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Jain H, Marsool MDM, Odat RM, Noori H, Jain J, Shakhatreh Z, Patel N, Goyal A, Gole S, Passey S. Emergence of Artificial Intelligence and Machine Learning Models in Sudden Cardiac Arrest: A Comprehensive Review of Predictive Performance and Clinical Decision Support. Cardiol Rev 2024:00045415-990000000-00260. [PMID: 38836621 DOI: 10.1097/crd.0000000000000708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/06/2024]
Abstract
Sudden cardiac death/sudden cardiac arrest (SCD/SCA) is an increasingly prevalent cause of mortality globally, particularly in individuals with preexisting cardiac conditions. The ambiguous premortem warnings and the restricted interventional window related to SCD account for the complexity of the condition. Current reports suggest SCD to be accountable for 20% of all deaths hence accurately predicting SCD risk is an imminent concern. Traditional approaches for predicting SCA, particularly "track-and-trigger" warning systems have demonstrated considerable inadequacies, including low sensitivity, false alarms, decreased diagnostic liability, reliance on clinician involvement, and human errors. Artificial intelligence (AI) and machine learning (ML) models have demonstrated near-perfect accuracy in predicting SCA risk, allowing clinicians to intervene timely. Given the constraints of current diagnostics, exploring the benefits of AI and ML models in enhancing outcomes for SCA/SCD is imperative. This review article aims to investigate the efficacy of AI and ML models in predicting and managing SCD, particularly targeting accuracy in prediction.
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Affiliation(s)
- Hritvik Jain
- From the Department of Internal Medicine, All India Institte of Medical Sciences (AIIMS), Jodhpur, India
| | | | - Ramez M Odat
- Department of Internal Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan
| | - Hamid Noori
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jyoti Jain
- From the Department of Internal Medicine, All India Institte of Medical Sciences (AIIMS), Jodhpur, India
| | - Zaid Shakhatreh
- Department of Internal Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan
| | - Nandan Patel
- From the Department of Internal Medicine, All India Institte of Medical Sciences (AIIMS), Jodhpur, India
| | - Aman Goyal
- Department of Internal Medicine, Seth GS Medical College and KEM Hospital, Mumbai, India
| | - Shrey Gole
- Department of Immunology and Rheumatology, Stanford University, CA; and
| | - Siddhant Passey
- Department of Internal Medicine, University of Connecticut Health Center, CT
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Ebrahimzadeh E, Dehghani A, Asgarinejad M, Soltanian-Zadeh H. Non-linear processing and reinforcement learning to predict rTMS treatment response in depression. Psychiatry Res Neuroimaging 2024; 337:111764. [PMID: 38043370 DOI: 10.1016/j.pscychresns.2023.111764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 11/05/2023] [Accepted: 11/09/2023] [Indexed: 12/05/2023]
Abstract
BACKGROUND Forecasting the efficacy of repetitive transcranial magnetic stimulation (rTMS) therapy can lead to substantial time and cost savings by preventing futile treatments. To achieve this objective, we've formulated a machine learning approach aimed at categorizing patients with major depressive disorder (MDD) into two groups: individuals who respond (R) positively to rTMS treatment and those who do not respond (NR). METHODS Preceding the commencement of treatment, we obtained resting-state EEG data from 106 patients diagnosed with MDD, employing 32 electrodes for data collection. These patients then underwent a 7-week course of rTMS therapy, and 54 of them exhibited positive responses to the treatment. Employing Independent Component Analysis (ICA) on the EEG data, we successfully pinpointed relevant brain sources that could potentially serve as markers of neural activity within the dorsolateral prefrontal cortex (DLPFC). These identified sources were further scrutinized to estimate the sources of activity within the sensor domain. Then, we integrated supplementary physiological data and implemented specific criteria to yield more realistic estimations when compared to conventional EEG analysis. In the end, we selected components corresponding to the DLPFC region within the sensor domain. Features were derived from the time-series data of these relevant independent components. To identify the most significant features, we used Reinforcement Learning (RL). In categorizing patients into two groups - R and NR to rTMS treatment - we utilized three distinct classification algorithms including K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Multilayer Perceptron (MLP). We assessed the performance of these classifiers through a ten-fold cross-validation method. Additionally, we conducted a statistical test to evaluate the discriminative capacity of these features between responders and non-responders, opening the door for further exploration in this field. RESULTS We identified EEG features that can anticipate the response to rTMS treatment. The most robust discriminators included EEG beta power, the sum of bispectrum diagonal elements in the delta and beta frequency bands. When these features were combined into a single vector, the classification of responders and non-responders achieved impressive performance, with an accuracy of 95.28 %, specificity at 94.23 %, sensitivity reaching 96.29 %, and precision standing at 94.54 %, all achieved using SVM. CONCLUSIONS The results of this study suggest that the proposed approach, utilizing power, non-linear, and bispectral features extracted from relevant independent component time-series, has the capability to forecast the treatment outcome of rTMS for MDD patients based solely on a single pre-treatment EEG recording session. The achieved findings demonstrate the superior performance of our method compared to previous techniques.
