TY - GEN
T1 - Wearable Devices Acquired ECG Signals Detection Method Using 1D Convolutional Neural Network
AU - Hui, Yi
AU - Yin, Zhendong
AU - Wu, Mingyang
AU - Li, Dasen
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/4/14
Y1 - 2021/4/14
N2 - According to reports, the number of people who die from sudden cardiac death in China every year is as high as 540,000, and the number of deaths due to arrhythmia accounts for about 90%. Although a single arrhythmia heartbeat may not seriously affect life, continuous arrhythmia can lead to fatal conditions. Therefore, it is very important to monitor the heart rhythm regularly to control and prevent arrhythmia. Because the ECG is too complex and large, sports bracelet on sale can only monitor the momentary ECG, we designed a wearable, portable device that can continuously monitor heart rhythm in real time. At the same time, a classification method with low complexity and high accuracy classification network is proposed to detect arrhythmia. The results show that compared with other existing algorithms, our proposed 1D CNN model has improved accuracy and reduced network complexity.
AB - According to reports, the number of people who die from sudden cardiac death in China every year is as high as 540,000, and the number of deaths due to arrhythmia accounts for about 90%. Although a single arrhythmia heartbeat may not seriously affect life, continuous arrhythmia can lead to fatal conditions. Therefore, it is very important to monitor the heart rhythm regularly to control and prevent arrhythmia. Because the ECG is too complex and large, sports bracelet on sale can only monitor the momentary ECG, we designed a wearable, portable device that can continuously monitor heart rhythm in real time. At the same time, a classification method with low complexity and high accuracy classification network is proposed to detect arrhythmia. The results show that compared with other existing algorithms, our proposed 1D CNN model has improved accuracy and reduced network complexity.
KW - CNN
KW - ECG
KW - hardware design
KW - network complexity
UR - https://www.scopus.com/pages/publications/85107262840
U2 - 10.1109/ISMICT51748.2021.9434935
DO - 10.1109/ISMICT51748.2021.9434935
M3 - 会议稿件
AN - SCOPUS:85107262840
T3 - International Symposium on Medical Information and Communication Technology, ISMICT
SP - 81
EP - 85
BT - Proceedings of 2021 15th International Symposium on Medical Information and Communication Technology, ISMICT 2021
A2 - Wang, Lin
PB - IEEE Computer Society
T2 - 15th International Symposium on Medical Information and Communication Technology, ISMICT 2021
Y2 - 14 April 2021 through 16 April 2021
ER -