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

Joint Authors

Dai, Honghua
Wang, Zongmin
He, Renke
Zhang, Hongpo
Xu, Mingliang

Source

Journal of Healthcare Engineering

Issue

Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2020-05-18

Country of Publication

Egypt

No. of Pages

11

Main Subjects

Public Health
Medicine

Abstract EN

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.

In this study, experiments are performed using the MIT-BIH AFDB data in 5-s data segments.

We achieve 99.03% sensitivity, 99.35% specificity, and 99.23% overall accuracy.

The SS-SWT and SI-CNN we propose simplify the feature extraction step, effectively extracts the features of ECG, and reduces the feature redundancy that may be caused by wavelet transform.

The results shows that the method can effectively detect atrial fibrillation signals and has potential in clinical application.

American Psychological Association (APA)

Zhang, Hongpo& He, Renke& Dai, Honghua& Xu, Mingliang& Wang, Zongmin. 2020. SS-SWT and SI-CNN: An Atrial Fibrillation Detection Framework for Time-Frequency ECG Signal. Journal of Healthcare Engineering،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1186379

Modern Language Association (MLA)

Zhang, Hongpo…[et al.]. SS-SWT and SI-CNN: An Atrial Fibrillation Detection Framework for Time-Frequency ECG Signal. Journal of Healthcare Engineering No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1186379

American Medical Association (AMA)

Zhang, Hongpo& He, Renke& Dai, Honghua& Xu, Mingliang& Wang, Zongmin. SS-SWT and SI-CNN: An Atrial Fibrillation Detection Framework for Time-Frequency ECG Signal. Journal of Healthcare Engineering. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1186379

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1186379