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Affiliation(s)
- Elias Ebrahimzadeh
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
| | - Amin Dehghani
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | | | - Hamid Soltanian-Zadeh
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
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Chen S, Jiang L, Xu F, Pang J, Pan C, Chen Y, Wang J, Li K. Electrical-mechanical dynamical coupling between electrocardiographic and photoplethysmographic signals during cardiopulmonary resuscitation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107809. [PMID: 37757567 DOI: 10.1016/j.cmpb.2023.107809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 09/04/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023]
Abstract
BACKGROUND AND OBJECTIVE Cardiac arrest (CA) remains a significant cause of death and disability. High-quality cardiopulmonary resuscitation (CPR) can improve the survival rate of CA. A challenging issue is to find physiological indicators for screening and evaluating the cardiovascular function associated with CPR. This study aimed to investigate the electrical-mechanical dynamic coupling between electrocardiographic (ECG) and photoplethysmographic (PPG) signals for indicating cardiovascular function in the progress of CPR. METHOD The ECG and PPG signals were simultaneously collected from a porcine CA model (n = 10) induced by ventricular fibrillation, and were further divided into four periods: Baseline, CA, CPR, and recovery of spontaneous circulation (ROSC). Recurrence quantitative analysis (RQA) was applied to examine the nonlinear dynamics of the ECG and PPG signals individually, and cross recurrence quantitative analysis (CRQA) was used to examine the ECG-PPG dynamical coupling. RESULTS The CA influenced the dynamic patterns of electrical and mechanical activities and the electrical-mechanical coupling, which can be observed from the reduced entropy (ENTR) (p < 0.01), reduced determinism (DET) (p < 0.01) and reduced trapping time (TT) (p < 0.01) at CA compared to Baseline. The recurrence rate (RR), ENTR, DET, and TT at CPR were significantly lower than the parameters at ROSC but higher than those at CA. CONCLUSIONS The electrical-mechanical dynamical coupling was sensitive to CPR and able to reflect the changes in cardiac function in the process of CPR.
