Features Based Fuzzy System Design For Sinus And Critical Cardiac Arrhythmia Detection
The cardiac health condition monitoring and autonomous critical disease diagnosis in case of heart beats irregularity is mainly focused in this paper. The cardiac dysrhythmia or cardiac arrhythmia is such condition of heart beats in which irregular heartbeat rates are occurred. The autonomous heart disease diagnosis is a difficult task. In most of the hospitals all over the world, the known critical heart-conditions are detected by latest measurement systems. Then the specific heart disease based on detected heart-condition is diagnosed by the expert doctors in the specific field. The proposed fuzzy system is designed to detect the normal and abnormal heart beats intelligently and to diagnose the critical disease based on critical heartbeats autonomously. In order to accomplish the goals the proposed algorithm works in the following ways: Each real time Electrocardiogram (ECG) signal is passed through six digital band pass filters and based on those outputs specific features are calculated. Then these features are used to train the fuzzy system intelligently for autonomous diagnosis of critical disease. The real time Electrocardiogram signals utilized in this paper for sinus and arrhythmia conditions of different patients are available in MIT-BIH arrhythmia database on https://physionet.org
Keywords— MIT-BIH Data Base, Finite Impulse Response (FIR) Filters, QRS Peaks, Features Extraction, Fuzzy Membership Functions, Fuzzy Rules, Defuzzification.