An Efficient Algorithm Based on Combined Encoding Techniques for Compression of ECG Data from Multiple Leads

Abstract

ECG (Electrocardiogram) is a test that analyzes the electrical behaviour of the heart. ECG is used in diagnosing most of the cardiac diseases. Large amount of ECG data from multiple leads needs to be stored and transmitted, which requires compression for effective data storage and retrieval. Proposed work has been developed with Singular Value Decomposition (SVD) followed by Run Length Encoding (RLE) combined with Huffman Encoding (HE) and Arithmetic Encoding (AE) individually. The ECG signal is first preprocessed. SVD is used to factorize the signal into three smaller set of values, which preserve the significant features of the ECG. Finally, Run Length Encoding combined with Huffman encoding (RLE-HE) and Arithmetic encoding (RLE-AE) individually are employed and the compression performance metrics are compared. The proposed method is evaluated with PTB Diagnostic database. Performance measures such as Compression Ratio (CR), Percentage Root mean square Difference (PRD) and Signal to Noise Ratio (SNR) of the reconstructed signal are used to evaluate the proposed technique. It is evident that the proposed method performs well than the techniques based on SVD and Huffman Encoding. The results show that this method can be efficiently used for compression of ECG signal from multiple leads.