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Rai R, Singh V, Ahmad Z, Jain A, Jat D, Mishra SK. Autonomic neuronal modulations in cardiac arrhythmias: Current concepts and emerging therapies. Physiol Behav 2024; 279:114527. [PMID: 38527577 DOI: 10.1016/j.physbeh.2024.114527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 03/21/2024] [Accepted: 03/22/2024] [Indexed: 03/27/2024]
Abstract
The pathophysiology of atrial fibrillation and ventricular tachycardia that result in cardiac arrhythmias is related to the sustained complicated mechanisms of the autonomic nervous system. Atrial fibrillation is when the heart beats irregularly, and ventricular arrhythmias are rapid and inconsistent heart rhythms, which involves many factors including the autonomic nervous system. It's a complex topic that requires careful exploration. Cultivation of speculative knowledge on atrial fibrillation; the irregular rhythm of the heart and ventricular arrhythmias; rapid oscillating waves resulting from mistakenly inconsistent P waves, and the inclusion of an autonomic nervous system is an inconceivable approach toward clinical intricacies. Autonomic modulation, therefore, acquires new expansions and conceptions of appealing therapeutic intelligence to prevent cardiac arrhythmia. Notably, autonomic modulation uses the neural tissue's flexibility to cause remodeling and, hence, provide therapeutic effects. In addition, autonomic modulation techniques included stimulation of the vagus nerve and tragus, renal denervation, cardiac sympathetic denervation, and baroreceptor activation treatment. Strong preclinical evidence and early human studies support the annihilation of cardiac arrhythmias by sympathetic and parasympathetic systems to transmigrate the cardiac myocytes and myocardium as efficient determinants at the cellular and physiological levels. However, the goal of this study is to draw attention to these promising early pre-clinical and clinical arrhythmia treatment options that use autonomic modulation as a therapeutic modality to conquer the troublesome process of irregular heart movements. Additionally, we provide a summary of the numerous techniques for measuring autonomic tone such as heart rate oscillations and its association with cutaneous sympathetic nerve activity appear to be substitute indicators and predictors of the outcome of treatment.
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Affiliation(s)
- Ravina Rai
- Department of Zoology, School of Biological Sciences, Dr. Harisingh Gour Central University, Sagar 470003 MP, India
| | - Virendra Singh
- School of Biochemical Engineering, Indian Institute of Technology (Banaras Hindu University), Varanasi 221005 UP, India
| | - Zaved Ahmad
- Department of Zoology, School of Biological Sciences, Dr. Harisingh Gour Central University, Sagar 470003 MP, India
| | - Abhishek Jain
- Sanjeevani Diabetes and Heart Care Centre, Shri Chaitanya Hospital, Sagar, 470002, MP, India
| | - Deepali Jat
- Department of Zoology, School of Biological Sciences, Dr. Harisingh Gour Central University, Sagar 470003 MP, India.
