A Novel ECG Data Compression Method Based on Nonrecursive Discrete Periodized Wavelet Transform

Adaptive Sampling of the Electrocardiogram Based on Generalized Perceptual Features

A non-uniform distribution of diagnostic information in the electrocardiogram (ECG) has been commonly accepted and is the background to several compression, denoising and watermarking methods. Gaze tracking is a widely recognized method for identification of an observer's preferences and interest areas. The statistics of experts' scanpaths were found to be a convenient quantitative estimate of medical information density for each particular component (i.e., wave) of the ECG record. In this paper we propose the application of generalized perceptual features to control the adaptive sampling of a digital ECG. Firstly, based on temporal distribution of the information density, local ECG bandwidth is estimated and projected to the actual positions of components in heartbeat representation. Next, the local sampling frequency is calculated pointwise and the ECG is adaptively low-pass filtered in all simultaneous channels. Finally, sample values are interpolated at new time positions forming a non-uniform time series. In evaluation of perceptual sampling, an inverse transform was used for the reconstruction of regularly sampled ECG with a percent root-mean-square difference (PRD) error of 3-5% (for compression ratios 3.0-4.7, respectively). Nevertheless, tests performed with the use of the CSE Database show good reproducibility of ECG diagnostic features, within the IEC 60601-2-25:2015 requirements, thanks to the occurrence of distortions in less relevant parts of the cardiac cycle.

Algorithm and VLSI Architecture Design of Low-Power SPIHT Decoder for mHealth Applications

A real-time cost and power-efficient (CPE) set partitioning in hierarchical trees (SPIHT) decoder design with low hardware complexity and low-power dissipation is introduced in one-dimension (1-D) wavelet-based quality-assured electrocardiograph (ECG) compression systems for mobile health (mHealth) applications. However, current SPIHT coding architectures are designed for image/video processing. These architectures require a large amount of memory as well as complicated sorting algorithms, which both require time-consuming tasks and are unsuitable for mobile ECG applications. Based on our previously modified SPIHT coding work, which used flags and check bits to reduce memory requirements and coding complexity by merging three search processes into one step. Therefore, to achieve the real-time design goal for mobile ECG applications, in this paper, we first introduce a hardware-oriented SPIHT decoding algorithm that is suitable for decoding the previously presented SPIHT coding work. Accordingly, an appropriate low-power hardware architecture is developed to implement a real-time high-performance and low-cost SPIHT VLSI design for our proposed decoder algorithm, which is appropriate for mobile ECG applications. Using the distinct ECG signals in the MIT-BIH arrhythmia database (sampling rate of 360 Hz), the final simulation and VLSI implementation results reveal that the proposed CPE SPIHT decoder design outperforms the state-of-the-art designs in terms of the average decoding time, the decoding quality, the VLSI speed, and the power consumption. Most importantly, the design can be exploited to a 1-D 1024 × 1 wavelet-based quality-assured ECG data compression system.

A Speed- and Power-Efficient SPIHT Design for Wearable Quality-On-Demand ECG Applications

In this paper, a speed and power-efficient set partitioning in hierarchical trees (SPIHT) design is introduced for one-dimensional (1-D) wavelet-based electrocardiography (ECG) compression systems with quality guarantee. To achieve real-time and low-power design objectives toward wearable quality-on-demand (QoD) ECG applications, we first propose a coding-time- and computation-efficient SPIHT algorithm using various types of coding status register files to overcome the disadvantages of low coding speeds and complicated hardware architectures characterizing prior SPIHT algorithms resulting from the necessity of dynamic computation and arrangement in the sorting and refinement processing phase. Second, a highly pipelined and power-efficient very large scale integration (VLSI) architecture is developed to implement a high-performance and low-power SPIHT design based on the proposed algorithm. The final simulation results demonstrate that our proposed algorithm can speed up the average coding time 1.52 to 2.74 times compared to prior work with an identical compression ratio for an 11-level $1024\times 1\,1-{\rm{D}}$ discrete wavelet transform at diverse target percentage root-mean-square differences (PRDT) on various MIT-BIH arrhythmia datasets. Applied to wearable wavelet-based QoD ECG applications, our proposed VLSI architecture attains a working frequency of 740 MHz and consumes an average of $\text{23}\ \mu {\text{W}}$ of power with Taiwan Semiconductor Manufacturing Company 90-nm CMOS technology, which shows the effectiveness of speed and power over the state-of-the-art designs.

A linear quality control design for high efficient wavelet-based ECG data compression

In ECG data compression, maintaining reconstructed signal with desired quality is crucial for clinical application. In this paper, a linear quality control design based on the reversible round-off non-recursive discrete periodized wavelet transform (RRO-NRDPWT) is proposed for high efficient ECG data compression. With the advantages of error propagation resistance and octave coefficient normalization, RRO-NRDPWT enables the non-linear quantization control to obtain an approximately linear distortion by using a single control variable. Based on the linear programming, a linear quantization scale prediction model is presented for the quality control of reconstructed ECG signal. Following the use of the MIT-BIH arrhythmia database, the experimental results show that the proposed system, with lower computational complexity, can obtain much better quality control performance than that of other wavelet-based systems.