Efficiency Evaluation of Autonomic Heart Control by Using the Principal Component Analysis of ECG P-Wave

2010 ◽  
Vol 49 (02) ◽  
pp. 161-167
Author(s):  
R. Simoliuniene ◽  
M. Tamosiunas ◽  
V. Saferis ◽  
A. Vainoras ◽  
L. Gargasas ◽  
...  
Keyword(s):  
Heart Rate ◽  
Principal Component Analysis ◽  
Quantitative Evaluation ◽  
Principal Component ◽  
Component Analysis ◽  
P Wave ◽  
Whole Body ◽  
Postural Orthostatic Tachycardia Syndrome ◽  
Control Mechanisms ◽  
Orthostatic Test

Summary Background: Cardiac output is controlled by the autonomic nervous system by changing the heart rate and/or the contractions of the heart muscle in response to the hemodynamic needs of the whole body. Malfunction of these mechanisms causes the postural orthostatic tachycardia syndrome and/or the chronic fatigue syndrome. Evaluation of functionality and efficiency of the control mechanisms could give valuable diagnostic information in the early stages of dysfunction of the heart control systems and help to monitor the healing process in rehabilitation period after interventions. Objectives: In this study we demonstrate how P-wave changes evoked by an ortho-static test could be quantitatively evaluated by using the method based on the principal component analysis. Methods: ECG signals were recorded during an orthostatic test performed according to the typical protocol in three groups of volunteer subjects representing healthy young and older persons, part of which had transient periods of supraventricular arrhythmias. Quantitative evaluation of P-wave morphology changes was performed by means of principal component analysis-based method. Results: Principal component-based estimates showed certain variety of P-wave shape during orthostatic test, what revealed a possibility to evaluate the properties of para-sympathetic heart control. Conclusions: Quantitative evaluation of ECG P-wave changes evoked by an orthostatic test by using a newly developed method provides a quantitative estimate for functionality and efficiency of the heart rate control mechanisms. The method could be used in eHealth systems.

scholarly journals Determination of number of clusters of ECG P-wave morphology estimates by testing hypothesis about uniformity of distribution

2021 ◽  
Vol 47 ◽  
Author(s):  
Renata Šimoliūnienė ◽  
Algimantas Kriščiukaitis ◽  
Viktoras Šaferis ◽  
Violeta Šimatonienė
Keyword(s):  
Heart Rate ◽  
Healing Process ◽  
Principal Component ◽  
Chronic Fatigue ◽  
P Wave ◽  
Statistical Hypothesis ◽  
Whole Body ◽  
Postural Orthostatic Tachycardia Syndrome ◽  
Control Mechanisms ◽  
Fatigue Syndrome

Cardiac output is controlled by the autonomic nervous system by changing the heart rate and/or the contractions of the heart muscle in response to the hemodynamic needs of the whole body. This control is a result of permanent competition between the sympathetic and the parasympathetic nervous systems. Malfunction of these mechanisms causes the postural orthostatic tachycardia syndrome and/or the chronic fatigue syndrome. Evaluation of functionality and efficiency of the control mechanisms can give valuable diagnostic information in the early stages of dysfunction of the heart control systems and help to monitor the healing process or rehabilitation period after interventions. Quantitative evaluation of ECG P-wave changes evoked by an orthostatic test (which evokes a sudden misbalance in the interplay between the sympathetic and the parasympathetic heart control) by using a newly developed method based on the principal component analysis and clusterization by testing statistical hypothesis of uniformity provides a quantitative estimate for functionality and efficiency of the heart rate control mechanisms.

Peculiarities of electrocardiogram P-wave morphology analysis using principal component analysis

2011 ◽  
Vol 44 (2) ◽  
pp. e30
Author(s):  
Algimantas Krisciukaitis ◽  
Renata Simoliuniene ◽  
Alfonsas Vainoras ◽  
Liudas Gargasas
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
P Wave ◽  
Morphology Analysis ◽  
Wave Morphology

CARDIAC ARRHYTHMIA DIAGNOSIS BY HRV SIGNAL PROCESSING USING PRINCIPAL COMPONENT ANALYSIS

2012 ◽  
Vol 12 (05) ◽  
pp. 1240032 ◽  
Author(s):  
S. VINITHA SREE ◽  
DHANJOO N. GHISTA ◽  
KWAN-HOONG NG
Keyword(s):  
Heart Rate ◽  
Principal Component Analysis ◽  
Nervous System ◽  
Standard Deviation ◽  
Probabilistic Neural Network ◽  
Principal Component ◽  
Component Analysis ◽  
Cardiac Cells ◽  
Ecg Signal ◽  
Mean Values

