In a simple words, the heart rate variability (HRV) refers to the divergence in heart complex wave (beat- to-beat) intervals. It is a reliable repercussion of many, psychological, physiological, also environmental factors modulating therhythm of the heart. Seriously, the HRV act as a powerful tool for observation the interaction between the sympathetic and parasympathetic nervous systems. However, it has a frequency that is great for supervision, surveillance, and following up the cases. Finally, the generating structure of heart complex wave signal is not simply linear, but also it involves the nonlinear contributions. Those two contributions are totally correlated.
HRV is stochastic and chaotic (stochaotic) signal. It has utmost importance in heart diseases diagnosis, and it needs a sensitive tool to analyze its variability. In early works, Rosenstein and Wolf had used the Lyapunov exponent (LE) as a quantitative measure for HRV detection sensitivity, but the Rosenstein and Wolf methods diverge in determining the main features of HRV sensitivity, while Mazhar-Eslam introduced a modification algorithm to overcome the Rosenstein and Wolf drawbacks.
The present work introduces a novel reliable method to analyze the linear and nonlinear behaviour of heart complex wave variability, and to assess the use of the HRV as a versatile tool for heart disease diagnosis. This paper introduces a declaration for the concept of the LE parameters to be used for HRV diagnosis and proposes a modified algorithm for a more sensitive parameters computation
A M ODIFIED M ETHOD F OR P REDICTIVITY OF H EART R ATE V ARIABILITYcsandit
Heart Rate Variability (HRV) plays an important rol
e for reporting several cardiological and
non-cardiological diseases. Also, the HRV has a pro
gnostic value and is therefore quite
important in modelling the cardiac risk. The nature
of the HRV is chaotic, stochastic and it
remains highly controversial. Because the HRV has u
tmost importance, it needs a sensitive tool
to analyze the variability. In previous work, Rosen
stein and Wolf had used the Lyapunov
exponent as a quantitative measure for HRV detectio
n sensitivity. However, the two methods
diverge in determining the HRV sensitivity. This pa
per introduces a modification to both the
Rosenstein and Wolf methods to overcome their drawb
acks. The introduced Mazhar-Eslam
algorithm increases the sensitivity to HRV detectio
n with better accuracy.
Heart Rate Variability (HRV) plays an important role for reporting several cardiological and noncardiological
diseases. Also, the HRV has a prognostic value and is therefore quite important in modelling
the cardiac risk. The nature of the HRV is chaotic, stochastic and it remains highly controversial. Because
the HRV has utmost importance, it needs a sensitive tool to analyze the variability. In previous work,
Rosenstein and Wolf had used the Lyapunov exponent as a quantitative measure for HRV detection
sensitivity. However, the two methods diverge in determining the HRV sensitivity. This paper introduces a
modification to both the Rosenstein and Wolf methods to overcome their drawbacks. The introduced
Mazhar-Eslam algorithm increases the sensitivity to HRV detection with better accuracy.
IOSR Journal of Mathematics(IOSR-JM) is an open access international journal that provides rapid publication (within a month) of articles in all areas of mathemetics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mathematics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Control of Nonlinear Heartbeat Models under Time- Delay-Switched Feedback Usi...idescitation
In this paper, we adopt the Zeeman nonlinear heart model to discuss its stability
and control its operation using emotional learning control (ELC). We also demonstrate the
control of the heart model under threats of possible time delay introduced in the sensing
loop. We compare the robustness of the ELC with other control methods such as the
classical PID and the model predictive control (MPC) for the heart model under time delay
attack. We have showed that ELC is more robust than the classical PID and the MPC.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A M ODIFIED M ETHOD F OR P REDICTIVITY OF H EART R ATE V ARIABILITYcsandit
Heart Rate Variability (HRV) plays an important rol
e for reporting several cardiological and
non-cardiological diseases. Also, the HRV has a pro
gnostic value and is therefore quite
important in modelling the cardiac risk. The nature
of the HRV is chaotic, stochastic and it
remains highly controversial. Because the HRV has u
tmost importance, it needs a sensitive tool
to analyze the variability. In previous work, Rosen
stein and Wolf had used the Lyapunov
exponent as a quantitative measure for HRV detectio
n sensitivity. However, the two methods
diverge in determining the HRV sensitivity. This pa
per introduces a modification to both the
Rosenstein and Wolf methods to overcome their drawb
acks. The introduced Mazhar-Eslam
algorithm increases the sensitivity to HRV detectio
n with better accuracy.
Heart Rate Variability (HRV) plays an important role for reporting several cardiological and noncardiological
diseases. Also, the HRV has a prognostic value and is therefore quite important in modelling
the cardiac risk. The nature of the HRV is chaotic, stochastic and it remains highly controversial. Because
the HRV has utmost importance, it needs a sensitive tool to analyze the variability. In previous work,
Rosenstein and Wolf had used the Lyapunov exponent as a quantitative measure for HRV detection
sensitivity. However, the two methods diverge in determining the HRV sensitivity. This paper introduces a
modification to both the Rosenstein and Wolf methods to overcome their drawbacks. The introduced
Mazhar-Eslam algorithm increases the sensitivity to HRV detection with better accuracy.
IOSR Journal of Mathematics(IOSR-JM) is an open access international journal that provides rapid publication (within a month) of articles in all areas of mathemetics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mathematics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Control of Nonlinear Heartbeat Models under Time- Delay-Switched Feedback Usi...idescitation
In this paper, we adopt the Zeeman nonlinear heart model to discuss its stability
and control its operation using emotional learning control (ELC). We also demonstrate the
control of the heart model under threats of possible time delay introduced in the sensing
loop. We compare the robustness of the ELC with other control methods such as the
classical PID and the model predictive control (MPC) for the heart model under time delay
attack. We have showed that ELC is more robust than the classical PID and the MPC.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
HEART RATE VARIABILITY ANALYSIS FOR ABNORMALITY DETECTION USING TIME FREQUENC...cscpconf
Heart rate variability (HRV) is derived from the time duration between consecutive heart beats. The HRV is to reflect the heart’s ability to adapt to changing circumstances by detecting and quickly responding to unpredictable stimuli to cardiac system. Depressed HRV is a powerful predictor of mortality and of arrhythmic complications in patients after diseases like acute
Myocardial Infarction. The degree of variability in the HR provides information about the nervous system control on the HR and the heart’s ability to respond .Spectral analysis of HRV is a frequency domain approach to assess the cardiac condition. In this paper one such method for analyzing HRV signals known as smoothed pseudo Wigner Ville distribution (SPWVD) is
applied, The sub-band decomposition technique used in SPWVD, based on Instantaneous Autocorrelation (IACR) of the signal provides time-frequency representation for very lowfrequency (VLF), low-frequency (LF) and high-frequency (HF) regions identified in HRV spectrum. Results suggest that SPWVD analysis provides useful information for the assessment of dynamic changes and patterns of HRV during cardiac abnormalities.
A STUDY ON IMPACT OF ALCOHOL AMONG YOUNG INDIAN POPULATION USING HRV ANALYSISijcseit
Heart Rate Variability (HRV) is the measure of time difference between two successive heart beats and its
variation occurring due to internal and external stimulation causes. HRV is a non-invasive tool for indirect
investigation of both cardiac and autonomic system function in both healthy and diseased condition. It has
been speculated that HRV analysis by nonlinear method might bring potentially useful prognosis
information into light which will be helpful for assessment of cardiac condition. In this study, HRV from
two types of data sets are analyzed which are collected from different subjects in the age group of 18 to 22.
