MUAP abnormalities and indicated anatomical changes.
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\r\n\tIn this book Advanced application of radionuclides are introduced. New global trends on safe application of radionuclides in human life is elucidated.
Despite normalization of renal function and improvement in volume control with kidney transplantation, the prevalence of post-transplant hypertension (HTN) is substantial. The prevalence of post-transplant HTN reported in the literature varies considerably depending on the study population and the criteria used to define HTN, although most studies report a prevalence of between 60-80% [1-3]. In one cross-sectional study of 409 adults with stable kidney allograft function, the prevalence of HTN, defined as BP > 150/90 mmHg, was 77.3% [4]. Most subjects (68.9%) required multiple antihypertensive medications. However, for patients with diabetes or estimated GFR below 60 mL/min, treatment guidelines recommend blood pressure (BP) goals below 130/80 mmHg. Applying these more stringent recommendations, the true prevalence of post-transplant HTN is likely in excess of 95%.
\n\t\tThe exact pathogenesis of post-transplant HTN is poorly understood, as multiple factors impact its development. Important risk factors include preexisting recipient factors, donor specific factors, use of immunosuppressive agents, extra-allograft related issues, and both acute and chronic allograft dysfunction [1,2]. Ultimately, post-transplant HTN is characterized by sodium retention, enhanced sympathetic nervous system activity, renal vasoconstriction and relatively lower levels of plasma renin [5].
\n\t\t\tPost-transplant HTN demonstrates a distinctive characteristic regarding ambulatory BP monitoring, with patients having a high prevalence of nocturnal HTN [3]. In one prospective study, nearly 75% of subjects with post-transplant HTN demonstrated absence or reversal of the normal nocturnal fall in BP (i.e. non-dippers) [3]. In non-kidney transplant recipients, loss of nocturnal dip is associated with left ventricular hypertrophy, lacunar stroke and microalbuminuria [6].
\n\t\tSystemic steroid use contributes to the development of post-transplant HTN through various mechanisms, including sodium retention with resultant volume expansion, decreased prostaglandin biosynthesis, and increased smooth muscle pressor response [9]. In one systematic review comparing post-transplant recipients on immunosuppression regimens containing steroids versus those on steroid-sparing regimens, steroid-attributable incidence of HTN was reported to be between 2 and 17% [10]. A more recent meta-analysis of steroid avoidance or steroid withdrawal protocols examined impact on both graft and cardiovascular outcomes [11]. Steroid avoidance or withdrawal was found to be significantly related to increased risk of acute rejection and elevated creatinine at end of follow-up, with no difference in patient survival, graft loss, or death-censored graft loss. At the same time, steroid avoidance was associated with significant reduction in the risk of cardiovascular risk factors, including a 10% reduction in risk of post-transplant HTN.
\n\t\t\tHowever, some evidence indicates that chronic steroid use may not alter BP control. Vincenti and colleagues evaluated one-year outcomes and cardiovascular risk factors in kidney transplant recipients randomized to immunosuppressive regimens with complete steroid avoidance, early steroid withdrawal, and chronic steroid therapy. There was no difference in systolic or diastolic BP between any of the groups and no difference in the percent of patients requiring antihypertensive therapy [12].
\n\t\t\tPerhaps the most important factor in the pathogenesis of post-transplant HTN is the use of calcineurin inhibitors. Calcineurin inhibitors lead to the development of HTN through a myriad of mechanisms, including sodium retention with resultant volume expansion, enhanced sympathetic nerve activity, up-regulation of intrarenal renin biosynthesis, and vasoconstriction of the preglomerular vasculature via decreased production of vasodilatory factors and increased production of vasoconstrictive factors [5,13-14]. It has been shown that cyclosporine-induced renal vasoconstriction precedes the development of HTN [13]. Additionally, cyclosporine has been implicated as contributing to the loss of the normal nocturnal drop in BP [3].
\n\t\t\tIn recent years, there has been a paradigm shift in the choice of calcineurin inhibitors from cyclosporine to tacrolimus, as short-term patient and graft survival appears to be equivalent between the two [15]. Likewise, the incidence of acute rejection is similar between the two groups when they are used in conjunction with mycophenolate mofetil. The advantage of tacrolimus over cyclosporine is a lower incidence of hyperlipidemia, hirsutism, and gingival hyperplasia. However, a difference between the two agents with regard to HTN is not as clear.
\n\t\t\tA large multi-center open label randomized controlled trial comparing cyclosporine-based versus tacrolimus-based immunosuppression regimens involving 412 kidney transplant recipients reported that at 3 years post-transplantation, the number of recipients requiring antihypertensive therapy was lower in the tacrolimus group, though this did not reach statistical significance (74.7% vs. 84.9%, p = 0.06) [16]. Another cross-sectional study evaluating predictors of post-transplant HTN, defined as documented diagnosis of HTN or use of antihypertensive medications, determined that significantly more patients on a cyclosporine-based regimen were prescribed 2 or more antihypertensive medications compared with those on a tacrolimus-based regimen [17].
\n\t\t\tThere has been interest in the use of a mammalian-target of rapamycin (m-TOR) inhibitor-based immunosuppression regimen as an alternative to calcineurin inhibitor-based regimens, partially due to concerns over long-term effects of calcineurin-induced HTN and chronic preglomerular vasoconstriction. Thus far, the data regarding the relationship between m-TOR inhibitor use and HTN have been mixed. Comparison of a small cohort of patients on tacrolimus-based vs. sirolimus-based immunosuppression demonstrated that patients receiving sirolimus had significantly lower systolic BP on 24-hour ambulatory BP monitoring, though there was no significant difference in preserved nocturnal drop in BP [18].
\n\t\t\tHowever, two large randomized controlled trials evaluating the conversion of kidney transplant recipients from cyclosporine to sirolimus demonstrated no difference in BP [19-20]. The CONCEPT study, evaluating efficacy of conversion from cyclosporin to sirolimus 3-months post-transplantation in 237 kidney transplant recipients demonstrated no difference in systolic or diastolic BP one year post-transplantation [20]. There was, however, a tendency toward no need of antihypertensive medications in the sirolimus group compared to the cyclosporine group (51% vs. 38%), though this was not statistically significant. The CONVERT study, which followed a cohort of 830 subjects randomized to either sirolimus conversion or continuation of calcineurin inhibitor, found a statistically significant decrease in systolic BP at one month and in diastolic BP up to three months post-conversion [19]. However, by study endpoint two years, there was no significant difference in systolic or diastolic BP between the groups.
\n\t\tTransplant renal artery stenosis (tRAS) is a potentially important contributor to refractory HTN and unexplained graft dysfunction. The incidence of tRAS has been reported to range from 1 to 23%, with most of this variance attributed to differences in definition and diagnostic technique employed [21]. Most episodes of tRAS in the first three post-operative months are attributed to surgical complications, such as donor vessel trauma, intra-operative kidney malpositioning, or stenosis at the surgical anastamosis [22]. Transplant RAS occurring greater than 3 months post-transplantation is rarely related to surgical complications.
