Selected HRV features for extraction.
\r\n\tThis necessitated a need to understand control theoretical concepts and system analysis in a discrete time domain, which gave rise to the area of discrete time control systems. This has helped control engineers and designers to theoretically ascertain the possibilities and limitations of a control system design implemented in a digital framework, whereas continuous time designs suffer from the essential mismatch in the nature of the underlying independent time variable in theoretical studies and practical implementation. Also, many practical systems are inherently discrete time in nature, sensors and transducers sample data only at fixed time intervals, and computers calculate the control input only in some finite time.
\r\n\tTraditionally, fundamental concepts of discrete time control systems are derived from the continuous time counterpart upon time discretization of the latter and subsequent formal analysis. This gave rise to discrete time counterparts of system models and controllers in z-domain as well as in state space form. However, discrete time control system design and analysis matured as a discipline in itself with the advent of optimal and adaptive techniques solely based on discrete time approach. Robust nonlinear discrete time controllers were also developed utilizing the ideas of sliding modes, model predictive control, etc.
\r\n\tThe techniques for parameter estimation and system identification are largely dominated by discrete time methods. Well-established Kalman filter and extended Kalman filters are developed in discrete time. Many discrete time stochastic filters are utilized in control systems to reduce the impact of noise and disturbance during practical implementation.
\r\n\tDespite the developments in discrete time control designs and their usefulness in control system implementation, there are a few challenges like discretization effect on systems stability, communication loss, etc. which are also areas of serious research. With all its usefulness and limitations, discrete time control systems have found vast areas of application from process control and automation, robotics, network control systems and internet of things, control of networks and multi-agent systems, etc.
\r\n\tThis book intends to provide the reader with an overview of detailed control system design methodologies in discrete time which are well-established in literature. Emerging areas of interest in discrete time systems catering to new and existing challenges are also welcomed.
In order to find new ways to tune, enhance and optimize the properties of novel materials designed for different applications, the thorough knowledge of their thermodynamics is essential. The determination of the thermodynamic data and the thermochemical investigation of the formation reactions are essential for evaluating the long term stability and compatibility when the compounds are used in different applications. A careful search for experimental values is very important so much the more the literature is rather scarce as concerns the quantitative thermodynamic data for many multicomponent systems exhibiting balances of competing interactions. The focus of our present work is on multiferroic ceramics, which represents a "complex materials" class presenting a combination of magnetic and electrical properties which make the studies of novel multifunctional structures a very important issue of research.
BiFeO3 (BFO) of perovskite structure it is well known for its magnetic and ferroelectric ordering temperatures (TN~640 K, TC~1100 K) being one of the most attractive single phase multiferroic materials. For this reason there have been extensive studies of the structure, magnetic and electrical properties in bulk and in thin films, as well. Nevertheless, the wide potential for magnetoelectric applications of BFO may be inhibited because some major problems. Besides the small remanent polarization, the high coercive field and the inhomogeneous magnetic spin structure, the large leakage current it is still one of the major problems limiting device applications of BFO. The high conductivity and leakage found especially at higher temperatures were firstly considered caused by the high difficulty to produce single phase of BiFeO3 (Catalan, 2009; Carvalho, 2008; Mitoseriu, 2005; Yuan, 2006). Very small impurities or parasitic phases segregated at grains at boundaries could have a doping effect and transform the dielectric into a semiconductor. Besides, as in most ferrites, the leakage current in BiFeO3 could be attributed to the spontaneous change of the oxidation state of Fe (the partially reduction of Fe3+ ions to Fe2+) causing a high number of oxygen vacancies as a result of electrical neutrality requirement, giving rise to thermal activated hoping conductivity and resulting in low electrical resistivity (Palkar, 2002; Wang, 2004). Impurities and oxygen vacancies are also important for thin films, because they are known to artificially enhance the remanent magnetization (Catalan, 2009).
Forming binary solutions with other perovskites with good dielectric properties (like BaTiO3, PbTiO3 or LnFeO3) was explored as a possible route for diminish the mentioned problems (Buscaglia, 2006; Ismailzade, 1981; Ianculescu, 2008; Kim, 2004, 2007; Kumar, 1999, 2000; Singh, 2008, 2009; Zhu 2004, Prihor Gheorghiu, 2010; Wang, 2005). The coexistence of ferroelectric and magnetic properties in Bi1-xBaxFe1-xTixO3 materials was noticed up to high temperatures (Kumar, 2000). However, in the BiFeO3-rich region, these ceramics show weak magnetoelectric (ME) coupling effect, the problem of losses being only partially solved (Kumar, 1999).
The substitution of other elements for the Bi- and/or Fe-site was shown to enhance the ferroelectric and magnetic ordering in BiFeO3. Doping with small amounts of rare earths proved to suppress the inhomogeneity of magnetic spin structure, stabilizing macroscopic magnetization of BiFeO3 (Ivanova, 2003; Jiang, 2006; Sahu, 2007). Even though there are still questions about the structural evolution with composition (Catalan, 2009), previous studies on La-substituted BiFeO3 indicate the decreasing of TC and the increasing of TN with La concentration (Chen, 2008; Sahu, 2007). On the other hand, substituting with Mn in BiFeO3-based compounds was shown to improve the leakage current density and to induce changes in the magnetic order of the system, especially in the thin films (Azuma, 2007; Singh, 2007; Sahu, 2007; Habouti, 2007, Fukumura, 2009, Takahashi, 2007; Selbach, 2009, Wang, 2010). The simultaneous influence of La and Mn substitutions on the structural and functional properties of BiFeO3 was discussed in a few papers (Bogatko, 1998; Ianculescu, 2009, Gagulin, 1997; Habouti, 2007; Kothari, 2007; Palkar, 2003; Pradhan, 2008; Zheng, 2010). Very little work was reported on the thermodynamic behaviour in the co-doped (Bi,La)(Fe,Mn)O3 solid solutions (Gagulin, 1997; Tanasescu, 2010).
Recent investigations using advanced techniques, motivated by the prospect of new applications, have uncovered rich complexities that had not previously been recognized, when the development of new multifunctional bismuth ferrite perovskites, that combine sensitive responses to electric, magnetic, and stress fields, is intended [Ederer, 2005; Ramesh, 2007; Stroppa, 2010]. These phenomena occur at the crossover from localized to itinerant electronic behaviour and from ferroelectric (FE) to antiferromagnetic (AFE) displacive transitions, and are associated with dynamic, cooperative local deformations that are invisible to conventional diffraction studies. Due to the progress in methods for experimental analyzing distribution of elements at interfaces, some information has been accumulated on the chemical stabilities and properties of micro and nanostructured multifunctional materials. However the fundamental understanding was limited to rather simple cases. Such analyses need the thermodynamic data, because the driving forces for chemical reactions and diffusion can be given properly in terms of thermodynamic properties. This constitutes a considerable field of investigation, which is starting to be explored for both basic and applicative purposes (Boyd, 2011; Selbach, 2009; Tanasescu, 2004, 2008).
The present studies are focused upon the investigation of the effect of different compositional variables on the thermochemical properties and thermodynamic stability of multiferroic ceramics. Some compounds from specific systems were selected for discussion: multiferroic lead-free ceramics based on the (1-x)BiFeO3 - xBaTiO3 (0 x 0.30) solid solutions and Bi1-xLaxFe1-yMnyO3 with x = 0.1; y = 0 - 0.5 perovskite-type materials.