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Affiliation(s)
- Shuxin Chen
- Institute of Intelligent Medical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China
| | - Lijun Jiang
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China; Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Institute of Emergency and Critical Care Medicine of Shandong University, Chest Pain Center, Qilu Hospital of Shandong University, Jinan, China; Key Laboratory of Emergency and Critical Care Medicine of Shandong Province, Key Laboratory of Cardiopulmonary Cerebral Resuscitation Research of Shandong Province, Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Shandong Key Laboratory: Magnetic Field-free Medicine & Functional Imaging, Qilu Hospital of Shandong University, Jinan, China; NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, Qilu Hospital of Shandong University, Jinan, China
| | - Feng Xu
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China; Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Institute of Emergency and Critical Care Medicine of Shandong University, Chest Pain Center, Qilu Hospital of Shandong University, Jinan, China; Key Laboratory of Emergency and Critical Care Medicine of Shandong Province, Key Laboratory of Cardiopulmonary Cerebral Resuscitation Research of Shandong Province, Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Shandong Key Laboratory: Magnetic Field-free Medicine & Functional Imaging, Qilu Hospital of Shandong University, Jinan, China; NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, Qilu Hospital of Shandong University, Jinan, China
| | - Jiaojiao Pang
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China; Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Institute of Emergency and Critical Care Medicine of Shandong University, Chest Pain Center, Qilu Hospital of Shandong University, Jinan, China; Key Laboratory of Emergency and Critical Care Medicine of Shandong Province, Key Laboratory of Cardiopulmonary Cerebral Resuscitation Research of Shandong Province, Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Shandong Key Laboratory: Magnetic Field-free Medicine & Functional Imaging, Qilu Hospital of Shandong University, Jinan, China; NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, Qilu Hospital of Shandong University, Jinan, China
| | - Chang Pan
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China; Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Institute of Emergency and Critical Care Medicine of Shandong University, Chest Pain Center, Qilu Hospital of Shandong University, Jinan, China; Key Laboratory of Emergency and Critical Care Medicine of Shandong Province, Key Laboratory of Cardiopulmonary Cerebral Resuscitation Research of Shandong Province, Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Shandong Key Laboratory: Magnetic Field-free Medicine & Functional Imaging, Qilu Hospital of Shandong University, Jinan, China; NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, Qilu Hospital of Shandong University, Jinan, China
| | - Yuguo Chen
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China; Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Institute of Emergency and Critical Care Medicine of Shandong University, Chest Pain Center, Qilu Hospital of Shandong University, Jinan, China; Key Laboratory of Emergency and Critical Care Medicine of Shandong Province, Key Laboratory of Cardiopulmonary Cerebral Resuscitation Research of Shandong Province, Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Shandong Key Laboratory: Magnetic Field-free Medicine & Functional Imaging, Qilu Hospital of Shandong University, Jinan, China; NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, Qilu Hospital of Shandong University, Jinan, China.
| | - Jiali Wang
- Department of Emergency Medicine, Qilu Hospital of Shandong University, Jinan, China; Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, Institute of Emergency and Critical Care Medicine of Shandong University, Chest Pain Center, Qilu Hospital of Shandong University, Jinan, China; Key Laboratory of Emergency and Critical Care Medicine of Shandong Province, Key Laboratory of Cardiopulmonary Cerebral Resuscitation Research of Shandong Province, Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine, Shandong Key Laboratory: Magnetic Field-free Medicine & Functional Imaging, Qilu Hospital of Shandong University, Jinan, China; NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, Qilu Hospital of Shandong University, Jinan, China.
| | - Ke Li
- Institute of Intelligent Medical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China.
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Panjaitan F, Nurmaini S, Partan RU. Accurate Prediction of Sudden Cardiac Death Based on Heart Rate Variability Analysis Using Convolutional Neural Network. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1394. [PMID: 37629684 PMCID: PMC10456609 DOI: 10.3390/medicina59081394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 07/17/2023] [Accepted: 07/27/2023] [Indexed: 08/27/2023]
Abstract
Sudden cardiac death (SCD) is a significant global health issue that affects individuals with and without a history of heart disease. Early identification of SCD risk factors is crucial in reducing mortality rates. This study aims to utilize electrocardiogram (ECG) tools, specifically focusing on heart rate variability (HRV), to detect early SCD risk factors. In this study, we expand the comparison group dataset to include five groups: Normal Sinus Rhythm (NSR), coronary artery disease (CAD), Congestive Heart Failure (CHF), Ventricular Tachycardia (VT), and SCD. ECG signals were recorded for 30 min and segmented into 5 min intervals, following the recommended HRV feature analysis guidelines. We introduce an innovative approach to HRV signal analysis by utilizing Convolutional Neural Networks (CNN). The CNN model was optimized by tuning hyperparameters such as the number of layers, learning rate, and batch size, significantly impacting the prediction accuracy. The findings demonstrate that the HRV approach, in conjunction with linear features and the DL method, achieved a higher accuracy rate, averaging 99.30%, reaching 97% sensitivity, 99.60% specificity, and 97.87% precision. Future research should focus on further exploring and refining DL methods in the context of HRV analysis to improve SCD prediction.
