Sensitivity and Positive Predictive Values for the SRBF and SSE methods applied on the clinical sounds set without and with additive Gaussian noise.
\r\n\t
\r\n\t-production; advances in decline curve analysis, determining of optimal well spacing, parent-child wells relation, frat hit, stress shadowing, well interference,
\r\n\t-completion; determining optimal fracture spacing, optimal pad volume, optimal proppant volume, size and type, fiber optics,
\r\n\t-environmental aspects; produced water management, environmentally sustainable operation, footprint, and water consumption,
\r\n\t-improved oil recovery; Huff and Puff gas injection, surfactant injection, pilot tests, upscaling of lab-results to pilot-scale and field-scale,
\r\n\t-economics; integration of gas utilization, reducing operational costs, and water treatment.
The advancement of technology has paved the way for signal processing methods to be implemented and applied in many simple tools useful in everyday life. This is most notable in the medical technology field where contributions involving the intelligent applications have boosted the quality of diagnosis. Proposing an objective signal processing methods able to extract relevant information from biosignals is a great challenge in telemedicine and auto-diagnosis fields.
\n\t\t\tFor the cardiac system, many signals can be treated and monitored; ElectroCardioGram (ECG), PhonoCardioGram (PCG), Echo/Doppler and pressure monitor, see Figure 1.
The cardiac activity with different measurable signals [1].
The interest of this book chapter is the PCG signal. PCG and auscultation are noninvasive, low-cost and accurate for diagnosing some heart diseases.
The PCG signal confirms, and mostly, refines the auscultation data and provides further information about the acoustic activity concerning the chronology of the pathological signs in the cardiac cycle, by locating them with respect to the normal heart sounds. The cardiac sounds are by definition non-stationary signals, and are located within the low frequency range, approximately between 10 and 750 Hz.
The analysis of the cardiac sounds, solely based on the human ear, remains insufficient for a reliable diagnosis of cardiac pathologies, and for a clinician to obtain all the qualitative and quantitative information about cardiac activity especially in the field of time intervals.
Information, such as the temporal localization of the heart sounds, the number of their internal components, their frequency content, and the significance of diastolic and systolic murmurs, could all be studied directly on the PCG signal. In order to recognize and classify cardiovascular pathologies, advanced methods and techniques of signal processing and artificial intelligence will be used.
For that, different approaches could be considered for improve the electronic stethoscope:
Tool with embedded autonomous analysis, simple for home use by the general public for the purpose of auto-diagnosis, monitoring and warning in case of necessity.
Tool with sophisticated analysis (coupled to a PC, Bluetooth link) for the use of professionals in order to make an in-depth medical diagnosis and to train the medical students.
Whatever the approach, one of the first and most important phases in the analysis of heart sounds, is the segmentation of heart sounds. Heart sound segmentation partitions the PCG signals into cardiac cycles and further into S1 (first heart sound), systole, S2 (second heart sound) and diastole.
Identification of the two phases of the cardiac cycle and of the heart sounds with robust differentiation between S1 and S2 even in the presence of additional heart sounds and/or murmurs is a first step in this challenge. Then there is a need to measure accurately S1 and S2 allowing the progression to automatic diagnosis of heart murmurs with the distinction of ejection and regurgitation murmurs.
This phase of autonomous detection, without the help of ECG is based on signal processing tools such as: Shannon energy [2], Hilbert Transform [3], high order statistics [1], hidden Markov model [4] …
In this chapter we present a new module for heart sounds segmentation based on time-frequency analysis (S-Transform). The goal of this study is to develop a generic tool, suitable for clinical and home monitoring use, robust to noise, and applicable to diverse pathological and normal heart sound signals without the necessity of any previous information about the subject. The proposed segmentation module can be divided into three main blocks: localization of heart sounds, boundaries detection of the localized heart sounds and classification block to distinguish between S1 and S2.
The proposed methods are evaluated based on a database of 80 subjects (40 pathologic). This study is made under the control of an experienced cardiologist, in with the aim of validating the results of each method.
This chapter is organized as follows: Section 2 describes the data base used in this study. It is followed by the Section 3 which describes the different methods proposed for the segmentation module (localization, boundaries detection and classification). The results and discussion are presented in Section 4 and Sections 5 and 6 give the future research and the conclusion.
Several factors affect the quality of the acquired signal, above all, the type of the electronic stethoscope, its mode of use, the patient’s position during auscultation, and the surrounding noise. According to the cardiologist’s experience, it’s preferable that the signals remain unrefined; filtration will only be applied subsequently in the purpose of signal analysis. For this reason we used prototype stethoscopes produced by Infral Corporation, and comprising an acoustic chamber in which a sound sensor is inserted. Electronics of signal conditioning and amplification are inserted in a case along with a Bluetooth standard communication module.
Different cardiologists equipped with a prototype electronic stethoscope have contributed to a campaign of measurements in the Hospital of Strasbourg. In parallel, 2 prototypes have dedicated to the MARS500 project promoted by ESA, in order to collect signals form 6 volunteers (astronauts). The use of prototype electronic stethoscopes by different cardiologists makes the database rich in terms of qualitative diversity of collected sounds, which in turn makes the heart sounds localization more realistic.
The sounds are recorded with 16 bits accuracy and 8000Hz sampling frequency in a wave format, using the software “Stetho” developed under Alcatel-Lucent license.
The dataset contains 80 subjects, including 40 cardiac pathologies sounds which contain different systolic murmurs. Each subject corresponds to one recording sound. The length of each sound is 8 seconds.
At first the original signal is decimated by factor 4 from 8000 Hz to 2000 Hz sampling frequency and then the signal is filtered by a high-pass filter with cut-off frequency of 30 Hz, to eliminate the noise collected by the prototype stethoscope. The filtered signal is refiltered reverse direction so that there is no time delay in the resulting signal. Then, the Normalization is applied by setting the variance of the signal to a value of 1. The resulting signal is expressed by:
The localization algorithms operating on PCG data try to emphasize heart sound occurrences with an initial transformation that can be classified into three main categories: frequency based transformation, morphological transformations and complexity based transformations [1]. The transformation try to maximize the distance between the heart sounds and the background noise, and the result is smoothed and tresholded in order to apply a peak detector algorithm. We note here, that the main goal of heart sound localization is to locate the first and the second heart sounds but without distinguishing the two from each other and without detecting the boundaries of located sounds.
We proposed the RBF method as a transformation to emphasize heart sounds and it was shown to have a good performance on low level noise signals [5]. However, In the presence of high level of noise, the performance of the RBF method decreases. This was not surprising because the method operates directly on the heart sound without any feature extraction step. To deal with this problem, we proposed a method for heart sounds localization named SRBF [6]. This method aims at extracting the envelope of the signal by applying the features extracted from the S-Transform matrix of the heart sound signal to the radial basis function (RBF) neural network. Compared with other existing methods for heart sounds localization, SRBF was shown to have a significant enhancement in term of sensitivity and positive predictive value and the robustness of this method was shown against additive white Gaussian noise.
We will briefly explain the different steps of the SRBF method:
Block Diagram of SRBF Method
The S-Transform of the heart sound is calculated. A frequency range of 0-100 Hz was used to cover the main frequency band of S1 and S2 and to avoid murmurs which have in general a spectral energy above the frequency of 100 Hz [7].
A sliding window of 50 ms (so 100 samples) was operated on the S-matrix and an overlap of 75% was chosen. The feature extraction is done by applying some standard statistical techniques and transformations like Root Mean Square (RMS), the maximum and the average of each column of the S-matrix. Each array (100 samples) was divided into 5 segments and the mean of calculated features of each segment was calculated and taken as input to the classifier. So for each step we have a 100 by 100 matrix which gives 15 descriptors.
