Heartbeat Case Determination Using the Fuzzy C-Means (FCM) Method on ECG Signals

• Main Authors: Jen-Der Guo, 郭建得
• Other Authors: Yun-Chi Yeh
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/58048189342571486634

碩士 === 健行科技大學 === 電子工程系碩士班 === 103 === This paper presents a simple and effective electrocardiogram (ECG) heartbeat species identification method, which includes: (1) ECG signal pre-processor: the aim is to enlarge the body taken from the patient to the ECG signal, and do all kinds of miscellaneous information removal process; (2) ECG signal transmission: the post-processing of the ECG signal to Wi-Fi wireless communication technology is transferred to the receiver; and (3) calculation of the original feature points feature value: according to the received Wi-Fi receiver ECG signal to the middle of the QRS complex, P T wave spread position, a characteristic feature of the original value of each point; selection (4) the main features of the point: the principal component analysis (Principal Component Analysis; PCA) to select the main feature points the aim is to reduce the time the heartbeat species identification; (5) the heartbeat species identification: fuzzy clustering average (Fuzzy C-Means) method to identify the type of cardiac patients heartbeat, the heartbeat of this paper can identify five species occur more frequently, contain normal heartbeat (NORM) and four kinds of abnormal heartbeat. Four kinds of irregular heartbeat were: a left bundle branch block (LBBB), right bundle branch block (RBBB), ventricular premature contractions (VPC), and atrial premature contraction (APC) and so on. Finally, this paper MIT-BIH arrhythmia database related files to assess the effectiveness of the proposed method, the actual testing, identification heartbeat category NORM, LBBB, RBBB, VPC, and APC''s Se were up 98.28%, 90.35 %, 86.97%, 92.19%, and 94.36%. The total average rate of correct judgment TCA was 93.57%.