\n\t\t\tSeveral risk factors have been implicated in the development of late tRAS, including graft rejection, CMV infection, prolonged cold ischemia time, delayed graft function, and pediatric donor source [22-26]. A recent case-control study of 29 transplant recipients with tRAS found that CMV infection was associated with a five-fold increase in the risk for tRAS, while DGF increased tRAS risk four-fold [22].
\n\t\t\tDoppler ultrasonography may be an appropriate first-line screening test for tRAS, as it is non-invasive and avoids exposure to iodinated contrast media [21]. Sensitivity and specificity have been reported as high as 94% and 100%, respectively [27]. However, this diagnostic test is very operator-dependent, and such impressive results may not be obtained in centers without strong experience in kidney transplant imaging. Ultimately, the gold standard remains angiography.
\n\t\t\tTherapeutic options for tRAS include conservative management, angioplasty with or without stenting, and surgical repair. A recent case series compared the outcomes of these three strategies and determined that the highest success rate, defined as improvement in graft function, occurred in those who underwent primary angioplasty (36% conservative therapy, 82% angioplasty, 44% surgery) [28]. Graft survival at five years post-transplantation was also highest in the primary angioplasty group (63% conservative therapy, 86% angioplasty, 65% surgery). The primary angioplasty cohort was the only group in which a sustained improvement in BP was observed, with 63% of participants in this subgroup reaching target BP with a single agent post-procedure. However, angioplasty is not without risks. Four participants (6%) who underwent initial angioplasty had to undergo a transplant nephrectomy due to post-intervention complications, specifically uncontrolled bleeding and/or thrombosis. Furthermore, the presence of large or multiple stenoses may not be appropriate for primary angioplasty, leaving surgical intervention as the only viable option.
\n\t\t\tAnother case-controlled series of patients evaluated the efficacy of angioplasty with or without stenting in participants with tRAS [22]. Both serum creatinine and BP control improved significantly post-procedure. Restenosis occurred in 27% of patients at a mean time of 26 months post-procedure, and 10% experienced immediate graft loss due to procedural complications.
\n\t\t\tTransplant RAS is an important entity to consider in subjects with new-onset or refractory HTN. It can be effectively treated in most cases with angioplasty, which appears to impact positively on graft function and potentially prolonging allograft life.
\n\t\tThe precise role of HTN on allograft outcome has been difficult to define due to the complex interactions between HTN and worsening allograft function. Hypertension is both a cause and consequence of kidney disease. The presence of post-transplant HTN is associated with an increased risk for acute rejection, and allograft recipients who experience an episode of acute rejection have a significantly higher BP than those without rejection [29-30]. In a historical cohort study of adult allograft recipients, Mange and colleagues characterized the relationship between BP and subsequent allograft function [31]. For each 10-mm Hg increment increase in systolic, diastolic and mean BP, there was a 15%, 27% and 30% reduction, respectively, in the rate of allograft survival. Another cohort study by Opelz and colleagues demonstrated that systolic BP greater than 140 mmHg was associated with increased risk of graft failure, regardless of diastolic BP or history of acute rejection [32].
\n\t\t\tPost-transplant HTN is associated with increased mortality, chronic allograft nephropathy, acute rejection, and graft loss [7,30,32]. It is also an independent risk factor for the development of cardiovascular disease, the leading cause of death in kidney transplant recipients. The fact that more severe HTN has been associated with a higher rate of graft dysfunction, worse graft survival and a higher frequency of proteinuria is suggestive of a causative relationship [8].
\n\t\tIn patients with chronic kidney disease (CKD), therapy for HTN slows the progression of renal insufficiency [33]. This suggests that treatment of post-transplant HTN may likewise ameliorate the loss of allograft function. Current KDOQI guidelines recommend kidney transplant recipients maintain a target BP < 130/80, largely based on extrapolation from outcomes data in CKD patients [34]. Due to various contributing factors, post-transplantation HTN can be difficult to control. Multiple retrospective cohort analyses report a significant proportion of subjects fail to reach target BP, even with use of multiple anti-hypertensive agents. A review of the Collaborative Transplant Study, a database involving nearly 30,000 chronic transplant recipients at 400 international transplant centers, demonstrated that only 44.5% achieved systolic BP < 140 mmHg and that 24.5% achieved systolic BP < 130mmHg [32]. A smaller cohort study of 150 transplant recipients demonstrated that over 60% of patients required three or more anti-hypertensive medications and that only 40% reached the target BP of < 130/80 mmHg [35]. Although the risk of HTN is well documented, there are few published reports on the management of post-transplant HTN that clearly elucidate ideal target BP or choice of individual antihypertensive agents [22].
\n\t\tCalcium channel blockers (CCB) are effective medications to lower BP in kidney transplant recipients. In the general population, they have proven to be robust agents to lower BP regardless of age, gender, ethnicity, and salt intake, which may explain why they are also effective in the kidney transplant population [36]. In addition, they also appear to reverse some of the intra-renal vasoconstriction caused by calcineurin inhibitors [36-38]. One trial of 65 transplant recipients receiving cyclosporine-based immunosuppression randomized to the CCB or placebo at the time of transplantation demonstrated that those taking felodipine had a significantly higher renal plasma flow at 6 weeks [39]. Additionally, those randomized to the felodipine also group had lower systolic and diastolic BP, higher renal plasma flow, and higher GFR (49ml/min vs. 40ml/min, p = 0.05) at 12 weeks post-transplantation, despite a greater proportion of patients in the placebo group receiving other anti-hypertensive agents.
\n\t\t\tIn a study of 123 immediate post-transplant recipients, subjects were randomized to nifedipine (CCB) or lisinopril as first line maintenance BP medication [40]. At three months post-transplantation 20% of all participants had achieved a goal diastolic BP of < 95 mmHg, with 38% in the CCB group reaching diastolic BP goal at one year. There was no difference in BP response between groups, but patients randomized to nifedipine had higher hemoglobin and lower creatinine levels compared to the lisinopril group at the study end. In an additional study comparing nifedipine and lisinopril, impact on left ventricular mass and function was assessed [41]. This study demonstrated that myocardial mass was significantly reduced in both groups one year post-transplantation, with a mean reduction of 15% in both groups. There was no statistically significant between-groups difference. The percentage of participants with persistent left ventricular hypertrophy (LVH) one year post-transplantation was similar between groups (45% nifedipine, 41% lisinopril p = NS). Another study of 99 kidney transplant recipients one year post-transplantation randomized subjects to 1 of 3 groups: (i) amlodipine (CCB) monotherapy, (ii) enalapril monotherapy, or (iii) combination amlodipine and enalapril [42]. At six months post-randomization, there was no difference amongst the three groups in terms of systolic BP or number of antihypertensive agents used. However, participants assigned to amlodipine monotherapy demonstrated improved creatinine clearance but no change in proteinuria, as compared with either angiotensin converting enzyme (ACE)-inhibitor monotherapy or combination. Results of this study should be interpreted with some caution, as they did not reach the target number of participants for adequate power.