A RBF neural network classifier is used and trained on two heart sounds samples (S1 and S2) and two no heart sound samples (systole, diastole) selected randomly from the database. The target is fixed to 1 for S1 or S2 and 0 for the other components. So the envelope of the signal is constructed by the output of the RBF neural network.
A new method for the localization of heart sounds is proposed in this study (SSE). It uses the S-matrix like the SRBF method (0-100 Hz) and it calculates the Shannon Energy (SE) of the local spectrum calculated by the S-transform for each sample of the signal x(t). Then, the extracted envelope is smoothed by applying an average filter (Figure 3).
Block Diagram of SSE Method
The S-Transform proposed in [8], of a time series x(t) is:
Where the window function w(τ-t) is chosen as:
And σ(f) is a function of frequency as:
The proposed SSE method calculates the Shannon energy of each column of the extracted S-matrix as follows:
Each column of the S-matrix represents the local frequency at a specific sample. The advantage of the Shannon energy transformation is its capacity to emphasize the medium intensities and to attenuate low intensities of the signal which represents the local spectrum in the case the SSE method. The main difference between the SSE and the SRBF method is the training phase needed for the RBF module. The RBF neural network in the SRBF method can be considered as a non-linear filter which is replaced with a simple average filter in the SSE method.
The boundaries detection algorithm aims at estimating the onset and the endpoint of the located heart sounds. Accurate boundaries estimation is a very important step in the heart sound segmentation module and it is essential for the extraction of meaningful features from each part of heart cycles in order to perform an auto-diagnosis process.
Different boundaries detection algorithms exists in the literature, in [2] the boundaries are estimated by applying a threshold on the extracted envelope of the signal, this is not be accurate for some cardiac cycles, because the envelope threshold level is used based on the average value of the whole recordings periods. The same authors propose another algorithm that employs the STFT (Short Time Fourier Transform) to explore the time-frequency domain of the signal [9]. Authors quantify the spectrogram at each segment to two values by applying a threshold that reserves 60% of the signal energy, however, it is not clear how the energy of the signal is calculated and the accuracy of the algorithm is not mentioned. In [10] authors use some biomedical features of heart sounds (S1 and S2) like the maximum duration of S1 and S2 to determine the limit of estimated boundaries, the disadvantage of this method is that the estimation of energy of the signal is based on the time domain only, so in the presence of high level of noise the performance of this method will decrease dramatically.
In this chapter, we propose a new algorithm to estimate the heart sounds boundaries. The proposed algorithm tries to optimize the energy concentration of the S-transform at each located sound by using a window width optimization method. The envelope of the optimized S-transform is then recalculated by using the SSE approach and an adaptive threshold is applied to determine the onset and the ending of each located heart sound. Let us assume that L is the time located sounds after applying the localization method on the heart sound and S(M,N) is the S-matrix of the heart sound where M represents the frequency domain and N the time domain.
The block diagram of the proposed algorithm (OSSE) is shown below (Figure 4).
The block diagram of the OSSE Method
Estimate the boundaries limit
The boundaries limits are estimated basing on the fact that the maximum duration of S1 and S2 is 150 ms [11]. So a 150ms window is applied in the proximity of detected S1 and S2 peaks which covers 75ms in the backward direction of the S1 or S2 peak and 75ms in the forward direction.
Optimized S-transform
Many studies tried to improve the TF representation of the S-transform[12-14]. The main study in the literature interested to optimize the energy concentration in the TF domain was in [14]. That is, to minimize the spread of the energy beyond the actual signal components. As it well known, the ideal time-frequency transformation should only be distributed along frequencies for the duration of signal components. So the neighboring frequencies would not contain any energy and the energy contribution of each component would not exceed its duration [15].
The energy concentration in the Time-Frequency (TF) domain is a very important parameter for the algorithms that aim to detect the duration of any given events in a signal. Therefore, it should hold the same importance for the boundaries detection algorithm of heart sounds based on time-frequency features. However, in some cases, the S-transform suffers from poor energy concentration in TF domain. Hence, the importance of an energy concentration optimization process to improve the boundaries estimation of the heart sounds.
The main approach is to optimize the width of the window used in the S-transform. The width of the Gaussian window can be controlled by several ways by adding a new parameter to the window equation. We use in this study the parameter p introduced in [14] and we investigate another parameter named α (see equation 6). Both of them control the Gaussian window width:
We note here that in this study when α vary, p is fixed to 1, and when p vary, α is fixed to 1. The optimal value can be calculated in two methods; the first method calculates one global parameter, which is recommended for signals with constant or very slowly varying frequency components. The second method calculates the time-varying parameter which is more suitable for signals with fast varying frequency components. The disadvantage of the second approach is its high computational complexity which makes it unsuitable for applications where time is an important factor.
Based on the first approach, the optimization algorithm is applied on both parameters p and α, separately. The performance measure against each parameter is compared in section (5.2). The performance measure is based on the concentration measure (CM) proposed in [16]. For each α (or p) from a given set, the CM (α) can be expressed by [14]:
With
The CM (α) and CM (p) are calculated and compared for all existing S1 and S2 sounds in the database. We note again that the main objective is to enhance the concentration energy of the S-transform in order to detect precisely the boundaries of the located heart sounds. We consider the parameter that reaches a higher CM to be more appropriate for the heart sound signals.
The Adaptive threshold
Performing an optimized S-transform before calculating the SSE envelope makes the choice of threshold less sensitive to the variation of different heart sounds. In this study, a threshold which equals 10 % of the maximum value of the SSE envelope is applied to refine the estimated boundaries.
Most of the existing methods for the segmentation of heart sounds use the feature of systole and diastole duration to classify the first heart sound (S1) and the second heart sound (S2) [1,17-18]. These time intervals can become problematic and useless in several clinical real life settings which are particularly represented by severe tachycardia or in tachyarrhythmia (Figure 5).
Example of an arrhythmic subject.
Consequently with the objective of development of a robust generic module for heart sound segmentation, we present in this chapter two feature extraction methods based on the Singular Value Decomposition (SVD) technique applied on the S-matrix, to classify S1 and S2. We investigate also, the ability of a new individual features based on the width of the optimized Gaussian window of the S-Transform, to discriminate between S1 and S2.
The SVD is a powerful tool that provides a compact matrix or compact significant information about single signal. Different ways exist in the literature aims to represent the time-frequency matrix in a compact manner by using the SVD technique. In [19] authors extracted the eigenvalues of the time-frequency matrix. In [20] authors extended the method to also incorporate information from the eigenvectors to classify EEG seizures. In [21] the last technique is applied on the S-matrix in the aim to extract features for systolic heart murmur classification. Following this approach, this study proposes a feature extraction method for S1 and S2 classification.
The time-frequency analysis is performed by the S-Transform. The S-matrix Si of the extracted heart sound Hi is decomposed by the SVD technique as follows:
Where U(M×M) and V(N×N) are orthonormal matrices so their squared elements can be considered as density function[20], and D(M×N) is a diagonal matrix of singular values. The columns of the orthonormal matrices U and V are called the left and right eigenvectors which contains in this case the time and frequency domain information, respectively. The eigenvectors related to the largest singular values contain more information about the structure of the signal.
Based on our experience, in this study, the first left eigenvector and the first right eigenvector that correspond to the largest singular values are used for the feature extraction process. The histogram (10 bins) for each related distribution function is calculated based on the density function. Five feature vectors obtained by this method are tested in the classification process; the eigentime histogram vector U1 (T-Features), the eigenfrequency histogram vector V1 (F-Features), the singular values vector D1 (SV Features) and the time-frequency vector U1&V1 (TF Features). All vectors have a length of 10 features except the time-frequency vector that has a length of 20.