(1-x)BiFeO3 – xBaTiO3 (0 x 0.30) ceramic samples were prepared by classical solid state reaction method from high purity oxides and carbonates: Bi2O3 (Fluka), Fe2O3 (Riedel de Haen), TiO2 (Merck) and BaCO3 (Fluka), by a wet homogenization technique in isopropyl alcohol. The place of the selected compositions on the BiFeO3 – BaTiO3 tie line of the quaternary Bi2O3 – BaO – Fe2O3 –TiO2 system is also presented in Fig. 1(a).
The mixtures were granulated using a 4 % PVA (polyvinyl alcohol) solution as binder agent, shaped by uniaxial pressing at 160 MPa into pellets of 20 mm diameter and ~3 mm thickness. The presintering thermal treatment was carried out in air, at 923 K, with 2 hours plateau. The samples were slowly cooled, then ground, pressed again into pellets of 10 mm diameter and 1- 2 mm thickness and sintered in air, with a heating rate of 278 K/min, for 1 hour at 973 and 1073 K, respectively (Ianculescu, 2000; Prihor, 2009; Prihor Gheorghiu, 2010).
Bi0.9La0.1Fe1−xMnxO3 (0 ≤ x ≤ 0.5) ceramics have been prepared by the same route, in the same conditions and starting from the same raw materials (Ianculescu, 2009). The place of the investigated compositions in the quaternary Bi2O3 – La2O3 – Fe2O3 – Mn2O3 system is presented in Fig. 1(b).
Place of the investigated compositions: (a) Bi1-xBaxFe1-xTixO3 in the quaternary Bi2O3 – BaO – Fe2O3 –TiO2 system; (b) Bi0.9La0.1Fe1-xMnxO3 in the quaternary Bi2O3 – La2O3 – Fe2O3 – Mn2O3 system
In both Bi1-xBaxFe1-xTixO3 and Bi0.9La0.1Fe1−xMnxO3 systems, the phase composition and crystal structure of the ceramics resulted after sintering were checked with a SHIMADZU XRD 6000 diffractometer with Ni-filtered CuK radiation ( = 1.5418 Å), 273.02 K scan step and 1 s/step counting time. To estimate the structural characteristics (unit cell parameter and rhombohedral angle) the same step increment but with a counting time of 10 s/step, for 2θ ranged between 293–393 K was used. Parameters to define the position, magnitude and shape of the individual peaks are obtained using the pattern fitting and profile analysis of the original X-ray 5.0 program. The lattice constants calculation is based on the Least Squares Procedure (LSP) using the linear multiple regressions for several XRD lines, depending on the unit cell symmetry.
A HITACHI S2600N scanning electron microscope SEM coupled with EDX was used to analyze the ceramics microstructure.
The solid-oxide electrolyte galvanic cells method was employed to obtain the thermodynamic properties of the samples. As shown in previous papers (Tanasescu, 1998, 2003, 2009) the thermodynamic stability limits of the ABO3-δ perovskite-type oxides are conveniently situated within the range of oxygen chemical potentials that can be measured using galvanic cells containing 12.84 wt.% yttria stabilized zirconia solid electrolyte and an iron-wüstite reference electrode. The design of the apparatus, as well as the theoretical and experimental considerations related to the applied method, was previously described (Tanasescu, 1998, 2011).
The measurements were performed in two principal different ways:
Under the open circuit conditions, keeping constant all the intensive parameters, when the electromotive force (EMF) measurements give information about the change in the Gibbs free energy for the virtual cell reaction. The EMF measurements were performed in vacuum at a residual gas pressure of 10-7 atm. The free energy change of the cell is given by the expression:
where E is the steady state EMF of the cell in volts;
By using the experimental values of the electromotive force of the cell and knowing the free energy change of the reference electrode (Charette, 1968; Kelley 1960, 1961), the values of the relative partial molar free energy of the solution of oxygen in the perovskite phase and hence the pressures of oxygen in equilibrium with the solid can be calculated:
The relative partial molar enthalpies and entropies were obtained according to the known relationships (Tanasescu, 1998, 2011):
The overall uncertainty due to the temperature and potential measurement (taking into account the overall uncertainty of a single measurement and also the quoted accuracy of the voltmeter) was ±1.5 mV. This was equivalent to ±0.579 kJ mol-1 for the free energy change of the cell. Considering the uncertainty of ±0.523 kJ mol-1 in the thermodynamic data for the iron-wüstite reference (Charette, 1968; Kelley 1960, 1961), the overall data accuracy was estimated to be ±1.6 kJ mol-1. For the enthalpies the errors were ±0.45 kJ mol-1 and for the entropies ±1.1 J mol-1 K-1. Errors due to the data taken from the literature are not included in these values because of the unavailability of reliable standard deviations.
By using a coulometric titration technique coupled with EMF measurements (Tanasescu, 2011), method which proved to be especially useful in the study of the compounds with properties highly sensitive to deviations from stoichiometry. The obtained results allow us to evidence the influence of the oxygen stoichiometry change on the thermodynamic properties. The titrations were performed in situ at 1073 K by using a Bi-PAD Tacussel Potentiostat. A constant current (I) is passed through the cell for a predetermined time (t). Because the transference number of the oxygen ions in the electrolyte is unity, the time integral of the current is a precise measure of the change in the oxygen content (Tanasescu, 1998; 2011). According to Faraday\'s law, the mass change
As one can see, a charge of 1 10-5 A sec, which is easily measurable corresponds to a weight change of only 8x 10-10 g. This makes it possible to achieve extremely high compositional resolution, and very small stoichiometric widths in both deficient and excess oxygen domains can be investigated. Thus, the effect of the oxygen stoichiometry can be correlated with the influence of the A- and B-site dopants.
After the desired amount of electricity was passed through the cell, the current circuit was opened, every time waiting till the equilibrium values were recorded (about three hours). Practically, we considered that EMF had reached its equilibrium value when three subsequent readings at 30 min intervals varied by less than 0.5 mV. After the sample reached equilibrium, for every newly obtained composition, the temperature was changed under open-circuit condition, and the equilibrium EMFs for different temperatures between 1073 and 1273 K were recorded.
Differential scanning calorimetric measurements were performed with a SETSYS Evolution Setaram differential scanning calorimeter (Marinescu, in press; Tanasescu, 2009). For data processing and analyses the Calisto–AKTS software was used. The DSC experiments were done on ceramic samples under the powder form, at a heating rate 10°C/min. and by using Ar with purity > 99.995% as carrier gas. For measurements and corrections identical conditions were set (Marinescu, in press). The critical temperatures corresponding to the ferro-para phase transitions, the corresponding enthalpies of transformations as well as heat capacities were obtained according to the procedure previously described (Marinescu, in press; Tanasescu, 2009).
The room temperature XRD patterns (Fig. 2(a)) show perovskite single-phase, in the limit of XRD accuracy for all the investigated compositions after pre-sintering at 923 K/2 h followed by sintering at 1073 K/1 h and slow cooling. For all investigated ceramics, perovskite structure of rhombohedral R3c symmetry was identified, with a gradual attenuation of the rhombohedral distortion with the increase of BaTiO3 content. This tendency to a gradual change towards a cubic symmetry with the BaTiO3 addition is proved by the cancellation of the splitting of the XRD (110), (111), (120), (121), (220), (030) maxima specific to pure BiFeO3 (2 ≈ 31.5°, 39 °, 51°, 57°, 66°, 70°, 75°), as observed in the detailed representation from Fig. 2(b). The evolution of the structural parameters provides an additional evidence for the influence of BaTiO3 admixture in suppressing rhombohedral distortion (Fig. 3). Besides, the expansion of the lattice parameters induced by an increasing barium titanate content in (1−x)BiFeO3 – xBaTiO3 system was also pointed out (Prihor, 2009).
a) Room temperature X-ray diffraction patterns of the (1−x)BiFeO3 – xBaTiO3 ceramics pre-sintered at 923 K/2 h, sintered at 1073 K/1 h and slow cooled; (b) detailed XRD pattern showing the cancellation of splitting for (1 1 1), (1 2 0) and (1 2 1) peaks, when increasing x.
