Low-cost detection of cardiovascular disease on chronic kidney disease and dialysis patients based on hybrid heterogeneous ECG features including T-wave alternans and heart rate variability

Tsu Wang Shen; Te-Chao Fang; Yi Ling Ou; Chih Hsien Wang

Low-cost detection of cardiovascular disease on chronic kidney disease and dialysis patients based on hybrid heterogeneous ECG features including T-wave alternans and heart rate variability

Tsu Wang Shen, Te-Chao Fang, Yi Ling Ou, Chih Hsien Wang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Accumulating evidence shows that cardiovascular disease (CVD) contributes substantial burden to dialysis patients, accounting for almost 50 percent of mortality in dialysis population. Traditional clinical risk factors may not totally explain and predict CVD high mortality. The aim of this research is to develop a non-invasive, low-cost method for dialysis patients to evaluate their risks on cardiovascular disease (CVD) by hybrid heterogeneous ECG features including T-wave alternans and heart rate variability. A decision-based neural network (DBNN) structure is used for feature fusion and it provides overall 71.07% accuracy for CVD identification.

Original language	English
Title of host publication	Computing in Cardiology
Pages	561-564
Number of pages	4
Volume	37
Publication status	Published - 2010
Externally published	Yes
Event	Computing in Cardiology 2010, CinC 2010 - Belfast, United Kingdom Duration: Sept 26 2010 → Sept 29 2010

Other

Other	Computing in Cardiology 2010, CinC 2010
Country/Territory	United Kingdom
City	Belfast
Period	9/26/10 → 9/29/10

ASJC Scopus subject areas

Computer Science Applications
Cardiology and Cardiovascular Medicine

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

Low-cost detection of cardiovascular disease on chronic kidney disease and dialysis patients based on hybrid heterogeneous ECG features including T-wave alternans and heart rate variability. / Shen, Tsu Wang; Fang, Te-Chao; Ou, Yi Ling et al.
Computing in Cardiology. Vol. 37 2010. p. 561-564 5738034.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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abstract = "Accumulating evidence shows that cardiovascular disease (CVD) contributes substantial burden to dialysis patients, accounting for almost 50 percent of mortality in dialysis population. Traditional clinical risk factors may not totally explain and predict CVD high mortality. The aim of this research is to develop a non-invasive, low-cost method for dialysis patients to evaluate their risks on cardiovascular disease (CVD) by hybrid heterogeneous ECG features including T-wave alternans and heart rate variability. A decision-based neural network (DBNN) structure is used for feature fusion and it provides overall 71.07% accuracy for CVD identification.",

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Y1 - 2010

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AB - Accumulating evidence shows that cardiovascular disease (CVD) contributes substantial burden to dialysis patients, accounting for almost 50 percent of mortality in dialysis population. Traditional clinical risk factors may not totally explain and predict CVD high mortality. The aim of this research is to develop a non-invasive, low-cost method for dialysis patients to evaluate their risks on cardiovascular disease (CVD) by hybrid heterogeneous ECG features including T-wave alternans and heart rate variability. A decision-based neural network (DBNN) structure is used for feature fusion and it provides overall 71.07% accuracy for CVD identification.

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BT - Computing in Cardiology

T2 - Computing in Cardiology 2010, CinC 2010

Y2 - 26 September 2010 through 29 September 2010

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Low-cost detection of cardiovascular disease on chronic kidney disease and dialysis patients based on hybrid heterogeneous ECG features including T-wave alternans and heart rate variability

Abstract

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ASJC Scopus subject areas

UN SDGs

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