In the last few years, the Empirical Mode Decomposition (EMD) has been applied in many fields one of which the biomedical signal analysis, like the emotion classification in natural speech [22], analysis of gastroesphageal information [23]. EMD has been applied to a simulated heart sounds in [24] authors show that EMD provides clear information about the components of S1 and S2 and their instantaneous frequency behaviour. In [25] authors presented a feature analysis approach of heart sound based on the improved Hilbert-Huang Transform, and applied the improved HHT by Hilbert spectrum analysis of various cases of heart sounds. In this study, a new feature extraction method based on EMD technique and Shannon energy is proposed for S1 and S2 classification.
As an alternative to the binomial TF transforms, EMD performs a multi-resolution analysis of non-stationary and nonlinear signals without the use of kernels or mother waveforms. To calculate the Intrinsic Mode Functions (IMFs), the local maxima and minima of extracted heart sound Hi(t)are calculated. They are interpolated by using the cubic spline curves which generates the upper and lower envelopes, respectively. Then the mean contour m1(t) is calculated, and the first component h1(t) is given as follows:
Now, h1 has to be refined by a sifting process. In the second sifting iteration we obtain:
Where m11 is an average contour between the upper and lower envelopes of h1. This operation is repeated k times until h1k can be considered as zero-mean according to some stopping criterion (Rilling et al., 2003). The first intrinsic mode function IMF1(t) is given as:
IMF1(t) should contain the finest scale or the shortest period component of the signal. The residue signal r1(t) is given by:
Considering r1 as a new signal the sifting process explained below is repeated to obtain the second IMF2(t). Similarly, a series of intrinsic mode functions are obtained and the final residue rn(t) is calculated. The stop criterion is when rn(t) becomes a monotonic function.
The initial signal Hi(t) can be reconstructed as follows:
For each IMF vector, the Shannon Energy is calculated as:
Where i=1,…,4 and N is the number of samples of IMFi the Shannon energy is smoothed by using a median filter, and the feature vector is obtained by applying the same SVD approach used in section 2.5.1 at each calculated IMF (Figure 6). For each extracted heart sound the first four IMF is calculated. The others IMF don’t contain relevant information about S1 and S2. Five feature vectors obtained by this method are tested in the classification process; FV1 (that correspond to IMF1 signal), FV2, FV3, FV4 and FV (that correspond to the average of calculated FVs). The length of each vector is 10.
Feature vector (FV) of Heart Sounds (Hi) extracted using EMD and Shannon Energy (SE) before applying the SVD technique.
The parameters α and p used to optimize the width of the Gaussian window of the S-Transform, are tested as a new individual features to discriminate between S1 and S2. It is known from a physiological point of view, that S1 is more complicated than S2 [26]. However, S2 in general contain higher frequency than S1. These physiological differences will necessarily lead to different time-frequency content behavior which we will aim to reveal with α and p parameters. Figure 7 shows a S1 and S2 signals examples with the corresponding optimized S-transform obtained with α=0.8 and 0.5, respectively.
S1 and S2 signals (top), Optimized S-transform obtained with α=0.8 for S1 and α=0.5 for S2 (bottom).
The performance of the SBRF and the SSE methods was measured as the methods capacity to locate S1 and S2 correctly. It was measured by sensitivity and positive predictive value:
And positive predictive value:
A sound is true positive (TP) if it is correctly located, all others detected sounds are considered as false positive (FP) and all missed sounds are considered as false negative (FN).
Results in Table 1 show that SRBF method reaches a higher PPV (98%) than the SSE method for the clinical signals without any additive noise. However, SSE reaches a higher sensitivity (96%) than the SRBF method (92%). The supervised approach performed by the RBF block in the SRBF method makes the extracted envelope more discriminative between the different parts of the signal than the unsupervised SSE method. Therefore, it is not surprising that the number of false detected sounds in the SRBF method is lower than the SSE method, which also explains the PPV results. The same reasons can also account for the false negative alarms which are higher in the SRBF method than the SSE method and which gives a higher sensitivity to the SSE method. In the presence of an additive white Gaussian noise, the performance of the SSE method is better with 93% sensitivity and 94% PPV. The robustness of both methods against noise is very significant. This is due to the advantage of performing a time-frequency analysis which makes methods more robust against noise. Figure 8 shows the envelopes extracted by the SSE and the SRBF method that correspond to a pathologic sound with a systolic murmur. Figure 9 shows the robustness of each method against white additive noise.
Method | Sensitivity | PPV | Sensitivity (Noise) | PPV (Noise) |
SRBF | 92% | 98% | 91% | 93% |
SSE | 96% | 95% | 93% | 94% |
Sensitivity and Positive Predictive Values for the SRBF and SSE methods applied on the clinical sounds set without and with additive Gaussian noise.
Envelope extraction (dashed lines) for a signal with systolic murmur, (top) SRBF envelope, (bottom) SSE envelope.
top) Envelope extraction for two normal PCG signal without and with additive Gaussian noise, (middle) their SRBF envelopes, (bottom) their SSE envelopes.
The performance measure against each parameter is compared (Table2). The values of α and p are chosen from a set; 0 <α< 2, 0<p<2, with a step of 0.1; so twenty values as total for each variable.
Heart Sounds | Optimal α | CM(α) | Optimal p | CM(p) | CM( α =1, p=1) |
S1 | 0.82±0.45 | 0.0185±0.0017 | 1.1±0.5 | 0.0186±0.0018 | 0.0177±0.0015 |
S2 | 0.55±0.3 | 0.0186±0.0015 | 1.37±0.5 | 0.0186±0.0018 | 0.0175±0.0014 |
Total | 0.68±0.37 | 0.0185±0.0016 | 1.23±0.5 | 0.0186±0.0018 | 0.0176±0.0015 |
Performance measure given by the maximum values of CM (α) and CM (p) for a given parameters set of α and p, respectively.
The optimal α is reached when CM(α) is maximized, and the optimal p is reached when CM (p) is maximized. Results from Table 2 show that there are no significant differences between the two parameters α and p concerning the performance measure. However, results show an important difference between optimized concentration measure and standard concentration that correspond to the standard S-transform with α=1 and p=1. The maximum values of concentration measures CM (α) and CM (p), that corresponds to the optimum α and p, respectively, are obtained with α <1 and p>1. This is can be explained by the fact that when α<1 and p>1, the Gaussian window of the S-transform is narrower (Figure 10), which improves the detection of the sudden changes in the signal, like the onset and the ending of the first and the second heart sounds. However, when a window is narrower in time domain, we loss in term of frequency resolution. The compromise is performed by the optimization process that operates on the variable that control the variance of the Gaussian window, α or p for example. The criterion of the performance is the concentration energy measure. The enhancement of energy concentration in the TF domain, influence clearly on the boundaries estimation results (Table 3).
Normalized Gaussian window for different values of p (left) and for different values of α (right).
Method | S1(ms) | S1(Noise) | S2(ms) | S2 (Noise) |
SSE | 122.4±7.2 | 127.8±9.6 | 95.2±8.3 | 101.2±7.4 |
OSSE | 110.7±4.32 | 113.6±6.5 | 69.1±5.4 | 77.9±8.2 |
Reference | 105.8±6 | 74.8±5.65 |
S1 and S2 durations (ms) estimated by the SSE and OSSE methods with and without additive noise.
