Comparative study of lossless ECG signal compression techniques for wireless networks

Electrocardiogram (ECG) is an important physiological signal which represents electrical activity of Heart. ECG plays important role in diagnosis of cardiovascular diseases. Telemedicine, telemonitoring requires huge amount of data to be stored for analysis and diagnosis purpose. Wireless sensor nodes consume lot of energy in data transmission. So Data Compression is needed for reducing storage space, transmission rate and effective utilization of bandwidth. This paper includes comparative study of various lossless compression methods for ECG signals in terms of compression ratio and execution time. It is found that minimum variance Huffman coding is best suited for ECG signal compression. Implementation is done in MATLAB software and database used is MITBIH Arrhythmia. 50% storage space can be saved with Minimum variance Huffman code with computational complexity of NLog
₂
N. Bandwidth is effectively utilized and buffer design complexity is also reduced.