\n\t\t\tA recent meta-analysis of randomized controlled trials involving antihypertensive agent use in renal transplant recipients was conducted [43]. This analysis concluded that use of a CCB versus placebo did not reduce the risk of death but did reduce the risk of graft lost by 25% at 12-months post-transplantation. Additionally, subjects receiving CCB had significantly higher estimated glomerular filtration rate (eGFR). When compared to ACE-inhibition. There was no difference detected in death, graft loss, or cardiovascular event risk.
\n\t\tUse of renin-angiotensin system (RAS) blockers in kidney transplant recipients was initially limited due to a number of concerns, including ineffectiveness in BP control, potential exacerbation of anemia, potential for inducing hyperkalemia, and the risk of precipitating acute kidney injury, [44-46]. The concern for ineffective BP control with renin-angiotensin blockade was related to the fact that post-transplant HTN, characterized by a low renin, volume expanded state, has been compared with the Goldblatt single-kidney, one-clip model of HTN, which potentially would not be very responsive to these agents [47]. However, this concern has not been borne out clinically, as multiple studies have demonstrated that RAS blockers have efficacy in reducing BP in post-transplant HTN [48].
\n\t\t\tRenin-angiotensin system blockade has now become commonplace in many transplant centers. Before 1990, approximately 9% of post-transplant subjects received treatment with an ACE inhibitor, which increased to roughly 47% in 2003 [49]. In the same retrospective review, only 38.5% (781 subjects) had never received an ACE inhibitor or an angiotensin receptor blocker (ARB). Six-hundred thirty eight subjects (31.4%) used ACE inhibitor or ARB therapy for the entirety of their follow-up, and 612 subjects (30.1%) received this therapy during various times of follow-up [49].
\n\t\t\tFurthermore, there are multiple theoretical benefits supporting the use of RAS blockers in the treatment of post-transplant HTN, such as (i) decreasing intraglomerular capillary pressure, (ii) decreasing the production and expression of the potentially damaging growth factors, (iii) decreasing proteinuria, (iv) for primary and secondary prevention of adverse cardiovascular outcomes, (v) decreasing cyclosporine nephrotoxicity, and (vi) blocking angiotensin type 1 (AT1) receptor antibodies that may be associated with vascular rejection [50-51]. In the general population, RAS blockers have been shown to reduce both primary and secondary cardiovascular events [52]. Despite these theoretical benefits for their RAS blocker use, there are no prospective studies demonstrating the advantage of RAS blockers for the protection against allograft loss or for prolonging patient survival.
\n\t\t\tThe largest study to date that has evaluated the efficacy of a RAS blocker is the SECRET trial, a multi-center double-blind randomized placebo-controlled trial involving 500 participants from several transplant centers in Europe [53] This trial was designed to evaluate the effects of Candesartan (ARB) therapy compared with placebo, on mortality, cardiovascular events, and graft failure. The study was discontinued prematurely due to a lower than expected event rate in both groups, which precluded conclusions regarding the primary endpoints. However, analysis of secondary endpoints revealed that reductions in BP and proteinuria were greater in the ARB group, but this was associated with a decrease in creatinine clearance and hemoglobin There was no significant difference in cardiovascular or graft outcomes between the two groups, though the overall event rate was quite low (5.1% with candesartan vs. 5.3% with placebo).
\n\t\t\tA much smaller study involving fifty recipients of living unrelated kidney transplants at least six months prior to enrollment were randomized to losartan (ARB) 50 mg daily or placebo for one year [54]. Of note, the subjects were not proteinuric at randomization. There was no difference in number of antihypertensives prescribed between the two groups and no difference in creatinine clearance at study end. However, systolic BP was significantly lower in the ARB group at 12 months (113mmHg vs. 126mmHg).
\n\t\t\tAlthough insufficient data exist to determine the impact of RAS blockade on overall cardiovascular outcomes, a small study has evaluated the impact of ACE inhibitor therapy on echocardiographic findings [55]. Evaluation of 74 transplant recipients randomized to lisinopril (ACE) or placebo and followed for 18 months demonstrated a significant decrease in left ventricular mass index in the ACE group while no difference was observed in the placebo group. There was no difference between the groups in terms of systolic BP, serum creatinine, urinary protein excretion, or number of antihypertensive agents used. The decrease in left ventricular mass index was observed exclusively in those concomitantly treated with ACE and cyclosporine, as opposed to tacrolimus. This small study is one more piece of evidence corroborating data from the general population, suggesting that drugs that block the RAS are capable of regressing left ventricular hypertrophy, both as part of their hemodynamic effect, but also through BP independent mechanisms. It is likely that regression of LVH may be a beneficial prognostic event that patients achieve with an appropriate BP control and an optimal class of antihypertensive therapy, with a potential for reducing adverse cardiovascular events. This study parallels efforts in older trials in the general population, illustrating the advantages of a RAS blocking drugs in the reduction of proteinuria and the risk for cardiovascular events and renal disease progression. Sadly, compelling data are still lacking in the kidney transplant population.
\n\t\t\tAlthough the number of randomized controlled trials regarding RAS blockade in transplant recipients has increased in recent years, much of the available data are from retrospective studies and systematic reviews. In one retrospective review of more than 2,000 recipients of kidney transplants at the University of Vienna, investigators noted that the ten-year patient survival rates were 74% in patients receiving either an ACE inhibitor or an ARB as part of their antihypertensive regimen and only 53% in patients not receiving these agents [49]. Their results were even more remarkable when one considers that the group receiving the RAS blockers were older, required a higher number of antihypertensive medications, and were more likely to have type 2 diabetes and evident cardiovascular disease, when compared to the group not receiving these agents. Although selection bias limits the power of this study, the data are intriguing and suggest that there may be an important advantage to employ RAS blocking drugs as part of an antihypertensive regimen in an effort to reduce cardiovascular events.
\n\t\t\tHeinze and colleagues [49], studied 436 kidney transplant recipients who had delayed graft function. Approximately half of those patients (n=181) were given either an ACE inhibitor or ARB at the time of transplantation. Those patients who received RAS blocker had improved ten-year graft survival, when compared to those who were not treated with RAS blockers (44% vs. 32%, respectively). Hiremath and colleagues [56] performed a systematic review of 21 randomized trials of 1,549 patients to determine the effect of ACE inhibitor or ARBs on graft function and patient survival after kidney transplantation. In this analysis, drugs that block the RAS were associated with a significant decrease in GFR (-5.8 mL/min), proteinuria (-470 mg/day), and hematocrit (-3.5%)[51,57]. However, there was insufficient data to determine their impact on patient or graft survival. Authors suggested that there may be a trade off between the beneficial effects of proteinuria reduction and potential cardiac protection with the development of possible anemia and lowered GFR.