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Affiliation(s)
- Febriyanti Panjaitan
- Doctoral Program of Engineering Science, Faculty of Engineering, Universitas Sriwijaya, Palembang 30128, Indonesia;
- Faculty of Science and Technology, Universitas Bina Darma, Palembang 30264, Indonesia
| | - Siti Nurmaini
- Intelligent System Research Group, Universitas Sriwijaya, Palembang 30128, Indonesia
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Yan SP, Song X, Wei L, Gong YS, Hu HY, Li YQ. Performance of heart rate adjusted heart rate variability for risk stratification of sudden cardiac death. BMC Cardiovasc Disord 2023; 23:144. [PMID: 36949420 PMCID: PMC10032001 DOI: 10.1186/s12872-023-03184-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 03/14/2023] [Indexed: 03/24/2023] Open
Abstract
PURPOSE As a non-invasive tool for the assessment of cardiovascular autonomic function, the predictive value of heart rate variability (HRV) for sudden cardiac death (SCD) risk stratification remains unclear. In this study, we investigated the performance of the individualized heart rate (HR) adjusted HRV (HRVI) for SCD risk stratification in subjects with diverse risks. METHODS A total of 11 commonly used HRV metrics were analyzed in 192 subjects, including 88 healthy controls (low risk group), 82 hypertrophic cardiomyopathy (HCM) patients (medium risk group), and 22 SCD victims (high risk group). The relationship between HRV metrics and HR was examined with long-term and short-term analysis. The performance HRVI was evaluated by area under the receiver operating characteristic curve (AUC) and covariance of variation (CV). RESULTS Most of the HRV metrics were exponentially decayed with the increase of HR, while the exponential power coefficients were significantly different among groups. The HRVI metrics discriminated low, medium and high risk subjects with a median AUC of 0.72[0.11], which was considerably higher than that of the traditional long-term (0.63[0.04]) and short-term (0.58[0.05]) HRV without adjustment. The average CV of the HRVI metrics was also significantly lower than traditional short-term HRV metrics (0.09 ± 0.02 vs. 0.24 ± 0.13, p < 0.01). CONCLUSIONS Subjects with diverse risks of SCD had similar exponential decay relationship between HRV metrics and HR, but with different decaying rates. HRVI provides reliable and robust estimation for risk stratification of SCD.
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Affiliation(s)
- Su-Peng Yan
- Department of Biomedical Engineering and Imaging Medicine, Army Medical University, 30 Gaotanyan Main Street, Chongqing, 400038, China
| | - Xin Song
- Department of Biomedical Engineering and Imaging Medicine, Army Medical University, 30 Gaotanyan Main Street, Chongqing, 400038, China
| | - Liang Wei
- Department of Biomedical Engineering and Imaging Medicine, Army Medical University, 30 Gaotanyan Main Street, Chongqing, 400038, China
| | - Yu-Shun Gong
- Department of Biomedical Engineering and Imaging Medicine, Army Medical University, 30 Gaotanyan Main Street, Chongqing, 400038, China
| | - Hou-Yuan Hu
- Department of Cardiology, Southwest Hospital, Army Medical University, Chongqing, 400038, China
| | - Yong-Qin Li
- Department of Biomedical Engineering and Imaging Medicine, Army Medical University, 30 Gaotanyan Main Street, Chongqing, 400038, China.
