NSTL国家科技图书文献中心

1. Perspective of Generalizing Deep Boltzmann Machine for ECG Signal Classification NSTL国家科技图书文献中心

Jeyaraj, Pandia Raja... | Asokan, Siva Prakash ... - 《National Academy Science Letters》 - 2025,48(1) - 39～43 - 共5页

摘要： Electrocardiogram (ECG) signal* from the extracted large amount of* | to meticulously classify physiological signal* like* | arrhythmia in real-time ECG* signals. The performance of* | classification of most of the arrhythmia ECG* and has high* | model performs consistently on various real-time ECG

关键词： Data analysis | Deep learning algorithm | Deep Boltzmann machine | ECG signal | Wearable devices

2. ECG Signal Classification with a Multi-stage Model Integrating CNN, SNN, and ResNet NSTL国家科技图书文献中心

Xiangyu Shi | Dianjing Cheng ... - 《Advanced Data Mining and Applications,Part I》 - International Conference on Advanced Data Mining and Applications - 2025, - 361～372 - 共12页

摘要：Electrocardiogram (ECG) signal* classification* | relatively low accuracy when applied to ECG signal | the performance of ECG* signal classification. The* | accuracy of ECG* signal classification, providing a novel* | from the ECG* signals, followed by SNN to capture*

关键词： Convolutional neural network (CNN) | Spiking neural network (SNN) | Residual network (ResNet) | Electrocardiogram (ECG) | Signal processing and classification

3. Detection of cardiac abnormalities in ECG signal using time-based signal processing algorithm NSTL国家科技图书文献中心

Kiran Kumar Patro | Allam Jaya Prakash ... - 《International journal of computational vision and robotics》 - 2025,15(1) - 59～74 - 共16页

摘要： signal characteristics, including R-R interval, T | and compared with NSR to determine ECG* cardiac* | detected using the time and amplitude features of the ECG | online Physionet ECG* databases (the AFDB, NSR, and MIT* | Electrocardiograms are bioelectrical signals

关键词： electrocardiogram | ECG | cardiac abnormalities | signal processing | bradycardia | tachycardia | atrial fibrillation | AF | left bundle branch block | LBBB...

4. Wavelet Integrated Convolutional Neural Network for ECG Signal Denoising NSTL国家科技图书文献中心

Takamasa Terada | Masahiro Toyoura - 《MultiMedia Modeling,Part IV》 - International Conference on MultiMedia Modeling - 2025, - 311～324 - 共14页

摘要：Wearable electrocardiogram (ECG) measurement | bands of ECG* and noise make noise reduction difficult* | features in a clean ECG. Testing confirms that the | proposed method effectively predicts accurate ECG | signal-to-noise ratio (SNR) range of - 10 - 10 are

关键词： Electrocardiogram | Wavelet transform | Denoising autoencoder | Convolutional neural network

5. Multi-criteria Bayesian optimization of Empirical Mode Decomposition and hybrid filters fusion for enhanced ECG signal denoising and classification: Cardiac arrhythmia and myocardial infarction cases NSTL国家科技图书文献中心

Bentaleb D. | Khatar Z. - 《Computers in Biology and Medicine》 - 2025,184 - 109462～109462 - 共13页

摘要： ECG signal. We employ a multi-criteria Bayesian | of ECG signals, focusing on the use of a hybrid | parameters to further improve the signal* quality* | © 2024 Elsevier LtdThis paper introduces a | new advanced model for denoising and classification

关键词： Arrhythmia classification | Cardiac diseases classification | ECG deep learning classification | ECG signal denoising | Myocardial infarction classification

6. A novel linear discriminant analysis-based classification of R-peaks in different ECG signal datasets NSTL国家科技图书文献中心

Varun Gupta - 《International journal of bioinformatics research and applications》 - 2025,21(2) - 153～170 - 共18页

摘要： electrocardiogram (ECG) signal* processing as reported by the same* | In the current scenario, there is a need to | develop efficient pre-processing and classification | techniques which can form the basis of an automated health | monitoring system. In this paper, independent component

关键词： electrocardiogram | ECG | adaptive notch filter | ANF | independent component analysis | ICA | linear discriminant analysis | LDA | signal-to-noise ratio | SNR

7. DBPF pre-processing-based improved ECG signal analysis in medical engineering applications NSTL国家科技图书文献中心

Gupta, Varun - 《International Journal of Engineering Systems Modelling and Simulation》 - 2025,16(2) - 111～118 - 共8页

摘要： form of electrocardiogram (ECG) signal. Unfortunately | envelope, and factor analysis is used for ECG signal | analysis. The analysis of ECG* signal is done by* | -BIH Arr) database and MIT-BIH Long-Term ECG* database* | Bioinformatics is the essential field to

关键词： bioinformatics | electrocardiogram | ECG | digital bandpass filter | Hilbert envelope | factor analysis | R-peaks