Then parameters of linear methods and three nonlinear methods, approximate entropy (ApEn), detrended
fluctuation analysis (DFA) and Poincare plot have been applied to analyze HRV among 158 subjects of
which 79 are control study and 79 are alcoholics. It has been clearly shown that the linear and nonlinear
parameters obtained from these two methods reflect the opposite heart condition of the two types of data
under study among alcoholics non-alcoholic’s by HRV measures. Poincare plot clearly distinguishes
between the alcoholics by analysing the location of points in the ellipse of the Poincare plot. In alcoholics
the points of the Poincare plot will be concentrated at the centre of the ellipse and in nonalchoholics the
points will be much concentrated along the periphery of the ellipse. The Approximate Entropy value will be
lesser than one in alcoholics and in nonalcoholics the entropy shows values greater than one. The
increased LF/HF value in alcoholics denotes the increase in sympathetic nervous system activities and
decrease of the parasympathetic activity which will be lesser in alcoholics subjects.
Heart Rate Variability (HRV) is the measure of time difference between two successive heart beats and its
variation occurring due to internal and external stimulation causes. HRV is a non-invasive tool for indirect
investigation of both cardiac and autonomic system function in both healthy and diseased condition. It has
been speculated that HRV analysis by nonlinear method might bring potentially useful prognosis
information into light which will be helpful for assessment of cardiac condition. In this study, HRV from
two types of data sets are analyzed which are collected from different subjects in the age group of 18 to 22.
Then parameters of linear methods and three nonlinear methods, approximate entropy (ApEn), detrended
fluctuation analysis (DFA) and Poincare plot have been applied to analyze HRV among 158 subjects of
which 79 are control study and 79 are alcoholics. It has been clearly shown that the linear and nonlinear
parameters obtained from these two methods reflect the opposite heart condition of the two types of data
under study among alcoholics non-alcoholic’s by HRV measures. Poincare plot clearly distinguishes
between the alcoholics by analysing the location of points in the ellipse of the Poincare plot. In alcoholics
the points of the Poincare plot will be concentrated at the centre of the ellipse and in nonalchoholics the
points will be much concentrated along the periphery of the ellipse. The Approximate Entropy value will be
lesser than one in alcoholics and in nonalcoholics the entropy shows values greater than one. The
increased LF/HF value in alcoholics denotes the increase in sympathetic nervous system activities and
decrease of the parasympathetic activity which will be lesser in alcoholics subjects.
Heart Rate Variability (HRV) analysis is the
ability to assess overall cardiac health and the state of the
autonomic nervous system (ANS), responsible for regulating
cardiac activity. ST-change due to ischemia and their HRV
analysis have not been well discussed in the previous works.
The proposed simple and time efficient TBC algorithm has
been tested in four sets of standard databases with selected
patient’s data having ischemic conditions (i.e.MIT-BIH
Normal-Sinus Rhythm Database (NSRDB), European ST-T
Database (EDB), MIT-BIH ST Change Database (STDB) &
Long-Term ST Database (LTSTDB))for the detection of R-peak
& HRV analysis. The pre-processing is done by MAF and DWT
to remove the baseline drift and noise induced in the ECG
signal. The mean/average of HR is calculated for each set of
databases and in case of EDB it is of 57 BPM (subjected to
bradycardia). The Probability with normal distribution is
analyzed by comparing the NSRDB data with the ischemic data sets. The performance of this algorithm is found to be 98.5%.
Review: Nonlinear Techniques for Analysis of Heart Rate VariabilityIJRES Journal
Heart rate variability (HRV) is a measure of the balance between sympathetic mediators of heart
rate that is the effect of epinephrine and norepinephrine released from sympathetic nerve fibres acting on the
sino-atrial and atrio-ventricular nodes which increase the rate of cardiac contraction and facilitate conduction at
the atrio-ventricular node and parasympathetic mediators of heart rate that is the influence of acetylcholine
released by the parasympathetic nerve fibres acting on the sino-atrial and atrio-ventricular nodes leading to a
decrease in the heart rate and a slowing of conduction at the atrio-ventricular node. Sympathetic mediators
appear to exert their influence over longer time periods and are reflected in the low frequency power(LFP) of
the HRV spectrum (between 0.04Hz and 0.15 Hz).Vagal mediators exert their influence more quickly on the
heart and principally affect the high frequency power (HFP) of the HRV spectrum (between 0.15Hz and 0.4
Hz). Thus at any point in time the LFP:HFP ratio is a proxy for the sympatho- vagal balance. Thus HRV is a
valuable tool to investigate the sympathetic and parasympathetic function of the autonomic nervous system.
Study of HRV enhance our understanding of physiological phenomenon, the actions of medications and disease
mechanisms but large scale prospective studies are needed to determine the sensitivity, specificity and predictive
values of heart rate variability regarding death or morbidity in cardiac and non-cardiac patients. This paper
presents the linear and nonlinear to analysis the HRV.
Analyzing Employee’s Heart rate using Nonlinear Cellular Automata modelIOSR Journals
Non-linear Cellular Automata model is a simulation tool which can be used to diagnosis the intensity of the disease. This paper aims to study the Heart rate behavior between normal respiratory patients and healthy controls/unhealthy controls. We also discuss about Heart Rate Variability (HRV) of employee’s through non-linear Cellular Automata model. Cellular Automata model gives us striking results for further studies
Review: Linear Techniques for Analysis of Heart Rate Variabilityinventionjournals
Heart rate variability (HRV) is a measure of the balance between sympathetic mediators of heart rate that is the effect of epinephrine and norepinephrine released from sympathetic nerve fibres acting on the sino-atrial and atrio-ventricular nodes which increase the rate of cardiac contraction and facilitate conduction at the atrio-ventricular node and parasympathetic mediators of heart rate that is the influence of acetylcholine released by the parasympathetic nerve fibres acting on the sino-atrial and atrio-ventricular nodes leading to a decrease in the heart rate and a slowing of conduction at the atrio-ventricular node. Sympathetic mediators appear to exert their influence over longer time periods and are reflected in the low frequency power(LFP) of the HRV spectrum (between 0.04Hz and 0.15 Hz).Vagal mediators exert their influence more quickly on the heart and principally affect the high frequency power (HFP) of the HRV spectrum (between 0.15Hz and 0.4 Hz). Thus at any point in time the LFP:HFP ratio is a proxy for the sympatho- vagal balance. Thus HRV is a valuable tool to investigate the sympathetic and parasympathetic function of the autonomic nervous system. Study of HRV enhance our understanding of physiological phenomenon, the actions of medications and disease mechanisms but large scale prospective studies are needed to determine the sensitivity, specificity and predictive values of heart rate variability regarding death or morbidity in cardiac and noncardiac patients. This paper presents the linear techniques to analysis the HRV
MEDITATION: ITS TREMENDOUS IMPACT ON HEART RATE VARIABILITYcscpconf
The heart is connected through the nervous system directly to major organs and is able to sense
their need. The heart also responds to adrenaline in the blood flowing through it. With these
inputs the heart is able to adjust its rate to accommodate the needs of the whole body. By its
heartbeat, it is able to broadcast a common signal to every cell. One measure of heart health is
Heart Rate Variability (HRV). HRV is defined in terms of how different the lengths of time
between each heart beat is. The greater the difference in times between heart beats, the
healthier is the heart thought to be. Individuals may be able to learn to increase their own HRV
and become healthier by a daily meditation practice. This paper uses Heart Variability as the
base signal for studying the impact of meditation on it. The paper brings out the difference
between pre and post meditation conditions in terms qualitative measure of Power Spectral
Density (PSD) variations using Smoothed Pseudo Wigner Ville (SPWVD) distribution method.The simulations highlight the meditation effects overtime period in PSDs
Comparison of Re-sampling Methods in the Spectral Analysis of RR-interval Ser...CSCJournals
The heart rate variability (HRV), refers to the beat-to-beat alterations in heart rate, is analyzed using RR-interval (RRI) series derived from the ECG signal as an interval between successive QRS complexes. For deciphering the true HRV spectrum using FFT, the RRI series should be resampled. But re-sampling often induces a noticeable distortion in the HRV power spectral estimates. Thus, the re-sampling operation should be accurate enough in reproducing the finest variation in the given signal. This paper compared three most widely used interpolation techniques: linear, cubicspline, and Berger’s, as re-sampling methods, in an attempt to propose an optimal method of interpolation for HRV analysis. The linear and cubicspline methods based PSD estimates, for artificially generated non-uniformly sampled RRI series, introduce linear phase shifting, and thus lower the HRV frequencies. On the contrary, Berger’s method efficiently reproduced the inherent frequencies in the underlying signal except some amplitude distortion. Further, similar trends in PSD estimates were obtained for real RRI series as well. Thus, it was concluded that at the expense of some increase in computational complexity, the spectral distortion has been significantly reduced using the Berger’s interpolation based re-sampling method as compared to the linear and cubicspline methods.