Evolution of the structural parameters versus BaTiO3 content.
Surface SEM investigations were performed on both presintered and sintered samples. The SEM image of BiFeO3 ceramic obtained after presintering at 923 K shows that the microstructure consists of intergranular pores and of grains of various size (the average grain size was estimated to be ~ 20 μm), with not well defined grain boundaries, indicating an incipient sintering stage (Fig. 4(a)). The SEM images of samples with x = 0.15 and x = 0.30 (Figs. 4(b) and Figs. 4(c) ) indicate that barium titanate addition influences drastically the microstructure. Thus, one can observe that BaTiO3 used as additive has an inhibiting effect on the grain growth process and, consequently, a relative homogeneous microstructure, with a higher amount of intergranular porosity and grains of ~ one order of magnitude smaller than those ones of non-modified sample, were formed in both cases analyzed here.
Surface SEM images of (1-x)BiFeO3 – xBaTiO3 ceramics obtained after presintering at 923 K/2 hours: (a) x = 0, (b) x = 0.15 and (c) x = 0.30
BiFeO3 pellet sintered at 1073 K/1h exhibits a heterogeneous microstructure with bimodal grain size distribution, consisting from large grains with equivalent average size of ~ 25 m and small grains of 3 - 4 m (Fig. 5(a)). The micrograph of the ceramic sample with x = 0.15 (Fig. 5(b)) shows that the dramatic influence of the BaTiO3 on the microstructural features is maintained also after sintering. Thus, a significant grain size decrease was observed for sample with x = 0.15. Further increase of BaTiO3 content to x = 0.30 (Fig. 5(c)) seems not to determine a further drop in the average grain size. Consequently, in both cases a rather monomodal grain size distribution and relative homogenous microstructures, consisting of finer (submicron) grains were observed (Ianculescu, 2008; Prihor, 2009). Irrespective of BaTiO3 content, the amount of intergranular porosity is significantly reduced in comparison with the samples resulted after only one-step thermal treatment. This indicates that sintering strongly contributes to densification of the Bi1-xBaxFe1-xTixO3 ceramics.
Surface SEM images of (1-x)BiFeO3 – xBaTiO3 ceramics obtained after presintering at 923 K/2 hours and sintering at 1073 K/1 hour: (a) x = 0, (b) x = 0.15 and (c) x = 0.30
Of particular interest for us is to evidence how the appropriate substitutions could influence the stability of the Bi1-xBaxFe1-xTixO3 perovskite phases and then to correlate this effect with the charge compensation mechanism and the change in the oxygen nonstoichiometry of the samples.
In a previous work (Tanasescu, 2009), differential scanning calorimetric experiments were performed in the temperature range of 773-1173 K in order to evidence the ferro-para phase transitions by a non-electrical method. Particular attention is devoted to the high temperature thermodynamic data of these compounds for which the literature is rather scarce. Both the temperature and composition dependences of the specific heat capacity of the samples were determined and the variation of the Curie temperature with the composition was investigated. The effect of the BaTiO3 addition to BiFeO3 was seen as the decrease of the Curie transition temperature and of the corresponding enthalpy of transformation and heat capacity values (Tanasescu, 2009) (Fig. 6). A sharp decline in the TC was pointed out for BiFeO3 rich compositions (Fig. 6). In fact, the Cp of the rhombohedral phase (x = 0) is obviously larger than that of the Bi1-xBaxFe1-xTixO3 perovskite phases, whereas the Cp of each phase shows a weak composition dependence below the peak temperature. In particular, the value of Cp for x = 0.3 was found to be fairly low, which we did not show in the figure. The decreasing of the ferroelectric – paraelectric transition temperature with the increase of the BaTiO3 amount in the composition of the solid solutions with x = 0 0.15 indicated by the DSC measurements is in agreement with the dielectric data reported by Buscaglia et al (Buscaglia, 2006).
Some reasons for this behaviour could be taken into account. First of all, these results confirm our observations that the solid solution system BiFeO3 – BaTiO3 undergoes structural transformations with increasing content of BaTiO3. The decrease of the ferroelectric-paraelectric transition temperature Tc observed for the solid solution (1-x)BiFeO3 – xBaTiO3 may be ascribed to the decrease in unit cell volume caused by the BaTiO3 addition. Addition of Ba2+ having empty p orbitals, reduces polarization of core electrons and also the structural distorsion. The low value obtained for Cp at x = 0.3 is in accordance with the previous result indicating that ferroelectricity disappears in samples above x ~ 0.3 (Kumar, 2000).
Variation of the Curie transition temperature TC and of the heat capacity Cp with composition. Inset: Variation of TC and enthalpy of transformation for BiFeO3 rich compositions (x=0; 0.05; 0.1) (Tanasescu, 2009)
At the same time, the diffused phase transitions for compositions with x > 0.15 could be explained in terms of a large number of A and B sites occupied by two different, randomly distributed cationic specimens in the perovskite ABO3 lattice. Previous reports on the substituted lanthanum manganites indicate that the mismatch at the A site creates strain on grain boundaries which affect the physical properties of an ABO3 perovskite (Maignan, 2000). Besides, the role of charge ordering in explaining the magnetotransport properties of the variable valence transition metals perovskite was emphasized (Jonker, 1953). Investigating the influence of the dopants and of the oxygen nonstoichiometry on spin dynamics and thermodynamic properties of the magnetoresistive perovskites, Tanasescu et al (Tanasescu, 2008, 2009) pointed out that the remarkable behaviour of the substituted samples could be explained not only qualitatively by the structural changes upon doping, but also by the fact that the magneto-transport properties are extremely sensitive to the chemical defects in oxygen sites.
Though the effects of significant changes in the overall concentration of defects is not fully known in the present system of materials, extension of the results obtained on substituted manganites, may give some way for the correlation of the electrical, magnetic and thermodynamic properties with the defect structure. The partial replacement of Bi3+ with Ba2+ cations acting as acceptor centers could generate supplementary oxygen vacancies as compensating defects, whereas the Ti4+ solute on Fe3+ sites could induce cationic vacancies or polaronic defects by Fe3+ → Fe2+ transitions. The presence of the defects and the change of the Fe2+/ Fe3+ ratio is in turn a function not only of the composition but equally importantly of the thermal history of the phase. Consequently, an understanding of the high temperature defect chemistry of phases is vital, if an understanding of the low temperature electronic and magnetic properties is to be achieved. To further evaluate these considerations, and in order to discriminate against the above contributions, experimental insight into the effects of defect types and concentrations on phase transitions and thermodynamic data could give a valuable help.
For discussion was chosen the compound Bi0.90Ba0.10Fe0.90Ti0.10O3 for which strong magnetoelectric coupling of intrinsic multiferroic origin was reported (Singh, 2008). The results obtained in the present study by using EMF and solid state state coulometric titration techniques are shown in the following.
Temperature dependence of EMF for Bi0.90Ba0.10Fe0.90Ti0.10O3
The recorded EMF values obtained under the open circuit condition in the temperature range 923-1273 K are presented in Fig. 7. The thermodynamic data represented by the relative partial molar free energies, enthalpies and entropies of the oxygen dissolution in the perovskite phase, as well as the equilibrium partial pressures of oxygen have been calculated and the results are depicted in Figs. 8-11. A complex behavior which is dependent on the temperature range it was noticed, suggesting a change of the predominant defects concentration for the substituted compound.