8. An ECG Signal Classification System Using EMD and Pipelined Yolo Network NSTL国家科技图书文献中心

M. Jyothi Prasad | C. Bennila Thangamma... - 《Circuits, systems, and signal processing》 - 2025,44(3) - 1983～1998 - 共16页

摘要： Chebyshev filter on the source ECG* signal to detect and* | source ECG* signal into either normal or Arrhythmia. The* | detecting abnormal electrocardiogram (ECG) signals. This | arrhythmia ECG* signals amidst normal ECG signals, targeting* | preprocessed ECG* signals are decomposed into number of sub*

关键词： ECG | Chebyshev filter | Arrhythmia | PYOLO | Dataset

9. Sleep Apnea Detection from Single-Lead ECG Signal Using Hybrid Deep CNN NSTL国家科技图书文献中心

Duc Thien Pham | Roman Moucek - 《Brain Informatics,Part I》 - International Conference on Brain Informatics - 2025, - 110～120 - 共11页

摘要： detect SA using a single-lead ECG* signal. We validated* | electrocardiogram (ECG) is vital for diagnosing SA due to its | Apnea-ECG dataset, which contains 70 single-lead ECG | accurately detecting SA from single-lead ECG* signals* | Sleep apnea (SA) is a prevalent disorder that

关键词： Sleep apnea | Deep CNN | Detection | Electrocardiogram

10. End-to-End ECG Signal Compression Based on Temporal Information and Residual Compensation NSTL国家科技图书文献中心

Xingyu Liu | Lesong Zheng ... - 《Circuits, systems, and signal processing》 - 2025,44(4) - 2779～2801 - 共23页 - 被引量：1

摘要： Electrocardiogram (ECG) signal* compression essential for efficient* | novel ECG* signal compression model that optimally* | the inherent temporal correlations of ECG* signals* | balances the compression ratio and signal* quality* | -dimensional residuals back into the original signal* space*

关键词： Data analysis | Deep learning algorithm | Deep Boltzmann machine | ECG signal | Wearable devices

摘要：Electrocardiogram (ECG) signal* classification* | relatively low accuracy when applied to ECG signal | the performance of ECG* signal classification. The* | accuracy of ECG* signal classification, providing a novel* | from the ECG* signals, followed by SNN to capture*

关键词： Convolutional neural network (CNN) | Spiking neural network (SNN) | Residual network (ResNet) | Electrocardiogram (ECG) | Signal processing and classification

摘要： signal characteristics, including R-R interval, T | and compared with NSR to determine ECG* cardiac* | detected using the time and amplitude features of the ECG | online Physionet ECG* databases (the AFDB, NSR, and MIT* | Electrocardiograms are bioelectrical signals

关键词： electrocardiogram | ECG | cardiac abnormalities | signal processing | bradycardia | tachycardia | atrial fibrillation | AF | left bundle branch block | LBBB...

摘要：Wearable electrocardiogram (ECG) measurement | bands of ECG* and noise make noise reduction difficult* | features in a clean ECG. Testing confirms that the | proposed method effectively predicts accurate ECG | signal-to-noise ratio (SNR) range of - 10 - 10 are

关键词： Electrocardiogram | Wavelet transform | Denoising autoencoder | Convolutional neural network

摘要： ECG signal. We employ a multi-criteria Bayesian | of ECG signals, focusing on the use of a hybrid | parameters to further improve the signal* quality* | © 2024 Elsevier LtdThis paper introduces a | new advanced model for denoising and classification

关键词： Arrhythmia classification | Cardiac diseases classification | ECG deep learning classification | ECG signal denoising | Myocardial infarction classification

摘要： electrocardiogram (ECG) signal* processing as reported by the same* | In the current scenario, there is a need to | develop efficient pre-processing and classification | techniques which can form the basis of an automated health | monitoring system. In this paper, independent component

关键词： electrocardiogram | ECG | adaptive notch filter | ANF | independent component analysis | ICA | linear discriminant analysis | LDA | signal-to-noise ratio | SNR

摘要： form of electrocardiogram (ECG) signal. Unfortunately | envelope, and factor analysis is used for ECG signal | analysis. The analysis of ECG* signal is done by* | -BIH Arr) database and MIT-BIH Long-Term ECG* database* | Bioinformatics is the essential field to

关键词： bioinformatics | electrocardiogram | ECG | digital bandpass filter | Hilbert envelope | factor analysis | R-peaks

关键词： ECG | Chebyshev filter | Arrhythmia | PYOLO | Dataset

摘要： detect SA using a single-lead ECG* signal. We validated* | electrocardiogram (ECG) is vital for diagnosing SA due to its | Apnea-ECG dataset, which contains 70 single-lead ECG | accurately detecting SA from single-lead ECG* signals* | Sleep apnea (SA) is a prevalent disorder that

关键词： Sleep apnea | Deep CNN | Detection | Electrocardiogram

关键词： ECG | Compression | Temporal correlations | Quantization | Residual

4008-161-200 800-990-8900

国家科技图书文献中心

关键词： Data analysis | Deep learning algorithm | Deep Boltzmann machine | ECG signal | Wearable devices