General anesthesia plays a crucial role in many surgical procedures. It is a drug-induced, reversible state characterized by unconsciousness, anti-nociception or analgesia, immobility and amnesia. On rare occasions, however, the patient can remain unconscious longer than intended, or may regain awareness during surgery. There are no precise measures for maintaining the correct dose of anesthetic, and there is currently no fully reliable instrument to monitor depth of anesthesia. Although a number of devices for monitoring brain function or sympathetic output are commercially available, the anesthetist also relies on clinical assessment and experience to judge anesthetic depth. The undesirable consequences of overdose or unintended awareness might in principle be ameliorated by improved control if we could understand better the changes in function that occur during general anesthesia. Coupling functions prescribe the physical rule specifying how the inter-oscillator interactions occur. They determine the possibility of qualitative transitions between the oscillations, e.g. routes into and out of phase synchronization. Their decomposition can describe the functional contribution from each separate subsystem within a single coupling relationship. In this way, coupling functions offer a unique means of describing mechanisms in a unified and mathematically precise way. It is a fast growing field of research, with much recent progress on the theory and especially towards being able to extract and reconstruct the coupling functions between interacting oscillations from data, leading to useful applications in cardio respiratory interactions. In this paper, a novel approach has been proposed for detecting the changes in synchronism of brain signals, taken from EEG machine. During the effect of anesthesia, there are certain changes in the EEG signals. Those signals show changes in their synchronism. This phenomenon of synchronism can be utilized to study the effect of anesthesia on respiratory parameters like respiration rate etc, and hence the quantity of anesthesia can be regulated, and if any problem occurs in breathing during the effect of anesthesia on patient, that can also be monitored.
A Novel Approach to Study the Effects of Anesthesia on Respiratory Signals by...IJECEIAES
General anesthesia plays a crucial role in many surgical procedures, and it therefore has an enormous impact on human health. There are no precise measures for maintaining the correct dose of anesthetic, and there is currently no fully reliable instrument to monitor depth of anesthesia. In this paper, a novel approach has been proposed for detecting the changes in synchronism of brain signals, taken from EEG machine. During the effect of anesthesia, there are certain changes in the EEG signals. Those signals show changes in their synchronism. This phenomenon of synchronism can be utilized to study the effect of anesthesia on respiratory parameters like respiration rate etc, and hence the quantity of anesthesia can be regulated, and if any problem occurs in breathing during the effect of anesthesia on patient, that can also be monitored.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Transfer entropy estimation supplements time domain beat-to-beat baroreflex s...eSAT Journals
Abstract Baroreflex mechanism plays a vital role in cardiovascular regulation by contributing in sympathetic-vagal imbalance triggered by baroreceptor reflex. This study estimates BRS (Baroreflex Sensitivity) index from beat-to-beat systolic blood pressure (SBP) and RR interval (RRi) series using time-domain windowed cross-correlation method. This index is supplemented by an information domain technique called Transfer Entropy (TE) is applied to decipher the directional coupling between SBP and RRi series. The study is performed on EUROBAVAR data. The results show that BRS index calculated is large (1.157±0.533) in supine position which supplemented by TE (SBP-RR) of (0.129±0.09) as compared to standing position for which BRS index is (0.866±0.472) and a TE (SBP-RR) of (0.04±0.03) for EUROBAVAR data with a p-value<0.05. Also, it is found that both the TE indices i.e. from SBP to RR and from RR to SBP show correlated results with the time domain BRS index in both supine and standing positions. Further, the cases with possible BRS failure are having very small values of BRS index, which are not assigned any value by prevalent methods. This shift is absent in BRS failure patients which is supplanted by TE (RR-SBP) sharing more information bits than TE (SBP-RR). Further, it is observed that patients having baroreflex impairment show reversal of information flow which is indicated by a larger TE (RR-SBP) index as compare to TE (SBP-RR) index. Time domain BRS index lacks directional information which is provided by TE and hence this supplemental information gives better interpretation of BRS index especially in patients having suppressed baroreflex. Index Terms: Baroreflex sensitivity, Directional coupling, Cross-correlation, and Transfer entropy
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
HEART RATE VARIABILITY ANALYSIS FOR ABNORMALITY DETECTION USING TIME FREQUENC...cscpconf
Heart rate variability (HRV) is derived from the time duration between consecutive heart beats. The HRV is to reflect the heart’s ability to adapt to changing circumstances by detecting and quickly responding to unpredictable stimuli to cardiac system. Depressed HRV is a powerful predictor of mortality and of arrhythmic complications in patients after diseases like acute
Myocardial Infarction. The degree of variability in the HR provides information about the nervous system control on the HR and the heart’s ability to respond .Spectral analysis of HRV is a frequency domain approach to assess the cardiac condition. In this paper one such method for analyzing HRV signals known as smoothed pseudo Wigner Ville distribution (SPWVD) is
applied, The sub-band decomposition technique used in SPWVD, based on Instantaneous Autocorrelation (IACR) of the signal provides time-frequency representation for very lowfrequency (VLF), low-frequency (LF) and high-frequency (HF) regions identified in HRV spectrum. Results suggest that SPWVD analysis provides useful information for the assessment of dynamic changes and patterns of HRV during cardiac abnormalities.