As one can see in Fig. 7, at low temperatures, between 923 and ~1000 K, EMF has practically the same value E=0.475 V. Then, Fig. 7 distinctly shows a break in the EMF vs. temperature relation at about 1003 K, indicating a sudden change in the thermodynamic parameters. A strong increase of the partial molar free energy and of the partial pressure of oxygen was observed until 1050 K (Figs. 8 and 9) which can be due to structural transformation related to the charge compensation of the material system. Then, on a temperature interval of about 40 K the increasing of the energies values is smaller. After ~ 1090 K a new change of the slope in the
Variation of ΔG¯O2with temperature - linear fit in the selected temperature ranges: 943-1003 K, 1003-1053 K, 1053-1093 K and 1093-1223 K
The plot of log pO2 vs. 1/T for the selected temperatures ranges
The break point at about 1003 K is mainly due to first order phase transition in Bi0.90Ba0.10Fe0.90Ti0.10O3 associated with the ferroelectric to the paraelectric transition TC. The 10% BaTiO3 substitution reduces the ferroelectric transition temperature of BiFeO3 with about 100K. This transition is also evident from calorimetric measurements (Tanasescu, 2009). The less abrupt first order transition at 1050 K is qualitatively in concordance with the transition to the\n\t\t\t\t\t\t polymorph which was previously identified in the literature for BiFeO3 at 1198-1203K (Arnold, 2010; Palai, 2008; Selbach, 2009).
In Fig. 10 we represented the partial molar free energies of oxygen dissolution obtained in this study for both Bi0.90Ba0.10Fe0.90Ti0.10O3 and BiFeO3 at temperatures lower than their specific ferroelectric transition temperatures. We would like to specify that in the case of BiFeO3, the EMF measurements were performed at temperatures not higher than 1073 K due to the instability of BiFeO3 at higher temperatures. As one can see in Fig. 10, at 923 K, the partial molar free energies of oxygen dissolution in BiFeO3 and Bi0.90Ba0.10Fe0.90Ti0.10O3 samples are near each other. With increasing temperature, the highest
Variation of ΔG¯O2with temperature - linear fit in the temperature range 943-1003 K for Bi0.90Ba0.10Fe0.90Ti0.10O3 (BFO-BTO) and 923-1073 K for BiFeO3 (BFO)
Further clarification could be achieved by determining
Formula: Eqn022 and ΔH¯O2as a function of BaTiO3 content (x) at temperatures lower than ferroelectric transition temperatures.
In order to further evaluate the previous results, the influence of the oxygen stoichiometry change on the thermodynamic properties has to be examined. The variation of the thermodynamic data of oxygen deficient Bi0.90Ba0.10Fe0.90Ti0.10O3-δ samples was analyzed at the relative stoichiometry change Δδ = 0.01. In Figures 12 (a) and (b), two sets of data obtained before and after the isothermal titration experiments are plotted. Higher
Variation of (a) log pO2and (b) ΔG¯O2with temperature and oxygen stoichiometry change for Bi0.90Ba0.10Fe0.90Ti0.10O3
Regarding the changes of
Formula: Eqn031 and ΔS¯O2 as a function of the oxygen stoichiometry change (Δδ = 0.01)
Presently, however, further details and measurements of the energy and entropy of oxygen incorporation into BiFeO3-based materials at different values of nonstoichiometry are necessary in order to make clear the vacancy distribution with the stoiochiometry change.
The room temperature X-ray diffraction pattern obtained for the presintered sample corresponding to the mixture 1 (Bi0.9La0.1FeO3) shows a single phase composition, consisting of the well-crystallized perovskite phase (Fig. 14(a)). A small Mn addition (x 0.1) does not change the phase composition. The increase of the manganese amount to x = 0.2 determines the segregation of a small amount of Bi36Fe2O57 secondary phase identified at the detection limit. For x 0.4 also small quantities of Bi2Fe4O9 was detected as secondary phase, indicating the beginning of a decomposition process (Fig 14(a)).
From the structural point of view the XRD data pointed out that all the samples exhibit hexagonal R3c symmetry, similar to the structure of the paternal non-modified BiFeO3 compound. Similar to Bi1-xBaxFe1-xTixO3 solid solutions, the increase of the manganese content does not determine the change of spatial group. However, certain distortions clearly emphasized by the cancellation of the splitting of some characteristic XRD peaks take place. Thus, Fig. 14(b) shows the evolution of the profile and position of the neighbouring (006) and (202) peaks specific to the Bi0.9La0.1O3 composition when Mn is added in the system. One can observe that an amount of only 10% Mn replacing Fe3+ in the perovskite structure is enough to eliminate the (006) peak in the characteristic XRD pattern. A shift of the position of the main diffraction peaks toward higher 2 values was also pointed out (for exampe the (002) peak shifts from 2 = 39.5 ° for Bi0.9La0.1FeO3 to 2 = 39.82° for Bi0.9La0.1Fe0.5Mn0.5O3).
The increase of the manganese concentration determines the decrease of both a and c lattice parameters (Fig. 15(a)) and therefore a gradual contraction of the unit cell volume (Fig. 15(b)). This evolution suggests that most of the manganese ions are more probably incorporated on the B site of the perovskite network as Mn4+, causing the decrease of the network parameters because of the smaller ionic radius of Mn4+ (0.60 Å), comparing with that one corresponding to Fe3+ (0.64 Å). These results are in agreement with those ones reported by Palkar et al (Palkar, 2003).
a) Room temperature X-ray diffraction patterns for Bi0.9La0.1Fe1-xMnxO3 ceramics thermally treated at 923 K for 2 hours; (b) detailed XRD pattern showing the disappearance of (0 0 6) peak
Evolution of the structural parameters versus Mn content for the Bi0.9La0.1Fe1-xMnxO3 ceramics sintered at 1073 K for 1 hour: (a) lattice parameters and (b) unit cell volume
As for Bi1-xBaxFe1-xTixO3 ceramics, SEM analyses were performed firstly on the pellets surface thermally treated at 923 K/2h. The surface SEM image of the Bi0.9La0.1FeO3 sample indicates the obtaining of a non-uniform and porous microstructure, consisting from grains of variable sizes and a significant amount of intergranular porosity (fig. 16(a)). For the sample with x = 0.20, the presence of the manganese in the system induces the inhibition of the grain growth process and has a favourable effect on the densification (Fig. 16(b)).
Surface SEM images of: (a) Bi0.9La0.1FeO3; (b) Bi0.9La0.1Fe0.8Mn0.2O3 and (c) Bi0.9La0.1Fe0.5Mn0.5O3 presintered at 925 K for 2 hours
The further increase of the Mn content to x = 0.50 enhances the densification, but seems to have not anymore a significant influence on the average grain size. Thus, the Bi0.9La0.1Fe0.5Mn0.5O3 ceramic shows a dense, fine-grained (average grain size of ~ 2 µm) and homogeneous microstructure with a monomodal grain size distribution (Fig. 16(c)).
The same trend of the decrease of the average grain size with the addition of both La and Mn solutes was also observed in the case of the ceramics sintered at 1073 K for 1 hour. Thereby, unlike the non-homogeneous, rather coarse-grained BiFeO3 sample (Fig. 5(a)), the average grain size in the La-modified Bi0.9La0.1FeO3 ceramic is of only ~ 4 m (Fig. 17(a)). The increase of Mn addition causes a further decrease of the average grain size to ~ 2 m and a tendency to coalescence of the small grains in larger, well-sintered blocks (Fig. 17(b) and 17(c)).
