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ECG Signal Compression Using Discrete Wavelet Transform

Varsha Nanaware, Dr. Sanjay Patil



A new method is proposed for compression of Electrocardiogram (ECG) signal using DWT. The necessity of ECG compression is to represent the signal information with fewer bits, by that reducing storage requirement. The proposed algorithm consists of five different steps: ECG signal is Preprocessing, Feature Extraction, QRS-Complex Estimation, DWT & Thresholding. The ECG signal is preprocessed by normalization and mean removal. Then, an error signal is formed as the difference between the preprocessed ECG signal and the estimated QRS-complex waveform. This error signal is wavelet transformed and the resulting wavelet coefficients are threshold by setting to zero all coefficients that are smaller than certain threshold levels. The compression algorithm was implemented & tested upon records selected from the MIT-BIH arrhythmia database. The simulation results of proposed algorithm shows better result for DWT as compare with existing algorithm. The tested result shows minimum percentage root mean square difference (PRD) and maximum compression ratio (CR). For example, the compression of record 123 using the proposed algorithm results in higher CR=94.11 with less PRD=0.65% .The main features of this compression algorithm are the high efficiency and high speed.


ECG Signal Compression, QRS-Complex Estimation, Discrete Wavelet Transform, Percentage Root Mean Square Difference (PRD), Compression Ratio (CR).

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