| dc.description.abstract | In this dissertation, some novel and efficient algorithms in two related research topics about ECG signals will be presented and discussed. In the first research topic, a simple and reliable method, called the Difference Operation Method (DOM), is proposed to detect the QRS complex of an electrocardiogram (ECG) signal. The proposed DOM includes two stages. The first stage is to find the point R by applying the difference equation operation to an ECG signal. The second stage looks for the points Q and S based on the point R and then finds the whole QRS complex. From the QRS complex, both T and P waves can be obtained by the existing methods. Some records (QRS complex and T, P waves) of ECG signals in MIT-BIH arrhythmia database are tested and thus it shows that the DOM has much more precise detection rate and faster speed than other existing methods. In the second research topic, three methods, named “Fuzzy Logic Method (FLM)”, “Linear Discriminant Analysis (LDA)”, and “Fuzzy C-Means (FCM)”, are applied for classifying the cardiac arrhythmia on ECG signals. The proposed methods can accurately classify the normal heartbeats and abnormal heartbeats. Abnormal heartbeats include Left Bundle Branch Block, Right Bundle Branch Block, Ventricular Premature Contractions and Atrial Premature Contractions. The proposed methods were evaluated using the MIT-BIH arrhythmia database and have the following advantages: (1) The average time required for processing 30-minute long records of ECG signals is less than 1 minute; (2) The maximum memory requirement is only about 2 MB (that is, 2100 × 432 × 16 bits) for 30-minute long (about 2100 beats) recordings with 16 bit sampling points; (3) Good detection results (High – Reliability). In the experiments, the average failure rate for processing 30-minute long records of ECG signals is 0.19% by DOM method. The total classification accuracy is 93.78%, 96.23% and 93.57% for FLM, LDA and FCM method, respectively. Thus, the proposed methods indeed provide efficient, simple and fast methods for classifying the cardiac arrhythmia from ECG signals.
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