The main method of this paper is based on obtaining the signal data from patient ECG signal, processing the data, then extract the feature of the ECGs. After that, using Linear classification technique was used for the automatic detection, which can help doctors make the diagnosis accurately and quickly.
Normal sinus rhythm, ventricular tachycardia and ventricular fibrillation signals which collect from MIT-BIH database were used for the analysis in current research including Autoregressive (AR) modeling, feature extraction and classification. The AR modeling was applied to normal sinus rhythm, ventricular tachycardia and ventricular fibrillation signals. AR model coefficients were utilized as the ECG features for the classification and diagnosis, and linear classifier was employed to classify the features.
Finally, the conclusion can be drown: It is feasible and practical that using ECG signal can diagnose the arrhythmia automatically through computers automatically.