Surface SEM images of: (a) Bi0.9La0.1FeO3; (b) Bi0.9La0.1Fe0.8Mn0.2O3 and (c)Bi0.9La0.1Fe0.5Mn0.5O3 ceramics sintered at 1073 K for 1 hour.
Emphasizing the role of charge ordering in explaining the magnetotransport properties of the manganites, Jonker and van Santen considered that the local charge in the doped manganites is balanced by the conversion of Mn valence between Mn3+ and Mn4+ and the creation of oxygen vacancies, as well (Jonker, 1953). Investigating the influence of the dopants and of the nonstoichiometry on spin dynamics and thermodynamic properties of the magnetoresistive perovskites, Tanasescu et al (Tanasescu, 2008, 2009) demonstrated that the formation of oxygen vacancies and the change of the Mn3+/ Mn4+ ratio on the B-site play important roles to explain structural, magnetic and energetic properties of the substituted perovskite.
In BiFeO3-BiMnO3 system was already pointed out that, even though the Mn substitution does not alter the space group of BiFeO3 for x ≤ 0.3, the possible variation of the valence state of Mn manganese together the oxygen hiperstoistoichiometry as a function of temperature and oxygen pressure could affect the crystallographic properties, electrical conductivity and phase stability of BiFe1-xMnxO3+δ (Selbach, 2009). Excepting the communicated results on DSC investigation of Bi1-xLaxFe1-yMnyO3 (x=0.1; y=0-0.5) (Tanasescu, 2010), no other work related to the thermodynamic behaviour of Bi1-xLaxFe1-yMnyO3 were reported in the literature. In that study the evolution of heat of transformation and heat capacity in the temperature range of 573 – 1173 K was analyzed. The ferroelectric transition was shifted to a lower temperature for Bi0.9La0.1FeO3 comparative to BiFeO3, in agreement with literature data (Chen, 2008). However, a non-monotonous change of TC, as well as of the thermochemical parameters is registered for the La and Mn co-doped compositions, depending on the Mn concentration, comparatively with undoped BiFeO3. A sharp decline in the Tc was pointed out for x=0.3. One reason to explain this behavior is sustained by the structural results which were already pointed out in the previous section. The increase of the manganese concentration determines the decrease of both a and c lattice parameters and therefore a gradual contraction of the unit cell volume. This evolution suggests that most of the manganese ions are more probably incorporated on the B-site as Mn4+. Besides, as already was shown, in our samples the decreasing of the average grain size with the addition of both La and Mn solutes was observed in the case of presintered, as well as ceramics sintered at 1273 K (Ianculescu, 2009; Tanasescu, 2010). For finer particles where defect formation energies are likely to be reduced, the lattice defects, oxygen nonstoichiometry etc. appear to be sizable and significant changes in overall defect concentration are expected. So, an excess of Mn4+ ions and an increased oxygen nonstoichiometry are more likely. Due to the linear relationship between the Mn-O distortion and the Mn3+ content, one could expect to find in our samples a strong dependence of the energetic parameters on the Mn3+/ Mn4+ ratio and the oxygen nonstoichiometry.
In order to understand how the thermodynamic properties are related to the oxygen and manganese content in the substituted BiFeO3, the thermodynamic properties represented by the relative partial molar free energies, enthalpies and entropies of oxygen dissolution in the perovskite phase, as well as the equilibrium partial pressures of oxygen have been obtained in a large temperature range (923-1123 K) by using solid electrolyte electrochemical cells method.
The obtained results are plotted in Figures 18 - 20. At low temperatures, between 923 and ~950 K for x=0.3 and between 923 and ~1000 K for x=0.2, the
In Fig. 19 we represented the partial molar free energies of oxygen dissolution obtained in this study for Bi0.9La0.1Fe0.8Mn0.2O3 and Bi0.9La0.1Fe0.7Mn0.3O3 samples at temperatures lower than their specific ferroelectric transition temperatures, and for comparison, the
Variation of ΔH¯O2(a) and ΔS¯O2(b) with temperature and Mn content (x)
Variation of ΔG¯O2with temperature - linear fit in the temperature ranges under the ferroelectric transition temperatures
Formula: Eqn046 and log pO2as a function of Mn content (x) at temperatures lower than TC
After the ferroelectric transition temperatures and until 1043 K (for x=0.2), respectively until 1023 K (for x=0.3), a sharp decrease of the
The obtained results evidenced the complex behavior of the partial molar thermodynamic data in substituted samples, suggesting a change of the predominant defects concentration as a function of temperature range and Mn concentration. Increasing the manganese content, the decreasing of the ferroelectric Curie temperature and of the transition temperature from paraelectric to γ phase it is noted.
To further evaluate the previous results, the influence of the oxygen stoichiometry change on the thermodynamic properties has been investigated. The variation of the partial molar thermodynamic data of Bi0.9La0.1Fe0.8Mn0.2O3 (noted as BLFM0.2) and Bi0.9La0.1Fe0.7Mn0.3O3 (noted as BLFM0.3) was examined before and after two successive titrations by the same relative oxygen stoichiometry change of Δδ = 0.02 in the oxygen excess region (Figures 21 and 22). Thus, the effect of the oxygen stoichiometry can be correlated with the influence of the substituent.
a) Variation of ΔG¯O2with temperature and oxygen stoichiometry change for Bi0.9La0.1Fe0.8Mn0.2O3+δ; (b) ΔG¯O2and ΔG¯O2 of Bi0.9La0.1Fe0.8Mn0.2O3+δ as a function of the oxygen stoichiometry change (Δδ = 0; 0.02; 0.04)
a) Variation of ΔG¯O2with temperature and oxygen stoichiometry change for Bi0.9La0.1Fe0.7Mn0.3O3+δ; (b) ΔH¯O2and ΔS¯O2 of Bi0.9La0.1Fe0.7Mn0.3O3+δ as a function of the oxygen stoichiometry change (Δδ = 0; 0.02; 0.04)
For x=0.2 higher values of the partial molar thermodynamic data are obtained after titration (Fig. 21). It is expected that the change in
Considering the partial pressure of oxygen as a key parameter for the thermodynamic characterization of the materials, we investigated the variation of
Variation of log pO2with temperature and oxygen stoichiometry change
It is obtained that for both compounds, after titration, at the same deviation of the oxygen stoichiometry,
The obtained results could be correlated with some previously reported conductivity measurements. Singh et al (Singh, 2007) reported that small manganese doping in thin films of BiFeO3 improved leakage current characteristic in the high electric field region, reducing the conductivity; others authors noted the increasing of the conductivity with increasing manganese content (Chung, 2006; Selbach, 2009, 2010). If the polaron hopping mechanism is supposed for the electrical conductivity at elevated temperatures, the electronic conductivity will increase in the samples with hiperstoichiometry. According to the evolution of the partial molar thermodynamic data of the oxygen dissolution, a decrease in the oxygen ionic conductivity (together the increasing of the electronic conductivity due to the electron-hole concentration increasing) will result in the sample with increased Mn content.
Even though there are disagreements between different works regarding the nature and the symmetry of the high temperature phases in the pure and substituted BiFeO3, based on our data, we would like to point out that, in the condition of our experimental work, we may close to the stability limit of the Mn doped materials at temperatures around 1123 K. This is in accordance with theoretical consideration of the stability of ABO3 compounds based on Goldschmidt tolerance factor relationship (Goldschmidt, 1926). The evolution with temperature and oxygen stoichiometry of the thermodynamic data suggest that excess oxygen causing an increase of the tolerance factor of the system will lead to the stabilization of the cubic phase at lower temperature with increasing the departure from stoichiometry.
