Exploiting multi-scale signal information in joint compressed sensing recovery of multi-channel ECG signals

Singh, Anurag; Dandapat, Samarendra

doi:10.1016/j.bspc.2016.05.008

Cited by 20 publications

(13 citation statements)

References 29 publications

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“…This helps it outperform other techniques in the form of reduced distortion levels at same CR (or M ) values. We also compared the recovery results of STSBL with our latest work [ 15 ]. In this work, we attempted to exploit the multi-scale signal information through a weighting approach and proposed a prior weighted mixed-norm minimisation (PWMNM) algorithm for JCS recovery.…”

Section: Resultsmentioning

confidence: 99%

“…However, the spatially correlated information that exists between different channels was ignored in the above works. The proposed work processes multiple channels simultaneously and thus exploits this We also compared the recovery results of STSBL with our latest work [15]. In this work, we attempted to exploit the multi-scale signal information through a weighting approach and proposed a prior weighted mixed-norm minimisation (PWMNM) algorithm for JCS recovery.…”

Section: Comparative Studymentioning

confidence: 99%

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Block sparsity‐based joint compressed sensing recovery of multi‐channel ECG signals

Singh

Dandapat

2017

Healthcare Technology Letters

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In recent years, compressed sensing (CS) has emerged as an effective alternative to conventional wavelet based data compression techniques. This is due to its simple and energy-efficient data reduction procedure, which makes it suitable for resource-constrained wireless body area network (WBAN)-enabled electrocardiogram (ECG) telemonitoring applications. Both spatial and temporal correlations exist simultaneously in multi-channel ECG (MECG) signals. Exploitation of both types of correlations is very important in CS-based ECG telemonitoring systems for better performance. However, most of the existing CS-based works exploit either of the correlations, which results in a suboptimal performance. In this work, within a CS framework, the authors propose to exploit both types of correlations simultaneously using a sparse Bayesian learning-based approach. A spatiotemporal sparse model is employed for joint compression/reconstruction of MECG signals. Discrete wavelets transform domain block sparsity of MECG signals is exploited for simultaneous reconstruction of all the channels. Performance evaluations using Physikalisch-Technische Bundesanstalt MECG diagnostic database show a significant gain in the diagnostic reconstruction quality of the MECG signals compared with the state-of-the art techniques at reduced number of measurements. Low measurement requirement may lead to significant savings in the energy-cost of the existing CS-based WBAN systems.

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Section: Resultsmentioning

confidence: 99%

Section: Comparative Studymentioning

confidence: 99%

Block sparsity‐based joint compressed sensing recovery of multi‐channel ECG signals

Singh

Dandapat

2017

Healthcare Technology Letters

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“…As stated above, in the literature, few studies are focused on the use of CS methods for multi-lead monitoring [7,[29][30][31]. Those methods outperform the single lead CS methods because they rely on the fact that the ECG signals from multi-lead channels are not independent but they have the electrical heart vector as a common source of information.…”

Section: Related Workmentioning

confidence: 99%

“…IoMT generally constitutes an elaborated paradigm, with medical things meaning wearable devices and smart sensors tied to human bodies (sometimes implanted in bodies), allowing the acquisition of biosignals and other vital parameters. Standard configurations of IoMT remote monitoring systems, where ECG signal is acquired at Nyquist rate, do not suit storage and transmission requirements as a huge amount of data need to be stored and transmitted [7], especially when a high number of patients is monitored [8]. Moreover, power consumption is often related to signal data rate, since data transmission is the main cause of energy dissipation in many interfaces (such as Wireless Local Area Network (WLAN) and Wireless Wide Area Network (WWAN) interfaces) [9].…”

Section: Introductionmentioning

confidence: 99%

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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“…Compressed sensing (CS) is a signal processing technique that enables signal reconstruction from a small set of linear projections, called measurements, provided the signal is sparse in some domain. Compressed sensing (CS) has emerged as a promising framework to address these challenges because of its energy-efficient data reduction procedure [44]. Compressive sensing samples the signal by a much smaller number of samples than required by the Nyquist-Shannon theorem.…”

Section: Compressive Sensingmentioning

confidence: 99%

Review of wireless body sensor networks

Wali¹,

Abdullah²

2020

IJET

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Wireless body area networks (WBANs) are emerging as important networks that are applicable in various fields. WBAN gives its users access to body sensor data and resources anywhere in the world with the help of the internet. These sensors offer promising applications in areas such as real-time health monitoring, interactive gaming, and consumer electronics. WBAN does not force the patient to stay in the hospital which saves a lot of physical movement. This paper reviews a review of WBANs. We study the following: prior researches, applications and architectures of WBAN, and compression sensing techniques.

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Exploiting multi-scale signal information in joint compressed sensing recovery of multi-channel ECG signals

Cited by 20 publications

References 29 publications

Block sparsity‐based joint compressed sensing recovery of multi‐channel ECG signals

Block sparsity‐based joint compressed sensing recovery of multi‐channel ECG signals

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

Review of wireless body sensor networks

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