On the wavelet-based compressibility of continuous-time sampled ECG signal for e-health applications

Maalej, Asma; Ben-Romdhane, Manel; Tlili, Mariam; Rivet, François; Dallet, Dominique; Rebai, Chiheb

doi:10.1016/j.measurement.2020.108031

Cited by 14 publications

(5 citation statements)

References 47 publications

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“…When proposing a compression method, a good practice consists in verifying that the compression does not alter significantly the clinical information contained in the signal. The performance of a compression method for ECG signals and other biosignals is typically evaluated by the percentage of root-mean-squared difference (PRD) [9,12,13,21,[23][24][25][26]28,29]. In this paper, the PRD is computed for the ECG signal related to each lead l:…”

Section: Implementation Of the Proposed Methodsmentioning

confidence: 99%

“…Although multiple electrodes and leads are actually adopted by measurement systems in biomedical field, ECG monitoring through CS has been addressed in the literature mainly in the case of one electrode with one lead [9,13,[21][22][23][24][25][26]. The aim of this paper is to propose a digital CS-based method for ECG monitoring of multi-lead signals.…”

Section: Introductionmentioning

confidence: 99%

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

Daponte

Vito

Iadarola

et al. 2021

Sensors

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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%.

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Section: Implementation Of the Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Daponte

Vito

Iadarola

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Asma Maalej et al [181] discusses the innovative wavelet-based compression scheme for ECG signals in e-Health cardiac online diagnostic applications. The compressibility of continuous-time sampled ECG signals has been investigated in this research using 75 ECG signals which are normal and pathologic and 54 different orthogonal and biorthogonal wavelets to determine the best wavelet for ECG compression.…”

Section: Cs Implementations-sensing and Sparsifying Matricesmentioning

confidence: 99%

Review on Compressive Sensing Algorithms for ECG Signal for IoT Based Deep Learning Framework

Kumar

Ramachandran

2022

Applied Sciences

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Nowadays, healthcare is becoming very modern, and the support of Internet of Things (IoT) is inevitable in a personal healthcare system. A typical personal healthcare system acquires vital parameters from human users and stores them in a cloud platform for further analysis. Acquiring fundamental biomedical signal, such as with the Electrocardiograph (ECG), is also considered for specific disease analysis in personal healthcare systems. When such systems are scaled up, there is a heavy demand for internet channel capacity to accommodate real time seamless flow of discrete samples of biomedical signals. So, there is a keen need for real time data compression of biomedical signals. Compressive Sensing (CS) has recently attracted more interest due to its compactness and its feature of the faithful reconstruction of signals from fewer linear measurements, which facilitates less than Shannon’s sampling rate by exploiting the signal sparsity. The most common biomedical signal that is to be analyzed is the ECG signal, as the prediction of heart failure at an early stage can save a human life. This review is for a vast use-case of IoT framework in which CS measurements of ECG are acquired, communicated through Internet to a server, and the arrhythmia are analyzed using Machine learning (ML). Assuming this use-case specific for ECG, in this review many technical aspects are considered regarding various research components. The key aspect is on the investigation of the best sensing method, and to address this, various sensing matrices are reviewed, analyzed and recommended. The next aspect is the selection of the optimal sparsifying method, and the review recommends unexplored ECG compression algorithms as sparsifying methods. The other aspects are optimum reconstruction algorithms, best hardware implementations, suitable ML methods and effective modality of IoT. In this review all these components are considered, and a detailed review is presented which enables us to orchestrate the use-case specified above. This review focuses on the current trends in CS algorithms for ECG signal compression and its hardware implementation. The key to successful reconstruction of the CS method is the right selection of sensing and sparsifying matrix, and there are many unexplored sparsifying methods for the ECG signal. In this review, we shed some light on new possible sparsifying techniques. A detailed comparison table of various CS algorithms, sensing matrix, sparsifying techniques with different ECG dataset is tabulated to quantify the capability of CS in terms of appropriate performance metrics. As per the use-case specified above, the CS reconstructed ECG signals are to be subjected to ML analysis, and in this review the compressive domain inference approach is discussed. The various datasets, methodologies and ML models for ECG applications are studied and their model accuracies are tabulated. Mostly, the previous research on CS had studied the performance of CS using numerical simulation, whereas there are some good attempts for hardware implementations for ECG applications, and we studied the uniqueness of each method and supported the study with a comparison table. As a consolidation, we recommend new possibilities of the research components in terms of new transforms, new sparsifying methods, suggestions for ML approaches and hardware implementation.

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“…The transform domain method is used to project an ECG signal to a transform domain utilizing a linear orthogonal transformation. In [12], the authors proposed a wavelet-based method for e-health cardiac online diagnostic systems; the method firstly utilizes a continuous-time level-crossing analog-to-digital converter (LC-ADC) to compress ECG signals, and then biorthogonal 3.1 wavelet is applied to further compress the ECG signal. In the receiving end, the inverse discrete wavelet transform and linear interpolation are used to reconstruct the original ECG signals.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for ECG Signal Compression and Reconstruction: Down-Sampling Operation and Signal-Referenced Network

et al. 2023

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Objective. The wearable electrocardiogram (ECG) monitoring device is an effective tool for diagnosing intermittent heart diseases. However, the massive amount of ECG data increases power consumption during wireless transmission, thereby reducing the monitoring duration of the wearable device. Here, we presented a novel method to solve this problem. Method. To reduce the data size, we used a down-sampling operation to compress signals. Then, we designed a signal-referenced network to reconstruct the original signals from the compressed ones. We validated the proposed method on the China Physiological Signal Challenge 2018 database, used the root mean square error (RMSE) to evaluate the performance of the proposed network, and evaluated the effectiveness of the reconstructed signals via the F1-score of an ECG signal classifier. Main Result. The classifier used in this paper achieved an F1-score of 84% on 500 Hz signals reconstructed from 25 Hz, 89% from 50 Hz, 90% from 125 Hz, and 95% from 250 Hz. The RMSE of these four sampling rates was 0.10 mV, 0.08 mV, 0.05 mV, and 0.04 mV, respectively. Significance. The experimental result shows that the proposed network has a good performance when reconstructing signals. Furthermore, our method can remove the computational load of compressing signals from wearable devices.

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On the wavelet-based compressibility of continuous-time sampled ECG signal for e-health applications

Cited by 14 publications

References 47 publications

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

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

Review on Compressive Sensing Algorithms for ECG Signal for IoT Based Deep Learning Framework

A Novel Method for ECG Signal Compression and Reconstruction: Down-Sampling Operation and Signal-Referenced Network

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