\n\t\tSince kidney transplant patients are at much greater risk for cardiovascular events compared to the general population [58], due to both traditional and non-traditional Framingham Heart Study risk factors, beta-blocker use is often advisable. This may be important both during the peri-operative period to protect against myocardial ischemia, but also in the long-term management of HTN and cardiovascular disease. However, these agents have not been extensively studied. A recent meta-analysis of randomized controlled trials involving antihypertensive therapy in renal transplant recipients identified four studies involving beta-blockers [43]. Currently, there is insufficient data to determine relative benefits and harms of these agents. However, data from these studies indicate that beta-blockers are effective in BP reduction without appreciable impact on renal function, proteinuria, or left ventricular mass [59-61].
\n\t\tIn addition to their antihypertensive effects, alpha-blockers are often used to facilitate prostatic relaxation. This is particularly important in many older patients who may have occult prostatic hypertrophy, or some degree of bladder detrusor neuropathy due to diabetes. However, these agents, in general, tend to cause significant orthostatic symptoms, and have not been proven to reduce mortality [62]. Both doxazosin and prazosin have been shown to decrease HTN in transplant recipients, although the literature remains sparse [63-64].
\n\t\tTaken together, the clinical trials of antihypertensive therapeutics in kidney transplant subjects illustrates that BP can be controlled. However, it usually requires multiple drugs. Although the data is not definitive, it appears that CCB and or RAS blockers should be included in an effective antihypertensive regimen. Subjects at risk for, or who have known coronary disease, may also derive benefit from beta-blockers. More studies are needed to define optimal levels of BP control and ideal combination of agents to facilitate better long-term patient and graft survival in kidney transplant recipients.
\n\t\tEMG is an electrodiagnostic technique used to record the electrical activity in skeletal muscles. EMG signals are complex and exhibit intricate patterns that are dependent on the anatomical properties of the muscle [1, 2, 3]. The signal manifests the neuromuscular activation underlying muscle contraction [1, 3]. Therefore, an abnormality in the contraction of a muscle due to an injury, nerve damage, or muscular or neurological disorder that causes motor dysfunction can be identified through EMG signal diagnosis. The motor neuron signal carries information from the CNS aimed for limb displacement by flexing and extending the joints [4, 5]. The dynamic electrical activity of these motor units is called motor unit action potentials (MUAPs). These are super-positioned and recorded by the EMG device [6]. EMG can be recorded using surface electrodes, fine wire electrodes as well as anal and vaginal probes for pelvic floor muscles [2]. A simple model of an EMG signal is given by Eq. (1), where, y(n) is the sampled EMG signal, a(r) is the MUAP, x(n) is point processed firing impulse, wn is the white Gaussian noise and N is the number of motor unit firing at a particular time.
Our aim in this article is to review EMG signal processing techniques that facilitate detection of gait and movement disorders. We discuss techniques from simple enveloping to complex computational machine learning algorithms that may help detect alterations in EMG patterns while performing daily life activities. We may note that there are number of highly cited review articles such as Raez et al. [7], and Chowdhury et al. [8], that review EMG processing and classification techniques. The novelty in our review is that in addition to discussing innovative processing techniques we have emphasized their applications, particularly focusing on lower limb disorders. In Section 2, we review the basic techniques such as EMG enveloping, followed by EMG onset/offset detection in Section 3. In Section 4, we review current literature on the decomposition of EMG signals into MUAPs and muscle synergies. In Section 5, we discuss the analysis of the EMG signal in the frequency and time-frequency domain to understand changes due to motor impairment. When working with a larger sample size, a machine learning system can be used to classify subjects with altered muscle activation and abnormal gait patterns [9, 10]. In Section 6, we discuss algorithms that employ supervised and unsupervised learning to detect patterns of gait disorders, followed by a discussion of future trends and conclusion in Section 7.
\nVisual inspection of the raw EMG plot or its envelope requires high dexterity and clinical experience to detect motor impairment. The methodology to obtain the EMG envelope includes preprocessing, signal filtering, rectification, smoothing, standardization, statistical testing, and intricate computational algorithms. Scientific recommendations by SENIAM project and International society of electromyography and Kinesiology (ISEK) suggest use of bandpass filters (10–500 Hz) to reduce aliasing effects when using a sampling frequency of 1 kHz. Intramuscular and needle recordings should be made with the low-pass cut-off set at 1500 Hz. Avoiding notch filter is recommended as it destroys the signal information [2]. De Luca et al. recommended root mean square (RMS) value to compute the signal amplitude of the EMG during voluntary contraction [3]. Methods to form EMG envelopes include moving average, root mean square, spline interpolation over local maxima, integrated EMG etc. EMG envelope can also be obtained from low pass Butterworth 6 Hz filter. Hilbert finite impulse response (FIR) filter computes magnitude of the analytic EMG signal.
\nA decrease in EMG amplitude was visually observable for chronic spinal cord injury (SCI) patients while walking for 3 min [11]. Biceps femoris (BF) and gastrocnemius medial (GM) revealed consistent activity, but that was not the case for tibialis anterior (TA) and rectus femoris (RF). The RMS magnitude of the signal from BF and GM muscles decreased with longer activity duration (10 min) followed by an EMG burst resulting from muscle spasm. Identification of chronic SCI was done by simple visual inspection of the raw EMG [11]. The inter-neuronal degradation was the cause of decreased locomotor performance [11]. The RMS amplitude of the EMG signal using a paired t-test showed a higher duration of muscle activity for BF and TA among cervical spondylotic myelopathic patients (CSM) [12]. The amplitude of the muscle burst activity was not statistically different between the healthy group and CSM [12]. The muscle stretch analyzed from kinematic data did not relate with spasticity, but the ratio of EMG RMS amplitude to the mechanomyogram data showed statistically significant results for healthy and myotonic control groups [12, 13].
\nThe stochastic and nonstationary nature of EMG signals makes it harder to study the innate patterns of the electrical activity of the muscles. Statistical tests such as Pearson’s, Pearson’s r, the Kolmogorov-Smirnov T-test, ANOVA F ratio and t-test, and Wilcoxon Signed Rank Test can demonstrate significant changes in the EMG profiles associated with different behavior [14, 15]. Domingo et al. performed an ANOVA on the normalized EMG amplitude of spinal cord injured patients, which led to the conclusion that with increased speed and no manual assistance the EMG pattern exhibited statistical significance when compared to the control group. The shape and timing of EMG patterns were less similar to controls [16]. Among stroke patients, the EMG activity displayed heterogeneity in comparison with healthy individuals [17]. Nieuwboer et al. [18] demonstrated that raw EMG and its linear envelopes of Parkinson’s patients during freezing episodes displayed abnormal activity of TA and GM. Nonparametric tests on the RMS EMG envelope of the hemiplegic patient showed statistical significance during push off and early stance phase [14]. EMG data acquired from Parkinson patients’ shoulder muscles revealed higher activation than those of healthy control subjects [19]. Average and maximum EMG amplitude were calculated for comparison [19].
