A novel technique for the detection of myocardial dysfunction using ECG signals based on hybrid signal processing and neural networks

Zeng, Wei; Yuan, Jian; Yuan, Chengzhi; Wang, Qinghui; Liu, Fenglin; Wang, Ying

doi:10.1007/s00500-020-05465-8

Cited by 18 publications

(5 citation statements)

References 80 publications

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“…Integrating these methodologies is essential for clinical ECG interpretation, enabling healthcare professionals to assess each patient's overall heart health thoroughly. Recently, there has been a focused effort to improve the classification of signals by incorporating features with ECG signals through a hybrid approach [97] , [98] . Deep learning models are predominantly considered black-box systems, making it challenging to conclusively determine whether these models rely solely on interval measurements or also take into account interval dispersion, potentially resulting in similar performance outcomes.…”

Section: Efficient Models For Ecg Cardiac Rhythm Classificationmentioning

confidence: 99%

A comprehensive review on efficient artificial intelligence models for classification of abnormal cardiac rhythms using electrocardiograms

Gupta,

Paluru,

Nankani

et al. 2024

Heliyon

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Section: Efficient Models For Ecg Cardiac Rhythm Classificationmentioning

confidence: 99%

A comprehensive review on efficient artificial intelligence models for classification of abnormal cardiac rhythms using electrocardiograms

Gupta,

Paluru,

Nankani

et al. 2024

Heliyon

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“…In many proposed studies, the following indicators are usually used to evaluate the performance of deep learning models: accuracy (ACC), sensitivity (SEN), specificity (SPE), and sometimes precision (PRE) [8,[51][52][53]. Accuracy indicates the percentage of correct prediction in the total; sensitivity indicates the proportion of all positive cases that are correctly classified, reflecting the classifier's ability to identify positive cases; specificity indicates the proportion of all counterexamples that are correctly classified and reflects the classifier's ability to identify counterexamples; precision indicates the proportion of positive examples that are positive examples.…”

Section: Evaluation Indexmentioning

confidence: 99%

Research Progress of ECG Monitoring Equipment and Algorithms Based on Polymer Materials

Zhang

Liu

2021

Micromachines

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Heart diseases such as myocardial ischemia (MI) are the main causes of human death. The prediction of MI and arrhythmia is an effective method for the early detection, diagnosis, and treatment of heart disease. For the rapid detection of arrhythmia and myocardial ischemia, the electrocardiogram (ECG) is widely used in clinical diagnosis, and its detection equipment and algorithm are constantly optimized. This paper introduces the current progress of portable ECG monitoring equipment, including the use of polymer material sensors and the use of deep learning algorithms. First, it introduces the latest portable ECG monitoring equipment and the polymer material sensor it uses and then focuses on reviewing the progress of detection algorithms. We mainly introduce the basic structure of existing deep learning methods and enumerate the internationally recognized ECG datasets. This paper outlines the deep learning algorithms used for ECG diagnosis, compares the prediction results of different classifiers, and summarizes two existing problems of ECG detection technology: imbalance of categories and high computational overhead. Finally, we put forward the development direction of using generative adversarial networks (GAN) to improve the quality of the ECG database and lightweight ECG diagnosis algorithm to adapt to portable ECG monitoring equipment.

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“…If the system tends to develop towards a certain steady state, the phase-space trajectory of the system shows an attractor shape, such as periodic attractors, quasi periodic attractors, and chaotic attractors, corresponding to periodic motion, quasi periodic motion, or chaotic motion. Such as, if the output signals of a piezoelectric dynamic system is reconstructed and the phase-space trajectory shows the shape of a periodic or chaotic attractor, the system is in periodic or chaotic motion [11,12]; After reconstructing the output signals of the tribological dynamic system, such as friction coefficient and temperature, the phase-space trajectory shows a chaotic attractor shape, resulting in a 'formation-maintenance -disappearance' chaotic dynamic evolution law [13]; After reconstructing normal and abnormal bioelectric signals such as electroencephalogram (EEG) and electrocardiogram (ECG), the phase-space trajectories exhibit different attractor shapes, enabling the recognition and detection of corresponding diseases and disorders [14,15]; etc.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear dynamics study of freestanding triboelectric nanogenerator system

Cao,

Bao,

Yin

et al. 2024

J. Phys. D: Appl. Phys.

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As a nonlinear dynamic system, the dynamic equation of triboelectric nanogenerator system is not only difficult to accurately establish, but also difficult to solve. The output signals of triboelectric nanogenerator system depends on the initial conditions of the system, which reflects the dynamic behavior of the system. Currently, there is little research on using the output signals to study the dynamic behavior of triboelectric nanogenerator system. Therefore, a dynamic simulation model of a freestanding triboelectric nanogenerator (F-TENG) system was established using COMSOL software. The mathematical model of the F-TENG system was fitted. Using chaos theory and its phase-space reconstruction technology, the phase-space trajectories of the F-TENG system were reconstructed through voltage signals under external factors, and the dynamic behavior of the F-TENG system was discussed and studied. Finally, validation experiments were carried out. The research shows that with the variation of internal factors and operating frequency, the mathematical model of the F-TENG system remains basically unchanged, and the variation law of output characteristics also remains basically unchanged. The F-TENG system is in periodic motion; With the variation of external factors such as resistance and friction layer spacing, the mathematical model of the F-TENG system will evolve in a complex direction, and the output characteristics will gradually distort. The F-TENG system will evolve from periodic motion to chaotic motion. The research results not only reveal the dynamic behavior and enrich the nonlinear dynamic theory of triboelectric nanogenerator system, but also provide theoretical guidance for increasing the working bandwidth and stability of triboelectric nanogenerator system.

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A novel technique for the detection of myocardial dysfunction using ECG signals based on hybrid signal processing and neural networks

Cited by 18 publications

References 80 publications

A comprehensive review on efficient artificial intelligence models for classification of abnormal cardiac rhythms using electrocardiograms

A comprehensive review on efficient artificial intelligence models for classification of abnormal cardiac rhythms using electrocardiograms

Research Progress of ECG Monitoring Equipment and Algorithms Based on Polymer Materials

Nonlinear dynamics study of freestanding triboelectric nanogenerator system

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