The “Reference” row in Table 3 represents the manual measures made by the cardiologists by using the software stetho developed under the license of Alcatel-Lucent. Results show the efficiency of optimizing the energy concentration of the S-transform in order to estimate more realistic boundaries for S1 and S2. Measures obtained by the SSE algorithm (without optimizing the S-transform) are always higher than the results given by the OSSE algorithm where an optimization process is performed. This is not surprising since the OSSE algorithm has a better energy concentration in the TF domain, which minimizes the spread of the energy beyond the S1 and the S2. Figure 11 shows the boundaries detection results, with and without optimization of the S-transform, applied on a S2 example and figure 12 shows the OSSE results applied on the entire heart sounds (normal and pathologic).
top) S2 signal with two detected boundaries calculated by the optimized S-transform and the standard S-transform (dashed line), S-transform with the optimum value α=0.5 (p=1), standard S-transform with α=1 (p=1), (bottom) SSE envelope for the optimized S-transform and standard S-transform (dashed line).
OSSE method applied on a normal heart sound (top) and pathological heart sound (bottom).
The localization of heart sounds is established by using the SSE method. The boundaries of the heart sounds are determined by the OSSE algorithm. The results were visually inspected by a cardiologist and erroneously extracted heart sounds were excluded from the study. The feature extraction process extracts a feature vector per extracted sound Si (S1 or S2) and each of these vectors is averaged across available extracted sounds from each subject. So from each subject in the database, we obtain one S1 feature vector and one S2 feature vector to use in the training and classification process.
A 3-Neirest Neighbor (KNN) classifier is used to evaluate the performance of the four feature vectors obtained by the two methods and the 5-fold approach is used for cross validation. The choice of KNN classifier was based on its simplicity of and its robustness to a noisy training data.
The time domain feature vector reaches 92% classification rate, however, the frequency feature vector reaches 85% classification rate (81% sensitivity and 88% specificity). The Time-Frequency vector (TF Features) reaches the higher classification rate with 95% sensitivity and 97% specificity. The singular values are almost indistinguishable from each other and it is shown by the low classification rate for the SV features. For the EMD based method, the FV feature vector reaches a high classification rate with 94% sensitivity and 97% specificity (Table4).
KNN | T- Features | F-Features | SV Features | TF Features | FV1 | FV2 | FV3 | FV4 | FV |
Sensitivity | 92% | 81% | 60% | 95% | 88% | 81% | 82% | 65% | 94% |
Specificity | 92% | 88% | 65% | 97% | 91% | 97% | 94% | 95% | 97% |
Sensitivity and specificity for the nine extracted feature vectors evaluated by a KNN classifier.
In most cases seen in the medical field, S2 has a higher frequency than S1. This is due to the fact that S2 is the heart sound associated with the closure of the aortic valve in a context of high left ventricular pressure, the mitral closing occurring at low left ventricular pressure (S1). However, this criterion cannot be generalized on all real life cases because some medical conditions are characterized by S2 frequency content lower than S1 frequency content. Hence, the importance of time-frequency and multi-resolution based features approach, especially in a generic module, which can explain the high performance obtained with the TF and FV features vectors.
The parameters used in the optimization process (section 3.3.2) to determine the boundaries of each extracted sound Si (S1 or S2) are averaged across available extracted sounds from each subject. So from each subject in the database, we obtain one S1 feature (α or p) and one S2 feature (α or p).
The main objective is to investigate the ability of these features to discriminate between S1 and S2. The probability that the two groups (S1 and S2) comes from distributions with different medians is calculated for each feature (α and p) by the Mann-Whitney-U-test (p<0.005). The receiver Operating Characteristic Curve (ROC) is also calculated for each feature and the Areas under the ROC Curve (AUC) are showed in figure 13.
The Results are presented in Table 5. Significant differences between the groups, with 95% confidence are found for both features α and p.
Feature | p-value | AUC | Sensitivity | Specificity |
α | <0.0001 | 0.83 | 0.79 | 0.72 |
p | 0.0047 | 0.64 | 0.609 | 0.671 |
Significant values (U-test), AUC values, sensitivity and specificity for the parameters α and p when used to distinguished between S1 and S2.
ROC curves for α and p parameters.
The classification results are promising for the parameter α (AUC =0.83). This is very interesting since this parameter was also used to refine the boundaries detection of S1 and S2. However, the results of the parameter p are significantly lower than the results of α (AUC =0.64). This gives a primary idea about the sensitivity of each parameter against the clinical signals. Further measures and tests should verify or deny this hypothesis.
A new time-frequency based feature is proposed and validated to distinguish with S1 and S2 (Section 4.3.2). Another parameter can be tested by applying another windows type at the S-transform like the arbitrary and varying shape window [13]. A combination of several features can also be used to classify S1 and S2 more accurately. This can be performed by combining the α parameter with the TF_Features vector (see section 4.3.1). Then a feature selection algorithm becomes necessary to select the most accurate features.
On another hand, the classification of normal and pathological heart sounds is the final objective of any heart sounds auto-diagnosis framework. The classification rate will depend first on the segmentation results, which was the main objective of this book chapter. Then classic steps of feature extraction, feature selection, designing and testing classification systems, will be needed to complete the classification process
One of the objectives of this study is to develop an auto diagnosis for various situations encountered in cardiology in real time. However, the S-Transform that can be considered as the heart of the proposed segmentation framework, suffers from a high computational burden. The implementation of a fast S-Transform algorithm on FPGA or GPU card will be necessary.
Introducing a smart stethoscope as a monitoring tool for home use, involves new problems related to sociological and psychological aspect of the user (patient). A smart stethoscope is a tool to facilitate the diagnosis process and to make it more objective and it will never replace the cardiologist and other advanced techniques of Cardiology. This should be taken into consideration in the deployment process in a telemedicine framework for example. The ergonomic aspect of the measuring instrument, the way to display the data and to transmit it, will be more than necessary elements to any future tool, simple for home use by the general public for the purpose of auto-diagnosis, monitoring and warning in case of necessity.
In this book chapter, a robust module for heart sounds segmentation has been proposed. The module is divided into three blocks; localization, boundaries detection, and classification of heart sounds (S1 and S2). Several methods are proposed during this study:
A heart sounds localization method based on the S-transform and Shannon Energy, named SSE, is proposed and evaluated against white additive Gaussian noise.
A method for boundaries detection named OSSE is proposed. It is based on an optimization process for the energy concentration in the TF domain provided by the S-transform.
A feature extraction methods based on Singular Value Decomposition (SVD) technique to distinguish between S1 and S2 are examined. The parameters used in the time-frequency optimization process to determine the boundaries of each extracted sound are also investigated and validated as discriminative features between S1 and S2.
Dividing the proposed segmentation method into three separate blocks, enable us to perform a targeted optimization at each level. This confers the feature of robustness to the proposed module, which is a more than necessary element to any auto-diagnosis module applicable in real life conditions.
The main objective of this study is to present a robust and generic PCG segmentation method useful in real life conditions (clinical use, home care, professional use …). The methods in the proposed framework are evaluated on a real data (80 subjects) with different noise levels and they are validated by the cardiologist.
More robustness tests against noisy signals, algorithms complexity, facility of implementation and more signals, would contribute to optimize the proposed module.
Lyon has always had a great tradition of orthopedic, and Charles Gabriel Pravaz was not only the inventor of the syringe, but he also created in Lyon a great orthopedic institute to treat scoliosis 200 years ago. The first Lyon brace, which was made of leather and steel, was created by Stagnara 70 years ago. It undergone a first change with the replacement of leather by polymethacrylate. This brace was used in adults in addition to surgery while waiting for the graft fusion, at a time when osteosynthesis did not have the current quality. In 2013, the use of adult ARTbrace in Europlex’O in polyamide and asymmetry allowed to avoid the plaster cast which has always been the characteristic of the Lyon management. The use of polyamide and digital allows treatment of thoracic and double major curves.
\nVanderpool et al. [1] shows that the frequency of scoliosis in adults increases steadily with age, from 6% of scoliosis after the patient reaches 40 years until it reaches 10% of the population at age 65. The sex ratio was 2 females to 1 male. It is women who have the most painful instabilities and imbalances. Their bone mass is lower than that of men with a vertebral fracture threshold at age 65. Pregnancy and menopause could be also aggravating factors [2].