\nTraditional statistical testing of the EMG uses ANOVA techniques that may not identify visually differentiable waveform features. McKay et al. [20] developed a more reliable statistical method to find the underlying patterns with the wavelet-based functional test (wfANOVA). Its performance to detect the changes in the magnitude and shape of EMG was more precise than the time domain ANOVA test. Wilcoxon signed rank tests were also used in studies with non-parametric data [12]. EMG envelope extraction using time domain features from multichannel sensors and their statistical tests can assist in the detection of altered myoelectric activity. Specific features such as EMG onset/offset, MUAP etc. can be analyzed from the envelopes for the diagnosis of gait disorders. Figure 1 shows signal envelope extracted from the EMG signal with RMS. MATLAB functions were used to extract envelope and perform a statistical hypothesis test for a healthy individual and other disorders.
\nRMS envelope from a healthy, a myopathic, and a neuropathic patient. A non-overlapping window of 200 samples was used and a paired student t-test revealed statistical significance (p < 0.05) between healthy and neuropathic, and healthy and myopathic conditions. The data was obtained from physionet [21].
EMG onset parameters define the duration for the muscles to stay active [2]. Onset estimation is useful to diagnose abnormality in muscle coordination. To detect the EMG onset, visual inspection or measurement of nerve conduction velocity may be used [22]. The basic thresholding method for onset detection is sensitive to the type of trials, EMG amplifiers and noise level in the signal. The thresholding based on SD baseline noise can be improved with local peak value. In a study [23], integrated EMG provided more information about early activation. During preconditioning, Teager-Kaiser Energy Operator (TKEO) also improved the onset detection accuracy by constricting the energy of the baseline noise [24, 25]. Staude et al. compared onset detection methods based on the statistical optimal decision threshold [26]. The simple threshold algorithm of Hodges and Bui [26] identifies the onset at a point where the mean of the samples within a fixed time window surpasses the baseline level by a defined multiple of standard deviation [27].
\nThe basic framework of the threshold detection algorithm includes signal conditioning (rectification, filtering, whitening etc.), detection (Test Function and Decision rule), and postprocessing [26]. A block diagram is shown in Figure 2.
\nEMG onset estimation framework; xk is Gaussian noise signal, yk is the processed signal, σ′o and μ′o are standard deviation and mean of samples, respectively, gk ≥ Th (Threshold) is the value to trigger an alarm ta, and t′o is the change time estimation.
Double threshold methods are considered better in comparison to single threshold methods [7]. The Bonato algorithm [28] includes pre-whitening filter and data sample squaring in the conditioning unit. The test function is computed between two successive samples from the conditioned EMG signal. The onset point identification is based on the following rules: (1) x out of y samples must exceed the threshold and (2) activation state of the muscle after surpassing the threshold should last for a certain number of samples or duration of time [26].
\nIn Lidierth [29] method, the signal conditioning unit performs full wave rectification. The test function and decision rule are based on Hodges [26]. Additional post-processing rules increase the efficiency of the algorithm. The test function unit detects the onset if the sEMG signal exceeds the threshold. Any decline in the activity below threshold within a defined duration, should not be longer than the defined range of samples [29]. The power spectral correlation coefficient method performs better than TKEO and utilizes the moving average method of Hodges and Bui [30]. The statistical estimation algorithm includes an optimal estimator and approximated generalized likelihood-ratio detector. The statistically optimized algorithms are more robust in terms of signal parameters [26]. Tenan et al. [25] reviewed three classes of standard EMG (linear envelope, entropy, TKEO) and six classes of statistical EMG onset detection (general time series/mean–variance, sequential change point detection with parametric and non-parametric methods, batch change point detection, and Bayesian change point analysis). The Bayesian Change Point analysis algorithm showed higher reliability and accuracy for the singular EMG onset detection.
\nMaximum voluntary contraction (MVC) is a common scaling technique for EMG onset detection. MVC is the largest RMS amplitude a muscle generates in maximum contraction [31]. MVC has a curvilinear relationship with the muscle force production, where less force production amount to muscle weakness. EMG onset on a normalized time series with MVC can help diagnose gait disorders associated with atrophy [2]. Muscle spasticity/co-contraction during tremors among patients with neurological gait disorder exhibited abnormality in EMG onset compared to healthy individuals [12, 32]. EMG envelope indicated alterations in EMG onset for patients with Parkinson’s during freezing episodes [20]. A premature activation of TA and GM muscles before a freezing episode was observed. In gait impairment, due to cervical spondylotic myelopathy, delayed onset and prolonged activation were present [12]. In cerebral palsy earlier onset suppression of EMG within cutaneous muscular reflex is associated with motor dysfunction, which results in inhibitory postsynaptic potentials [33].
\nRaw EMG signal consists of superpositioned motor unit activation potentials (MUAP) and noise components. Muscle crosstalk is a major issue during recording of the biological signals. The crosstalk is dependent on factors such as anatomical site for the placement of electrodes, type of movement, and skin thickness. Since it is harder for sEMG to detect the origin of muscle electrical activity, the chances of muscle crosstalk are higher in sEMG than needle EMG [13]. Besides, low spatial resolution, high movement artifact, and narrow frequency range makes needle EMG more promising as a diagnostic tool in nerve conduction studies for assessing neurological disorders [13]. Changes in the shape of MUAPs, large dynamic range of action potential among motor units and superposition of motor units pose major challenges to decomposing the sEMG.
\nFang et al. [34] decomposed EMG into MUAP by wavelet transform. The technique utilized spectrum matching in wavelet domain as opposed to waveform matching. De Luca et al. [35] proposed a method to decompose the sEMG into MUAP during cyclic dynamic contractions. The algorithm solved two main problems, the first associated with the displacement of the electrode on the surface of the skin leading to alteration in the shape of MUAPs, and second regarding lengthening and shortening of the muscle fibers while undergoing those contractions. The algorithm was an extension of the algorithm by Nawab et al. The process was followed as an extracting time-varying time template parameter, performing time-varying filter analysis, clustering on MUAP trains, shape refinement, test, and decomposition. If the test failed, the iterations were done again for shape refinement of MUAPs. Precision Decomposition I (PD I), which was earlier used to decompose needle EMG data was updated to decompose sEMG and referred as PD (III). An updated approach of PD III reported by Nawab et al. has PD-IPUS (Integrated Processing and Understanding) and PD-IGAT (Iterative Generate and Test) [36, 37]. Another method to decompose sEMG into MUAP trains included a hybrid approach of K-means clustering and convolution kernel compensation method. K-means clustering was performed to estimate the pulse trains, which were later updated iteratively by convolution kernel compensation method [38].
\nThe question arises, what changes may a neurological disorder or injury bring to MUAPs? The features of a MUAP (rise time, duration, amplitude, phases/turns, recruitment and, stability) are vital to diagnosing the cause of abnormality in muscle coordination leading to gait or other movement disorders. A normal motor unit and a motor unit after injury (axonal injury) are distinguishable [32, 39, 40, 41]. MUAPs from needle EMG are not only adequate in diagnosing neuropathy (nerve injury) but can also determine the severity of the neuropathic condition [41]. Abnormal motor units constitute polyphasic potentials, unlike diphasic or triphasic potentials that exist in healthy individuals. Polyphasic potentials are a result of nascent potentials and terminal collateral sprouting [40]. Rodriguez-Carreno et al. [6] reported MUAPs shape abnormality pertinent to the anatomical phenomena shown in Table 1. A study conducted on mice with amyotrophic lateral sclerosis (ALS) using single unit extracellular recording within the spinal cord and EMG revealed gait variability [32]. In ALS mice, the low frequency of motor neuron and irregularities in the motor burst were co-occurring with fractionated EMG.
