A new automated compression technique for 2D electrocardiogram signals using discrete wavelet transform

Background: The long-term electrocardiogram (ECG) signals are one of the crucial tools for the detection of severe heart diseases. However, a huge data is generated in its acquisition process, which affects the storage, transmission, and power efficiency of a system. Thus, there is a strong need for the development of new compression algorithms for effective ECG data management. Method: In this regard, an automated compression algorithm is proposed using 1D Cohen-Daubechies-Feauveau 9/7 wavelet transform and particle swarm optimization (PSO) for the compression of 2D ECG signals. It is applied to columns and rows of 2D ECG signals, which significantly increases sparsity in the transform domain and improves compression performance. The quantization of transform coefficients is performed using the optimized midtread quantizer. Its input parameters are also optimized using PSO to develop a more effective and user-independent compression algorithm. The quantized coefficients are then encoded by adaptive Huffman encoding to further improve the compression performance. Findings: The experimental work is performed on MIT-BIH arrhythmia database and also compared with the existing compression techniques to validate the proposed method. Furthermore, different wavelets such as sym2, haar, db5, coif4, and beta wavelet are also used and compared with the proposed work. The performance of proposed algorithm is measured in terms of compression ratio, signal-to-noise ratio, percent root-mean-square difference, and quality score. The obtained results validate the efficacy of the proposed work and ensure the applications in telemedicine, mobile healthcare, and wearable monitoring devices for optimal utilization of storage, bandwidth, and power resources.