At this point further studies are in progress, so that correlations could be established with the observed properties at different departures of oxygen stoichiometry, in both deficit and excess region for Bi0.9La0.1Fe1-xMnxO3 materials.
Bi1-xBaxFe1-xTixO3 (0 ≤ x ≤ 0.30) and Bi0.9La0.1Fe1-xMnxO3 (0 ≤ x ≤ 0.50) ceramics were prepared by the conventional mixed oxides route, involving a two-step sintering process. Single phase perovskite compositions resulted for all the investigated ceramics, in the limit of XRD accuracy. For both cases, the presence of foreign cations replacing Bi3+ and/or Fe3+ in the perovskite lattice induces the diminishing of the rhombohedral distortion and causes significant microstructural changes, mainly revealed by the obvious decrease of the average grain size.
In order to evidence how the appropriate substitutions could influence the stability of the perovskite phases and then to correlate this effect with the charge compensation mechanism, the thermodynamic data represented by the relative partial molar free energies, enthalpies and entropies of the oxygen dissolution in the perovskite phase, as well as the equilibrium partial pressures of oxygen have been obtained by solid state electrochemical (EMF) method. The influence of the oxygen stoichiometry change on the thermodynamic properties was examined using the data obtained by a coulometric titration technique coupled with EMF measurements.
New features related to the thermodynamic stability of the multiferroic Bi1-xBaxFe1-xTixO3 and Bi0.9La0.1Fe1-xMnxO3 ceramics were evidenced, the thermodynamic behavior being explained not only by the structural changes upon doping, but also by the fact that the energetic parameters are extremely sensitive to the chemical defects in oxygen sites.
The decreasing of the ferroelectric – paraelectric transition temperature in the substituted samples was evidenced by both EMF and DSC measurements. Besides, the phase transition qualitatively corresponding to the phase transformation from paraelectric to a new high temperature phase was evidenced and the partial molar thermodynamic data describing the different phase stability domains were presented for the first time.
Bearing in mind the role of charge ordering and of the defects chemistry in explaining the electrical, magnetic and thermodynamic behavior of the doped perovskite-type oxides, it should be possible to find new routes for modifying the properties of these materials by controlling the average valence in B-site and the oxygen nonstoichiometry. Preparation method also strongly could influence the behavior of the powder in terms of non-stoichiometry, which ultimately will affect its electrical properties since they are dependent upon the presence of oxygen ion vacancies in the lattice. Besides the doping with various foreign cations, the decreasing of the grain sizes, as well as the thin film technology could be efficient methods for tuning the electrical, magnetic and thermodynamic properties of BiFeO3-based compounds to be used as multiferroic materials.
Support of the EU (ERDF) and Romanian Government that allowed for acquisition of the research infrastructure under POS-CCE O 2.2.1 project INFRANANOCHEM - Nr. 19/01.03.2009, is gratefully acknowledged. This work also benefits from the support of the PNII-IDEAS program (Project nr. 50 / 2007).
Human body is interacting between each other where it consists of many different interacting systems. Any changes in human body will generate response to all parts of the body include the autonomic nervous system (ANS) [1]. ANS controls the system that regulates bodily functions such as the digestion, respiratory rate, heart rate, pupillary response, urination, and sexual arousal. Any changes in ANS can be detected by heart rate variability (HRV) since HRV and ANS is directly related.
\nHeart rate can be defined as the number of heart beats per minute while heart rate variability (HRV) is the fluctuation in the time intervals between adjacent heartbeats. HRV refers to the time series of the interval variation between consecutive heart beats and it can be analyzed in time, frequency and nonlinear domains [2]. The fluctuations in HRV value reflects neurocardiac function of the body as it is generated through heart-brain connection and autonomic nervous system (ANS) dynamics [3, 4].
\nHRV is a common measurement that can be extracted from the physiological measurement and helps to monitor the psychological stress [5]. It is because, HRV has direct connection with the autonomic nervous system (ANS) where any changes that occurred in human body can be directly detected by the HRV. The common methods to get the HRV are by using the ECG. However, there are several difficulties to record the ECG signal. First, it requires at least three surface electrodes to be placed on the skin to get single lead channel [6]. This clearly shows bulky of wires are needed for the recording and might cause distraction and uncomfortable feeling to the patient. Furthermore, it requires several times to set up the ECG before start the recording.
\nIn deriving the HRV signal, appropriate QRS algorithms need to be applied to detect the peaks and its R wave, to obtain the interval of RR, and to find acceptable interpolation and resampling to produce a consistently sampled tachogram. By using the ECG signal, the resultant HRV could have several errors in the HRV signal due to drift, electromagnetic and biological disturbance, and the complicated morphology of the ECG signal [6].
\nTherefore, a simple recording system in deriving the HRV signal is needed. PPG which is an electro-optical technique that detect the changes of blood volume in the microvascular bed of the tissue is believed able to overcome the problem that faced by ECG signal and has been suggested as an alternative method to derive the HRV signal [7].
\nThe PPG sensor’s system is equipped with a light source and a detector, it also developed with red and infrared (IR) light-emitting diodes (LEDs) that commonly used as the light source. The light intensity of the PPG sensor monitor has been changed via the reflection from or transmission through the tissue. Figure 1 shows the signal from ECG and PPG signal. Derivation HRV signal from ECG is calculated from R-R interval, while the calculation of HRV signal from PPG signal is used inter-beat interval (IBI) or pulse interval (PPI) [8].
\nECG and PPG signals.
The light traveling through biological tissue passes many materials, including pigments in the skin, bone, and arterial and venous blood. The changes of blood flow mainly occur in the arteries and arterioles (but not in the veins). For example, during the systolic phase of the cardiac cycle, the arteries contain more blood volume than the diastolic phase. PPG sensors optically detect changes in the blood flow volume, for instance, changes in the detected light intensity in the microvascular bed of tissue through the reflection from or transmission through the tissue [9].
\nAs previously discussed, both ECG and PPG system are able to provide information on cardiovascular activities. While ECG system allow better depiction of real cardiac movement through the measurement of the electrical signals produced by the action potential of the tissue, PPG allow adequate cardiovascular measurements such as heart rate and cardiac output only through pulsatile flow of blood in the arteries. Several studies have shown that the cardiovascular parameters collected through PPG systems are highly correlative and comparable to the measurements taken through standard ECG system [8, 10, 11]. This proves that despite not being able to illustrate exact cardiac waveforms or ectopic beats, PPG could serve as better alternative for portable heart monitoring device.
\nIn terms of measurement accuracy, there are several factors to be considered to ensure the reliability of data collection. Topographical factor such as position of sensor placement on the body plays an important factor since different area of the body constitutes different accuracy of perfusion readings. The most accurate perfusion readings are recorded in earlobe; however, the wrist does allow perfusion readings with appropriate accuracy [9]. PPG watch is not subjected to electrical interference and drying or dropping-off of electrodes [8].
\nTherefore, this study proposes a PPG recording system for heart rate variability measurement that can be further used for mental stress assessment.
