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The present paper proposes a complexity sorting and coupled chaotic map mutation mechanism for compression-then-encryption of the Electrocardiogram (ECG) signals. The compressed-then-encrypted ECG is wirelessly transmitted using orthogonal frequency division multiplexing scheme modified to perform impair sample correction. The compression based on complexity sorting involves following steps: Beat Detection, 2D ECG array formation, Period Normalization, Dc Equalization, Complexity Sorting, Codec Quantization and JPEG2000 codec. The 2D compression results in reduced memory requirements for the clinical data storage. The coupled chaotic based on mutation mechanism for ECG encryption randomizes the ECG array to prevent the attackers from deducing the confidential information from it. The compressed-then-encrypted ECG bitstream is transmitted through the Rayleigh fading wireless channel. At the receiver end, the erroneous sample is corrected by moving median filtering (MMF) mechanism to reduce Percentage Root mean square Difference (PRD). The 2D compressed and the encrypted ECG remains diagnosable after reconstruction. The average compressor metrics Compression Ratio (CR), PRD, and Quality Score (QS) were 72.81 ± 15.90, 2.57 ± 1.68%, and 44.36 ± 29.92 respectively on MIT-BIH Arrhythmia database. Furthermore, 2D compressed ECG is effectually encrypted as validated by the histogram analysis, Information Entropy(En), Entropy Score (ES), and the correlation coefficient analysis. The En, ES, and the correlation values were equal to 7.996, 0.999, and 0.0042 respectively for an 8-bit quantization resolution. Moreover, due to MMF approach at the particular Channel Signal to Noise Ratio (SNRc = 15 dB) & BER = 10−2, the PRD reduces from 45% (without erroneous sample correction) to 2.2% (with erroneous ECG sample correction). [ABSTRACT FROM AUTHOR]

This paper proposes an effectual sample entropy (SampEn) based complexity sorting pre-processing technique for two dimensional electrocardiogram (ECG) data compression. The novelty of the approach lies in its ability to compress the quasi-periodic ECG signal by exploiting the intra and inter-beat correlations. The proposed method comprises the following steps: (1) QRS detection, (2) Length normalization, (3) Dc equalization, (4) SampEn based nonlinear complexity sorting and (5) Compression using JPEG2000 Codec. The performance has been evaluated over 48 records from the MIT-BIH arrhythmia database. The average quality score (QS) measurements at different residual errors were 42.25, 4.73, and 2.75 for percentage root mean square difference (PRD), PRD1024, and PRD Normalized respectively. The work also reports extensive experimentations on the compressor for various durations of the ECG records (5–30 min, with 5-min increment). The proposed algorithm demonstrates significantly better performance in comparison to the contemporary state-of-the-art works present in the literature. [ABSTRACT FROM AUTHOR]