A new method for early detection of myocardial ischemia: cardiodynamicsgram (CDG)

Dong, Xunde; Ou, Shanxing; Wang, Wei; Hu, Junmin; Yang, Feifei

doi:10.1007/s11432-015-5309-7

Cited by 26 publications

(21 citation statements)

References 41 publications

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“…This theory is essentially based on concepts and theories of system identification, adaptive control and RBF networks. By the deterministic learning algorithm, the dynamics of a temporal pattern can be accurately modelled, and the temporal features of the pattern can be effectively extracted and represented in a way that is invariant in time [19,18,22]. In deterministic learning, a temporal or dynamical pattern is defined as the periodic or recurrent (quasi-periodic, almost periodic and even chaotic signals (or trajectories) generated from the following general nonlinear dynamical system:…”

Section: Deterministic Learning Theorymentioning

confidence: 99%

“…It has been shown in [18,21,22] that for almost every temporal pattern f z , the unknown dynamics f i ðx; pÞ can be accurately modelled along the trajectory f z :…”

Section: Deterministic Learning Theorymentioning

confidence: 99%

“…Identity recognition based on ECG signals is actually a temporal pattern recognition problem. The existing classifiers developed for static patterns might prove inappropriate for ECGbased identity recognition [18]. It is supposed in [19] that methods for temporal pattern recognition should be fundamentally different from those for static pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, a novel algorithm called deterministic learning theory [18,21,22] was proposed to process the temporal patterns. The dynamics of a temporal pattern can be accurately modelled and represented in a CONTACT Weiyi Huang dong.xunde@mail.scut.edu.cn, 18924114669@189.cn time-independent manner by using deterministic learning [19].…”

Section: Introductionmentioning

confidence: 99%

“…Especially, the representation contains complete information of the temporal pattern. Based on the representation, a mechanism to measure the similarity between temporal patterns was proposed in [18,22]. As the dynamics of temporal patterns contain complete information, it is more appropriate to recognize temporal patterns with large variations in morphology (e.g.…”

Section: Introductionmentioning

confidence: 99%

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ECG-based identity recognition via deterministic learning

Dong

Huang

2018

Biotechnology & Biotechnological Equipment

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In this paper, a novel method based on electrocardiogram (ECG) signals is proposed for identity recognition. A unique feature called dynamics, which is fundamentally different from features used in literature, is extracted from ECG signals and used for identity recognition. Deterministic learning, a recently proposed machine learning approach, is used to model the dynamics of training ECG signals. A set of estimators employing the modelling results of training ECG signals is constructed. Through comparing the test ECG signal (measured from a subject to be recognized) with the estimators, a set of errors can be obtained and used to measure the similarity between the test and the training ECG signals. The test ECG signal is recognized in accordance with the smallest error, and then the subject can be recognized rapidly. Experimental results indicate that the proposed method is reliable and efficient for identity recognition.

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Section: Deterministic Learning Theorymentioning

confidence: 99%

“…It has been shown in [18,21,22] that for almost every temporal pattern f z , the unknown dynamics f i ðx; pÞ can be accurately modelled along the trajectory f z :…”

Section: Deterministic Learning Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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ECG-based identity recognition via deterministic learning

Dong

Huang

2018

Biotechnology & Biotechnological Equipment

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ECG‐based cardiodynamicsgram can reflect anomalous functional information in coronary artery disease

Wang

Sun

Kim

et al. 2023

Clinical Cardiology

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Background: The cardiodynamicsgram (CDG), a novel noninvasive method, extracts dynamic ST-T segment information from an electrocardiogram (ECG) through deterministic learning. Hypothesis:The CDG can reflect anomalous functional information in coronary artery disease (CAD).Methods: We retrospectively enrolled 456 patients with suspected CAD who underwent coronary computed tomography angiography (CCTA) from January 2020 to 2022, followed immediately by standard 12-lead ECG acquisition. Positivity for CAD were defined as CCTA ≥ 50% or CT-derived fractional flow reserve (CT-FFR) ≤ 0.8. A CDG value <0 was considered negative; otherwise, it was considered positive. We also evaluated the diagnostic performance of the CDG in the ECGdiagnosis-negative subgroup and in patients who had undergone invasive coronary angiography (ICA) after CCTA.Results: Of 362 patients, 168 (46.41%) were positive for CAD, and 178 (49.17%) were men. The median age was 59 (52−66) years. The accuracy of the CDG in the diagnosis of CAD was 79.56%, with a sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) of 75.60%, 82.99%, and 0.836 (95% CI: 0.794−0.878), respectively. Similarly, in the ECG-diagnosis-negative subgroup (n = 223), the accuracy of the CDG was 80.27%, with an AUC of 0.842 (95% CI: 0.790−0.895). Among the 11 patients with CAD confirmed by ICA, 10 were diagnosed positive by the CDG. Furthermore, the CDG values and CT-FFR were correlated (r = −.395; p < .001). Conclusions:The ECG-based CDG has relatively high specificity and accuracy for the diagnosis of CAD and reflects functional cardiac information to some extent. It has

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An ECG denoising method combining variational modal decomposition and wavelet soft threshold

Yu²,

2022

Concurrency and Computation

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Cardio‐cerebrovascular disease is one of the malignant diseases with the highest morbidity and mortality in the world. Medical research shows that the factors that induce heart blood cerebrovascular disease are diverse, and electrocardiogram (ECG) has important reference significance for the diagnosis and pathological research of cardiovascular and cerebrovascular diseases. This paper proposes a method of denoising ECG signals by combining variational mode decomposition with wavelet soft‐threshold. First perform variational modal decomposition on the ECG signal to obtain several modal components, and then the decomposed modals components are denoised by wavelet threshold, and finally the signal reconstruction of the denoised modal components is carried out through Matlab simulation experiment analysis. Compared with the soft‐threshold denoising method of ensemble empirical mode decomposition (EEMD), the soft‐threshold denoising method of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the variational modal decomposition wavelet threshold denoising algorithm combined with ECG signal denoising has better denoising effect. This method is used to denoise the ECG signal from the MIT‐BIH database of the Massachusetts Institute of Technology. Under the condition of ensuring the smoothness of the ECG signal image, the signal‐to‐noise ratio value is 27.1121 and the mean square error value is 0.0535, which has achieved good results.

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A new method for early detection of myocardial ischemia: cardiodynamicsgram (CDG)

Cited by 26 publications

References 41 publications

ECG-based identity recognition via deterministic learning

ECG-based identity recognition via deterministic learning

ECG‐based cardiodynamicsgram can reflect anomalous functional information in coronary artery disease

An ECG denoising method combining variational modal decomposition and wavelet soft threshold

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