Using Correlation Coefficient in ECG Waveform for Arrhythmia Detection
| Autor: | Tong-Hong Lin, 林東宏 |
|---|---|
| Rok vydání: | 2004 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 92 Arrhythmia is one kind of diseases that gives rise to the death and possibly forms the immedicable danger. The most common cardiac arrhythmia is the ventricular premature beat (extrasystole). The analysis of the electrocardiogram (ECG) signal is the most readily available method for diagnosing cardiac arrhythmias. The main purpose of this study is to develop an efficient arrhythmia detection algorithm based on the morphology characteristics of arrhythmias in ECG signal. There are two main processes. First, we detect whether the RR-interval duration locates in the normal range. Secondly, we detect whether the morphology of QRS complex is arrhythmia. Subjects for experiments included normal subjects, patients with atrial premature contraction (APC), and patients with ventricular premature contraction (PVC). So and Chan’s algorithm was used to find the locations of QRS complexes. When the QRS complexes were detected, the correlation coefficient was utilized to calculate the similarity of arrhythmias. The algorithm was tested using MIT-BIH arrhythmia database and every QRS complex was classified in the database. The total number of test data was 538, 9 and 24 for normal beats, APCs and PVCs, respectively. The results are presented in terms of sensitivity, positive predication, and performance. High overall performance (99.3%) for the classification of the different categories of arrhythmic beats was achieved. The accuracy results of the system reach 99.44%, 100% and 95.35% for normal beats, APCs and PVCs, respectively. The sensitivity results of the system are 99.81%, 81.82% and 95.83% for normal beats, APCs and PVCs, respectively. Results revealed that the system is accurate and efficient to classify arrhythmias of APC and PVC from ECG signals. The proposed arrhythmia detection algorithm is therefore helpful to the clinical diagnosis. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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