\nECG and PPG signal has been collected from 12 healthy subjects randomly selected with no prior symptoms of autonomic or cardiovascular disorder, ages between 20 and 30 years old. The data was collected with duration of 30 min including 10 min of adjustment, 10 min of rest (baseline) and 10 min of mental arithmetic testing. As a type of mental stress test, participants were needed to conduct an internet arithmetic test for 10 min in order to evaluate HRV under stress conditions such as time constraint. Lead II ECG setup with three electrodes were placed on the skin of the subject. For PPG signal, the wristband was placed on the left wrist. The subject was asked to sit down and make sure they are familiarized with the procedure. The ECG and PPG were recorded simultaneously after device was setup. The data was imported to the MATLAB software for the signal processing (Figure 2).
\nExperiment setup.
The recorded PPG and ECG signals were then pre-processed to extract the HRV using MATLAB software (Figure 3).
\nPan and Tompkins algorithm for ECG signal analysis and slope sum function (SSF) for PPG signal analysis.
The PPG signal began with the band pass filter to attenuate noises contained in the signals. The band-pass filter was made of cascaded lowpass and high-pass filters. The cut-off frequencies that have been used 5 and 11 Hz. The low pass filter (LPF) eliminates the noise from other part of body, such as the muscle noise and also 50 Hz power line noise. The high pass filter (HPF) which is used to remove the motion artifacts [12].
\nAfter that, the PPG signal undergo the slope sum function (SSF). This method is to enhance the systolic peak of the PPG pulse and to suppress the balance of the pressure waveform by using equation in Eq. (1) [13].
\nwhere w and sk are the length of the analyzing window and the filtered PPG signal, respectively. The SSF algorithm initialize the localization of the onset and offset of SSF then the pulse peak is identified as the local maxima within the range. The SSF signal produced coincides completely with the PPG pulse onset and offset and the pulse peaks appeared within the range of SSF pulse [14].
\nFor ECG processing, Pan and Tompkins algorithm was implemented to get the HRV signal [15]. The Pan and Tompkins procedure are more complex as ECG signal contains superimposition of several waves (P, QRS, and T waves) as seen in Figure 3 [16]. After initial denoising using BPF, the waveform undergoes differentiation process to obtain slope information overcome baseline drift. The next step is to perform signal squaring to emphasize higher frequency signal components (QRS waves) while attenuating components of low frequency. Resultant signal obtained through the squaring phase was then smoothed using moving average filter with a moving window integrator at 80 ms. A thresholding process is required to ensure that only the true QRS complex detected and the adaptive thresholds have been set for the classification of the locations of the detected R points.
\nThe N-N interval was then computed and outliers presented in the signal was removed. Some of the data segment loss through the outlier extraction method was substituted by a new data segment using a linear interpolation method that resulted in NN intervals with nonequivalent moment sampling. However, the use of irregularly sampled NN intervals during HRV analysis characteristics such as frequency and TF analysis would cause generation of additional harmonic components and artifacts in (Figure 4) [16].
\nOverview of HRV signal processing using Pan and Tompkins algorithm.
Therefore, the HRV signals were resampled at standard sampling frequency of 4 Hz [17]. Finally, the NN interval was passed through detrending process to overcome irregular trends.
\nThe following HRV features (Table 1) were computed based on the guidelines provided by Task Force of The European Society of Cardiology (ESC) [18].
\nProcessing method | \nHRV features | \nNo. of features | \n
---|---|---|
Time analysis | \nHR, SDNN, SDANN, RMSSD, HTI, NN50, pNN50 | \n7 | \n
Frequency analysis | \nVLF, LF, HF, LF/HF, LFnu, HFnu, TP | \n7 | \n
Nonlinear analysis | \nShannon entropy: LF, HF, LF/HF, Total(O); Renyi entropy: LF, HF, LF/HF, Total(O) | \n8 | \n
\n | Total | \n22 | \n
Selected HRV features for extraction.
In this study, the time domain has been analyzed from HRV signal. Besides that, HRV features were extracted which are standard deviation of the normal-to-normal intervals (SDNN), standard deviation of the average of normal-to-normal intervals (SDANN) and root mean square successive difference (RMSSD). SDNN, SDANN and RMSSD were calculated by using equations in Eq. (2), Eq. (3) and Eq. (4) respectively.
\nwhere N is total window length and NN is normal-to-normal time interval.
\nwhere N5 is 5 min window length and NN is normal-to-normal time interval.
\nwhere N is total window length.
\nFor this research, AR using the Burg estimation technique has been used to optimize forward and backward prediction errors. The power spectrum of the AR technique using the Burg estimation can be calculated as follows,
\nwhere êp represents the sum of both forward and backward prediction errors or the total least square error while p denotes the model order and â (l) indicate pth order of the AR coefficient.
\nNonlinear analysis was performed using Modified B-distribution (MBD) as the technique is capable of providing high resolution TF distribution without cross-terms for HRV analysis. [16]. The kernel for the MBD as follows,
\nwhere Γ defines as gamma function and β is a real positive number between 0 and 1 that regulates the trade-off between component resolution and cross-cutting elimination.
\nIn order to investigate the statistical significance (p-value < 0.05), Spearman’s correlation is conducted between HRV features of multiple length under both resting and stress conditions. It is performed to determine the correlation between the HRV features produced through PPG signal in comparing with standardized ECG signal. A nonparametric Wilcoxon signed-rank test was performed to observe the difference between resting (baseline) and arithmetic stress test.
\nThis chapter presented the results obtained through pre-processing, feature extraction of HRV and multiscale comparison and correlation analysis along with relevant discussions of the findings.
\nThe results of each pre-processing phase for HRV assessment and the resulting PPG signal HRV are shown in Figures 5 and 6, whereas the resulting ECG signal HRV is shown in Figures 7 and 8.
\nThe output of pulse peak detection from PPG signals using SSF.
The HRV signal obtained from pulse peak detected in SSF signal.
The output of QRS peak detection from ECG signals using Pan and Tompkins algorithm.
The HRV signal obtained from RR peak detected.
Figure 5 presented the attenuation of the PPG signal pulses after the application of the SSF conversion. The pulse peaks became more distinct throughout the entire signal duration using SSF conversion as lower ectopic beats were also amplified to match ordinary pulse peaks that facilitate peak detection during thresholding method. Figure 6 showed the resulting HRV signal that was obtained after removal, resampling and detrending of the outlier.
\nFigure 7 showed the changes in the ECG signal throughout the Pan and Tompkins algorithm processes. It can be seen that the algorithm was able to detect the R-R intervals throughout the signal excerpt. This method was chosen due to the simplicity and efficacy of this algorithm in QRS detection among adult subjects with 99.3% accuracy rate [15]. The subsequent HRV signal produced (illustrated in Figure 8) after smoothing procedure was then used for feature analysis. Smoothing process which comprised of the outlier removal, resampling and detrending.
\nWhile evaluating the algorithm necessary for the HRV signal acquisition, it can be said that the pre-processing of the HRV signal recorded using the PPG system is simpler, as the signal contained only one type of wave (blood pulse) compared to the ECG signals usually containing a combination of three waves (P, QRS and T waves). Despite that, the HRV signal produced through both recordings do have relatively consistent magnitude.
\nThe analysis discussed in this section focuses mainly on the HRV features extracted using PPG method. Generally, the features selected have been associated with significant reactivity under stress conditions.
\nThe HRV signal obtained under resting and stress conditions were subsequently plotted in Figure 9 which also showed the HRV obtained with time excerpts of 10 min duration. In addition, different lengths of HRV excerpts carry different weightage of information on the HRV of the sample. Longer HRV excerpts allow better visualization of fluctuations in the HRV measurements in both conditions. However, it is difficult to distinguish the difference of HRV changes between resting and stress testing through visual inspection only.
