ECG compressed sensing method with high compression ratio and dynamic model reconstruction

Šaliga, Ján; Andráš, Imrich; Dolinský, Pavol; Michaeli, L.; Kováč, Ondrej; Kromka, Jozef

doi:10.1016/j.measurement.2021.109803

Cited by 23 publications

(17 citation statements)

References 53 publications

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“…The main advantage of the proposed method is the dynamic ECG evaluation. In other CS implementations for ECG monitoring, the sensing matrix Φ is usually randomly constructed according to a probability distribution [9,13,21,25,29]. As an example, in [21], the reconstruction performance of different CS methods is compared by considering several distributions, such as Bernoulli or Gaussian.…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…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: The Proposed Methodsmentioning

confidence: 99%

Section: Implementation Of the Proposed Methodsmentioning

confidence: 99%

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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show abstract

“…It is a straightforward, intuitive, and easy-to-understand key indicator. Use CR (compressing ratio, CR) to represent the data compression ratio, and the range is set to (0, 1); then, the compression ratio is defined as follows [ 34 ]:

…”

Section: Comprehensive Evaluation Index Of Vibration Data Compression...mentioning

confidence: 99%

Compression Reconstruction and Fault Diagnosis of Diesel Engine Vibration Signal Based on Optimizing Block Sparse Bayesian Learning

Bai

Wen

et al. 2022

Sensors

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It is critical to deploy wireless data transmission technologies remotely, in real-time, to monitor the health state of diesel engines dynamically. The usual approach to data compression is to collect data first, then compress it; however, we cannot ensure the correctness and efficiency of the data. Based on sparse Bayesian optimization block learning, this research provides a method for compression reconstruction and fault diagnostics of diesel engine vibration data. This method’s essential contribution is combining compressive sensing technology with fault diagnosis. To achieve a better diagnosis effect, we can effectively improve the wireless transmission efficiency of the vibration signal. First, the dictionary is dynamically updated by learning the dictionary using singular value decomposition to produce the ideal sparse form. Second, a block sparse Bayesian learning boundary optimization approach is utilized to recover structured non-sparse signals rapidly. A detailed assessment index of the data compression effect is created. Finally, the experimental findings reveal that the approach provided in this study outperforms standard compression methods in terms of compression efficiency and accuracy and its ability to produce the desired fault diagnostic effect, proving the usefulness of the proposed method.

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“…Jahanshahi et al [ 19 ] put forward a method with low-rank constraints, using the Kronecker sparsifying base to exploit the temporal and spatial structures of the ECG signals. Saliga et al [ 20 ] adopted a dynamic ECG model in which the parameters were learnt from the measurement by the Differential Evolution algorithm. This model reduced the noise of the interfering signals and maintained the signals’ structures.…”

Section: Introductionmentioning

confidence: 99%

Deep Compressive Sensing on ECG Signals with Modified Inception Block and LSTM

Hua

Rao

Peng

et al. 2022

Entropy

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In practical electrocardiogram (ECG) monitoring, there are some challenges in reducing the data burden and energy costs. Therefore, compressed sensing (CS) which can conduct under-sampling and reconstruction at the same time is adopted in the ECG monitoring application. Recently, deep learning used in CS methods improves the reconstruction performance significantly and can removes of some of the constraints in traditional CS. In this paper, we propose a deep compressive-sensing scheme for ECG signals, based on modified-Inception block and long short-term memory (LSTM). The framework is comprised of four modules: preprocessing; compression; initial; and final reconstruction. We adaptively compressed the normalized ECG signals, sequentially using three convolutional layers, and reconstructed the signals with a modified Inception block and LSTM. We conducted our experiments on the MIT-BIH Arrhythmia Database and Non-Invasive Fetal ECG Arrhythmia Database to validate the robustness of our model, adopting Signal-to-Noise Ratio (SNR) and percentage Root-mean-square Difference (PRD) as the evaluation metrics. The PRD of our scheme was the lowest and the SNR was the highest at all of the sensing rates in our experiments on both of the databases, and when the sensing rate was higher than 0.5, the PRD was lower than 2%, showing significant improvement in reconstruction performance compared to the comparative methods. Our method also showed good recovering quality in the noisy data.

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ECG compressed sensing method with high compression ratio and dynamic model reconstruction

Cited by 23 publications

References 53 publications

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

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

Compression Reconstruction and Fault Diagnosis of Diesel Engine Vibration Signal Based on Optimizing Block Sparse Bayesian Learning

Deep Compressive Sensing on ECG Signals with Modified Inception Block and LSTM

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