dc.contributor.authorSwapna, Goutham
dc.contributor.authorGhista, Dhanjoo N.
dc.contributor.authorMartis, Roshan Joy
dc.contributor.authorAng, Alvin P. C.
dc.contributor.authorSree, Subbhuraam Vinitha
dc.date.accessioned2013-07-16T01:48:20Z
dc.date.available2013-07-16T01:48:20Z
dc.date.copyright2012en_US
dc.date.issued2012
dc.identifier.citationSwapna, G., Ghista, D. N., Martis, R. J., Ang, A. P. C., & Sree, S. V. (2012). ECG signal generation and heart rate variability signal extraction: Signal processing, features detection, and their correlation with cardiac diseases. Journal of Mechanics in Medicine and Biology, 12(04), 1240012-.en_US
dc.identifier.urihttp://hdl.handle.net/10220/11490
dc.description.abstractThe sum total of millions of cardiac cell depolarization potentials can be represented by an electrocardiogram (ECG). Inspection of the P–QRS–T wave allows for the identification of the cardiac bioelectrical health and disorders of a subject. In order to extract the important features of the ECG signal, the detection of the P wave, QRS complex, and ST segment is essential. Therefore, abnormalities of these ECG parameters are associated with cardiac disorders. In this work, an introduction to the genesis of the ECG is given, followed by a depiction of some abnormal ECG patterns and rhythms (associated with P–QRS–T wave parameters), which have come to be empirically correlated with cardiac disorders (such as sinus bradycardia, premature ventricular contraction, bundle-branch block, atrial flutter, and atrial fibrillation). We employed algorithms for ECG pattern analysis, for the accurate detection of the P wave, QRS complex, and ST segment of the ECG signal. We then catagorited and tabulated these cardiac disorders in terms of heart rate, PR interval, QRS width, and P wave amplitude. Finally, we discussed the characteristics and different methods (and their measures) of analyting the heart rate variability (HRV) signal, derived from the ECG waveform. The HRV signals are characterised in terms of these measures, then fed into classifiers for grouping into categories (for normal subjects and for disorders such as cardiac disorders and diabetes) for carrying out diagnosis.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesJournal of mechanics in medicine and biologyen_US
dc.rights© 2012 World Scientific Publishing Company.en_US
dc.subjectDRNTU::Engineering::Mechanical engineering
dc.titleECG signal generation and heart rate variability signal extraction : signal processing, features detection, and their correlation with cardiac diseasesen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1142/S021951941240012X


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