A STUDY ON IMPACT OF ALCOHOL AMONG YOUNG INDIAN POPULATION USING HRV ANALYSISijcseit
Heart Rate Variability (HRV) is the measure of time difference between two successive heart beats and its
variation occurring due to internal and external stimulation causes. HRV is a non-invasive tool for indirect
investigation of both cardiac and autonomic system function in both healthy and diseased condition. It has
been speculated that HRV analysis by nonlinear method might bring potentially useful prognosis
information into light which will be helpful for assessment of cardiac condition. In this study, HRV from
two types of data sets are analyzed which are collected from different subjects in the age group of 18 to 22.
Then parameters of linear methods and three nonlinear methods, approximate entropy (ApEn), detrended
fluctuation analysis (DFA) and Poincare plot have been applied to analyze HRV among 158 subjects of
which 79 are control study and 79 are alcoholics. It has been clearly shown that the linear and nonlinear
parameters obtained from these two methods reflect the opposite heart condition of the two types of data
under study among alcoholics non-alcoholic’s by HRV measures. Poincare plot clearly distinguishes
between the alcoholics by analysing the location of points in the ellipse of the Poincare plot. In alcoholics
the points of the Poincare plot will be concentrated at the centre of the ellipse and in nonalchoholics the
points will be much concentrated along the periphery of the ellipse. The Approximate Entropy value will be
lesser than one in alcoholics and in nonalcoholics the entropy shows values greater than one. The
increased LF/HF value in alcoholics denotes the increase in sympathetic nervous system activities and
decrease of the parasympathetic activity which will be lesser in alcoholics subjects.
Heart Rate Variability (HRV) is the measure of time difference between two successive heart beats and its
variation occurring due to internal and external stimulation causes. HRV is a non-invasive tool for indirect
investigation of both cardiac and autonomic system function in both healthy and diseased condition. It has
been speculated that HRV analysis by nonlinear method might bring potentially useful prognosis
information into light which will be helpful for assessment of cardiac condition. In this study, HRV from
two types of data sets are analyzed which are collected from different subjects in the age group of 18 to 22.
Then parameters of linear methods and three nonlinear methods, approximate entropy (ApEn), detrended
fluctuation analysis (DFA) and Poincare plot have been applied to analyze HRV among 158 subjects of
which 79 are control study and 79 are alcoholics. It has been clearly shown that the linear and nonlinear
parameters obtained from these two methods reflect the opposite heart condition of the two types of data
under study among alcoholics non-alcoholic’s by HRV measures. Poincare plot clearly distinguishes
between the alcoholics by analysing the location of points in the ellipse of the Poincare plot. In alcoholics
the points of the Poincare plot will be concentrated at the centre of the ellipse and in nonalchoholics the
points will be much concentrated along the periphery of the ellipse. The Approximate Entropy value will be
lesser than one in alcoholics and in nonalcoholics the entropy shows values greater than one. The
increased LF/HF value in alcoholics denotes the increase in sympathetic nervous system activities and
decrease of the parasympathetic activity which will be lesser in alcoholics subjects.
Heart Rate Variability (HRV) analysis is the
ability to assess overall cardiac health and the state of the
autonomic nervous system (ANS), responsible for regulating
cardiac activity. ST-change due to ischemia and their HRV
analysis have not been well discussed in the previous works.
The proposed simple and time efficient TBC algorithm has
been tested in four sets of standard databases with selected
patient’s data having ischemic conditions (i.e.MIT-BIH
Normal-Sinus Rhythm Database (NSRDB), European ST-T
Database (EDB), MIT-BIH ST Change Database (STDB) &
Long-Term ST Database (LTSTDB))for the detection of R-peak
& HRV analysis. The pre-processing is done by MAF and DWT
to remove the baseline drift and noise induced in the ECG
signal. The mean/average of HR is calculated for each set of
databases and in case of EDB it is of 57 BPM (subjected to
bradycardia). The Probability with normal distribution is
analyzed by comparing the NSRDB data with the ischemic data sets. The performance of this algorithm is found to be 98.5%.
Review: Nonlinear Techniques for Analysis of Heart Rate VariabilityIJRES Journal
Heart rate variability (HRV) is a measure of the balance between sympathetic mediators of heart
rate that is the effect of epinephrine and norepinephrine released from sympathetic nerve fibres acting on the
sino-atrial and atrio-ventricular nodes which increase the rate of cardiac contraction and facilitate conduction at
the atrio-ventricular node and parasympathetic mediators of heart rate that is the influence of acetylcholine
released by the parasympathetic nerve fibres acting on the sino-atrial and atrio-ventricular nodes leading to a
decrease in the heart rate and a slowing of conduction at the atrio-ventricular node. Sympathetic mediators
appear to exert their influence over longer time periods and are reflected in the low frequency power(LFP) of
the HRV spectrum (between 0.04Hz and 0.15 Hz).Vagal mediators exert their influence more quickly on the
heart and principally affect the high frequency power (HFP) of the HRV spectrum (between 0.15Hz and 0.4
Hz). Thus at any point in time the LFP:HFP ratio is a proxy for the sympatho- vagal balance. Thus HRV is a
valuable tool to investigate the sympathetic and parasympathetic function of the autonomic nervous system.
Study of HRV enhance our understanding of physiological phenomenon, the actions of medications and disease
mechanisms but large scale prospective studies are needed to determine the sensitivity, specificity and predictive
values of heart rate variability regarding death or morbidity in cardiac and non-cardiac patients. This paper
presents the linear and nonlinear to analysis the HRV.
Analyzing Employee’s Heart rate using Nonlinear Cellular Automata modelIOSR Journals
Non-linear Cellular Automata model is a simulation tool which can be used to diagnosis the intensity of the disease. This paper aims to study the Heart rate behavior between normal respiratory patients and healthy controls/unhealthy controls. We also discuss about Heart Rate Variability (HRV) of employee’s through non-linear Cellular Automata model. Cellular Automata model gives us striking results for further studies
Review: Linear Techniques for Analysis of Heart Rate Variabilityinventionjournals
Heart rate variability (HRV) is a measure of the balance between sympathetic mediators of heart rate that is the effect of epinephrine and norepinephrine released from sympathetic nerve fibres acting on the sino-atrial and atrio-ventricular nodes which increase the rate of cardiac contraction and facilitate conduction at the atrio-ventricular node and parasympathetic mediators of heart rate that is the influence of acetylcholine released by the parasympathetic nerve fibres acting on the sino-atrial and atrio-ventricular nodes leading to a decrease in the heart rate and a slowing of conduction at the atrio-ventricular node. Sympathetic mediators appear to exert their influence over longer time periods and are reflected in the low frequency power(LFP) of the HRV spectrum (between 0.04Hz and 0.15 Hz).Vagal mediators exert their influence more quickly on the heart and principally affect the high frequency power (HFP) of the HRV spectrum (between 0.15Hz and 0.4 Hz). Thus at any point in time the LFP:HFP ratio is a proxy for the sympatho- vagal balance. Thus HRV is a valuable tool to investigate the sympathetic and parasympathetic function of the autonomic nervous system. Study of HRV enhance our understanding of physiological phenomenon, the actions of medications and disease mechanisms but large scale prospective studies are needed to determine the sensitivity, specificity and predictive values of heart rate variability regarding death or morbidity in cardiac and noncardiac patients. This paper presents the linear techniques to analysis the HRV
MEDITATION: ITS TREMENDOUS IMPACT ON HEART RATE VARIABILITYcscpconf
The heart is connected through the nervous system directly to major organs and is able to sense
their need. The heart also responds to adrenaline in the blood flowing through it. With these
inputs the heart is able to adjust its rate to accommodate the needs of the whole body. By its
heartbeat, it is able to broadcast a common signal to every cell. One measure of heart health is
Heart Rate Variability (HRV). HRV is defined in terms of how different the lengths of time
between each heart beat is. The greater the difference in times between heart beats, the
healthier is the heart thought to be. Individuals may be able to learn to increase their own HRV
and become healthier by a daily meditation practice. This paper uses Heart Variability as the
base signal for studying the impact of meditation on it. The paper brings out the difference
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A Novel Approach to Study the Effects of Anesthesia on Respiratory Signals by...IJECEIAES
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Transfer entropy estimation supplements time domain beat-to-beat baroreflex s...eSAT Journals
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Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
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GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
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The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
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Gopinath Rebala
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• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Communications Mining Series - Zero to Hero - Session 1
A novel reliable method assess hrv for
1. International Journal of Biomedical Engineering and Science (IJBES), Vol. 3, No. 1, January 2016
DOI : 10.5121/ijbes.2016.3104 45
A NOVEL RELIABLE METHOD ASSESS HRV FOR
HEART DISEASE DIAGNOSIS USING BIPOLAR MVF
ALGORITHM
Mazhar B. Tayel and Eslam I AlSaba
Electrical Engineering Department, Faculty Of Engineering., Alexandria University,
Alexandria, Egypt
ABSTRACT
In a simple words, the heart rate variability (HRV) refers to the divergence in heart complex wave (beat-
to-beat) intervals. It is a reliable repercussion of many, psychological, physiological, also environmental
factors modulating therhythm of the heart. Seriously, the HRV act as a powerful tool for observation the
interaction between the sympathetic and parasympathetic nervous systems. However, it has a frequency
that is great for supervision, surveillance, and following up the cases. Finally, the generating structure of
heart complex wave signal is not simply linear, but also it involves the nonlinear contributions. Those two
contributions are totally correlated.