\nAkbarnia et al. [3] described the key features as curve stiffness, degeneration of the discs, osteoporosis, spinal imbalance both coronal and sagittal, rotary subluxation, spinal stenosis, and higher rate of complications (pulmonary, etc.). The esthetic aspect is not negligible, and even surgery performed during adolescence does not solve everything. Edgar and Mehta [4] has shown that self-image representation and social life is different after surgery in adolescence. 82% of adult scoliosis without surgery was married compared to 60% of scoliosis operated in adolescence. O’Brien [5] analyzes the consequences of scoliosis in adulthood. He noted that for adult scoliosis abnormal physical appearance and diminished self-esteem may always be present, but breathing limitations, inability to function, and other quality of life issues generally become the driving forces for clinical examination, diagnosis, and treatment.
\nThe complications were analyzed by many authors. For Baron and Albert [6] the incidence of medical complications ranges between 40 and 86%. Local complications include infection, pseudarthrosis or failure of instrumentation, and neurological and adjacent-level degeneration or instability. Common medical complications include pneumonia, atelectasis, ileus, delirium, and cerebrovascular incidents. Smith et al. [7] studied the incidence of complications according to age. His conclusions were the following: the oldest age group (65–85 years) has nearly four times the number of minor complications and nearly five times the number of major complications when compared with the youngest age group (25–44 years). As invasive surgical therapy needs a perfect understanding of risk/benefit, Ogilvie [8] suggests that the decision to proceed with surgical treatment even if justified in many cases must be based on a thorough understanding of the anticipated benefits from surgical treatment and the risk of serious complications. These potential complications lead to multiple surgeries with results that can be less desirable than the original condition. The results of conservative orthopedic treatment are more difficult to assess. Kluba et al. [9] compares surgical and conservative treatment for degenerative lumbar scoliosis. He finds a significantly higher rate of spinal stenosis and degenerative spondylolisthesis in the group of patients with surgery. However no significant difference was evident between the two groups in terms of lumbar back pain after 4 years, respectively.
\nEverett and Patel [10] conducted a systematic review of non-operative treatment. There is indeterminate, level III/IV evidence on the effectiveness of any conservative option; level IV evidence on the role of physical therapy, chiropractic care, and bracing; and level III evidence for injections in the conservative treatment of adult deformity. The use of rigid or hard bracing in adult scoliosis is generally not recommended. This is due to the risk of muscle weakening effects from hard bracing and the fact that this could accelerate the degenerative process in some cases. Chuah et al. [11] notes that bracing may sometimes help the symptoms, but it has no effect on curve progression.
\nPain is not synonymous with deformity progression. Some stable scoliosis patient report pain, and others evolve without pain. It will be necessary to try to make the difference between the “physical” pain and the “emotional” suffering when the patient does not support his deformation anymore.
\n\n
Thoracolumbar pain often corresponds to minor joint instability.
The pain of convexity is of muscular origin.
The pain of the concavity is posterior: facet syndrome.
The lumbosacral pain is of ligament origin.
These pains respond perfectly to physiotherapy.
\nWhen scoliosis progresses, it is either (1) the evolution in adulthood of an adolescent idiopathic scoliosis, (2) a de novo scoliosis usually of discal origin, or (3) a camptocormia of muscular origin. In all cases, there may be a disc disease with sometimes rotatory dislocation, postural impairment with imbalance, extrapyramidal muscle involvement, and bone involvement (osteoporosis). In these progressive cases of instability, bracing or surgery may be necessary.
\n\n
From 20 to 30 years old, the main problem is the anatomical pain.
From 30 to 50 years old, the main problem is the discal decompensation.
After 50 years old, there are two main problems: degenerative scoliosis very rigid with arthrosis and camptocormia reducible with paravertebral muscular atrophy.
Early works on scoliosis progression in adulthood were pessimistic [12], but at this time, idiopathic scoliosis, especially rachitic infantile, is mixed with neurological poliomyelitis that no longer exists.
\nIn 2003 Weinstein published the spontaneous evolution of 117 idiopathic scolioses over more than 50 years [13]. Thoracic curves of more than 50 degrees at skeletal maturity progressed with an average of 29.4 degrees. Thoracolumbar curves between 50 and 75 degrees increased with an average of 22.3 degrees. Lumbar curves had the most progression, especially when the L5 vertebra was not well seated and when the apical rotation was greater than 33%. He does not observe a functional respiratory or painful repercussion below 70°. This angulation could be currently the functional surgical Cobb limit. Pregnancy does not change the progression of scoliosis in adulthood, except in cases of twin pregnancy.
\nIn 2007 Marty-Poumarat [14] describes two specific adult scoliosis entities: adolescent scoliosis in adult (ASA) and degenerative de novo scoliosis (DDS).
\nGroup A (ASA) = adult progression of AIS > 40° with first dislocation at 45 years. The progression can be sometimes regular, sometimes chaotic.
\nGroup B (DDS) = de novo scoliosis with low Cobb after 50°, first dislocation at 52 years after menopause. DDS is more progressive than AIS. Because DDS is a result of degenerative disc instability, it is almost always progressive. Lumbar and thoracolumbar are the most progressive degenerative curves. Duval-Beaupere and Dubousset [15] have first described the mechanism of rotatory subluxation. Following their work, many authors have insisted on the importance of the lumbo-pelvic parameters [16, 17, 18].
\nThe radiological risk factors for instability are (1) rotatory dislocation with lateral olisthesis (Figure 1), (2) L3–L4 inclination, (3) hypolordosis, and (4) increased thoracolumbar kyphosis [19, 20].
\nDe novo scoliosis with constitution of a rotatory dislocation in 2 years, then scoliosis worsening by osteoporotic cuneiformization.
The physical activity and fracture rate of adult scoliosis is identical to that of the general population, except for operated patients who have less physical activity [21]. Unlike adolescence, when bracing is systematic when scoliosis progresses, the corrective bracing indication in adults is less related to Cobb angulation but more to the instability which results in pain, abnormal angular evolution, or imbalances (Figure 2).
\nClinical imbalances in the frontal and the sagittal planes.
From a database started in 1998, we selected all adult scoliosis in which conservative orthopedic treatment has been proposed to, even if the treatment had not been achieved by the patient. Scoliosis treated during adolescence and monitored in adulthood were excluded [22]. In this case series study, we analyzed 779 patients referred for nonsurgical treatment, and we correlated three parameters: the etiology, age, and Cobb angulation (Table 1).
\nIndications ARTbrace adult (n = 779) | \nRate % | \nMean age | \nMean angulation | \n
---|---|---|---|
Rotatory dislocation (n = 361) | \n46.5% | \n59.73 y ± 13.50 | \n39.08° ± 16.56 | \n
Segmental instability (n = 150) | \n19% | \n46.03 y ± 15.49 | \n25.29° ± 12.29 | \n
Instability post-surgery (n = 86) | \n11% | \n53.09 y ± 12.91 | \n40.49° ± 15.38 | \n
Camptocormia (n = 68) | \n9% | \n69.78 y ± 12.19 | \n38.09° ± 14.23 | \n
Kyphosis (thoracolumbar) (n = 62) | \n8% | \n60.73 y ± 15.51 | \n43.34° ± 21.48 | \n
Disabling pain (n = 33) | \n4% | \n48.36 y ± 13.73 | \n36.45° ± 21.48 | \n
Spondylolisthesis and spinal stenosis (n = 19) | \n2.5% | \n\n | \n |
Main indications for adult scoliosis bracing with frequency classification.
The rate of dropout patients not wearing the brace is 17% which is not excessive, especially since the plaster cast at that time was made before the brace discouraged patients.
