Determining Heartbeat Types by Principal Component Analysis and Fuzzy Inference

• Main Authors: Shou-Ru Chen, 陳首儒
• Other Authors: 黃有評
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/hu49a9

碩士 === 國立臺北科技大學 === 電機工程系研究所 === 100 === For the past few years due to the changes in diet habits, patients with cardiovascular disease become progressively younger, and the rate of deaths caused by heart disease rises; therefore, using new technologies to monitor heart disease play an important role. Traditional ECG inspection procedures can be very complicated and need professional assistance. When minor abnormality occurs the patient’s heart often returns to normal before he/she takes a detailed examination. Thus, the cause of abnormality remains unsolved. In this study, we proposed a suitable design of heartbeat monitoring ECG real-time detection system for home care, which uses the ECG sensors and a wireless sensor network technology to detect the user''s heartbeat rates and their variations. This study also integrates with the cloud system, showing the monitoring results on web pages as diagnosing assistance. In addition, the Massachusetts Institute of Technology MIT-BIH database is used to analyze arrhythmia, which is used to select main features from common types of arrhythmia, namely, NORM, LBBB, RBBB, VPC, APC and PB. Based on the selected features an arrhythmia detection module is devised to detect the arrhythmia that is determined by the corresponding fuzzy rules. In order to reduce the system complexity without increasing the false positive rate, we cut heartbeat characteristics from six to five. Besides, the fuzzy rules are simplified from 66 to rules. The experimental results show that the proposed system is able to detect normal and arrhythmia heartbeat detection. For NORM, LBBB, RBBB, VPC, APC and PB the discriminative accuracy rates are 97.5%, 87.5%, 92.5%, 100%, 95% and 100%, respectively. The experimental result shows that the proposed system is suitable for distinguishing the types of arrhythmia heartbeat.