HRV is stochastic and chaotic (stochaotic) signal. It has utmost importance in heart diseases diagnosis,
and it needs a sensitive tool to analyze its variability. In early works, Rosenstein and Wolf had used the
Lyapunov exponent (LE) as a quantitative measure for HRV detection sensitivity, but the Rosenstein and
Wolf methods diverge in determining the main features of HRV sensitivity, while Mazhar-Eslam introduced
a modification algorithm to overcome the Rosenstein and Wolf drawbacks.
The present work introduces a novel reliable method to analyze the linear and nonlinear behaviour of heart
complex wave variability, and to assess the use of the HRV as a versatile tool for heart disease diagnosis.
This paper introduces a declaration for the concept of the LE parameters to be used for HRV diagnosis and
proposes a modified algorithm for a more sensitive parameters computation.
KEYWORDS
Lyapunov exponent (LE), stochastic, Chaotic, stochaotic, Sensitive Dependence (SED), Transform domain,
Heart Rate Variability (HRV), sympathetic and parasympathetic, Diagnosis, bipolar Mazhar-Eslam
Variability Frequency, Variability Frequency, sensitivity.
1. INTRODUCTION
In sinus rhythm the HRV mean the temporal variations in (beat-to-beat) intervals corresponding
to Heart Rates instant (HRs).
HRV considered a universal instrument to analyze the heart neural control.Also explain the
degree of communication between sympathetic and parasympathetic impact on (HRs). Different
pattern can describe the fluctuations in HR such as (linear, non-linear pattern), which classified
2. International Journal of Biomedical Engineering and Science (IJBES), Vol. 3, No. 1, January 2016
46
into periodical and a periodical oscillation. These patterns can be quantified in time domain using
statistical analysis, in order to estimation the RR-fluctuations intervals.
The analysis by spectroscopy for HRV reveals that the presence of two clear frequency bands
variations of HR. High and low frequency band in the range vary from (0.16-0.4) Hz and (0.04-
0.15) Hz respectively. The higher band frequent act as a marker of vagal modulation, and the
lower frequent band indicates the overwhelmingly sympathetic tone and barore flex activity
[1,2,3]. Each of time domain and frequency domain methods were studied assuming that, the
signal of HRV are linear [4], although it failed in full quantification the dynamic structure of the
HR signals in order to extraction a highly sensitive diagnostic method for HR diseases.
Really, HRV represent a result for both linear and nonlinear fluctuations. Some interference can
modify the linear content of the variability, while constant nonlinear fluctuations. Moreover, the
reverse may occur: interference, which up till now have been approved to make cardiovascular
fluctuations depends on monitoring with linear methods, can just as well modulate the nonlinear
fluctuations. This represent a great work in new drug improvement for patients’ treatment.
In the present work introduces a novel reliable method to analyze the linear and nonlinear
behaviour of heart complex wave variability, and to assess the use of the HRV as a versatile tool
for heart disease diagnosis. The introduced new algorithm based on the Mazhar-Eslam algorithm
considering whole cases of linear and nonlinear behaviour for the HRV signal and pattern unlike
in Wolf and Rosenstein algorithms [5]. It is a Novel Reliable Method to verify the importance of
using the modified Mazhar-Eslam algorithm as a precise predicting tool for HRV diagnosis. It
allows to analyze the linear and nonlinear behaviour of HRV. Moreover, it can be considered as
a computer aided diagnosis (CAD) of HRV diseases.
2. HEART RATE VARIABILITY PHENOMENON
It describes the temporally variation in (beat-to-beat) intervals. Limitation of HR diagnosis
method depens on many factors like physical, psychological, and surrounding environmental
stressors [5] Backsstated that the validity of the autonomic component, using data from a
different research study, in which many central and peripheral psycho-physiological observations
were combined together while performing one or double duties which had a diverse physical
requirements. For independence, sympathetic and parasympathetic nervous system were tested,
using different data From various research, the Principles components analysis factors calculated
on raw ECG data supported useful information like various autonomic modes of control were not
clear in heart beats. The objective was to verify if factors taken out using residual HR as a sign
variable validly reflected cardiac sympathetic activity.If the solutions obtained from raw and
baseline corrected data were in acquiescencewith each other. This information about the
underlying autonomic activity may raise the efficiency for diagnosis of HR(Figure 1 HR
variation -control case).
3. International Journal of Biomedical Engineering and Science (IJBES), Vol. 3, No. 1, January 2016
47
Figure 1.Heart rate variation of a normal subject [6].
3. NEW (MAZHAR - ESLAM) APPROACH
Recently, a novel of new approach titled(Mazhar-Eslam) algorithm clarified [5], which used
Discrete Wavelet Transform (DWT) considering the merits of DWT over that of FFT. Although
the FFT has widely studied, there are still under demands due to its benefits which are not given
by FFT. Several points give the priority in choosen DWT instead of FFT. 1st
is the hardness of
FFT algorithm pruning [5]. Which is not suitable for case of few non-zero inputs located
randomly. In simple words, sparse signal does not provide increase to faster algorithm.
The 2nd
cause was the accuracy and its speed. Structure part of FFT are one unit and they have
equal importance. Thus, it is difficult to choice which part of the FFT structure to delete in case of
speed is crucial and error occurring. In simple words, the FFT is conceded a single speed also
single accuracy algorithm, which is not suitable for sensitive dependence (SED) cases.
The other reason for not selecting FFT is that there is no built-in noise reduction capacity.
