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Low-Complexity Compressed Analysis in Eigenspace with Limited Labeled Data for Real-Time Electrocardiography Telemonitoring

Abstract: 

To achieve real-time electrocardiography (ECG) telemonitoring, we need to overcome the scarce bandwidth. Compressed sensing (CS) emerges as a promising technique to greatly compress ECG signal with little computation. Furthermore, with edge-classification, we can reduce the data rate by transmitting abnormal ECG signals only. However, there are three main limitations: limited number of labeled ECG signal, tight battery constraint of edge devices and low response time requirement. Task-driven dictionary learning (TDDL) appears as an appropriate classifier to render low complexity and high generalization. Combining CS with TDDL directly (CA-N) will degrade classification and need higher complexity model. In this paper, we proposed an eigenspace-aided compressed analysis (CA-E) integrating principal component analysis (PCA), CS and TDDL, sustaining not only light complexity but high performance under exiguous labeled ECG data set. Simulation results show CA-E reduces about 67% parameters, 76% training time, 87% inference time and has the smaller accuracy variance to CA-N counterpart.

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Paper Details

Authors:
Kai-Chieh Hsu, Bo-Hong Cho, Ching-Yao Chou, and An-Yeu (Andy) Wu
Submitted On:
12 December 2018 - 10:09pm
Short Link:
Type:
Presentation Slides
Event:
Presenter's Name:
Kai-Chieh Hsu
Paper Code:
BIO-L.2.4
Document Year:
2018
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GlobalSIP_slides.pdf

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[1] Kai-Chieh Hsu, Bo-Hong Cho, Ching-Yao Chou, and An-Yeu (Andy) Wu, "Low-Complexity Compressed Analysis in Eigenspace with Limited Labeled Data for Real-Time Electrocardiography Telemonitoring", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3718. Accessed: Apr. 19, 2019.
@article{3718-18,
url = {http://sigport.org/3718},
author = {Kai-Chieh Hsu; Bo-Hong Cho; Ching-Yao Chou; and An-Yeu (Andy) Wu },
publisher = {IEEE SigPort},
title = {Low-Complexity Compressed Analysis in Eigenspace with Limited Labeled Data for Real-Time Electrocardiography Telemonitoring},
year = {2018} }
TY - EJOUR
T1 - Low-Complexity Compressed Analysis in Eigenspace with Limited Labeled Data for Real-Time Electrocardiography Telemonitoring
AU - Kai-Chieh Hsu; Bo-Hong Cho; Ching-Yao Chou; and An-Yeu (Andy) Wu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3718
ER -
Kai-Chieh Hsu, Bo-Hong Cho, Ching-Yao Chou, and An-Yeu (Andy) Wu. (2018). Low-Complexity Compressed Analysis in Eigenspace with Limited Labeled Data for Real-Time Electrocardiography Telemonitoring. IEEE SigPort. http://sigport.org/3718
Kai-Chieh Hsu, Bo-Hong Cho, Ching-Yao Chou, and An-Yeu (Andy) Wu, 2018. Low-Complexity Compressed Analysis in Eigenspace with Limited Labeled Data for Real-Time Electrocardiography Telemonitoring. Available at: http://sigport.org/3718.
Kai-Chieh Hsu, Bo-Hong Cho, Ching-Yao Chou, and An-Yeu (Andy) Wu. (2018). "Low-Complexity Compressed Analysis in Eigenspace with Limited Labeled Data for Real-Time Electrocardiography Telemonitoring." Web.
1. Kai-Chieh Hsu, Bo-Hong Cho, Ching-Yao Chou, and An-Yeu (Andy) Wu. Low-Complexity Compressed Analysis in Eigenspace with Limited Labeled Data for Real-Time Electrocardiography Telemonitoring [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3718