摘要：Electrocardiogram (ECG) signal classification | relatively low accuracy when applied to ECG signal | the performance of ECG signal classification. The | accuracy of ECG signal classification, providing a novel | from the ECG signals, followed by SNN to capture

关键词： Convolutional neural network (CNN) | Spiking neural network (SNN) | Residual network (ResNet) | Electrocardiogram (ECG) | Signal processing and classification

摘要： signal characteristics, including R-R interval, T | and compared with NSR to determine ECG cardiac | detected using the time and amplitude features of the ECG | online Physionet ECG databases (the AFDB, NSR, and MIT | Electrocardiograms are bioelectrical signals

关键词： electrocardiogram | ECG | cardiac abnormalities | signal processing | bradycardia | tachycardia | atrial fibrillation | AF | left bundle branch block | LBBB...

摘要：Wearable electrocardiogram (ECG) measurement | bands of ECG and noise make noise reduction difficult | features in a clean ECG. Testing confirms that the | proposed method effectively predicts accurate ECG | signal-to-noise ratio (SNR) range of - 10 - 10 are

关键词： Electrocardiogram | Wavelet transform | Denoising autoencoder | Convolutional neural network

摘要： ECG signal. We employ a multi-criteria Bayesian | of ECG signals, focusing on the use of a hybrid | parameters to further improve the signal quality | © 2024 Elsevier LtdThis paper introduces a | new advanced model for denoising and classification

关键词： Arrhythmia classification | Cardiac diseases classification | ECG deep learning classification | ECG signal denoising | Myocardial infarction classification

摘要： electrocardiogram (ECG) signal processing as reported by the same | In the current scenario, there is a need to | develop efficient pre-processing and classification | techniques which can form the basis of an automated health | monitoring system. In this paper, independent component

关键词： electrocardiogram | ECG | adaptive notch filter | ANF | independent component analysis | ICA | linear discriminant analysis | LDA | signal-to-noise ratio | SNR

摘要： form of electrocardiogram (ECG) signal. Unfortunately | envelope, and factor analysis is used for ECG signal | analysis. The analysis of ECG signal is done by | -BIH Arr) database and MIT-BIH Long-Term ECG database | Bioinformatics is the essential field to

关键词： bioinformatics | electrocardiogram | ECG | digital bandpass filter | Hilbert envelope | factor analysis | R-peaks

关键词： ECG | Chebyshev filter | Arrhythmia | PYOLO | Dataset

摘要： detect SA using a single-lead ECG signal. We validated | electrocardiogram (ECG) is vital for diagnosing SA due to its | Apnea-ECG dataset, which contains 70 single-lead ECG | accurately detecting SA from single-lead ECG signals | Sleep apnea (SA) is a prevalent disorder that

关键词： Sleep apnea | Deep CNN | Detection | Electrocardiogram

摘要： Electrocardiogram (ECG) signal compression essential for efficient | novel ECG signal compression model that optimally | the inherent temporal correlations of ECG signals | balances the compression ratio and signal quality | -dimensional residuals back into the original signal space

关键词： ECG | Compression | Temporal correlations | Quantization | Residual

4008-161-200 800-990-8900

国家科技图书文献中心

摘要：Electrocardiogram (ECG) signal* classification* | relatively low accuracy when applied to ECG signal | the performance of ECG* signal classification. The* | accuracy of ECG* signal classification, providing a novel* | from the ECG* signals, followed by SNN to capture*

摘要： signal characteristics, including R-R interval, T | and compared with NSR to determine ECG* cardiac* | detected using the time and amplitude features of the ECG | online Physionet ECG* databases (the AFDB, NSR, and MIT* | Electrocardiograms are bioelectrical signals

摘要：Wearable electrocardiogram (ECG) measurement | bands of ECG* and noise make noise reduction difficult* | features in a clean ECG. Testing confirms that the | proposed method effectively predicts accurate ECG | signal-to-noise ratio (SNR) range of - 10 - 10 are

摘要： ECG signal. We employ a multi-criteria Bayesian | of ECG signals, focusing on the use of a hybrid | parameters to further improve the signal* quality* | © 2024 Elsevier LtdThis paper introduces a | new advanced model for denoising and classification

摘要： electrocardiogram (ECG) signal* processing as reported by the same* | In the current scenario, there is a need to | develop efficient pre-processing and classification | techniques which can form the basis of an automated health | monitoring system. In this paper, independent component

摘要： form of electrocardiogram (ECG) signal. Unfortunately | envelope, and factor analysis is used for ECG signal | analysis. The analysis of ECG* signal is done by* | -BIH Arr) database and MIT-BIH Long-Term ECG* database* | Bioinformatics is the essential field to

摘要： detect SA using a single-lead ECG* signal. We validated* | electrocardiogram (ECG) is vital for diagnosing SA due to its | Apnea-ECG dataset, which contains 70 single-lead ECG | accurately detecting SA from single-lead ECG* signals* | Sleep apnea (SA) is a prevalent disorder that