Therefore, it is not useful to be used. According to the previous, the DWT is better than FFT
especially in the SED calculations used in HRV, because each small variant in HRV indicates the
important data and information. Thus, all variants in HRV should be calculated.
The Mazhar-Eslam algorithm depends to some extend on Rosenstein algorithm’s strategies [5] to
estimate lag and mean period, and uses the Wolf algorithm [5] for calculating the MVF (Ωெ)
except the first two steps, whereas the final steps are taken from Rosenstein’s method. Since the
MVF (Ωெ) measures the degree of the SED separation between infinitesimally close trajectories
in phase space, as discussed before, the MVF (Ωெ) allows determining additional invariants.
Consequently, the Mazhar-Eslam algorithm allows to calculate a mean value for the MVF (Ωெ),
that is given by
Ωெ
തതതതത = ∑
Ωಾ
ୀଵ (1)
Note that the Ωெs contain the largest Ωெ and variants Ωெs that indicate to the helpful and
important data. Therefore, the Mazhar-Eslam algorithm is a more SED prediction quantitative
measure. Therefore, it is robust quantitative predictor for real time, in addition to its sensitivity
for all time whatever the period.
Apply the Mazhar-Eslam algorithm to the HRV of the normal case given in fig. 1, it is found that
the mean MVF (Ωெ
തതതതത ) as 0.4986 Hz, which is more accurate than Wolf (0.505 Hz) and Rosenstein
(0.7586 Hz).Figure 2 flowcharts for calculating Mazhar-Eslam MVF algorithm.
4. International Journal of Biomedical Engineering and Science (IJBES), Vol. 3, No. 1, January 2016
48
Table (1)shows the different results of the normal case among Mazhar-Eslam, Wolf, and
Rosenstein algorithms. The MVF of ideal or optimum case should be 0.5 Hz as shown in table 1,
thus the accurate one for the normal case should be so close of the range of 0.5 Hz. The table 1
shows how algorithms’ MVF close to this range. The Mazhar-Eslam algorithm is the most
sensitive as it is the closest on of the 0.5 range. The deviation shown in table one, the Rosenstein
is highly deviated as its deviation is 0.2586. The Wolf success to reach a good sensitive level as
its deviation 0.005, but it unlike Mazhar-Eslam algorithm because Mazhar-Eslam algorithm’s
deviation is 0.0014. Also, from table 1 it is seen that, the Rosenstein algorithm has the lowest
SED because of its quite high error( D =51.72 % ) comparing to the optimum, while the Wolf
algorithm takes a computational place for SED (D = 1 % ). However, the Mazhar-Eslam
algorithm shows more sensitivity (D = 0.28 %) than Wolf algorithm as shown in fig. 3. Besides,
the optimum value of variance (Var) for healthy or normal case should tend to 0.25, thus it is
found the Rosenstein is the worst one its variance value is 0.058274. However the Wolf’s
variance is acceptable as it is 0.245025, it is insufficient because HRV depends on the heart and
body matter and it needs the most powerful and sensitive tool to analysis it. The variance of
Mazhar-Eslam is highly close to optimum as its value is 0.248602. Therefore, table 1 provide that
Mazhar-Eslam algorithm is the most suitable for HRV analysis as discussed previously. The
patient case deviation D for normal HRV case is calculated as
݊݅ݐܽ݅ݒ݁ܦሺܦሻ = |Ωெ − Ωெ௦| (2)
the cases percentage deviation is to be calculated as
%ܦ =
× 100% (3)
and, the variance for algorithms should be calculated as
ݎܽݒ = ሺΩெ − ܦሻଶ
(4)
Table 1.The results of the three algorithms for the normal case shown in fig. 1
method
parameter
Optimum Rosenstein Wolf Mazhar-Eslam
Ωெ 0.500000 0.758600 0.505000 0.498600
D 0.00000 0.258600 0.005000 0.001400
D% 0.000000 51.720000 1.000000 0.280000
Var 0.250000 0.058274 0.245025 0.248602
The bar diagram in fig. 4 shows the percentage deviation of the three algorithms. From this figure
it is seen that the Mazhar-Eslam algorithm gives the best result as it has the lowest percentage
deviation (D = 14). At the same time, when calculating the variance to determine the accurate and
best method, Mazhar-Eslam algorithm gives the best value. Figure 5 shows the bar diagram of
the variance for normal control case using the HRV for Wolf, and Mazhar-Eslam algorithms. It is
clear that the Mazhar-Eslam algorithm is more powerful and accurate than Wolf, because its
variance better than Wolf by 0.0036. This result comes because the Mazhar-Eslam considers all
the variability mean frequenciesΩெ
തതതതതs unlike the Wolf method as it takes only the largest. Each
interval of the HRV needs to be well monitored and taken into account because the variant in
HRV is indication of cases.
5. International Journal of Biomedical Engineering
Figure 2.The flowchart of the (Mazhar
From the bar diagram in fig. 5
sensitive comparing to Wolf and Rosenstein
International Journal of Biomedical Engineering and Science (IJBES), Vol. 3, No. 1, January 2016
Start
select initial
condition
Select an
embedded point
in the attractor
randomly
Form a delay
vector dE
Generate the reference
trajectory (nearest
neighbor vector)
select
another
trajectory
searching for the
point that minimizes
the distance to the
particular reference
point
d
min
The divergence
between the two
vectors compute
Samples
=3
Selecte the new
vector was
minimized length
and angular
separation
reference
trajectory has
gone over the
entire data
sample
calculate the divergence
and Ω_݅ܯ ݏ
Calculate the
ሺΩ_ܯ ሻ ̅
End
No
Yes
No Yes
No
Yes
The flowchart of the (Mazhar-Eslam) algorithm.
it is seen that the Mazahar-Eslamalgorithm is most useful and
sensitive comparing to Wolf and Rosenstein algorithms.
and Science (IJBES), Vol. 3, No. 1, January 2016
49
is most useful and
6. International Journal of Biomedical Engineering and Science (IJBES), Vol. 3, No. 1, January 2016
50
Figure 3.The three algorithms deviation for the normal case in fig. 1.
Figure 4.The three algorithms Percentage deviation (D%) for the normal case fig. 1.
Figure 5.The Variance of Wolf and Mazhar-Eslam algorithm for normal case fig. 1.
0
20
40
60
Mazhar-EslamWolfRosenstein
D%
0.243
0.244
0.245
0.246
0.247
0.248
0.249
Mazhar-EslamWolf
Var
7. International Journal of Biomedical Engineering and Science (IJBES), Vol. 3, No. 1, January 2016
51
4. BIPOLAR MVF ALGORITHM
The MVF "Ωெ"diversity of initially closely trajectories in state-space is connected with folding
of them. The presence of a positive part MVF ሺΩெ > 0ሻ, for all initial conditions in a restricted
dynamical system, is the vastly used definition of deterministic chaos. Thus, to distinguish
between periodic signals chaotic and, dynamics the MVF Ωெ are predominantly used. The
trajectories of chaotic signals in state-space pursue typical patterns. Nearlydiverge trajectories
diverge and converge exponentially, proportional to each other. A negative MVFሺΩெ < 0ሻmeans
that the orbit enticesto a settled point or stable periodic orbit. Negative MVFs are distinguishing
of non-fogyish systems. Like systems display asymptotic stability. For more stability, the MVF is
more negative. When MVF tends to infinity i.e. Ωெ= − ∞, it is mean the excessive stable
periodicity.