\nSamples of PPG-derived HRV for 10 min from same sample between resting and stress condition.
The PSD can be classified into three components which are VLF band between 0.0033 and 0.04 Hz, LF band between 0.04 and 0.15 Hz and HF band between 0.15 and 0.4 Hz [18].
\nBased on the findings in Figure 10, the LF components increases during stress testing while HF components relatively decreases.
\nSamples of PSD generated from PPG-derived HRV for 10 min from same sample between resting and stress condition.
For the plotted Figure 11, it was observed that more complex changes experienced during stress testing in 10 min. TFD plot was able to provide supplementary visualization of more complex changes within the HRV features during stress phase. Next, the changes within VLF and LF frequency bands were also more noticeable in TFD analysis.
\nSamples of TFD generated from PPG-derived HRV for 10 min from same sample between resting and stress condition.
Based on this finding, it can be seen that most of the HRV features extracted using the PPG device produced similar measurements as the ECG, especially for the TA and FA features. However, for more sophisticated measurements, such as nonlinear TF characteristics, the correlation between the two techniques was less important, particularly for smaller HRV characteristics. This could be due to the fact that PPG waveform mainly reflects the central artery properties which means factors such as artery stiffness may attenuate the signal and resulted in differences of NN intervals obtained between different individuals [19]. The PPG signals are also influenced by other parasympathetic activity such as temperature variations [20] and could significantly changes due to factors such as body age, vascular age, physical status, sleeping hours, physical activities [21].
\nCorrelation analysis was performed to assess the interdependence between PPG-derived HRV and ECG-derived HRV as shown in Table 2.
\n\n | Features | \nr Rest | \nr Stress | \n
---|---|---|---|
Time analysis | \nHR* | \n0.964 | \n0.970 | \n
SDNN* | \n0.893 | \n0.920 | \n|
RMSSD* | \n0.793 | \n0.801 | \n|
SDANN* | \n0.909 | \n0.964 | \n|
NN50* | \n0.659 | \n0.907 | \n|
pNN50* | \n0.716 | \n0.851 | \n|
HTI* | \n0.800 | \n0.773 | \n|
Frequency analysis | \nVLF | \n0.918 | \n0.491 | \n
LF* | \n0.773 | \n0.764 | \n|
HF* | \n0.845 | \n0.718 | \n|
LF/HF* | \n0.936 | \n0.873 | \n|
TP | \n0.827 | \n0.364 | \n|
LFnu* | \n0.936 | \n0.873 | \n|
Hfnu* | \n0.936 | \n0.864 | \n|
Nonlinear analysis | \nShEn LF* | \n0.800 | \n0.818 | \n
ShEn HF* | \n0.645 | \n0.836 | \n|
ShEn LFHF* | \n0.727 | \n0.818 | \n|
ShEn O* | \n0.709 | \n0.773 | \n|
ReEn LF | \n−0.064 | \n0.255 | \n|
ReEn HF* | \n0.873 | \n0.909 | \n|
ReEn LFHF* | \n0.873 | \n0.791 | \n|
ReEn O* | \n0.909 | \n0.945 | \n
Correlation between multi-length HRV features with standard of 10 min.
In bold, Spearman’s correlation coefficient (rho) greater than 0.6 and resulted correlation significant (prho < 0.05); and based on results, the time domain HRV features (except HTI) maintained a significantly high correlation coefficient. Frequency domain features at 10 minutes showed consistent significant correlation with the equivalent standard HRV features during both resting and stress phases. For non-linear analysis, Shanon Entropy measurements (ShEn LF, ShEn HF, ShEn LFHF and ShEn O) showed to be highly correlate with standard excerpt for HRV features at 10 minutes. HR—mean of heart rate; SDNN—standard deviation of NN intervals; RMSSD—root mean square of the successive differences; SDANN—standard deviation of average NN intervals; NN50—NN intervals differing by more than 50 ms; pNN50—percentage of NN50 count; HTI—HRV triangular index; VLF—very low frequency; LF—low frequency; HF—high frequency; TP—total power; Lfnu—low frequency normalized unit; Hfnu—high frequency normalized unit; ShEn LF—Shannon entropy measurements; and ReEn—Renyi entropy measurements.*Correlation is significant at the 0.01 level (2-tailed).
In general, HRV features resulted less correlated in resting than during stress conditions. This is most likely due to the fact that HRV showed a more depressed dynamic during stress phase. Other than that, HRV features such as HR, NN50, TP, VLF, LF, HF, Lfnu, Hfnu, LF/HF, and Renyi entropy (LF, HF and Total(O)) has also showed significant correlation between the values measured for HRV excerpts collected using PPG and ECG. This prove that PPG is able to produce HRV signal with equivalent significant to HRV signal produced by ECG during stress testing [8, 22]. Besides, it can be deduced that HR, RMSSD, LF/HF, Lfnu and Hfnu features showed consistent characteristics as valid surrogate of the standard HRV which means regardless of length of HRV signal (between 1 and 10 min), these features would produce values that high correlate to value produced with standard HRV excerpt.
\nThis study intends to investigate if there is different length of HRV excerpts provide valid measurement of HRV indices with comparison to standard 5-min excerpt for detection of mental stress. Although many studies have shown that HRV analyzes provide a reliable quantification technique for mental stress, it is hard to compare the precision of each method as their experimental design (i.e., duration of HRV characteristics) differs. Although it was claimed that the excerpt of 5-min HRV is the gold standard [18], the growing demand for wearable devices to instantly evaluate mental stress has increased interest in HRV computing characteristics shorter than the 5-min HRV standard [2]. In order to investigate the utility of various length of HRV excerpts in quantifying HRV features, 22 features were extracted at each time interval. The agreement between features at each time interval was compared with standard 5-min excerpt under both resting and stress phases. Overall, TA features (except HTI) conform significantly across all excerpts in correlation to standard excerpt while FA features (i.e., VLF, LF, HF, and TP) showed significant correlation across excerpts longer than 3 min while LFnu, HFnu and LF/HF showed consistent high correlation for all excerpts. As for time-frequency analysis, Shannon entropy measurements showed significant correlation for signal excerpts longer than 4 min while for Renyi entropy, only HF and Total(O) measurements showed significant correlation throughout all time excerpts.
\nDespite that, the limitation of these analyses is that correlation coefficient is blind to the possibility of bias caused by the difference in the mean or standard deviation between two measurements [23].
\nIn comparison to conventional ECG, a correlation assessment between HRV characteristics obtained by PPG was also performed to observe any variation between the extracted measurements and analyze whether the PPG system is sufficiently robust to obtain HRV characteristics according to clinical standards. For this research, an ultra-short and short-term HRV feature was presumed to be a valid surrogate of the equivalent standard HRV if the feature sustained at a high correlation (i.e., rho > 0.6 and prho < 0.05) with the equivalent 5-min standard feature over all time scales and produced consistent trend and significant difference (p-value < 0.05) during the rest and stress phase. Therefore, it can be deduced that HR, RMSSD, LF/HF, LFnu and HFnu features showed consistent characteristics as valid surrogate of the standard HRV which means regardless of length of HRV signal (between 1 and 10 min), these features would produce values that high correlate to value produced with standard HRV excerpt. In the future, methods such as machine learning may be applied to test the accuracy between the use different PPG specifications such as measurement site, probe contact force and LED wavelengths which affect the reliability of its recordings or between different experimental protocol such as type of stressor and subject conditions.
\nThis study was supported by the Research University Funding from Universiti Teknologi Malaysia, Malaysia (FRGS: R.J130000.7851.5F219).
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