Research and improvement of ECG compression algorithm based on EZW

New ECG Compression Method for Portable ECG Monitoring System Merged with Binary Convolutional Auto-Encoder and Residual Error Compensation

In the past few years, deep learning-based electrocardiogram (ECG) compression methods have achieved high-ratio compression by reducing hidden nodes. However, this reduction can result in severe information loss, which will lead to poor quality of the reconstructed signal. To overcome this problem, a novel quality-guaranteed ECG compression method based on a binary convolutional auto-encoder (BCAE) equipped with residual error compensation (REC) was proposed. In traditional compression methods, ECG signals are compressed into floating-point numbers. BCAE directly compresses the ECG signal into binary codes rather than floating-point numbers, whereas binary codes take up fewer bits than floating-point numbers. Compared with the traditional floating-point number compression method, the hidden nodes of the BCAE network can be artificially increased without reducing the compression ratio, and as many hidden nodes as possible can ensure the quality of the reconstructed signal. Furthermore, a novel optimization method named REC was developed. It was used to compensate for the residual between the ECG signal output by BCAE and the original signal. Complemented with the residual error, the restoration of the compression signal was improved, so the reconstructed signal was closer to the original signal. Control experiments were conducted to verify the effectiveness of this novel method. Validated by the MIT-BIH database, the compression ratio was 117.33 and the root mean square difference (PRD) was 7.76%. Furthermore, a portable compression device was designed based on the proposed algorithm using Raspberry Pi. It indicated that this method has attractive prospects in telemedicine and portable ECG monitoring systems.

ECG compression with Douglas-Peucker algorithm and fractal interpolation

In this paper, we propose a new ECG compression method using the fractal technique. The proposed approaches utilize the fact that ECG signals are a fractal curve. This algorithm consists of three steps: First, the original ECG signals are processed and they are converted into a 2-D array. Second, the Douglas-Peucker algorithm (DP) is used to detect critical points (compression phase). Finally, we used the fractal interpolation and the Iterated Function System (IFS) to generate missing points (decompression phase). The proposed (suggested) methodology is tested for different records selected from PhysioNet Database. The obtained results showed that the proposed method has various compression ratios and converges to a high value. The average compression ratios are between 3.19 and 27.58, and also, with a relatively low percentage error (PRD), if we compare it to other methods. Results depict also that the ECG signal can adequately retain its detailed structure when the PSNR exceeds 40 dB.

Reconfigurable Architecture for Multi-lead ECG Signal Compression with High-frequency Noise Reduction

Electrocardiogram (ECG) is a record of the heart's electrical activity over a specified period, and it is the most popular noninvasive diagnostic test to identify several cardiac diseases. It is an integral part of a typical eHealth system, where the ECG signals are often needed to be compressed for long term data recording and remote transmission. Reconfigurable architecture offers high-speed parallel computation unit, particularly the Field Programmable Gate Array (FPGA) along with adaptable software features. Hence, this type of design is suitable for multi-channel signal processing units like ECGs, which usually require precise real-time computation. This paper presents a reconfigurable signal processing unit which is implemented in ZedBoard- a development board for Xilinx Zynq -7000 SoC. The compression algorithm is based on Fast Fourier Transformation. The implemented system can work in real-time and achieve a maximum 90% compression rate without any significant signal distortion (i.e., less than 9% normalized percentage of root-mean-square deviation). This compression rate is 5% higher than the state-of-the-art hardware implementation. Additionally, this algorithm has an inherent capability of high-frequency noise reduction, which makes it unique in this field. The confirmatory analysis is done using six databases from the PhysioNet databank to compare and validate the effectiveness of the proposed system.

A novel ECG signal compression method using spindle convolutional auto-encoder

BACKGROUND AND OBJECTIVES

With rapid development of telehealth system and cloud platform, traditional 12-ECG signals with high resolution generate heavy burdens in data storage and transmission. This problem is increasingly addressed with various ECG compression methods. The important objective of compression method is to achieve a high-ratio and quality guaranteed compression. Consequently, to achieve this objective, this work presents a deep-learning-based spindle convolutional auto-encoder. The spindle structure achieves the high-ratio compression by reducing the dimension and guarantees the quality by increasing the dimension and end-to-end framework.

