ECG-based real-time arrhythmia monitoring using quantized deep neural networks: A feasibility study. (April 2022)

• Record Type: Journal Article
• Authors: De Melo Ribeiro, Henrique; Arnold, Ahran; Howard, James P.; Shun-Shin, Matthew J.; Zhang, Ying; Francis, Darrel P.; Lim, Phang B.; Whinnett, Zachary; Zolgharni, Massoud
• Is Part Of: Computers in biology and medicine. Volume 143(2022)
• Publication Date: 2022-04
• Subjects: Arrhythmia -- ECG -- Deep learning -- Continuous monitoring -- Heart disease; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.105249
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Continuous ambulatory cardiac monitoring plays a critical role in early detection of abnormality in at-risk patients, thereby increasing the chance of early intervention. In this study, we present an automated ECG classification approach for distinguishing between healthy heartbeats and pathological rhythms. The proposed lightweight solution uses quantized one-dimensional deep convolutional neural networks and is ideal for real-time continuous monitoring of cardiac rhythm, capable of providing one output prediction per second. Raw ECG data is used as the input to the classifier, eliminating the need for complex data preprocessing on low-powered wearable devices. In contrast to many compute-intensive approaches, the data analysis can be carried out locally on edge devices, providing privacy and portability. The proposed lightweight solution is accurate (sensitivity of 98.5% and specificity of 99.8%), and implemented on a smartphone, it is energy-efficient and fast, requiring 5.85 mJ and 7.65 ms per prediction, respectively. Highlights: Continuous ambulatory cardiac monitoring is of great interest. Proposed approach can reliably detect abnormal arrhythmia. Neural network is lightweight and fast for real-time application on smartphones.