This paper presents an innovative method for multiple lead electrocardiogram (ECG) monitoring based on Compressed Sensing (CS). The proposed method extends to multiple leads signals, a dynamic Compressed Sensing method, that were previously developed on a single lead. The dynamic sensing method makes use of a sensing matrix in which its elements are dynamically obtained from the signal to be compressed. In this method, for the application to multiple leads, it is proposed to use a single sensing matrix for which its elements are obtained from a combination of multiple leads. The proposed method is evaluated on a wide set of signals and acquired on healthy subjects and on subjects affected by different pathologies, such as myocardial infarction, cardiomyopathy, and bundle branch block. The experimental results demonstrated that the proposed method can be adopted for a Compression Ratio (CR) up to 10, without compromising signal quality. In particular, for CR= 10, it exhibits a percentage of root-mean-squared difference average among a wide set of ECG signals lower than 3%.

ECG Monitoring Based on Dynamic Compressed Sensing of Multi-Lead Signals.

Arrhythmia is less frequent than a normal heartbeat in an electrocardiogram signal, and the analysis of an electrocardiogram measurement can require more than 24 hours. Therefore, the efficient storage and transmission of electrocardiogram signals have been studied, and their importance has increased recently due to the miniaturization and weight reduction of measurement equipment. The polygonal approximation method based on dynamic programming can effectively achieve signal compression and fiducial point detection by expressing signals with a small number of vertices. However, the execution time and memory area rapidly increase depending on the length of the signal and number of vertices, which are not suitable for lightweight and miniaturized equipment. In this paper, we propose a method that can be applied in embedded environments by optimizing the processing time and memory usage of dynamic programming applied to the polygonal approximation of an ECG signal. The proposed method is divided into three steps to optimize the processing time and memory usage of dynamic programming. The first optimization step is based on the characteristics of electrocardiogram signals in the polygonal approximation. Second, the size of a data bit is used as the threshold for the time difference of each vertex. Finally, a type conversion and memory optimization are applied, which allow real-time processing in embedded environments. After analyzing the performance of the proposed algorithm for a signal length L and number of vertices N, the execution time is reduced from O(L
 2
N) to O(L), and the memory usage is reduced from O(L
 2
N) to O(LN). In addition, the proposed method preserve a performance of fiducial point detection. In a QT-DB experiment provided by Physionet, achieving values of -4.01 ± 7.99 ms and -5.46 ± 8.03 ms.

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Advanced Real-Time Dynamic Programming in the Polygonal Approximation of ECG Signals for a Lightweight Embedded Device

A Telemedicine network that uses connectivity and information technology to transmit medical signals such as neurological signals Electroencephalography (EEG) has become a reality for medical services of long distances. In the monitoring of mobile healthcare, these signals need to be compressed for the efficient utilization of bandwidth and the confidentiality of these signals, where compression is a critical tool to solve storage and transmission problems and can then retrieve the original signal (OS) from the compressed signal. The objective of this manuscript is to achieve higher compression gains at a low bit rate while maintaining the integrity of clinical details and also encrypting the signal to keep it private, except for specialists. Thresholding techniques are utilized in the compression stage, the Discrete Wavelet Transform (DWT). Instead, Huffman Encoding (HuFE) is utilized for compression and EEG signal encryption with chaos. This manuscript addresses the encoding of EEG signals with consistency for Telemedicine applications. To test the proposed method, overall compression and reconstruction (ComRec) time (T) was measured, the root mean square (RMSE), and the compression ratio (CR). Findings from the simulation show that the addition of HuFE after the DWT algorithm gives the best CR and complexity efficiency. The findings show that the consistency of the reconstructed signal (Rs) is maintained at a low PRD while yielding better findings in compression. Utilizing the DWT as a loss compression algorithm followed by the HuFE as a lossy compression algorithm, CR = 92.9% at RMS = 0.16 and PRD = 5. 4131%.

A Tele Encephalopathy Diagnosis Based on EEG Signal Compression and Encryption.

A correction to this article has been published and is linked from the HTML version of this paper. The error has not been fixed in the paper.

/pdf/publisher-correction-efficient-ecg-compression-and-qrs-10irgfg14j.pdf

Publisher Correction: Efficient ECG Compression and QRS Detection for E-Health Applications

One of the primary issues is cardiovascular disease in various countries worldwide. Proper diagnosis of heart disease at the right time can minimise the loss of life in heart patients. One of the most prevalent approaches for identifying and classifying heart-related disorders is the electrocardiogram (ECG). ECG can give information mainly on heart functioning, recording heart rate, and tracking heart rhythms. ECG denoising is one of the important tasks in medical diagnostics because it contains life-saving information about a patient. Before analysing, compressing, and classifying, the ECG signal has to be denoised for better performance. This work proposes a new RTL architecture to perform ECG signal denoising using the Fast Normalised Least Mean Square (FNLMS) technique. The proposed technique achieved higher accuracy when compared with the conventional signal denoising technique. The proposed work is implemented with a Field Programmable Gate Array device (FPGA) to analyse hardware resource utilisation. This work achieves RMSE of 0.027, PRD of 42.126 and SNR of 33.125. This result is high performance when compared with existing works. This work consumed less computational time and hardware resource utilisation when compared with the conventional ECG denoising technique. 

An ECG Signal Compression Technique Using Fast Normalised Least Mean Square Algorithm

An electrocardiogram (ECG) is an electrical record of heart activity. ECG compression is the biggest concern for many applications in the biomedical community. ECG lossless compression (ECG LC) is data recovery for diagnostic and analysis purposes. The aim here develops an efficient algorithm ECG LC. This algorithm uses the transform based on wavelet followed by the arithmetic coding (AC) on the residual. The parameters of performance measurement for the ECG signal such as CR (Compression Ratio), PRD (Percent Root mean square Distortion). The proposed algorithm achieves high compression ratios compared to other compressing algorithms. Outcomes display that this algorithm works well for various kinds of patient recordings and is even able to provide lossless compression for event-related potentials. According the outcomes, the higher CR. The highest CR is obtained is 75.5, and the lowest PRD is obtained is 0.18 according to the patient records that have the highest CR.

Nawar Ahmed Irsan Nawar

Papers

A Proposed Technique Based on Wavelet Transform for Electrocardiogram Signal Compression