Vyas, P.; Pandit, D. Heartbeat Abnormality Detection using Machine Learning Models and Rate Variability (HRV) Data. Preprints2018, 2018070488. https://doi.org/10.20944/preprints201807.0488.v1
APA Style
Vyas, P., & Pandit, D. (2018). <em></em>Heartbeat Abnormality Detection using Machine Learning Models and Rate Variability (HRV) Data. Preprints. https://doi.org/10.20944/preprints201807.0488.v1
Chicago/Turabian Style
Vyas, P. and Diptangshu Pandit. 2018 "<em></em>Heartbeat Abnormality Detection using Machine Learning Models and Rate Variability (HRV) Data" Preprints. https://doi.org/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.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