An electrocardiogram (ECG) signal represents the sum total of millions of cardiac cells' depolarization potentials. It helps to identify the cardiac health of the subject by inspecting its P-QRS-T wave. The heart rate variability (HRV) data, extracted from the ECG signal, reflects the balance between sympathetic and parasympathetic components of the autonomic nervous system. Hence, HRV signal contains information on the imbalance between these two nervous system components that results in cardiac arrhythmias. Thus in this paper, we have analyzed HRV signal abnormalities to determine and classify arrhythmias. The HRV signals are non-stationary and non-linear in nature. In this work, we have used continuous wavelet transform (CWT) coupled with principal component analysis (PCA) to extract the important features from the heart rate signals. These features are fed to the probabilistic neural network (PNN) classifier, for automated classification. Our proposed system demonstrates an average accuracy of 80% and sensitivity and specificity of 82% and 85.6%, respectively, for arrhythmia detection and classification. Our system can be operated on larger data sets. Our CWT–PCA analysis resulted in eigenvalues which constituted the HRV signal analysis parameters. We have shown and plotted the distribution of the parameters' mean values and the standard deviation for arrhythmia classification. We found some overlap in the distribution of these eigenvalue parameters for the different arrhythmia classes, which mitigates the effective use of these parameters to separate out the various arrhythmia classes. Therefore, we have formulated a HRV Integrated Index (HRVID) of these eigenvalues, and determined and plotted the mean values and standard deviation of HRVID for the various arrhythmia classifications. From this information, it can be seen that the HRVID is able to distinguish among the various arrhythmia classes. Hence, we have made a case for the employment of this HRVID as an index to effectively diagnose arrhythmia disorders.

scholarly journals Dynamic heart rate estimation using principal component analysis

2015 ◽  
Vol 6 (11) ◽  
pp. 4610 ◽  
Author(s):  
Yong-Poh Yu ◽  
P. Raveendran ◽  
Chern-Loon Lim ◽  
Ban-Hoe Kwan
Keyword(s):  
Heart Rate ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Rate Estimation ◽  
Heart Rate Estimation

scholarly journals How Do Movement Patterns in Weightlifting (Clean) Change When Using Lighter or Heavier Barbell Loads?—A Comparison of Two Principal Component Analysis-Based Approaches to Studying Technique

2021 ◽  
Vol 11 ◽  
Author(s):  
Inge Werner ◽  
Nicolai Szelenczy ◽  
Felix Wachholz ◽  
Peter Federolf
Keyword(s):  
Principal Component Analysis ◽  
Repeated Measures ◽  
Principal Component ◽  
Movement Pattern ◽  
Component Analysis ◽  
Whole Body ◽  
Load Effects ◽  
The One ◽  
Coordination Patterns ◽  
Initial Elevation

This study compared whole body kinematics of the clean movement when lifting three different loads, implementing two data analysis approaches based on principal component analysis (PCA). Nine weightlifters were equipped with 39 markers and their motion captured with 8 Vicon cameras at 100 Hz. Lifts of 60, 85, and 95% of the one repetition maximum were analyzed. The first PCA (PCAtrial) analyzed variance among time-normed waveforms compiled from subjects and trials; the second PCA (PCAposture) analyzed postural positions compiled over time, subjects and trials. Load effects were identified through repeated measures ANOVAs with Bonferroni-corrected post-hocs and through Cousineau-Morey confidence intervals. PCAtrial scores differed in the first (p < 0.016, ηp2 = 0.694) and fifth (p < 0.006, ηp2 = 0.768) principal component, suggesting that increased barbell load produced higher initial elevation, lower squat position, wider feet position after squatting, and less inclined arms. PCAposture revealed significant timing differences in all components. We conclude, first, barbell load affects specific aspects of the movement pattern of the clean; second, the PCAtrial approach is better suited for detecting deviations from a mean motion trajectory and its results are easier to interpret; the PCAposture approach reveals coordination patterns and facilitates comparisons of postural speeds and accelerations.

Intelligent alarms using principal component analysis on hemodynamic monitoring (Preprint)

2019 ◽  
Author(s):  
Silje Skeide Fuglerud ◽  
Mikael Dyb Wedeld ◽  
Harald Martens ◽  
Nils Kristian Skjærvold
Keyword(s):  
Heart Rate ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Classification Problem ◽  
Component Analysis ◽  
Waveform Data ◽  
Alarm Fatigue ◽  
Physiological Variability ◽  
Monitoring Devices

BACKGROUND Patient monitors in modern hospitals give heartbeat waveform data that is reduced to aggregated variables and simple thresholds for alarms. Often, the monitors give a steady stream of non-specific alarms, leading to alarm fatigue in clinicians. An alarm can be seen as a classification problem, and by applying Principal Component Analysis (PCA) to the heart rate waveform of readily available monitoring devices, the accuracy of the classification of abnormality could be highly increased. OBJECTIVE To investigate whether physiological changes could be detected by looking at the heart rate waveform. METHODS A dataset of a healthy volunteer monitored with electrocardiography (ECG) and invasive blood pressure (BP) experiencing several tilts on a tilting table was investigated. A novel way of splitting continuous data based on the heartbeat was introduced. PCA was applied to classify the heartbeats. RESULTS A classification using only the aggregated variables heart rate (HR) and BP was able to correctly identify 20.7% of the heartbeats in the vertical tilt as abnormal. A classification using the full waveforms and combining the ECG and BP signals was able to correctly identify 83.5% of the heartbeats in the vertical tilt as abnormal. A humanistic machine learning (ML) method is then proposed based on the PCA classification. CONCLUSIONS A ML method for classification of physiological variability was described. The main novelty lies in the splitting of an ECG and BP signal by the heart rate and performing a PCA on the data-table.

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