Development of computer algorithm for electrocardiogram (ecg) signal interpretation applied in 3-lead ecg machine
Interpretation of ECG signal allows diagnosis of wide range of heart conditions. Nowadays, the ECG signal interpretation is mostly still done manually by high skilled cardiologist which is found not so time efficient. To deal with this problem, an automatic ECG interpretation software is developed in this research. The methods used in this automated system are signal filtering/preprocessing, QRS complex detection, beat features extraction, and beat classification. The algorithm used was adopted from Tompkins? method that had been modified by Hamilton (2002). The beat classification classifies heart conditions based on the beat rhythm and the characteristics of QRS complex (normal beat or PVC beat- Premature Ventricular Contraction). To verify the performance of this software, downloaded data from physionet database were used. This software was originally developed for ECG signal with sampling rate of 200 Hz. The beat annotations and cardiologists? qualitative analysis were compared to this software performance and resulted accuracy of 99.38 % for data with sampling rate of 200 Hz, 98.46 % for data with sampling rate of 250 Hz and 97.02 % for data with sampling rate of 360Hz.
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