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2
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Karakayalı M, Artac I, Ilis D, Omar T, Rencuzogullari I, Karabag Y, Altunova M, Arslan A, Guzel E. Evaluation of Outpatients in the Post-COVID-19 Period in Terms of Autonomic Dysfunction and Silent Ischemia. Cureus 2023; 15:e40256. [PMID: 37440812 PMCID: PMC10335598 DOI: 10.7759/cureus.40256] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/11/2023] [Indexed: 07/15/2023] Open
Abstract
INTRODUCTION AND OBJECTIVE In this context, the objective of this study is to evaluate the 24-hour ambulatory electrocardiography (ECG) recordings, autonomous function with heart rate variability (HRV), and silent ischemia (SI) attacks with ST depression burden (SDB) and ST depression time (SDT) of post-COVID-19 patients. Materials and methods: The 24-hour ambulatory ECG recordings obtained >12 weeks after the diagnosis of COVID-19 were compared between 55 consecutive asymptomatic and 73 symptomatic post-COVID-19 patients who applied to the cardiology outpatient clinic with complaints of palpitation and chest pain in comparison with asymptomatic post-COVID-19 patients in Kars Harakani state hospital. SDB, SDT, and HRV parameters were analyzed. Patients who had been on medication that might affect HRV, had comorbidities that might have caused coronary ischemia, and were hospitalized with severe COVID-19 were excluded from the study. RESULTS There was no significant difference between symptomatic and asymptomatic post-COVID-19 patients in autonomic function. On the other hand, SDB and SDT parameters were significantly higher in symptomatic post-COVID-19 patients than in asymptomatic post-COVID-19 patients. Multivariate analysis indicated that creatine kinase-myoglobin binding (CK-MB) (OR:1.382, 95% CI:1.043-1.831; p=0.024) and HRV index (OR: 1.033, 95% CI:1.005-1.061; p=0.019) were found as independent predictors of palpitation and chest pain symptoms in post-COVID-19 patients. CONCLUSION The findings of this study revealed that parasympathetic overtone and increased HRV were significantly higher in symptomatic patients with a history of COVID-19 compared to asymptomatic patients with a history of COVID-19 in the post-COVID-19 period. Additionally, 24-hour ambulatory ECG recordings and ST depression analysis data indicated that patients who experienced chest pain in the post-COVID-19 period experienced silent ischemia (SI) attacks.
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Affiliation(s)
| | - Inanc Artac
- Cardiology, Kafkas University School of Medicine, Kars, TUR
| | - Dogan Ilis
- Cardiology, Kafkas University School of Medicine, Kars, TUR
| | - Timor Omar
- Cardiology, Kafkas University School of Medicine, Kars, TUR
| | | | - Yavuz Karabag
- Cardiology, Kafkas University School of Medicine, Kars, TUR
| | - Mehmet Altunova
- Cardiology, Mehmet Akif Ersoy Thoracic and Cardiovascular Surgery Training Research Hospital, Kars, TUR
| | - Ayça Arslan
- Cardiology, Kafkas University School of Medicine, Kars, TUR
| | - Ezgi Guzel
- Cardiology, Kafkas University School of Medicine, Kars, TUR
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3
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Huang Y, Li H, Yu X. A novel time representation input based on deep learning for ECG classification. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
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Chou L, Gong S, Yang H, Liu J, Chou Y. A fast sample entropy for pulse rate variability analysis. Med Biol Eng Comput 2023:10.1007/s11517-022-02766-y. [PMID: 36826631 DOI: 10.1007/s11517-022-02766-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 12/22/2022] [Indexed: 02/25/2023]
Abstract
Sample entropy is an effective nonlinear index for analyzing pulse rate variability (PRV) signal, but it has problems with a large amount of calculation and time consumption. Therefore, this study proposes a fast sample entropy calculation method to analyze the PRV signal according to the microprocessor process of data updating and the principle of sample entropy. The simulated data and PRV signal are employed as experimental data to verify the accuracy and time consumption of the proposed method. The experimental results on simulated data display that the proposed improved sample entropy can improve the operation rate of the entropy value by a maximum of 47.6 times and an average of 28.6 times and keep the entropy value unchanged. Experimental results on PRV signal display that the proposed improved sample entropy has great potential in the real-time processing of physiological signals, which can increase approximately 35 times.
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Affiliation(s)
- Lijuan Chou
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou, 215500, Jiangsu, China
- School of Computer and Information Technology, Northeast Petroleum University, Daqing, 163318, Heilongjiang, China
| | - Shengrong Gong
- School of Computer and Information Technology, Northeast Petroleum University, Daqing, 163318, Heilongjiang, China
- School of Computer Science and Engineering, Changshu Institute of Technology, Suzhou, 215500, Jiangsu, China
| | - Haiping Yang
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou, 215500, Jiangsu, China
| | - Jicheng Liu
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou, 215500, Jiangsu, China
| | - Yongxin Chou
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou, 215500, Jiangsu, China.
