Pattern Extraction and Classification of ECG Signals using Neuro-Fuzzy Techniques
Electrocardiography is a technique which provides the interpretation of the electrical activity of the heart over a period of time. The condition of cardiac health is given by Electrocardiogram (ECG) and heart rate. This paper represents the techniques, which extracts features from the ECG signals and classify them. ECG signal were taken from preselected data segment of the MIT‐BIH Arrhythmia Database. Pattern extraction provides the PQRST-wave values, heart rate and the intervals of the ECG signals, which are useful to calculate the cardiac health of the patient. In the result section, it was shown that two different techniques were applied for comparative study of Artificial Neural Network (ANN) as well as Fuzzy Interface System (FIS) on the same ECG signal. ANN and FIS classifier was used for the classification and an accuracy of 95.69% and 97.87% was achieved, respectively.
Keywords: Electrocardiography, cardiac, ECG, PQRST-wave, ANN
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