Preprint Article Version 1 This version is not peer-reviewed

Heartbeat Abnormality Detection using Machine Learning Models and Rate Variability (HRV) Data

Version 1 : Received: 15 June 2018 / Approved: 25 July 2018 / Online: 25 July 2018 (14:22:10 CEST)

How to cite: Vyas, P.; Pandit, D. Heartbeat Abnormality Detection using Machine Learning Models and Rate Variability (HRV) Data. Preprints 2018, 2018070488 (doi: 10.20944/preprints201807.0488.v1). Vyas, P.; Pandit, D. Heartbeat Abnormality Detection using Machine Learning Models and Rate Variability (HRV) Data. Preprints 2018, 2018070488 (doi: 10.20944/preprints201807.0488.v1).

Abstract

The use of machine learning techniques in predictive health care is on the rise with minimal data used for training machine-learning models to derive high accuracy predictions. In this paper, we propose such a system, which utilizes Heart Rate Variability (HRV) as features for training machine learning models. This paper further benchmarks the usefulness of HRV as features calculated from basic heart-rate data using a window shifting method. The benchmarking has been conducted using different machine-learning classifiers such as artificial neural network, decision tree, k-nearest neighbour and naive bays classifier. Empirical results using MIT-BIH Arrhythmia database shows that the proposed system can be used for highly efficient predictability of abnormality in heartbeat data series.

Subject Areas

heart rate variability; machine learning; abnormality detection; window shifting; high accuracy prediction

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