\nMUAP abnormality | \nAnatomical relation to changes | \n
---|---|
Increased amplitude | \nIncrement in connective tissues, loss of muscle fibers | \n
Decreased amplitude | \nMuscle fibers grouping | \n
Decreased duration | \nLoss of muscle fibers | \n
Increased duration | \nIncreased muscle fibers | \n
Increased spike duration | \nVariation in muscle diameter and increased endplate thickness | \n
Increase in number of turns and phases | \nSlow conduction of terminal axons/increased diameter of muscle fiber and end plate | \n
Increase in firing rate | \nLoss of motor units | \n
Increase in the jiggle | \nAtypical neuromuscular transmission | \n
MUAP abnormalities and indicated anatomical changes.
Among patients with myopathy, short, small, long duration, polyphasic and early recruitment of MUAPs were observed [39]. Different myopathy disorder studies in relation to MUAP trains were conducted using needle EMG by Paganoni et al. [39]. In early phases of disorders due to loss in muscle fibers the compound muscle action potential amplitude is lower. The result was short, small and early recruitment of MUAPs, but in Lambert-Eaton Myasthenic Syndrome, higher CMAP amplitude was observed. The shapes of MUAPs also alter with chronicity. Instead of positive sharp wave and fibrillation in the needle EMG, a mixture of long and short duration of EMG is prevalent [39]. Use of sEMG in comparison to needle EMG for postural disorder is preferable. sEMG is very good at detecting kinesiological disorders such as myotonia, myoclonus and tremors [13]. It can further be decomposed into MUAPs with the PD (III) algorithm, or hybrid of K-means and convolution kernel compensation method.
\nLinear decomposition of multi-source EMG signal is another method to diagnose the alteration in EMG patterns of patients with gait disorders [5, 42]. The muscle synergy hypothesis can be employed to understand better the physiological aspects of gait disorders using a number of linear decomposition algorithms such as principal component analysis (PCA), factor analysis (FA), independent component analysis (ICA), and non-negative matrix factorization algorithm (NNMF). Each algorithm is unique and extracts the synergy structure based on the assumption made on the synergy (e.g. orthogonality, non-negativity, statistical independence, etc.). After applying the factorization algorithm, the multi-electrode EMG signal is decomposed into the activation coefficients and synergies. The synergy vectors from the healthy group can be compared with a group suffering from the neurological or non-neurological disorder [43]. Statistical tests including cosine correlation, Pearson correlation or cluster analysis are generally used to compare the similarity and alterations in synergy structures [44, 45]. The application of a clustering algorithm for diagnosing gait disorder is discussed in a later section. Patients with thoracic spinal cord injury revealed lesser modules, higher co-contraction and, less directional tuning in relation to healthy individuals [46]. It is likely that the number of dimensional space was affected due to the choice of preprocessing [47]. A review cum research by Kieliba et al. [47] supported that increase in the cut off frequency of the filter decreases the variance, accounts for a particular component and increases dimensional space of synergies to be extracted. EMG acquired from children with cerebral palsy and from individual’s post-stroke has shown that the choice of preprocessing (filtering, normalization) had an effect on the number of synergies and differentiation of physiological traits [48, 49]. Figure 3 displays how the choice of low pass filter (10 and 20 Hz), a second-order Butterworth filter, effects the dimensional space. Filters are generally used to remove movement artifact. The principal component variance is higher for 10 than 20 Hz.
\nA variance threshold ≥0.9 reveals five synergies for 10 Hz low pass filter and four synergies for 20 Hz low pass filter for 9-channel EMG data.
From a neurophysiological perspective, the recruitment of fewer spinal modules during movement is due to the loss of supraspinal inflow that results in simple muscle coordination (neuroadaptation). In upper extremities, the neuroadaptation was similarly perceived in the form of changes in the dimensional space of muscle synergy structures. Alteration of synergy structures was also present in patients with chronic stroke (upper extremity), and cerebral palsy [42, 43, 45, 50]. The linear envelopes extracted from the EMG data are subjected to MS extraction. The synergy hypothesis is well suited for capturing the physiological aspects of motor impairment [19]. In chronic stroke, merging and fractionation of synergies were observed. Merging of muscle synergies results in poor muscle coordination. In children with cerebral palsy, the dimensional space was smaller than it was in the control participants (unimpaired) [42]. However, the modules for cerebral palsy were higher for Duchenne muscular dystrophy (DMD) and typical developing (TD) children [43]. Rodriguez et al. revealed that fewer modules were recruited while walking on treadmill among Parkinson’s patients. Thus, the size of dimensional space is crucial for the assessment of gait disorder such as cerebral palsy and Parkinson’s [51, 52]. It is also important to properly choose preprocessing before analyzing the synergies as the dimensional space is sensitive to the preprocessing methods.
\nEMG power spectrum estimation methods can be categorized into parametric and nonparametric techniques. The spectral methods include fast Fourier transform (FFT), multitaper analysis and short-time Fourier transform (STFT) and wavelet transform. The difference between FFT and Wavelet Transformation is that FFT is localized to the frequency domain whereas the latter is localized to time-frequency analysis. Hu [53] recorded cortical and spinal somatosensory evoked potential (CSEP and SSEP), cortical motor evoked potential (CMEP) and spinal cord evoked potential (SCEP). The short time Fourier transformation was applied to the CSEP signal with a Hanning window [53]. The results revealed that the time-frequency analysis is a better marker for spinal injury than time domain analysis. The peak power after spinal injury had lesser energy with more dispersion in time-frequency scale.
\nThe EMG time series signal can be analyzed in the frequency domain for the diagnosis of gait disorders. The frequency spectrum for EMG signals is in range of 0–500 Hz [54]. The FFT algorithm [55] computes the discrete Fourier transform (DFT) of EMG signal more efficiently. The FFT decomposes the EMG signals into periodic sine and cosine waves. We computed the FFT of EMG signal recorded from the Vastus Medialis (VM) during walking (Figure 4).
\n(A) sEMG signal from VM during walking in time domain; (B) frequency domain representation of the signal using FFT.
