Analysis of ECG Signals by Dynamic Mode Decomposition

Ingabire, Honorine Niyigena; Wu, Kangjia; Amos, Joan Toluwani; He, Sixuan; Peng, Xiaohang; Wang, Wen-An; Li, Min; Chen, Jinying; Feng, Yanqiu; Rao, Nini; Ren, Peng

doi:10.1109/jbhi.2021.3130275

Cited by 16 publications

(6 citation statements)

References 56 publications

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“…In engineering, there are many subtypes of communication models. Since much previous literatures have demonstrated that the brain is a complex and large-scale system composed of many subsystems [8], [9], [19], this paper adopted the PCM. Thus, investigation of how information is transmitted within EEG subsystems provides a more complete frame of brain activity.…”

Section: Discussionmentioning

confidence: 99%

“…Only our proposed approach can quantitatively evaluate MIT in the space-time-frequency domain. In our method, DMD was first employed to unveil the MIT in the space-time-frequency domain, and then parameters extracted from the PCM model were used to quantify MIT [8], [9]. (2) Unlike previous literature, our method does not regard MS as a closed system, but rather as an open system; they constantly exchange information with the outside environment.…”

Section: A Introduction To Multivariate Information Transmissionmentioning

confidence: 99%

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Assessment of Multivariate Information Transmission in Space-Time-Frequency Domain: A Case Study for EEG Signals

Han

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Objective: Multivariate signal (MS) analysis, especially the assessment of its information transmission (for example, from the perspective of network science), is the key to our understanding of various phenomena in biology, physics and economics. Although there is a large amount of literature demonstrating that MS can be decomposed into space-timefrequency domain information, there seems to be no research confirming that multivariate information transmission (MIT) in these three domains can be quantified. Therefore, in this study, we attempted to combine dynamic mode decomposition (DMD) and parallel communication model (PCM) together to realize it. Methods: We first regarded MS as a large-scale system and then used DMD to decompose it into specific subsystems with their own intrinsic oscillatory frequencies. At the same time, the transition probability matrix (TPM) of information transmission within and between MS at two consecutive moments in each subsystem can also be calculated. Then, communication parameters (CPs) derived from each TPM were calculated in order to quantify the MIT in the space-time-frequency domain. In this study, multidimensional electroencephalogram (EEG) signals were used to illustrate our method. Results: Compared with traditional EEG brain networks, this method shows greater potential in EEG analysis to distinguish between patients and healthy controls. Conclusion:This study demonstrates the feasibility of measuring MIT in the space-time-frequency domain simultaneously. Significance: This study shows that MIT analysis in the spacetime-frequency domain is not only completely different from the MS decomposition in these three domains, but also can reveal many new phenomena behind MS that have not yet been discovered.

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Section: Discussionmentioning

confidence: 99%

Section: A Introduction To Multivariate Information Transmissionmentioning

confidence: 99%

Assessment of Multivariate Information Transmission in Space-Time-Frequency Domain: A Case Study for EEG Signals

Han

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…It evaluates how well the heart contracts in response to either external or internal jolts. Variations in the time that elapses between two consecutive heartbeats may be analyzed with the use of RR intervals [19,20]. The HRV time series is used in order to quantify this variance.…”

Section: Introductionmentioning

confidence: 99%

MBFRDH: Design of a Multimodal Bioinspired Feature Representation Deep Learning Model for Identification of Heart-Diseases

Jaisinghani,

Malik

2023

IJRITCC

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Electrocardiograms (ECGs) are generated by checking different beating patterns of heart, and are widely used for identification of multiple heart-related issues. Existing deep learning models that are proposed for ECG analysis are either highly complex, or showcase lower scalability when applied to clinical scans. To overcome these issues, this text proposes design of a novel multimodal bioinspired feature representation deep learning model for identification of heart-diseases. The proposed model initially collects large-scale ECG datasets, and extracts Fourier, Cosine, iVector, Gabor, and Wavelet components. These components are given to a Grey Wolf Optimization (GWO) based feature selection model, which assists in identification of high-inter-class variance feature sets. This is done via modelling a variance-based fitness function and fusing it with an Iterative Learning Model (ILM) that use feedback-accuracy levels for optimization of selected feature sets. The extracted features are used to incrementally train a custom 1D Binary-Augmented Convolutional Neural Network (1D BACNN) that can be trained for multiclass scenarios. The BACNN Model is trained individually for each of the heart diseases. Each BACNN categorizes input ECG samples between ‘Normal’, and ‘Heart-Disease’ categories. Due to use of this binary-type classification, the proposed model is able to achieve a consistent 99.9% accuracy for multiple heart disease sets, which is found to be higher than most of the existing multiclass techniques. The model was tested for Angina, Arrhythmia, Valve disease, and Congenital heart conditions, and was observed to achieve 3.5% higher precision, 4.9% higher accuracy, with 1.2% increase is computational delay, which makes it highly suitable for real-time clinical use cases.

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“…Ambulatory ECG is performed with an ambulatory electrocardiograph to record the electrocardiographic activity of patients for 24 h or longer under daily living conditions in order to provide an important objective basis for clinical diagnosis, treatment and assessment of therapeutic effects [8] . The clinical application of ambulatory ECG is significant in clinical practice [9] . AF is predicted to be one of the most common cardiovascular diseases in the world within the upcoming 50 y. AF is a common clinical arrhythmia and a secondary symptom caused by multiple heart diseases.…”

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confidence: 99%

Diagnostic Efficiency of Ambulatory Electrocardiography in Atrial Fibrillation with Long RR Intervals

Ke¹,

Qiu²

2023

IJPS

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The current research was conducted to evaluate the diagnostic efficiency of ambulatory electrocardiography in atrial fibrillation with long RR intervals. A total of 80 patients with atrial fibrillation with long RR intervals shown by ambulatory electrocardiography who were hospitalized in our institution between January 2020 and December 2022 were recruited. The baseline patient profiles were recorded and the causative factors related to long RR intervals were analyzed. The patients were stratified into a study group (long RR intervals during sleep) and a control group (long RR intervals during non-sleep periods). The mean heart rate, number of prolonged RR episodes and prolonged RR intervals of patients were analyzed. Premature ventricular beats, ventricular escape, and crossover escape were risk factors for atrial fibrillation with long RR intervals (p<0.05). Significantly more patients showed long RR intervals during sleep than during non-sleep periods (p<0.05). Patients with long RR intervals during sleep had a higher heart rate and, fewer and shorter long RR intervals vs. those with long RR intervals during non-sleep periods (p<0.05). Ambulatory electrocardiography in the diagnosis of atrial fibrillation with long RR intervals provides rapid detection of the relevant causative factors and frequency of disease occurrence, thereby allowing accurate assessment of the patient's condition and the implementation of appropriate therapeutic measures to effectively improve treatment outcomes.

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Analysis of ECG Signals by Dynamic Mode Decomposition

Cited by 16 publications

References 56 publications

Assessment of Multivariate Information Transmission in Space-Time-Frequency Domain: A Case Study for EEG Signals

Assessment of Multivariate Information Transmission in Space-Time-Frequency Domain: A Case Study for EEG Signals

MBFRDH: Design of a Multimodal Bioinspired Feature Representation Deep Learning Model for Identification of Heart-Diseases

Diagnostic Efficiency of Ambulatory Electrocardiography in Atrial Fibrillation with Long RR Intervals

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