\nA tentative classification according to etiology, age, and angulation is proposed (Figure 3).
\nIndications of nonsurgical treatment by etiology (n = 739).
More than half of the indications concern the rotational dislocation, which is the specific complication of adult scoliosis. The rotary dislocation is visible on the CT scan with subluxation and joint narrowing on the sliding side and widening of the articular space on the opposite side.
\nOne-fourth of the indications concern disc instability, which can be considered as the early stage of rotational dislocation.
\nThe other etiologies are less frequent: lumbar-pelvic-femoral kyphosis, secondary instability under arthrodesis, root pain, and rarely spinal stenosis which requires neurosurgery. Camptocormia is linked to weakness of the deep posterior musculature [23]. The patient increases kyphosis gradually to tighten his weak paravertebral muscles. There is often an extrapyramidal context of Parkinson’s disease [23]. MRI cross sections highlight the fatty degeneration. Some authors have mentioned paravertebral myopathy [24].
\nAccording to age, there is no Cobb angle difference between patients aged 39 and 80 years old, even if we notice a slight worsening between patients aged 80 and 90 years old. It can be concluded that after 40 years, for the same angulation, the risk of decompensation does not depend on age [22].
\nIf we examine in more detail the distribution of patients according to Cobb angle, we find that Cobb angle is not a discriminating factor like aging.
\nOne of the bracing eligibility tests especially for camptocormia is self-correction by using the hands on the thighs, even if this self-correction does not last long in time. The second test of reducibility is carried out in supine position. The occipital patient must rely on the plane of the examination table. The placement of the ARTbrace is performed by the patient who stabilizes the brace at the pelvic level then unrolls the spine using the rigidity of the posterior bar and finally blocks the upper part. As for children, the “mayonnaise tube” effect of the two lateral hemi-valves completes the correction in the sagittal plane.
\nAdult scoliosis bracing is performed only in technically equipped medical clinics. Hospitalization is not essential because the use of the brace must be integrated into the patient’s environment. On the other hand, physiotherapy scoliosis-specific exercises (PSSE) is mandatory.
\nThe brace wearing time protocol is a total time of 24 hours a day during 3 weeks with a plaster cast (or digital cast) to adjust the length of the ligaments with plastic deformation and, then, at least 4 hours per day for a minimum of 6 months, including systematically for 2 hours after the practice of sports activity (Table 2).
\nManagement | \nWearing time | \nParticularity | \nFollow-up examination | \n
---|---|---|---|
First 3 weeks | \nTotal time 24/24 | \nOnly 10′ for shower, no work interruption | \nAt the end of total time without X-ray | \n
First 6 months | \n4 hours/24 | \nSystematically for 2 hours after physical activity | \nAt 6 months with X-ray | \n
6 m to 2 years | \nOn demand and 2 hours after sport | \nIn case of pain, in prevention before major efforts | \nAt 2 years with X-ray | \n
After 2 years | \nNo specific indication | \nBrace is kept for safety | \nAT 5 years with X-ray, then every 5 years | \n
Adult bracing management (Lyon ARTbrace).
Wearing the brace for a “total time” allows the patient to relearn all the gestures of daily living in a good posture, for example, the sitting writing posture with feet behind the chair and buttocks in front of the seat. The lower part of the chest touches the anterior edge of the table, and the forearms rest on the desktop.
\nThe digital cast is made in three blocks according to the deviations as in the teenager, but in deep inspiration. In many cases, only a scan in maximum corrective posture perfectly balanced is performed. The corrective posture is derived from Schroth. The sagittal plane and the frontal plane are simultaneously corrected, ensuring the overall balance of the spine. The spine is placed in maximum extension to promote lumbar lordosis and reduce thoracic hyperkyphosis. The convex hand is placed on the vertical support, the concave hand is placed on the head, and the operator supports the patient’s elbow (Figure 4).
\nDigital cast with simultaneous correction in the frontal and in the sagittal planes.
The thickness of Europlex’O used in adults is 3 mm. The digital cast is made in blocks according to the deviations as in the teenager, but in deep inspiration. The advantages are manifold: (1) The patient can maintain the maximum corrected position for a few seconds while standing; (2) breathing is controlled, and the patient can be asked to perform maximum inspiration; and (3) the accuracy of the eight structure sensors is less than 1 mm. The 3 mm Europlex’O with very high rigidity can be used instead of polyethylene. It is possible to work bare skin, but the thin optical vest in jersey allows the use of landmarks for the superposition of the three blocks. The processing with a specific software allows the creation of a positive which will be milled by a digital milling machine. The CPO has all the tools to rework on the captured shapes. After a period of 3 weeks of “total time,” the brace is worn for a minimum of 4 hours/24 for 6 months, then on demand.
\nInstability pain management is obtained by:
A skin contact of the brace like a massage.
A discharge of the lumbar discs and vertebral body by the “composite beam effect.” The discharge of 30% is provided by the waist grip in the frontal plane, while the sagittal plane is free to prevent an excessive abdominal pressure.
A rebalancing spine in the frontal and sagittal plane.
A limitation of extreme postures.
The rigid brace is an active brace. The patient spontaneously tends to contract the paravertebral musculature in the sense of self-active axial elongation. Associated physiotherapy is however essential.
\nThe brace can reshape the waist. It can also symmetrize the body for the largest scoliotic curves by the adjunction of a foam cushion in the concavity.
\nThe lock automatically performed by the brace facilitates motion and strengthens the musculature of the lower limbs. There is also a better mobility of shoulder girdle because of the stabilization of shoulder blades in a more physiological position.
\nThe wearing of a rigid brace is obligatorily supplemented by physiotherapy scoliosis-specific exercises. The ideal is to act when the spine begins to disrupt or becomes painful, indicating instability. The therapeutic progression is usual:
\n\n
Analgesia.
Preventing muscle atrophy lumbo-abdominal strengthening in isometric and improving paravertebral deep muscles (Figure 5).
Promoting more flexible self-active axial elongation (Figure 6).
Correcting 3D spine balance: in the frontal plane, rebalance of the occipital axis; in the sagittal plane, restoration of sagittal lumbar and pelvic curvatures (pelvic anteversion and lumbar lordosis (strengthening of the iliopsoas)); and in the horizontal plane, dissociation of pelvic and shoulder girdles.
Developing compensation at the lower and upper limbs: relaxation under pelvic extension (hamstring stretching) (Figure 7).
Stimulating the mechanisms of postural correction with reharmonization of the paravertebral tensions (muscular chains) (Figure 8).
Isometric strengthening of the deep front line with correction of thoracolumbar kyphosis.
Self-active axial elongation in closed kinetic chain (hands/espalier).
Posture of stretching posterior chains of the lower limbs.
Reharmonization of paravertebral tensions with mirror control.
The main differences between adolescent and adult scoliosis are demonstrated in Table 3.
\nPhysiology and biomechanics | \nAdolescent | \nAdult | \n
---|---|---|
No specific pain in adolescents. Painful instability in adults | \nNo pain relief techniques | \nPain relief techniques, massage, and others | \n
Flat back in the teenager. Loss of lordosis and hyperkyphosis in adults | \nRestoration of physiological sagittal curves (arms projected forward) | \nPhysiotherapy in lumbar lordosis (hands crossed in the back) | \n
The brace aims to stiffen the spine (rust the spring). Spine mobilization in adults can lead to curve progression | \nSpine mobilization during cast and brace in all the amplitudes | \nNo spine mobilization beyond the corrected posture | \n
Strengthening muscle fibers (adult sarcopenia) | \nReinforcement of the reticulospinal system (aerobic) | \nReinforcement of voluntary musculature in anaerobic metabolism. | \n
Translation along the vertical axis | \nActive axial self-elongation in standing position (grand porter) Open kinetic chain | \nActive axial self-elongation trunk bent at 90°, hands resting on the espalier. Closed kinetic chain | \n
Lumbo-pelvic region | \nOpening the iliolumbar angle | \nAnterior lumbo-pelvic strengthening (iliopsoas, abdo, quad) | \n
Lower limbs | \nNo specific stretching. Global training without excessive resistance | \nStretching of the posterior chain at the level of the lower limbs | \n
One-third of the thorax volume develops after the end of the stature growth | \nResistance breathing exercises (inflating a balloon) | \nBreathing exercises in forced expiration | \n
Main differences between adolescent and adult scoliosis Lyon method physiotherapy.
