Pal Hardev Singh, Kumar A, Vishwakarma Amit, Lee Heung-No
Discipline of Electronics and Communication Engineering, PDPM Indian Institute ofInformation Technology, Design and Manufacturing Jabalpur, Jabalpur 482005, India.
School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 500712, Republic of Korea.
ISA Trans. 2023 Nov;142:335-346. doi: 10.1016/j.isatra.2023.07.033. Epub 2023 Jul 24.
The electrocardiogram (ECG) signals are commonly used to identify heart complications. These recordings generate large data that needed to be stored or transferred in telemedicine applications, which require more storage space and bandwidth. Therefore, a strong motivation is present to develop efficient compression algorithms for ECG signals. In the above context, this work proposes a novel compression algorithm using adaptive tunable-Q wavelet transform (TQWT) and modified dead-zone quantizer (DZQ). The parameters of TQWT and threshold values of DZQ are selected using the proposed Sparse-grey wolf optimization (Sparse-GWO) algorithm. The Sparse-GWO is proposed in this work to reduce the computation time of the original GWO. Moreover, it is also compared with some popular algorithms such as original GWO, particle swarm optimization (PSO), Hybrid PSOGWO, and Sparse-PSO. The DZQ has been utilized to perform thresholding and quantization. Then, run-length encoding (RLE) has been used to encode the quantized coefficients. The proposed work has been performed on the MIT-BIH arrhythmia database. Quality assessment performed on reconstructed signals ensure the minimal impact of compression on the morphology of reconstructed ECG signals. The compression performance of proposed algorithm is measured in terms of the following evaluation matrices: percent root-mean-square difference (PRD), compression ratio (CR), signal-to-noise ratio (SNR), and quality score (QS). The obtained average values are 3.21%, 20.56, 30.62 dB, and 7.79, respectively.
心电图（ECG）信号通常用于识别心脏并发症。这些记录会生成大量数据，在远程医疗应用中需要进行存储或传输，这需要更多的存储空间和带宽。因此，开发高效的ECG信号压缩算法具有很强的动机。在上述背景下，这项工作提出了一种使用自适应可调Q小波变换（TQWT）和改进的死区量化器（DZQ）的新型压缩算法。TQWT的参数和DZQ的阈值使用所提出的稀疏灰狼优化（Sparse-GWO）算法进行选择。这项工作中提出Sparse-GWO是为了减少原始灰狼优化算法的计算时间。此外，它还与一些流行算法进行了比较，如原始灰狼优化算法、粒子群优化算法（PSO）、混合PSO-GWO算法和稀疏PSO算法。DZQ已被用于执行阈值处理和量化。然后，游程编码（RLE）已被用于对量化系数进行编码。所提出的工作是在MIT-BIH心律失常数据库上进行的。对重建信号进行的质量评估确保了压缩对重建ECG信号形态的影响最小。所提出算法的压缩性能根据以下评估指标进行衡量：均方根误差百分比（PRD）、压缩率（CR）、信噪比（SNR）和质量分数（QS）。获得的平均值分别为3.21%、20.56、30.62 dB和7.79。
