A 2D electrocardiogram signal compression algorithm using 1D discrete wavelet transform

Electrocardiogram (ECG) signals are frequently acquired nowadays to detect various heart diseases. Nowadays, IoT-enabled wearable devices are in demand for distant or telemedicine-based healthcare applications. However, the acquisition process of ECG signals generates a huge amount of data, which negatively impacts the storage and transmission efficiency of these devices. As a result, an efficient compression algorithm is needed for effective ECG data management. Therefore, a compression algorithm for 2D ECG signals is proposed that employs the 1D Cohen-Daubechies-Feauveau 9/7 wavelet transform on 2D ECG signals. The proposed method effectively improves compression performance by increasing sparsity among the transform coefficients. Following that, obtained coefficients are quantized, and significant ones are retained using the target-based reconstruction error. The adaptive Huffman encoding is used to further enhance the compression once the quantized coefficients have been encoded. The experimental work is tested on MIT-BIH arrhythmia database, and the effect of different anomalies on compression performance is also assessed. The compression efficacy is evaluated in comparison to existing compression methods, and other wavelet transforms such as sym2, sym4, haar, db5, coif4, and beta wavelets. The proposed algorithm's performance is assessed in terms of quality score, percent root-mean-square difference, signal-to-noise ratio, and compression ratio. These factors were averaged out to get values of 30.23, 5.07, 26.78 dB, and 7.21, respectively. Results are evident that the proposed method can significantly improve storage efficiency and may also improve bandwidth utilization during real-time data transfer.