CLASSIFICATION AND COMPRESSION OF CARDIAC VASCULAR DISEASE TO ENHANCE RURAL HEALTH CARE SYSTEM USING SOFTCOMPUTING TECHNIQUES

The detection of Cardiac Vascular Disease (CVD) is to save a life of a heart patients, with the help of Public Health Care Centers by transmitting ECG signals to nearby hospital server. In this paper we analyze the abnormalities found in the ECG signals by identifying the Normal, Bradycardia Arrhythmia, Tachycardia Arrhythmia and Ischemia signal using the method of Neuro Fuzzy Classifier. DWT coefficients are used toextract the relevant information from the ECG input data. The extracted features are analyzed and classified using Adaptive Neuro Fuzzy Inference System (ANFIS) as a Neuro Fuzzy classifier. The compression algorithm is performed by using Huffman coding. Unit blocks size optimization, adaptive threshold adjustment, and 4-bit-wise Huffman coding methods are applied to reduce the processing cost while maintaining the signal quality.