ECG Arrhythmia Detection Using Fuzzy Classifiers

An electrocardiogram (ECG) arrhythmia detection system has been developed. Piecewise continuous trapezoidal fuzzy membership functions and defuzzification schemes have been designed to be used in a fuzzy classifier. Fourteen types of arrhythmias and abnormalities can be detected implementing the classifier. We have evaluated the algorithm on MIT-BIH database. The classifier achieved a sensitivity of 99.18% plusmn 2.75 and a positive predictivity of 98.00% plusmn 4.45 in detecting twelve out of fourteen arrhythmias, but a sensitivity of 53.12% plusmn 34.04 and a positive predictivity of 36.80% plusmn 40.26 are designated to the other two. Due to the acceptable results, the novelty of the classification procedure and its fast application, the method is recommended for further study and practical implementation.