First week. Physiotherapy is for analgesic purposes and is performed in the supine position by soft traction and a muscular work with irradiation of the short external rotators. Breathing is controlled because of the limitation of the abdominal expansion. The thoracic breathing is facilitated by the mobilization of the intercostal muscles.
\nSecond week. The iliolumbar angle is mobilized to adjust tension at the iliolumbar level. The hump can be modeled with progressive closure of the ratcheting buckle. Physiotherapy is performed in sitting position.
\nThird week. Physiotherapy is more global, more general, more tonic, and stronger. The lever arm of shoulder and pelvic girdles is used. The sessions are made in standing position.
\nWe first determine the sagittal direction of muscular work, usually lordosis for lumbar and thoracolumbar scoliosis. For each session there is a progression from supine to sitting and standing position.
\nRib hump erasing. Having refocused the spine from the vertical in the sagittal plane and in the frontal plane, the patient is asked to lengthen from the brace at the rib hump level. The movement is controlled manually. The trapezius muscle is relaxed.
\nSagittal tensioning girdles. The aim is to relax the posterior chain muscles while avoiding cervical lordosis. The exercise is made with control of inspiration breathing.
\nSelf-axial lengthening. The patient straightens his head, his hands resting on the anterosuperior part of the brace. It can be done in a sitting position using a proprioceptive system. When the head is at the correct high position, a sound and a light stimulate the patient. If the spine is close to a wall, a cushion at the cervical level must be stabilized by the patient. This exercise can be completed with the upper limb extension.
\nPosture memorization. Exercise can be more complete with the work of the lower limbs. The starting position is knees bent for self-axial elongation of the spine; the upper limbs are fixed on the espalier. The patient is asked to stand up to a position of global extension. This exercise improves the quadricep muscle that will be key to saving the spine.
\nStrengthening of weak muscles: quadriceps and abdominals. The exercise will be started in a supine position. The pelvis is locked in the brace posture. This work is associated with an isometric tension of the posterior chain and expiration. This exercise is completed by a stabilization of the shoulder girdle with a stick and control of the rotation of the hip by a ball between the knees. The solicitation is obtained by an oblique manual push on the side of the patient. By gradually lowering the legs, it also seeks the rectus femoris. The anterior chain has been stretched, and it is in this posture of extension that strengthening is performed with isometric contract-relax muscular work.
\nStretching strong muscles: hamstrings and short external rotators. It starts at the lumbosacral junction with pelvic-femoral, tricep, and hamstring stretch in lumbar lock controlled by the brace. It also stretches the psoas and rectus femoris. We can stimulate muscular work by manual push on the pelvis. The buttocks and the latissimus dorsi are solicited in the prone position, emphasizing the control of the cervical lordosis. When sitting, it stretches the anterior chain by adjusting the hip. Stretching can also be controlled at home on a stair. The exercise at the bar also allows global stretching.
\nProprioceptive rehabilitation. On a Klein Vogelbach ball, it transfers the body weight in all plans, with emphasis on relaxation of tone and breathing control. The muscle tonicity is improved by changes in posture, standing, and lying and by the addition of loads. The global proprioceptive work prepares the patient for the definitive weaning of the brace.
\nIn case of major disc degeneration, physiotherapy will be conducted in physiological lordosis, rather than in a standing position.
\nIn case of major facet joint degeneration, physiotherapy will be conducted in physiological lordosis in prone position, legs bent or in a sitting position.
\nIn case of leg length discrepancy, the feet imbalances adjustment with a shoe lift of 5 mm if it improves both pelvic and spine alignment.
\nIn the sagittal plane, one can use small high heel stubs from 3 to 5 cm to reduce a lumbar kyphosis.
\nThe food control helps to reduce overweight.
\nThe postural control concerns mainly the workstation.
\nThe regular practice of physical activity outside is essential. It is necessary to insist on the strict brace wearing during 2 hours after the sports activity.
\nExcessive mobilization of passive structures may lead to a progression of scoliosis, so the hyper flexibility is avoided and a position closest to that of the brace is better.
\nHigh thoracic breathing is less efficient than the usual abdominal breathing, and we must insist on improving the vital capacity for thoracic or double major curves. If lumbar scoliosis is treated, the risk of an increase of scoliosis during inspiration is low; however, breathlessness is to be avoided.
\nAs the brace can be asymmetrical in the direction of the rebalancing of the spine, it will, however, always ensure the balance of the shoulder girdle.
\nWhen the body is fully developed, we advise high-impact sports such as running and dance, to favor the fixation of the calcium on the bone and the constitution of an important bony mass.
\nIn a specific way when ribs are asymmetric, we recommend avoiding deep and quick inhalation which favors the vertebral rotation and therefore the breathlessness during the practice of sports.
\nFor lumbar curves, we advise, as well, against the quick flexions of the trunk forward or the position extending with an anterior flexion of the trunk.
\nDuring the period of maximal tensegrity up to 40 years, all sports can be performed at a high level as long as the spine is straight.
\nAfter 40 years, decreased intervertebral disc height and sarcopenia reduce the body’s performance.
\nAfter 65 years, osteoarthritis is predominant. Swimming avoids overloading the lower limbs and helps maintain lumbar lordosis (Table 4).
\nAge (girls) | \nPhysiology | \nActivity (example) | \n
---|---|---|
15–21 years | \nBefore complete bone mass | \nJogging and running Axial impact and spiral chains | \n
21–40 years | \nBefore sarcopenia and osteopenia (tensegrity) | \nFitness, sports reinforcing spiral chains | \n
40 to retirement | \nBefore extrapyramidal weakness (postural system) | \nNordic walking, cycling | \n
Retirement | \nOsteoarthritis, Pisa syndrome | \nSwimming | \n
Sports activity according to the age.
Immobilization braces made of polyethylene have been used for more than 50 years in case of mechanical pain. They complement classical physiotherapy by reducing load by 30% at the lumbar spine. We specifically studied the 158 patients with 5-year follow-up from our prospective database [25].
\nThe principle of bracing is completely different from that of adolescent scoliosis. Indeed, we try to:
Decompress the discs with the “sandglass effect” lifting the trunk under the ribs and transferring the load on the pelvis.
Rebalance the spine in both the frontal plane and in the sagittal planes, mostly by recreating lumbar lordosis.
Relieve pain by the analgesic effect of rigid low back brace.
A specific frame is used to stabilize the patient in the most corrective posture in the frontal and the sagittal plane.
\nFor those patients who had a progressive scoliosis, Cobb angle is stabilized or improved by more than 5° in 80% of cases, and only 20% of scoliosis remain candidates for surgery [25].
\nThe frontal and horizontal clinical parameters are improved, but not the sagittal parameters with the forward trunk projection (Figure 9).
\nInsufficient correction in the sagittal plane.
The sternoclavicular support is poorly tolerated, and due to reduced dexterity in the older person, lateral closure is a handicap for elderly patients, even if adaptations are possible, that is why we currently use the 3 mm Europlex’O.