METHODS

The spindle convolutional auto-encoder provides a high-ratio and quality-guaranteed ECG compression. It is composed of two parts as convolutional encoder and convolutional decoder with functional layers. By convolutional operation, the local information can be extracted. The spindle structure is increasing dimension in first few layers to obtain sufficient information to guarantee compression quality. And it is reducing dimension in last few layers to merge the information into a code for high-ratio compression. Meanwhile, the end-to-end framework is to obtain the optimum encoding for compression to improve the reconstruction performance.

RESULTS

Compression performance is validated with records from MIT-BIH database. The proposed method achieves high compression ratio of 106.45 and low percentage root mean square difference of 8.00%. Compared with basic convolutional auto-encoder, the spindle structure improves the compression quality with lower losses.

CONCLUSIONS

The spindle convolutional auto-encoder performs a high-ratio and quality-guaranteed compression. It can be considered as a promising compression technique used in tele-transmission and data storage.

BAQALC: Blockchain Applied Lossless Efficient Transmission of DNA Sequencing Data for Next Generation Medical Informatics

Due to the development of high-throughput DNA sequencing technology, genome-sequencing costs have been significantly reduced, which has led to a number of revolutionary advances in the genetics industry. However, the problem is that compared to the decrease in time and cost needed for DNA sequencing, the management of such large volumes of data is still an issue. Therefore, this research proposes Blockchain Applied FASTQ and FASTA Lossless Compression (BAQALC), a lossless compression algorithm that allows for the efficient transmission and storage of the immense amounts of DNA sequence data that are being generated by Next Generation Sequencing (NGS). Also, security and reliability issues exist in public sequence databases. For methods, compression ratio comparisons were determined for genetic biomarkers corresponding to the five diseases with the highest mortality rates according to the World Health Organization. The results showed an average compression ratio of approximately 12 for all the genetic datasets used. BAQALC performed especially well for lung cancer genetic markers, with a compression ratio of 17.02. BAQALC performed not only comparatively higher than widely used compression algorithms, but also higher than algorithms described in previously published research. The proposed solution is envisioned to contribute to providing an efficient and secure transmission and storage platform for next-generation medical informatics based on smart devices for both researchers and healthcare users.

N-WRETS: Near-Lossless Wireless Real-time Efficient Electroencephalogram Transmission Solution to Support Sleep Disorder Monitoring Platforms

BACKGROUND

Sleep disorders lead to many adverse complications and chronic diseases. Sleep disorder-related healthcare costs are tens of billions of dollars worldwide. Sleep monitoring solutions have thus been the focus of research and industrial interest. However, the problem of limited bandwidth and battery consumption hinders the accuracy and practical use of sleep monitoring aids.

INTRODUCTION

The aim of this study is to propose Near-Lossless Wireless Real-time Efficient electroencephalogram Transmission Solution (N-WRETS) solution that solves the issue of limited bandwidth and battery consumption, thereby supporting platforms dedicated to sleep disorder monitoring.

MATERIALS AND METHODS

Electroencephalography (EEG) data materials were obtained from the Physionet PhysioBank database. The CAP Sleep Database was used. C programming was used for development.

RESULTS

To evaluate transmission efficiency, the compression ratio (CR) was compared to prior studies. The N-WRETS CR of 11.34 exceeded other reported values.

DISCUSSION

Compared to prior related research, N-WRETS showed the highest compression performance for EEG, but showed the lowest stability, which was a trade-off for its high efficiency. This article opens a possibility for future research to improve the performance of EEG compression algorithms according to sleep disease type. N-WRETS is also near-lossless, which is fit for priceless EEG data that contain important information on the patient's health. The proposed solution also supported wireless real-time transmission, which was another distinctive characteristic compared to related studies.

CONCLUSIONS

N-WRETS may provide a platform in which sleep disorder patients may be properly monitored in real time. The system could overcome the problems of limited bandwidth and battery consumption.

A Novel ECG Eigenvalue Detection Algorithm Based on Wavelet Transform

This study investigated an electrocardiogram (ECG) eigenvalue automatic analysis and detection method; ECG eigenvalues were used to reverse the myocardial action potential in order to achieve automatic detection and diagnosis of heart disease. Firstly, the frequency component of the feature signal was extracted based on the wavelet transform, which could be used to locate the signal feature after the energy integral processing. Secondly, this study established a simultaneous equations model of action potentials of the myocardial membrane, using ECG eigenvalues for regression fitting, in order to accurately obtain the eigenvalue vector of myocardial membrane potential. The experimental results show that the accuracy of ECG eigenvalue recognition is more than 99.27%, and the accuracy rate of detection of heart disease such as myocardial ischemia and heart failure is more than 86.7%.