A Study on Dictionary Selection in Compressive Sensing for ECG Signals Compression and Classification

Fira, Monica; Costin, Hariton; Goraș, Liviu

doi:10.3390/bios12030146

Cited by 11 publications

(6 citation statements)

References 34 publications

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“…On the other hand, sample recordings in ambient conditions and PD identification from continuous speech are pursued less in the literature. Moreover, none of the reviewed solutions attempts to solve this problem by using CNN [46][47][48]. As such, the speech assessment workflow proposed in this article is aimed towards the assessment of continuous speech acquired in a noisy environment.…”

Section: Present Studymentioning

confidence: 99%

“…Thus, the CNN improves the structure and performance of traditional artificial networks, and the architecture of these models is suitable for recognizing certain patterns, i.e., features from the structure of 2D images [47]. As a mode of use, the CNN achieved very good results in the analysis of medical images, image segmentation, or in the field of visual recognition [48].…”

Section: Cnn-based Spectrogram Classificationmentioning

confidence: 99%

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CNN-Based Identification of Parkinson’s Disease from Continuous Speech in Noisy Environments

et al. 2023

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Parkinson’s disease is a progressive neurodegenerative disorder caused by dopaminergic neuron degeneration. Parkinsonian speech impairment is one of the earliest presentations of the disease and, along with tremor, is suitable for pre-diagnosis. It is defined by hypokinetic dysarthria and accounts for respiratory, phonatory, articulatory, and prosodic manifestations. The topic of this article targets artificial-intelligence-based identification of Parkinson’s disease from continuous speech recorded in a noisy environment. The novelty of this work is twofold. First, the proposed assessment workflow performed speech analysis on samples of continuous speech. Second, we analyzed and quantified Wiener filter applicability for speech denoising in the context of Parkinsonian speech identification. We argue that the Parkinsonian features of loudness, intonation, phonation, prosody, and articulation are contained in the speech, speech energy, and Mel spectrograms. Thus, the proposed workflow follows a feature-based speech assessment to determine the feature variation ranges, followed by speech classification using convolutional neural networks. We report the best classification accuracies of 96% on speech energy, 93% on speech, and 92% on Mel spectrograms. We conclude that the Wiener filter improves both feature-based analysis and convolutional-neural-network-based classification performances.

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Section: Present Studymentioning

confidence: 99%

Section: Cnn-based Spectrogram Classificationmentioning

confidence: 99%

CNN-Based Identification of Parkinson’s Disease from Continuous Speech in Noisy Environments

et al. 2023

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“…Experiments have shown that if the ECG signal of the same patient for 24 hours needs to be detected in the ambulatory ECG. Then the PSCCS method can be chosen, and if the purpose is to identify and classify abnormalities in the ECG signal, the CPCS method with the presence of pre-processing as well as segmentation is more effective [ 45 ].…”

Section: Literature Reviewmentioning

confidence: 99%

ECG signal classification in wearable devices based on compressed domain

et al. 2023

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Wearable devices are often used to diagnose arrhythmia, but the electrocardiogram (ECG) monitoring process generates a large amount of data, which will affect the detection speed and accuracy. In order to solve this problem, many studies have applied deep compressed sensing (DCS) technology to ECG monitoring, which can under-sampling and reconstruct ECG signals, greatly optimizing the diagnosis process, but the reconstruction process is complex and expensive. In this paper, we propose an improved classification scheme for deep compressed sensing models. The framework is comprised of four modules: pre-processing; compression; and classification. Firstly, the normalized ECG signals are compressed adaptively in the three convolutional layers, and then the compressed data is directly put into the classification network to obtain the results of four kinds of ECG signals. We conducted our experiments on the MIT-BIH Arrhythmia Database and Ali Cloud Tianchi ECG signal Database to validate the robustness of our model, adopting Accuracy, Precision, Sensitivity and F1-score as the evaluation metrics. When the compression ratio (CR) is 0.2, our model has 98.16% accuracy, 98.28% average accuracy, 98.09% Sensitivity and 98.06% F1-score, all of which are better than other models.

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“…The paper proposed by Monica fira et al [228] discusses the study on dictionary selection for ECG signals in CS domain. This article proposes the construction of dictionaries which are directly constructed from R waves.…”

Section: Learning On Reconstructed Ecg Signalmentioning

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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A Study on Dictionary Selection in Compressive Sensing for ECG Signals Compression and Classification

Cited by 11 publications

References 34 publications

CNN-Based Identification of Parkinson’s Disease from Continuous Speech in Noisy Environments

CNN-Based Identification of Parkinson’s Disease from Continuous Speech in Noisy Environments

ECG signal classification in wearable devices based on compressed domain

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

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