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Data compression is an effective means for saving storage space and channel bandwidth. There are two main types of compression lossy and lossless. This paper will deal with lossless compression techniques named Huffman, Arithmetic, LZ-78 and Golomb coding. The paper attempts to do comparative analysis in terms of their compression efficiency and speed. The test files used for this include English text files, Log files, Sorted word list and geometrically distributed data text file. The implementation results of these compression algorithms suggest the efficient algorithm to be used for a certain type of file to be compressed taking into consideration both the compression ratio and speed of operation. In terms of compression ratios, Golomb is best suited for very low frequency Text files, arithmetic for moderate and high frequency. Implementation is done using MATLAB software. General TermsText Compression.

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.

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ECG of a person is being recorded for diagnosis of heart diseases, regular checkups, fitness and many other diseases also. Hence, huge amount of ECG data is being generated daily in hospitals. ECG recording and monitoring consume lots of memory space of digital computers. Data compression plays a vital role in reducing storage space and utilizing transmission bandwidth effectively. Objective of the research work is to propose a robust and effective method for ECG signal compression. This paper includes extraction of key morphological features, statistical features from decomposed signal, analysis in wavelet domain and classification of feature set. To reduce dimensionality of feature set, principle component analysis (PCA) is applied. Accuracy achieved with 15 principle components is same as pure wavelet transform with significant improvement in compression ratio (CR) by a factor of 4:1. MITBIH Arrhythmia database is used for experimentation. Accuracy obtained with K nearest neighborhood (KNN) classifier is 82.60% and sensitivity 92.30%. Research work carried out here is an improvement in the existing telemedicine technology.