Cardiac Health Prediction using Electrocardiography
Pratishtha Agnihotri1, Monika Jain2
1Pratishtha Agnihotri, Department of Electronics and Communication, PSIT College of Engineering, Kanpur, (U.P.), India.
2Monika Jain, Department of Information Technology, IIITD, New Delhi, India.
Manuscript received on 21 March 2019 | Revised Manuscript received on 27 March 2019 | Manuscript published on 30 July 2019 | PP: 5703-5711 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3284078219/2019©BEIESP | DOI: 10.35940/ijrte.B3284.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Amongst various physiological signals, that can be collected from the human body, Electrocardiogram (ECG) is one widely used signal that gives an overview of individual’s health non-invasively. Some prognostic tools, based on ECG, have already been introduced in the past. However, the diagnostic information contained in ECG is still under used. In the present study, we propose an algorithm that predicts the cardiac health (both present and future) by analyzing subject’s ECG. The prediction is based on diagnostic information like Blood Pressure (BP), Arrhythmia and Heart Rate Variability (HRV), where BP and Arrhythmia are used to predict the present cardiac health, and Arrhythmia and HRV are used to predict the future cardiac health associated with an individual. To verify the algorithm, we use: (1) Linear Regression Model to extract BP based on parameters extracted from ECG; (2) Neural Network Pattern Recognition Application to detect Arrhythmia- Right and Left bundle branch block beat, Atrial premature contraction beat, Premature ventricular contraction beat and Premature or ectopic supraventricular beats, in any ECG signal; (3) Self-Organized Maps for HRV analysis using ECG. These models are used on ECG of 30 subjects chosen from an existing database. Based on the outputs of these models our algorithm predicts the present as well as the future cardiac health of 30 subjects under study. Our predictions are compared with the present and future cardiac health of these subjects already documented in the database. The prediction accuracy showed that present and future cardiac health risk of an individual can be satisfactorily determined using the proposed algorithm, which, in future, can be easily incorporated in any health monitoring device which can record ECG.
Keywords: Blood Pressure; Arrhythmia; Heart Rate Variability; Cardiac Health Monitoring.
Scope of the Article: Health Monitoring and Life Prediction of Structures