\nInstability in adulthood is frequent, and surgery is the most frequently offered solution despite the high rate of complications, as there was no alternative to date for thoracic and thoracolumbar curves. Only overlapped bivalve polyethylene braces were used for lumbar scoliosis with good frontal stabilization but no control in the sagittal plane (Figure 9). The ARTbrace in Europlex’O which allows an average reduction of 70% for the children has been used since 2015 in the adult for all the deviations.
\nThe results of a consecutive series of 62 patients (6.2% of all ARTbrace patients) were treated between 2015 and 2016, as an alternative to surgery.
\nNine patients (15%) which constitute the dropout were not seen at 6 months, which is very little considering the general condition and age of patients. The percentage of dropouts is identical to the previous series of lumbar curves treatments. Despite the very high rigidity, Europlex’O which needs a precision of 1 mm is therefore as well tolerated as polyethylene.
\nIn the frontal plane, the average in-brace reduction is 27%, slightly higher for lumbar and thoracolumbar curvatures. The reduction to 2 years without brace is 15%, and especially the symptomatology of instability disappears. It is now possible to stabilize all thoracolumbar, thoracic, and double major scoliosis (Figure 10).
\nReduction in the frontal plane after decompensation upon arthrodesis.
In the sagittal plane, the average in-brace reduction is 32% and at 2 years without brace of 25% (Figure 11).
\nCorrection of kyphosis in the sagittal plane.
In the horizontal plane, some characteristic case study with EOS 3D confirms that adult ARTbrace is indeed, as in the child, a detorsion brace. Adult ARTbrace is the only brace to correct kyphosis and thus compensate for the insufficiency of polyethylene whose sternoclavicular support was not tolerated (Figure 12).
\nEOS 3D confirms thoracolumbar spine detorsion in ARTbrace.
Adult deformity is a major demographic health issue in the geriatric population. Surgeons are often very conservative in the treatment of adult scoliosis because of the complication rates associated with the surgeries and the marginal bone quality endemic to this population. Medical complications are a major concern in adult spinal deformity surgery [26]. The incidence ranges between 40% and 86%, but there is indeterminate level III/IV evidence on the effectiveness of any usual conservative care option. There is currently a lack of consensus on the most efficacious conservative treatments for adult deformity.
\nVery few results have been published concerning scoliosis adult bracing. Most of them only concern low back pain [27, 28]. Pain is the usual reason of medical consultation. Pain means instability when combined with the following clinical signs:
Frontal and sagittal Imbalance. The lumbar kyphoscoliosis is due to pelvic retroversion. The hips are extended under a retroverted pelvis, femurs were oriented downward and forward, and knees and ankles compensate with flexion deformity. Pelvic retroversion is limited by osteoarthritis of the hip, flexion deformity of the knee is poorly tolerated, and the patient will use a walking stick to walk. The thorax can enter in conflict with the pelvis at the concavity level pushing the viscera down. The patient suffers from breathing difficulty; digestive disorders are common and promote abdominal hypertension and sphincter disorders. The loss of lumbar lordosis has multiple causes: a decrease in the anterior height of the disc, hypertrophy of the facet joints and spinous process increasing the posterior height, and loss of extensors muscle strength [29].
In the horizontal plane, there is a rotation of the shoulder girdle as if the patient looks on the concave side of thoracic scoliosis. The pelvis is drawn by lumbar scoliosis. The convex hemi-pelvis moves back, and the hip is placed in internal rotation, while the concave hemi-pelvis moves forward, and the hip is placed in external rotation.
On each occasion when examining a patient at least every 5 years, verification X-ray is necessary in order to define a progression while being aware that in many cases the progression is chaotic.
The most characteristic sign of decompensating is the disc height loss that can sometimes exceed 10 mm. The disc corruption results in loss of physiological lordosis and ligament instability by hypermobility.
The losses of the gluteal muscles are very distinct when we make the plaster cast. It explains in part the pelvic retroversion; the spine tends to relocate along the line of gravity.
Muscular atrophy is a common criticism for rigid braces. In fact, the conservative orthopedic treatment does not suffer approximation. Its teamwork incorporates a specific physical therapy, the continuation of normal activity, and the practice of regular physical activity. No patient is wearing the brace for pleasure. The risk of overtreatment is zero.
\nUsually the total time bracing relieves pain, and the partial time bracing extends the improvement obtained. When the patient is not relieved, we can discuss the surgery with better arguments. The nonsurgical treatment treats the cause of lumbar instability mainly by discharging the pressure in the disc and stabilizing the lumbar area in lordosis to restore the tensegrity of the spine.
\nThe esthetic improvement of the rib hump and asymmetrical waist is logical; the orthopedic brace is the best way to remodel a trunk. The cosmetic result continues 5 years after starting the treatment, with improvement of the rib hump measured with the plumb line and the Bunnel angle of trunk rotation (Figure 9).
\nThe nonsurgical treatment can fit into a therapeutic progression. The indications may be progressive: observation, physiotherapy, medicine, conservative orthopedic Treatment, and surgery.
\nThe good surgical indications concern the degenerative scoliosis not relieved by bracing, or relieved by total time, but insufficiently by partial time and especially if there is a spinal stenosis. It can also be used to complete surgery if remaining instability.
\nThe Greek study [30] associating Schroth and Chêneau brace shows that patients have great difficulty to follow the protocol. For the quarter of patients following the protocol, the results are correct on pain and posture, but in 39% of patients, Cobb angle continues to increase.
\nJosette Bettany [31] confirms that for adult scoliosis, there are only a few studies on the effectiveness of PSSEs and a conclusion cannot yet be drawn. Recently a RCT proves the effectiveness of a motor and cognitive rehabilitation [32].
\nThe motivation of the patient is fundamental. The brace should be designed as a tool to facilitate physiotherapy.
\nThe use of an instantaneous and accurate CAD/CAM is better because the adult patient can only maintain the corrected position for a few seconds.
\nThe scan is made in deep inspiration to not limit the vital capacity.
\nThe management is 4 hours a day including systematically for 2 hours after any physical activity. Physiotherapy is even more important than during adolescence [33].
\nThe frequency of adult scoliosis makes it a public health problem. The new digital technologies have changed the adult scoliosis bracing, and conservative care in general may be a helpful option for adult deformity, but evidence for this decision was lacking. Lyon nonsurgical treatment is effective and offers new perspectives to adult scoliosis bracing. Not only does the brace relieve pain and support the spine, but for the first time, it corrects deviations in the frontal, sagittal, and horizontal planes. Immobilization braces in polyethylene allow a treatment of the cause of pain without side effects. Worn a few hours in the day, they complement physiotherapy. The first results confirm the excellent tolerance of Europlex’O adult ARTbrace with its ease of implementation and corrections unmatched to date in adults. These corrections make it possible to restore stability of the deviations without surgery. Adult scoliosis bracing as an alternative to surgery could be possible. Initially reserved for the most severe cases, this management deserves to be more widely used for adult scoliosis. The increasing number of CPO using the most modern CAD/CAM technologies should facilitate research in the field of very high rigidity.
\nThanks to my daughter Agnès Thornton de Mauroy, for proofreading in English.
\nARTbrace | asymmetrical rigid torsion brace |
ASA | adolescent scoliosis in adult |
CAD/CAM | computer-aided design/computer-aided manufacturing |
CPO | certified prosthetist/orthotist |
CT scan | computed tomography scan |
DDS | degenerative de novo scoliosis |
EOS | low-dose X-ray imaging |
MRI | magnetic resonance imaging |
PSSE | physiotherapy scoliosis-specific exercises |
RCT | randomized controlled trial |
This is a brief overview of the main steps involved in publishing with IntechOpen Compacts, Monographs and Edited Books. Once you submit your proposal you will be appointed a Author Service Manager who will be your single point of contact and lead you through all the described steps below.
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