4.1 BIPOLAR (MAZHAR-ESLAM) VARIABILITY FREQUENCY (BMVF)
Generally, the HRV for healthy person (normal) seems to be periodic stochaotic signal, while the
HRV for a patient is usually a periodic stochaotic signal. Thus, in case will be stable if variability
signal (i.e. stochaotic signal) is periodic. Also, it gives an indication of utmost important
information. Subsequently, stochaotic signal of the HRV periodicity should be studied and
analysed for variability prediction.
It is clear that the MVF is the most suitable and sensitive tool for predicting the HRV. Therefore,
in the following it would be used to predict and verify the importance of HRV stochaotic
periodicity. Also, it was stated that the positive part indicates case status and the negative part
indicates the stability and periodicity. This explains the necessity to consider both polarities
(positive and negative) of the MVF, (i.e. Ωெ > 0andΩெ < 0). Also, it was stated that in the
present work the stochaotic periodicity and variation in the HRV the negative and positive MVFs
Ωெs should be taken in account. Thus, a new approach to be defined as Bipolar Mazhar-Eslam
Variability Frequency method is introduced.
Theoretically speaking, the introduced Bipolar Mazhar-Eslam Variability Frequency (BMVF)
calculated from ideal HRV, that comes from ideal Electrocardiograph (ECG) is equal to 0.5 for
positive MVF indicating a healthiest case, and is equal to – 1 for the negative MVF indicating
more HRV stochaotic periodic signal. Thence, the difference of the introduced Bipolar Mazhar-
Eslam Variability Frequency Ωெಳ
തതതതതത is -0.5, and around this value, the case is healthy. For the
normal (control) case shown in (Fig. 1), the introduced Bipolar Mazhar-Eslam BMVF Ωெಳ
തതതതതത is
0.4986 and – 0.9832. The difference of the BMVF for normal (control) case is – 0.4846, which is
very close to – 0.5. Consequently, the introduced Bipolar Mazhar-Eslam Variability Frequency
BMVF has a great role in monitoring, predicting and diagnosing the HRV stochaotic signal. The
BMVF has ability to monitor and follow up the patient case. It shows the disease by the positive
BMVF (Ωெಳ
തതതതതത > 0) part and shows the periodicity by the negative BMVF (Ωெಳ
തതതതതത < 0) part.
To verify and show the benefits of the BMVF some critical diseases data from the MIT-BIH are
used. The table 2 discusses the introduced Bipolar Mazhar-Eslam Variability Frequency BMVF
in many different cases from the MIT-BIH and compares it with Mazhar-Eslam, Wolf, and
Rosenstein algorithms results. The figures 6 and 7 present the MVF distribution.
8. International Journal of Biomedical Engineering
Table 2.The MVF results of different methods using normal case and the MIT
Serial
Parameter
Method
Case
Rosenstein
1 Normal 0.7586
2 101 0.2500
3 102 0.1600
4 104 0.2100
5 106 0.2300
6 107 0.2000
7 109 0.2200
8 111 0.2400
9 112 0.2400
10 115 0.2800
11 117 0.2300
12 118 0.2500
13 119 0.2700
14 121 0.2500
15 122 0.2300
16 123 0.2300
17 124 0.2500
18 200 0.2300
19 203 0.2300
20 212 0.2100
21 221 0.2100
22 230 0.2100
23 231 0.2200
Rosenstein
International Journal of Biomedical Engineering and Science (IJBES), Vol. 3, No. 1, January 2016
The MVF results of different methods using normal case and the MIT-BIH sample cases
.
MVF BMVF
Rosenstein Wolf Mazhar-Eslam
Ωெಳ
തതതതതത> 0
0.5050 0.4986 0.4986
0.1700 0.0830 0.0830
0.1300 0.0530 0.0530
0.1300 0.0700 0.0700
0.1500 0.0770 0.0770
0.1300 0.0667 0.0667
0.1400 0.0733 0.0733
0.1600 0.0800 0.0800
0.1700 0.0800 0.0800
0.1700 0.0930 0.0930
0.1600 0.0770 0.0770
0.1600 0.0833 0.0833
0.1700 0.0900 0.0900
0.1600 0.0840 0.0840
0.1600 0.0770 0.0770
0.1500 0.0770 0.0770
0.1700 0.0840 0.0840
0.1500 0.0770 0.0770
0.1500 0.0770 0.0770
0.1400 0.0700 0.0700
0.1400 0.0700 0.0700
0.1400 0.0700 0.0700
0.1500 0.0740 0.0740
Figure 6.MVF distribution
0
5
10
Rosenstein Wolf Mazhar-Eslam
and Science (IJBES), Vol. 3, No. 1, January 2016
52
BIH sample cases
BMVF
ത Ωெಳ
തതതതതത< 0
-0.9832
-0.1200
-0.0830
-0.1100
-0.1130
-0.1030
-0.1100
-0.1230
-0.1170
-0.1330
-0.1100
-0.1230
-0.1370
-0.1200
-0.1140
-0.1140
-0.1300
-0.1130
-0.1140
-0.1100
-0.1070
-0.1030
-0.1100
9. International Journal of Biomedical Engineering
4.2 DISCUSSIONS
To study and analyse table 2, the cases must be arranged related to field study. The results of the
previous proposal is based on studding of
introduced in next.
4.2.1 STUDY OF SENSITIVITY D
The sensitivity dependence (SED) in prediction of the HRV is so important.
results of computation of MVF for the four algorithms namely: Rosenstein, Wolf, Mazhar
and the introduced Bipolar Mazhar
relative the sensitivity. The next
Wolf, and Mazhar-Eslam. The results were rearranged depending on Wolf algorithm results
compared to Mazhar-Eslam and Rosenstein algorithms results, since the Wolf distributed has
same variability in many difference cases.
The results in table 3 were grouped depend on the Wolf values. This table shows the sensitivity
of the three MVF algorithms. The Wolf results show the cases in same area of diseases. Actually,
they are different although these have the same main problem
ventricular ectopy, they have other diseases. Thus, the BMVF is used to predict any tiny change
in HRV that depends on many information and different diseases. However, the BMVF
algorithms success to predict sensitively t
sensitivity. From the table 3, it is clear that the Rosenstein algorithm is unsuitable for predict the
critically heart disease cases and the long data set as however its sensitivity. The Rosenstein is
sensitive tool but its variation is not small enough to be accepted for HRV prediction because any
tiny change should be predicted. The Wolf algorithm scores better accuracy than Rosenstein.
Unfortunately, its results are stable in many cases and the HRV
tool to be predicted. Consequently, the table 5.4 shows that depending on the result and cases
status in MIT-BIH medical reports data, the Mazhar
MVF tool for HRV. The figure 8
figure 9 summarize the sensitivity dependence (SED) of MVF algorithms.