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5
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Chou L, Liu J, Gong S, Chou Y. A life-threatening arrhythmia detection method based on pulse rate variability analysis and decision tree. Front Physiol 2022; 13:1008111. [PMID: 36311226 PMCID: PMC9614148 DOI: 10.3389/fphys.2022.1008111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 09/23/2022] [Indexed: 01/11/2023] Open
Abstract
Extreme bradycardia (EB), extreme tachycardia (ET), ventricular tachycardia (VT), and ventricular flutter (VF) are the four types of life-threatening arrhythmias, which are symptoms of cardiovascular diseases. Therefore, in this study, a method of life-threatening arrhythmia recognition is proposed based on pulse rate variability (PRV). First, noise and interference are wiped out from the arterial blood pressure (ABP), and the PRV signal is extracted. Then, 19 features are extracted from the PRV signal, and 15 features with highly important and significant variation were selected by random forest (RF). Finally, the back-propagation neural network (BPNN), extreme learning machine (ELM), and decision tree (DT) are used to build, train, and test classifiers to detect life-threatening arrhythmias. The experimental data are obtained from the MIMIC/Fantasia and the 2015 Physiology Net/CinC Challenge databases. The experimental results show that the DT classifier has the best average performance with accuracy and kappa coefficient (kappa) of 98.76 ± 0.08% and 97.59 ± 0.15%, which are higher than those of the BPNN (accuracy = 94.85 ± 1.33% and kappa = 89.95 ± 2.62%) and ELM (accuracy = 95.05 ± 0.14% and kappa = 90.28 ± 0.28%) classifiers. The proposed method shows better performance in identifying four life-threatening arrhythmias compared to existing methods and has potential to be used for home monitoring of patients with life-threatening arrhythmias.
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Affiliation(s)
- Lijuan Chou
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou, China,School of Computer and Information Technology, Northeast Petroleum University, Daqing, China
| | - Jicheng Liu
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou, China
| | - Shengrong Gong
- School of Computer and Information Technology, Northeast Petroleum University, Daqing, China,School of Computer Science and Engineering, Changshu Institute of Technology, Suzhou, China
| | - Yongxin Chou
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou, China,*Correspondence: Yongxin Chou,
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Marzog HA, Abd HJ. Machine Learning ECG Classification Using Wavelet Scattering of Feature Extraction. APPLIED COMPUTATIONAL INTELLIGENCE AND SOFT COMPUTING 2022; 2022:1-8. [DOI: 10.1155/2022/9884076] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/02/2023] Open
Abstract
The heart’s electrical activity is registered by an electrocardiogram (ECG), which consists of a wealth of pathological data on heart diseases such as arrhythmia. However, with increasing complexity and nonlinearity, direct observation of ECG signals and analysis is very tough. The highest accuracy of classification performance for machine learning approaches are 99.7 for neural network with wavelet scattering features extraction and 99.92 for SVM also with wavelet scattering features extraction. Through wavelet cascades with a neural network, the wavelet scattering transform can yield a translation invariant and deflection depictions of ECG signals. We suggested a new wavelet scattering transform-based method for automatically classifying three types of ECG heart diseases as follows: arrhythmia (ARR), congestive heart failure (CHF), and normal sinus rhythm (NSR). The bandwidth of the scaling function is used to critically downsample the wavelet scattering transform in time. As a result, each of the scattering paths has 16-time windows. Beat classification performance is classified by utilizing the MIT-BIH arrhythmia dataset. The suggested method is able to conduct high accuracy arrhythmia classification, with a 99.7% and 99.92% accuracy rate of the neural network (NN) and support vector machine (SVM), respectively, and will aid physicians in ECG explanation.
