Deterministic Compressed Sensing of heart sound signals

Daponte, P.; Vito, Luca De; Iadarola, Grazia; Picariello, Francesco; Rapuano, Sergio

doi:10.1109/memea52024.2021.9478766

Cited by 15 publications

(7 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This means that the matrix C is row-sparse; that is, it has few nonzero rows. Exploiting this consideration, the matrix X can be reconstructed by starting from the matrix of compressed samples Y (11), the dynamic sensing matrix Φ (10), and the Mexican hat wavelet matrix Ψ (12) and by solving a joint sparse recovery problem that can be expressed as the following [35]:…”

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%

“…Relying on sparsity property, the technique of Compressed Sensing (CS) has been proposed in order to reduce the number of samples representing the signals of interest and to reconstruct their digital version without compromising signal quality [10]. The CS has been vastly investigated in the field of biomedical signal processing not only for ECG monitoring but also for electroencephalogram, electromyogram, electrooculogram, galvanic skin response, and heart sound signals [9,11,12]. Adopting CS entails a reduction in data rate with respect to signal information content.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Daponte

Vito

Iadarola

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

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%.

show abstract

Section: The Proposed Methodsmentioning

confidence: 99%

Section: Implementation Of the Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Daponte

Vito

Iadarola

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…In other words, any input signal may be reconstructed from a set of measurements of a minimum number that is

, and, the lower the coherence is, the lower the required number M is. The incoherence property can be easily satisfied, for example, if the measurement matrix is chosen with random entries from a Bernoulli or Gaussian distribution [ 5 , 26 , 28 , 29 ], but it can also be satisfied when the measurement matrix is properly built with deterministic entries [ 30 , 31 , 32 ].…”

Section: Analog-to-information Convertersmentioning

confidence: 99%

Over the Limits of Traditional Sampling: Advantages and Issues of AICs for Measurement Instrumentation

Iadarola

Daponte

Vito

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

Data acquisition systems have shown the need of wideband spectrum monitoring for many years. This paper describes and discusses a recently proposed architecture aimed at acquiring efficiently wideband signals, named the Analog-to-Information Converter (AIC). AIC framework and working principle implementing the sub-Nyquist sampling are analyzed in general terms. Attention is specifically focused on the idea of exploiting the condition of the signals that, despite their large bandwidth, have a small information content in the frequency domain. However, as clarified in the paper, employing AICs in measurement instrumentation necessarily entails their characterization, through the analysis of their building blocks and the corresponding non-idealities, in order to improve the signal reconstruction.

show abstract

“…Pasquale Daponte et al [212] investigated his study on heart sound signals based on CS using Deterministic Binary Block Diagonal (DBBD) matrix as sensing matrix. The major benefit of using this is that it does not require generation of random numbers in the acquisition node and the computational complexity is also less at the compression phase.…”

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

View full text Add to dashboard Cite

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.

show abstract

Deterministic Compressed Sensing of heart sound signals

Cited by 15 publications

References 25 publications

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

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

Over the Limits of Traditional Sampling: Advantages and Issues of AICs for Measurement Instrumentation

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

Contact Info

Product

Resources

About