Key result
Novel hybrid ECG compression algorithm achieves ~97% classification accuracy with high signal fidelity.
Why the study?
Efficient compression algorithms are needed to reduce memory, power, and transmission time requirements for long-term ECG recordings.
Efficient compression reduces memory requirement in long term recording and reduces power and time requirement in transmission.A new compression algorithm combining Linear Predictive coding (LPC) and Discrete Wavelet transform is proposed in this study.Our coding algorithm offers compression ratio above 85% for records of MIT-BIH compression database.The performance of algorithm is quantified by computing distortion measures like percentage root mean square difference (PRD), wavelet-based weighted PRD (WWPRD) and Wavelet energy based diagnostic distortion (WEDD).The PRD is found to be below 6 %, values of WWPRD and WEDD are less than 0.03.Classification of decompressed signals, by employing fuzzy c means method, is achieved with accuracy of 97%.
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Ardhapurkar et al. (2012) studied Cardiac arrhythmias (n=55). Hybrid Algorithm combining Linear Predictive Coding (LPC) and Discrete Wavelet Transform (DWT) was evaluated on Compression ratio, percentage root mean square difference (PRD), and classification accuracy. The proposed hybrid algorithm combining Linear Predictive coding and Discrete Wavelet transform offered an ECG compression ratio above 85% with a PRD below 6% and a classification accuracy of 97%.
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