The FFT allows computation of power spectra by squaring of FFT’s magnitude [56]. In Parkinson disease, the spectral power of the signal has lower amplitude for the usual tremor than for the unusual tremor, which has peak amplitude of 4–6 Hz during an atypical tremor [15]. The signals associated with nonperiodic tremors are differentiable with FFT [57]. The EMG signal from neuropathic patients with SCI also exhibited distinct power spectrum density and amplitude in comparison to healthy individuals [58]. The application of FFT to the EMG envelope revealed muscle burst discharge in frequency domain ranging from 4 to 7 Hz [15]. Average power spectra computed from fractionated EMG of ALS mice by FFT was significantly higher than the control group. In the ALS group the spectra were skewed towards higher frequency content but single unit recordings revealed the absence of higher motor neuron (MN) frequencies or shortening of MN frequency in ALS mice [32], due to small type firing neurons improperly increasing firing frequency. This phenomenon results in co-contraction thus producing fractionated EMG. Co-contraction in muscles can also be observed in spinal cord injured patients [32]. In a study, EMG signals from lower limbs of dystonic and non-dystonic participants while walking were recorded. The non-dystonic participants were also patients suffering from other gait disorders. The power spectral density was computed using FFT with the Welch method of 50% overlap. The median power frequency (MdPF) and total power in low frequency were calculated for each muscle. The results revealed that MdPF for dystonic muscles had shifted to low frequencies and a concurrent increase in total power percentage in low-frequency range was observed [59]. Thus, frequency analysis of EMG signal not only provides us with distinction between normal and abnormal gait behavior but also specific gait abnormalities can be distinguished.
\nShort-time Fourier transformation (STFT) is used to analyze a nonstationary signal in the frequency-domain. The signal is sliced and subjected to Fourier transform. Segmenting the signal is called time domain windowing, and the time localized signal is defined by \n
Mitchell et al. [60] used cross time-frequency analysis to diagnose hypertension of the GM muscle. The study included 57 elderly people with 10 younger adults. Reduced Interference distribution (RID) was utilized to remove cross terms implementing time smoothing window and frequency smoothing window. A Hanning frequency smoothing window was chosen. In the study of gait, it is necessary to consider a time-localized cross-correlation between two signals, such as left and right muscle groups responsible for gait [60]. Hence, cross Wigner distribution (CWD) was selected to preserve the phase information. The results revealed statistical significance for several time-frequency parameters of sEMG between control group and persons with neuropathy, diabetes, osteoporosis, and arthritis patients [60]. STFT does not adopt an optimal time window or frequency resolution for non-stationary signals [7]. For the implementation of FFT and STFT the signals are considered to be stationary [8]. The problem or resolution can be overcome by continuous wavelet transform (CWT) [8]. Multitaper analysis is another and perhaps more efficient method for power spectral analysis to deal with non-stationary signals [61, 62].
\nWavelet transform such as Multitaper is well suited for non-stationary signals. Wavelet transform elicits good localization of energy when the MUAP shape matches that of the wavelet [8]. Continuous wavelet transform (CWT) of bandpass filtered EMG showed alteration in the motor unit among stroke patients when a foot drop stimulator device was used (FDS) [63]. Energy localization below 100 Hz that resulted from foot drop was caused by slow motor unit recruitment. The neuromuscular activation improved with FDS. The time-frequency plot for Gastrocnemius showed that peak energy localization shifted from 50 to 100 Hz as a neuromuscular strategy [63]. Instantaneous mean frequency (IMNF) is the average frequency of power density spectrum of a signal and is computed from time-frequency distribution, W(f, t) [63], where W is obtained from continuous wavelet transformation defined by (3) and (4).
In the above, x is the scaling factor that controls the width of the wavelet, y controls its location in time, \n
Time and frequency domain features of the EMG signal may be used to diagnose gait disorders. For example, an image processing technique can be used to detect pathological gait affected by abnormal firing of MUs [65]. Machine learning algorithms are important tools in detecting the pattern of normal and abnormal gait [66, 67]. They do so by making minimum assumptions about the data generating system, as it does not need a carefully controlled experimental design [9]. Application of machine learning algorithms to detect and classify gait disorders is suited to big data. Machine Learning is further divided into: (1) Supervised learning and (2) unsupervised learning. We will now discuss techniques to detect gait disorders using supervised and unsupervised learning algorithms.
\nUnsupervised learning can be used to find structures in the EMG data. For example, cluster analysis has been used to identify alteration in the gait patterns, which are undetected by statistical tests. Patients with Parkinson’s disease can be distinguished from a healthy individual by using cluster analysis of dimensionally reduced feature vector [68, 69]. K-means clustering is a very common clustering technique that initially estimates K centroids randomly or selectively. The algorithm iterates between two steps, data assignment steps and updating centroid. The aim is to minimize objective function, which is given by (5).
where V(j) is the objective function, n is the number of data points in jth cluster, k is the number of clusters and \n
The hypothesis of muscle synergies has been applied in several studies [44, 45, 70]. Unsupervised Learning helps in grouping identical synergies and can be helpful in diagnosing gait disorders. Kim et al. [70] identified synergies using iterative K-mean clustering and intraclass correlation. Hierarchical, model-based, fuzzy c means clustering has been employed to group gait patterns [69, 71, 72, 73]. Dolatabadi et al. [71] used mixture model clustering on spatiotemporal gait pattern to classify pathological gait. Pathological disorders such as cerebral palsy that show higher inter-stride variability can be analyzed with a hierarchical clustering method proposed by Rosati et al. [72]. Feature Fusion technique with Davies Bouldin Index (DBI) based on fuzzy C means algorithm was used in a trip/fall study [73]. The DBI can be used to evaluate the clustering algorithm. We have used K mean cluster analysis to cluster normal gait and gait with constraints, which are displayed in Figure 5.
\nA total of four clusters were chosen to group sEMG signal based on 93% variability in data within each cluster. The clusters were plotted for the first two principal components for walking with and without constraint.
In supervised learning, the predictive models are based on the input and output data. Some of the widely used learning algorithms are decision trees, Bayesian networks, support vector machine, artificial neural networks, and linear discriminant analysis (LDA). After feature extraction and classification, the EMG time series can be modeled to control prosthetic or rehabilitative device. The fundamental approach to classification of EMG signal is shown in Figure 6 [66].
\nBlock diagram of an EMG Signal classification system.
The performance of different algorithms (SVM, LDA, MLP) in classifying gait disorders (Cerebral Palsy) was compared [74]. SVM classifier, compared to LDA and MLP, performed better when the analysis was done on kinematic data [74]. The normalization of the EMG data from different limb configurations increased classification accuracy [74, 75]. Feature level fusion is used to extract the feature space from daily life activities [73]. Patients with Parkinson’s were classified with high accuracy using SVM with leave-one-out cross-validation [75]. Results from Nair et al. [76] suggest that least square kernel algorithm performed better than LDA, Neural Network, MLP and learning vector quantification (LVQ) for patients with arthritis. Decision Tree (DT) classifier used to classify toe walking gait disorder revealed three major toe-walking patterns [77]: (1) muscle weakness of TA and quadriceps and spasticity of Tibialis Surae; (2) severe spasticity of Tibialis Surae with limited range of ankle motion; and, (3) hamstring spasticity. The MLP, on the other hand, exhibited higher accuracy while classifying gait disorders associated with myopathy and neuropathy. Based on the literature studied, normalization, feature extraction and selection are important steps for accurately classifying gait disorders [75, 76].