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Affiliation(s)
- Heyam A. Marzog
- Electrical Engineering Department, College of Engineering, University of Babylon, Hilla, Babil, Iraq
- Engineering Technical College/Najaf, Al-Furat Al-Awsat Technical University, Al Najaf 31001, Iraq
| | - Haider. J. Abd
- Electrical Engineering Department, College of Engineering, University of Babylon, Hilla, Babil, Iraq
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Wei Y, Nie L, Gao L, Zhong L, Sun Z, Yang X, Yue J, Zeng Y, Li L, Sun J, Zang H. An Integrated Strategy to Identify and Quantify the Quality Markers of Xinkeshu Tablets Based on Spectrum-Effect Relationship, Network Pharmacology, Plasma Pharmacochemistry, and Pharmacodynamics of Zebrafish. Front Pharmacol 2022; 13:899038. [PMID: 35677447 PMCID: PMC9170229 DOI: 10.3389/fphar.2022.899038] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
Xinkeshu tablets (XKST), a traditional Chinese patent medicine (CPM), have served in the clinical treatment of cardiovascular diseases (CVDs) for decades. However, its pharmacodyamic material basis was still unclear, and the holistic quality control has not been well established due to the lack of systematic research on the quality markers. In this experiment, the heart rate recovery rate of a zebrafish larva was used to evaluate the traditional pharmacological effect of XKST i.e., antiarrhythmic effect. The HPLC fingerprints of 16 batches of XKST samples were obtained, and antiarrhythmic components of XKST were identified by establishing the spectrum-effect relationship between HPLC fingerprints and heart rate recovery rate of zebrafish larva with orthogonal signal correction and partial least squares regression (OSC-PLSR) analysis. The anticardiovascular disease components of XKST were identified by mapping the targets related to CVDs in network pharmacology. The compounds of XKST absorbed and exposed in vivo were identified by ultra-high performance liquid chromatography Q-Exactive high-resolution mass spectrometry (UHPLC-Q-Exactive HRMS). Based on the earlier studies, combined with five principles for identifying quality markers and verified by a zebrafish arrhythmia model, danshensu, salvianolic acid A, salvianolic acid B, daidzein, and puerarin were identified as quality markers of XKST. In total, 16 batches of XKST samples were further quantified with the method established in this study. Our study laid the foundation for the quality control of XKST. The integrated strategy used in the study of XKST could be applied for the identification and quantification of quality markers of other CPMs as well.
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Affiliation(s)
- Yongheng Wei
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Lei Nie
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Lele Gao
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Liang Zhong
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Zhongyu Sun
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Xiangchun Yang
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Jianan Yue
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yingzi Zeng
- Shandong Wohua Pharmaceutical Technology Co., Ltd., Weifang, China
| | - Lian Li
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China.,Key Laboratory of Chemical Biology (Ministry of Education), Shandong University, Jinan, China
| | - Jing Sun
- Qinghai Provincial Key Laboratory of Qinghai-Tibet Plateau Biological Resources, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, China
| | - Hengchang Zang
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China.,National Glycoengineering Research Center, Shandong University, Jinan, China.,Key Laboratory of Chemical Biology (Ministry of Education), Shandong University, Jinan, China.,Qinghai Provincial Key Laboratory of Qinghai-Tibet Plateau Biological Resources, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, China
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Sayed Ismail SNM, Ab. Aziz NA, Ibrahim SZ, Nawawi SW, Alelyani S, Mohana M, Chia Chun L. Evaluation of electrocardiogram: numerical vs. image data for emotion recognition system. F1000Res 2022; 10:1114. [PMID: 35685688 PMCID: PMC9171287 DOI: 10.12688/f1000research.73255.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/23/2022] [Indexed: 11/20/2022] Open
Abstract
