SS-SWT and SI-CNN: An Atrial Fibrillation Detection Framework for Time-Frequency ECG Signal.

Journal of Healthcare Engineering
Hongpo ZhangZongmin Wang

Abstract

Atrial fibrillation is the most common arrhythmia and is associated with high morbidity and mortality from stroke, heart failure, myocardial infarction, and cerebral thrombosis. Effective and rapid detection of atrial fibrillation is critical to reducing morbidity and mortality in patients. Screening atrial fibrillation quickly and efficiently remains a challenging task. In this paper, we propose SS-SWT and SI-CNN: an atrial fibrillation detection framework for the time-frequency ECG signal. First, specific-scale stationary wavelet transform (SS-SWT) is used to decompose a 5-s ECG signal into 8 scales. We select specific scales of coefficients as valid time-frequency features and abandon the other coefficients. The selected coefficients are fed to the scale-independent convolutional neural network (SI-CNN) as a two-dimensional (2D) matrix. In SI-CNN, a convolution kernel specifically for the time-frequency characteristics of ECG signals is designed. During the convolution process, the independence between each scale of coefficient is preserved, and the time domain and the frequency domain characteristics of the ECG signal are effectively extracted, and finally the atrial fibrillation signal is quickly and accurately identified....Continue Reading

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Citations

Jun 19, 2021·Physical and Engineering Sciences in Medicine·Hocine HamilDjaffar Ould Abdeslam
Sep 4, 2021·Computer Methods and Programs in Biomedicine·Hongpo ZhangZongmin Wang
Sep 14, 2021·Frontiers in Physiology·Ricardo Salinas-MartínezFrida Sandberg

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Methods Mentioned

BETA
feature extraction

Software Mentioned

CNN
Tensorflow1
SS
Keras
SWT
SI

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