Key result
Disease-aware multi-lead ECG compression achieves a ~21x overall compression ratio while preserving affected lead accuracy.
A novel disease-aware multi-lead ECG compression technique achieved high compression ratios while preserving pathological information in affected leads.
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May support efficient multi-lead ECG archiving; hypothesis-generating and requires clinical validation before adoption.
In this paper, we describe a multi-lead electrocardiogram (MECG) compression technique, which preserves pathological information in different affected leads while achieving high overall compression. For non-affected leads, the principal component decomposed expansion coefficients were optimally quantized using a feed-forward neural network. For affected leads, the wavelet decomposed coefficients were quantized using a fixed level. The proposed technique was evaluated with 130 ECG records with three major classes of myocardial infarction under Physionet. An average overall compression ratio of 21.25, with low values of percentage root mean squared difference of 2.45 for the affected lead group, was obtained.
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Bera et al. (2020) studied Myocardial infarction (n=130). Disease-aware Compression of Multi-lead Electrocardiogram Using Intelligent Hybrid Encoder was evaluated on Average overall compression ratio and percentage root mean squared difference. A disease-aware multi-lead ECG compression technique achieved an average overall compression ratio of 21.25 and a percentage root mean squared difference of 2.45 for affected leads.
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