Background: The electrocardiogram (ECG) is a physiological signal used to diagnose and monitor cardiovascular disease, usually using 2- D ECG. Numerous studies have proven that ECG can be used to detect human emotions using 1-D ECG; however, ECG is typically captured as 2-D images rather than as 1-D data. There is still no consensus on the effect of the ECG input format on the accuracy of the emotion recognition system (ERS). The ERS using 2-D ECG is still inadequately studied. Therefore, this study compared ERS performance using 1-D and 2-D ECG data to investigate the effect of the ECG input format on the ERS. Methods: This study employed the DREAMER dataset, which contains 23 ECG recordings obtained during audio-visual emotional elicitation. Numerical data was converted to ECG images for the comparison. Numerous approaches were used to obtain ECG features. The Augsburg BioSignal Toolbox (AUBT) and the Toolbox for Emotional feature extraction from Physiological signals (TEAP) extracted features from numerical data. Meanwhile, features were extracted from image data using Oriented FAST and rotated BRIEF (ORB), Scale Invariant Feature Transform (SIFT), KAZE, Accelerated-KAZE (AKAZE), Binary Robust Invariant Scalable Keypoints (BRISK), and Histogram of Oriented Gradients (HOG). Dimension reduction was accomplished using linear discriminant analysis (LDA), and valence and arousal were classified using the Support Vector Machine (SVM). Results: The experimental results show 1-D ECG-based ERS achieved 65.06% of accuracy and 75.63% of F1 score for valence, and 57.83% of accuracy and 44.44% of F1-score for arousal. For 2-D ECG-based ERS, the highest accuracy and F1-score for valence were 62.35% and 49.57%; whereas, the arousal was 59.64% and 59.71%. Conclusions: The results indicate that both inputs work comparably well in classifying emotions, which demonstrates the potential of 1-D and 2-D as input modalities for the ERS.
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Affiliation(s)
| | - Nor Azlina Ab. Aziz
- Faculty of Engineering, Multimedia University, Bukit Beruang, Melaka, 75450, Malaysia
| | - Siti Zainab Ibrahim
- Faculty of Information Science & Technology, Multimedia University, Bukit Beruang,, Melaka, 75450, Malaysia
| | - Sophan Wahyudi Nawawi
- School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai, Johor Bahru, 81310, Malaysia
| | - Salem Alelyani
- Center for Artificial Intelligence, King Khalid University, Abha, 61421, Saudi Arabia
- College of Computer Science, King Khalid University, Abha, 61421, Saudi Arabia
| | - Mohamed Mohana
- Center for Artificial Intelligence, King Khalid University, Abha, 61421, Saudi Arabia
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Stracina T, Ronzhina M, Redina R, Novakova M. Golden Standard or Obsolete Method? Review of ECG Applications in Clinical and Experimental Context. Front Physiol 2022; 13:867033. [PMID: 35547589 PMCID: PMC9082936 DOI: 10.3389/fphys.2022.867033] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 03/15/2022] [Indexed: 12/14/2022] Open
Abstract
Cardiovascular system and its functions under both physiological and pathophysiological conditions have been studied for centuries. One of the most important steps in the cardiovascular research was the possibility to record cardiac electrical activity. Since then, numerous modifications and improvements have been introduced; however, an electrocardiogram still represents a golden standard in this field. This paper overviews possibilities of ECG recordings in research and clinical practice, deals with advantages and disadvantages of various approaches, and summarizes possibilities of advanced data analysis. Special emphasis is given to state-of-the-art deep learning techniques intensely expanded in a wide range of clinical applications and offering promising prospects in experimental branches. Since, according to the World Health Organization, cardiovascular diseases are the main cause of death worldwide, studying electrical activity of the heart is still of high importance for both experimental and clinical cardiology.
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Affiliation(s)
- Tibor Stracina
- Department of Physiology, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Marina Ronzhina
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czech Republic
| | - Richard Redina
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czech Republic.,International Clinical Research Center, St. Anne's University Hospital Brno, Brno, Czech Republic
| | - Marie Novakova
- Department of Physiology, Faculty of Medicine, Masaryk University, Brno, Czech Republic
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Automatic cardiac arrhythmia classification based on hybrid 1-D CNN and Bi-LSTM model. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.02.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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