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Towards better heartbeat segmentation with deep learning classification

Computer Science

Towards better heartbeat segmentation with deep learning classification

P. Silva, E. Luz, et al.

This paper presents an innovative real-time method for validating heartbeat segmentation using convolutional neural networks (CNNs), designed to minimize false positive alarms. With application evaluations on the MIT-BIH and CYBHI databases, conducted by Pedro Silva, Eduardo Luz, Guilherme Silva, Gladston Moreira, Elizabeth Wanner, Flavio Vidal, and David Menotti, this approach shows promise for real-time applications and could potentially be integrated into dedicated hardware.

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Playback language: English
Abstract
This paper proposes a real-time approach for validating heartbeat segmentation using convolutional neural networks (CNNs). The method aims to reduce false positive alarms by classifying heartbeat patterns as either 'heartbeat' or 'not a heartbeat'. A seven-layer CNN is used, and the approach is evaluated on the MIT-BIH and CYBHI databases. Compared to the Pan-Tompkins algorithm, the CNN approach improves positive prediction while slightly decreasing sensitivity, demonstrating feasibility for real-time applications and potential for embedding in dedicated hardware.
Publisher
Scientific Reports
Published On
Nov 26, 2020
Authors
Pedro Silva, Eduardo Luz, Guilherme Silva, Gladston Moreira, Elizabeth Wanner, Flavio Vidal, David Menotti
Tags
heartbeat segmentation
convolutional neural networks
real-time validation
false positive reduction
MIT-BIH database
CYBHI database

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