\nArtificial neural networks (ANNs) are considered better at discovering nonlinear relationships in data. Ozsert et al. [78] classified biceps, frontalis and abductor muscles using ANN. The authors used wavelet transform for pre-processing the sEMG signal and an AR model to train the ANN. Senanayake et al. [79] used EMG RMS value and soft tissue deformation parameter (STDP) extracted from the video recordings to train a feed-forward-backward propagation neural network (FFBPN) to identify gait patterns. The proposed evaluation scheme improved classification accuracy between healthy and injured subject’s gait patterns as Vastus Medialis and Lateralis revealed higher positive correlation between EMG and STDP for healthy individuals [79].
\nAn adaptive neuro-fuzzy inference system (ANFIS) successfully diagnosed neurological disorders [8, 80]. In a number of studies, ANN and SVM worked well in diagnosing the gait pathology [7, 8, 71, 81]. Naik et al. [82] decomposed needle EMG from brachial biceps with ensemble empirical mode decomposition (EMD). The authors used Fast ICA and LDA classifier with majority voting to diagnose healthy participants from ALS, and myopathic individuals [82]. The algorithm of Naik et al. [83] for walking, sitting and standing tasks, achieved 86% classification accuracy for participants with and 96% without knee pathology. ICA via entropy bound minimization, time domain feature extraction, and feature selection with fisher score were performed prior to LDA classification. Ai et al. [30] used fused accelerometer and EMG data to discriminate among four participants including an amputee; more amputees in the study could provide better insight of the suggested technique [30].
\nThere is no perfect machine learning algorithm to detect gait disorders. Signal processing techniques for feature extraction and selection, and standardization of the time series play a crucial role in enhancing classification accuracy. We also see consistent improvement in the existing models with increased classification accuracy [84]. ANN classifier has some deficiencies, such as high training process time and overfitting. Extreme Machine Learning algorithm (EML) improves on these anomalies at no cost to classification accuracy [8]. SVM accuracy was low for eight daily life activities including falling. The accuracy for detecting trip fall improved with weighted genetic algorithm [73]. A wide variety of time domain, frequency domain, and time-frequency domain features, and optimization techniques provide multiple options to enhance the classification accuracy of gait diagnosis. The performance of each algorithmic class discussed in this review with respect to the abnormal physiological condition is shown in Table 2.
\nClassifier | \nAuthors | \nYear | \nConditions | \nClassification | \nPerformance | \n
---|---|---|---|---|---|
Neural networks | \nSenanayake et al. | \n2014 | \nSoft tissue deformation | \nGait pattern identification between healthy and injured | \nAccuracy = 98% | \n
\n | Nair et al. | \n2010 | \nOsteoarthritis | \nEMG of healthy and osteoarthritis | \nAccuracy = 89.4 ± 11.8% | \n
\n | Nair et al. | \n2010 | \nRheumatoid arthritis | \nEMG of healthy and rheumatoid arthritis | \nAccuracy = 57 ± 1 8% | \n
\n | Kamruzzaman and Begg. | \n2006 | \nCerebral palsy | \nGait pattern identification using stride length and cadence | \nAccuracy = 94.87% | \n
LDA | \nNaik et al. | \n2018 | \nKnee pathology | \nMovement classification for healthy and patients with knee pathology | \nAccuracy = 86% (Unhealthy) and 96% (Healthy) | \n
\n | Nair et al. | \n2010 | \nRheumatoid arthritis | \nEMG of healthy and rheumatoid arthritis | \nAccuracy = 72 ± 20% | \n
\n | Ai et al. | \n2017 | \nNormal and amputated | \nMovement-based classification for normal and amputee subject | \nAccuracy = 95.6 ± 2.2% | \n
\n | Kamruzzaman and Begg. | \n2006 | \nCerebral palsy | \nGait pattern identification using stride length and cadence | \nAccuracy = 93.59% | \n
SVM | \nKamruzzaman and Begg. | \n2006 | \nCerebral palsy | \nGait pattern identification using stride length and cadence | \nAccuracy = 96.8% | \n
\n | Kugler et al. | \n2013 | \nParkinson | \nDifferentiate between healthy and Parkinson patients by auto-step segmentation | \nSpecificity = 90% and Sensitivity = 90% | \n
\n | Ai et al. | \n2017 | \nNormal and amputated | \nMovement-based classification for normal and amputee subject | \nAccuracy = 98.1 ± 1.6% | \n
\n | Xi et al. | \n2018 | \nFall | \nGait recognition for daily life activities including Fall | \nAccuracy = 100% | \n
Decision tree | \nArmand et al. | \n2006 | \nToe Walking disorders | \nIdentification of ankle kinematic patterns for toe walkers | \nAccuracy = 81% | \n
Least square Kernel Algorithm | \nNair et al. | \n2010 | \nRheumatoid arthritis | \nEMG of healthy and rheumatoid arthritis | \nAccuracy = 91% | \n
\n | Nair et al. | \n2010 | \nOsteoarthritis | \nEMG of healthy and osteoarthritis | \nAccuracy = 97% | \n
EMG classification methods.
The computational methods reviewed in this study have evolved over several decades and continue to do so. For example, ANOVA test’s inability to detect visually observable waveform due to abnormal gait behavior had been improved with wfANOVA test [20]. Apart from factorization algorithms and PCA, artificial neural network were implemented for synergy extraction [5]. New time and frequency domain features and hybrid methods for feature selection have been developed and introduced over the years [67]. In these examples, the conventional techniques were enhanced or detection of gait disorders. There is a consistent effort to augment current computational techniques and improve the EMG based detection methods for motor behavior abnormalities. Optimization algorithms, feature level fusion, and advances in computational methodology point to a future for detecting intricate EMG patterns EMG associated with abnormal gait behavior in machine learning. Recently, application of deep learning algorithms to detect abnormal EMG patterns appears more promising [85], and performs well with EMG acquired directly from the muscles. The main issue in clinical application of deep learning is its real-time implementation. The development of powerful graphics processing unit (GPU) and faster training algorithms will likely resolve such issues in near future.
\nIn conclusion, in this article we reviewed the existing literature on EMG processing techniques from simple thresholding to complex computation algorithms and their application in detecting gait disorders. The pros and cons of the techniques discussed are summarized in Table 3. Besides discussing these techniques in detail, our study cites pertinent literature where these techniques were successfully used to detect gait abnormalities. This study clearly points towards the recent trend in assessing gait disorders from EMG data using an intelligent system. Examples of such systems using supervised and unsupervised learning were also reviewed.
\nEMG method | \nPros | \nCons | \n
---|---|---|
Visual inspection of raw EMG | \n\n
| \n\n
| \n
EMG envelope/onset detection | \n\n
| \n\n
| \n
Frequency and time-frequency analysis | \n\n
| \n\n
| \n
MUAP decomposition | \n\n
| \n\n
| \n
Muscle synergy decomposition | \n\n
| \n\n
| \n
Pros and cons of EMG processing techniques discussed.
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