0.220.240.27
Rosenstein
International Journal of Biomedical Engineering and Science (IJBES), Vol. 3, No. 1, January 2016
Figure 7.MVF distribution
, the cases must be arranged related to field study. The results of the
previous proposal is based on studding of field is sensitivity dependence (SED) of algorithms is
DEPENDENCE (SED)
The sensitivity dependence (SED) in prediction of the HRV is so important. Table 2
results of computation of MVF for the four algorithms namely: Rosenstein, Wolf, Mazhar
and the introduced Bipolar Mazhar-Eslam (BMVF). From this table 2 the results rearranged
relative the sensitivity. The next table 3 discusses the SED of three MVF algorithms: Rosenstein,
Eslam. The results were rearranged depending on Wolf algorithm results
Eslam and Rosenstein algorithms results, since the Wolf distributed has
same variability in many difference cases.
were grouped depend on the Wolf values. This table shows the sensitivity
of the three MVF algorithms. The Wolf results show the cases in same area of diseases. Actually,
they are different although these have the same main problem like supra ventricular ectopy and
ventricular ectopy, they have other diseases. Thus, the BMVF is used to predict any tiny change
in HRV that depends on many information and different diseases. However, the BMVF
algorithms success to predict sensitively the HRV signal, they have variation in the accuracy and
, it is clear that the Rosenstein algorithm is unsuitable for predict the
critically heart disease cases and the long data set as however its sensitivity. The Rosenstein is
sensitive tool but its variation is not small enough to be accepted for HRV prediction because any
tiny change should be predicted. The Wolf algorithm scores better accuracy than Rosenstein.
Unfortunately, its results are stable in many cases and the HRV needs more sensitive and accurate
tool to be predicted. Consequently, the table 5.4 shows that depending on the result and cases
BIH medical reports data, the Mazhar-Eslam is the most sensitive and accurate
figure 8 that presents the bar diagram of MVF algorithms SED and
summarize the sensitivity dependence (SED) of MVF algorithms.
0
2
4
6
8
0.0530.070.0740.080.0840.0930.140.160.222
Rosenstein Wolf Mazhar-Eslam
and Science (IJBES), Vol. 3, No. 1, January 2016
53
, the cases must be arranged related to field study. The results of the
field is sensitivity dependence (SED) of algorithms is
Table 2gives the
results of computation of MVF for the four algorithms namely: Rosenstein, Wolf, Mazhar-Eslam,
the results rearranged
three MVF algorithms: Rosenstein,
Eslam. The results were rearranged depending on Wolf algorithm results
Eslam and Rosenstein algorithms results, since the Wolf distributed has
were grouped depend on the Wolf values. This table shows the sensitivity
of the three MVF algorithms. The Wolf results show the cases in same area of diseases. Actually,
ventricular ectopy and
ventricular ectopy, they have other diseases. Thus, the BMVF is used to predict any tiny change
in HRV that depends on many information and different diseases. However, the BMVF
he HRV signal, they have variation in the accuracy and
, it is clear that the Rosenstein algorithm is unsuitable for predict the
critically heart disease cases and the long data set as however its sensitivity. The Rosenstein is
sensitive tool but its variation is not small enough to be accepted for HRV prediction because any
tiny change should be predicted. The Wolf algorithm scores better accuracy than Rosenstein.
needs more sensitive and accurate
tool to be predicted. Consequently, the table 5.4 shows that depending on the result and cases
Eslam is the most sensitive and accurate
presents the bar diagram of MVF algorithms SED and
11. International Journal of Biomedical Engineering and Science (IJBES), Vol. 3, No. 1, January 2016
55
Figure 9.MVF algorithms SED
(Figure 8, and 9) show that the Mazhar-Eslam algorithm is the most sensitive MVF tool.
The Mazhar-Eslam results change with a range of diseases but with a sensitive variation.
The Rosenstein algorithm is out of comparison because its results have a huge variation
for the same disease. The Wolf algorithm is sensitive, but its sensitivity is not enough
because it is stable for many cases although they are different. Thence, it is so clear that
the Mazhar-Eslam is the best method and as a quantities measure.
5. CONCLUSIONS
Heart Rate Variability (HRV) is presented in manydiseases whatever it related to the heart or not.
Besides, it has good prediction class or level and importance in modelling the heart risk. HRV is
stochaotic signal that remains highly controversial. In order to have utmost importance, HRV
needs a sensitive tool to analyse it. It is concluded that Mazhar-Eslam variability mean frequency,
is a better qualitative measure of sensitivity than others. The Rosenstein algorithm presented
lower sensitive ܨܸܯestimates than the Wolf algorithm to getvariations in local dynamic stability
from small data sets. The data confirming the idea that latest outcome observations from the
inability and ability of the Wolf algorithm and Rosenstein algorithm, respectively, to estimate
adequately MVF of attractors with significant of convergence. Therefore, the Mazhar-Eslam
algorithm seems to be more suitable to evaluate local dynamic stability for any data sets
especially small one like HRV. When the data set size is raise, it be provided to make the
observations of the Mazhar-Eslam algorithm more convenient, although other means as raising
the sample size might have a same impact. The Mazhar-Eslam algorithm takes the same strategy
of Rosenstein method for initial step to calculate the lag and mean period, but it uses the merits of
Discrete Wavelet Transform (DWT) instead of Fats Fourier Transform (FFT) unlike Rosenstein.
After that, it completes steps of calculating Ωெas Wolf method. The Mazhar-Eslam method care
of all variants especially the small ones like that are in HRV. These variants may contain many
important data to diagnose diseases as R-R interval has many variants. Thus, the Mazhar-Eslam
algorithm for MVF Ωெ
തതതതതtakes all of Ωெs. That makes it to be robust predictor, that appear in
different results among Mazhar-Eslam, Wolf, and Rosenstein. The Mazhar-Eslam algorithm
presents a new chapter for HRV prediction. It contains a positive part for HRV as it is stochaotic
signal. The introduced Bipolar Mazhar-Eslam MVF supports to follow-up the cases and it has the
ability of monitoring. The Bipolar Mazhar-Eslam MVF shows that Mazhar-Eslam algorithm is
0
0.05
0.1
0.15
0.2
0.25
0.3
112
124
115
122
118
231
200
106
221
109
107
ΩM
MIT-BH Cases
Rosenstein Wolf Mazhar-Eslam
12. International Journal of Biomedical Engineering and Science (IJBES), Vol. 3, No. 1, January 2016
56
more accurate and sensitive than Wolf and Rosenstein MVF. The next table 4 discusses the
sensitivity and accuracy of MVF algorithms as it is clear the Mazher-Eslam is the best MVF
algorithm for HRV.
Table 4. SED of MVF algorithms.
Cases No. Mazher– Eslam (ME) Wolf
(W)
Rosenstein (R) SED
112, 101, 124, 119
and 115
0.0800, 0.0830, 0.0840, 0.0900,
0.0930
0.1700 0.2400, 0.2500, 2500, 2700,
2800
ME>W>R
102, 107 and 104 0.0530, 0.0667, 0.0700 0.1300 0.1600, 0.2000, 02100 ME>W>R
117, 122, 111, 118
and 121
0.0770, 0.0770, 0.0800, 0.0833,
0,0840,
0.1600 0.2300, 0.2300, 0.2400, 0.2500,
0.2500
ME>W>R
212, 221, 230 and
109
0.0700, 0.0700, 0.0700, 0.0733 0.1400 0.2100, 0.2100, 0.2100, 02200 ME>W>R
231, 203, 200,123
and106
0.0740, 0.0770, 0.0770, 0.0770,
0.0770
0.1500 0.2200, 0.2300, 0.2300, 0.2300,
0.2300
ME>W>R
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