Design of Low-Power SPIHT Decoder Based ECG Compression and Reconstruction System

This article proposes a 1D biomedical signal encoding scheme to allow embedding of metadata and to protect privacy. The compression of ECG signal and its reconstruction is implemented. The design concentrates on an overview of the criteria for safe and effective m-health storage, transmission, and access to medical tests. However, existing architectures for encoding SPIHT are designed to process images/videos. Significant memory and complex sorting algorithms are required for both architectures, and they all require time-consuming tasks that do not apply to mobile ECG applications. On the basis of our previously updated SPIHT coding research, we used flags and bit controls to reduce memory needs and code complexity through a combination of three search processes in one phase. The goal of real-time architecture for mobile ECG applications is therefore to be accomplished. In order first, to solve the disadvantages of the low-encryption speed of coded and complex hardware architectures that characterize previous SPIHT algorithms, we propose a SPIHT coding algorithm that uses several types of state registry files because of its need for dynastic c to attain real-time and performance design objectives. Secondly, a highly piped and efficient VLSI architecture is used to implement a high-efficiency and low-power SPIHT design based on the proposed algorithm.

ELECTROCARDIOGRAM DATA COMPRESSION TECHNIQUES IN 1D/2D DOMAIN

Electrocardiogram (ECG) is one of the best representatives of physiological signal that provides the state of the autonomic nervous system, primarily responsible for the cardiac activity. The ECG data compression plays a significant role in localized digital storage or efficient communication channel utilization in telemedicine applications. The lossless and lossy compression system’s compressor efficiency depends on the methodologies used for compression and the quality measure used to evaluate distortion. Based on domain ECG, data compression can be performed either one-dimensional (1D) or two-dimensional (2D) for utilization of inter and inter with intra beat correlation, respectively. In this paper, a comparative study between 1D and 2D ECG data compression methods was taken out from the existing literature to provide an update in this regard. ECG data compression techniques and algorithms in 1D and 2D domain have their own merits and limitations. Recently, numerous research and techniques in 1D ECG data compression have been developed, including direct and transformed domain. Additionally, 2D ECG data compression research is reported based on period normalization and complexity sorting in recent times. Finally, several practical issues highlight the assessment of reconstructed signal quality and performance comparisons with an average comparative of exhaustive existing 1D and 2D ECG compression methods based on the utilized digital signal processing systems.

Multi-lead ECG Compression Based on Compressive Sensing

Compressed Sensing (CS) has been considered a very effective means of reducing energy consumption at the energy-constrained wireless body sensor networks for monitoring the multi-lead Electrocardiogram (MECG) signals. This paper develops the compressed sensing theory for sparse modeling and effective multi-channel ECG compression. A basis matrix with Gaussian kernels is proposed to obtain the sparse representation of each channel, which showed the closest similarity to the ECG signals. Thereafter, the greedy orthogonal matching pursuit (OMP) method is used to obtain the sparse representation of the signals. After obtaining the sparse representation of each ECG signal, the compressed sensing theory could be used to compress the signals as much as possible. Following the compression, the compressed signal is reconstructed utilizing the greedy orthogonal matching pursuit (OMP) optimization technique to demonstrate the accuracy and reliability of the algorithm. Moreover, as the wavelet basis matrix is another sparsifying basis to sparse representations of ECG signals, the compressed sensing is applied to the ECG signals using the wavelet basis matrix. The simulation results indicated that the proposed algorithm with Gaussian basis matrix reduces the reconstruction error and increases the compression ratio.