State-Based General Gamma CUSUM for Modeling Heart Rate Variability   Using Electrocardiography Signals

doi:10.1109/TASE.2015.2503284

CORC > 北京大学 > 工学院

	State-Based General Gamma CUSUM for Modeling Heart Rate Variability Using Electrocardiography Signals
	Chen, Lili ; Zhang, Xi
刊名	IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
	2017
关键词	Cumulative sum (CUSUM) disease diagnosis general gamma distribution heart rate variability OBSTRUCTIVE SLEEP-APNEA OXYGEN-SATURATION RECORDINGS ACUTE MYOCARDIAL-INFARCTION ARRHYTHMIA DIAGNOSIS ELECTRICAL-ACTIVITY CLASSIFICATION CHARTS PERFORMANCE FEATURES
DOI	10.1109/TASE.2015.2503284
英文摘要	Traditional approaches based on short-term heart rate variability for cardiovascular disease diagnosis fail to capture the long-term dynamic information and individual effect from electrocardiography signals among subjects when examining the physiological condition. These shortages may lead to incorrect disease detection and weaken diagnosis performance. To address these problems, this paper proposes a new disease detection approach by considering the long-term dynamics and meanwhile the individual effect existing among subjects. Specifically, a multistate general Gamma cumulative sum (GGCUSUM) scheme is developed for signal state detection. Further, a backward elimination algorithm based on the exponential likelihood ratio test (ELRT) is proposed to reduce the risk of incorrect detection of change points. A general disease severity index is then designed based on our approach to satisfy the clinical requirement for disease diagnosis. A real clinical case from one of cardiovascular diseases, is given to validate the proposed approach, of which the result demonstrates the effectiveness with a satisfactory detection performance. Note to Practitioners-Automatic diagnosis of cardiovascular diseases based on physiological signals is critical for healthcare service improvement. The information loss and unreliability, as well as inconsistency in the signal pattern for different individuals, increase the probability of incorrect diagnosis for current diagnostic methods. This paper aims to overcome these limitations existing in current studies by proposing a general Gamma CUSUM-based detection scheme. To fully implement this approach, it is necessary (i) to preprocess the heart rate variability signals and eliminate unexpected signal points; (ii) to establish prior distribution for the hyperparameter of reference state distribution based on the training data; and (iii) to collect the sufficient dataset to further enhance the performance. A real-world case study has shown that the proposed diagnostic approach provided a satisfactory diagnostic accuracy.; SCI(E); ARTICLE; 2; 1160-1171; 14
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/474265]
专题	工学院
推荐引用方式 GB/T 7714	Chen, Lili,Zhang, Xi. State-Based General Gamma CUSUM for Modeling Heart Rate Variability Using Electrocardiography Signals[J]. IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING,2017.
APA	Chen, Lili,&Zhang, Xi.(2017).State-Based General Gamma CUSUM for Modeling Heart Rate Variability Using Electrocardiography Signals.IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING.
MLA	Chen, Lili,et al."State-Based General Gamma CUSUM for Modeling Heart Rate Variability Using Electrocardiography Signals".IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2017).