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Ebrahimzadeh E, Fayaz F, Rajabion L, Seraji M, Aflaki F, Hammoud A, Taghizadeh Z, Asgarinejad M, Soltanian-Zadeh H. Machine learning approaches and non-linear processing of extracted components in frontal region to predict rTMS treatment response in major depressive disorder. Front Syst Neurosci 2023; 17:919977. [PMID: 36968455 PMCID: PMC10034109 DOI: 10.3389/fnsys.2023.919977] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 02/13/2023] [Indexed: 03/12/2023] Open
Abstract
Predicting the therapeutic result of repetitive transcranial magnetic stimulation (rTMS) treatment could save time and costs as ineffective treatment can be avoided. To this end, we presented a machine-learning-based strategy for classifying patients with major depression disorder (MDD) into responders (R) and nonresponders (NR) to rTMS treatment. Resting state EEG data were recorded using 32 electrodes from 88 MDD patients before treatment. Then, patients underwent 7 weeks of rTMS, and 46 of them responded to treatment. By applying Independent Component Analysis (ICA) on EEG, we identified the relevant brain sources as possible indicators of neural activity in the dorsolateral prefrontal cortex (DLPFC). This was served through estimating the generators of activity in the sensor domain. Subsequently, we added physiological information and placed certain terms and conditions to offer a far more realistic estimation than the classic EEG. Ultimately, those components mapped in accordance with the region of the DLPFC in the sensor domain were chosen. Features extracted from the relevant ICs time series included permutation entropy (PE), fractal dimension (FD), Lempel-Ziv Complexity (LZC), power spectral density, correlation dimension (CD), features based on bispectrum, frontal and prefrontal cordance, and a combination of them. The most relevant features were selected by a Genetic Algorithm (GA). For classifying two groups of R and NR, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Multilayer Perceptron (MLP) were applied to predict rTMS treatment response. To evaluate the performance of classifiers, a 10-fold cross-validation method was employed. A statistical test was used to assess the capability of features in differentiating R and NR for further research. EEG characteristics that can predict rTMS treatment response were discovered. The strongest discriminative indicators were EEG beta power, the sum of bispectrum diagonal elements in delta and beta bands, and CD. The Combined feature vector classified R and NR with a high performance of 94.31% accuracy, 92.85% specificity, 95.65% sensitivity, and 92.85% precision using SVM. This result indicates that our proposed method with power and nonlinear and bispectral features from relevant ICs time-series can predict the treatment outcome of rTMS for MDD patients only by one session pretreatment EEG recording. The obtained results show that the proposed method outperforms previous methods.
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Affiliation(s)
- Elias Ebrahimzadeh
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
- *Correspondence: Elias Ebrahimzadeh
| | - Farahnaz Fayaz
- Biomedical Engineering Department, School of Electrical Engineering, Payame Noor University of North Tehran, Tehran, Iran
| | - Lila Rajabion
- School of Graduate Studies, SUNY Empire State College, Manhattan, NY, United States
| | - Masoud Seraji
- Department of Psychology, University of Texas at Austin, Austin, TX, United States
| | - Fatemeh Aflaki
- Department of Biomedical Engineering, Islamic Azad University Central Tehran Branch, Tehran, Iran
| | - Ahmad Hammoud
- Department of Medical and Technical Information Technology, Bauman Moscow State Technical University, Moscow, Russia
| | - Zahra Taghizadeh
- Department of Bioengineering, George Mason University, Fairfax, VA, United States
| | - Mostafa Asgarinejad
- Department of Cognitive Neuroscience, Institute for Cognitive Sciences Studies, Tehran, Iran
| | - Hamid Soltanian-Zadeh
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
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Kolk MZH, Deb B, Ruipérez-Campillo S, Bhatia NK, Clopton P, Wilde AAM, Narayan SM, Knops RE, Tjong FVY. Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies. EBioMedicine 2023; 89:104462. [PMID: 36773349 PMCID: PMC9945642 DOI: 10.1016/j.ebiom.2023.104462] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 01/19/2023] [Accepted: 01/19/2023] [Indexed: 02/11/2023] Open
Abstract
BACKGROUND Ventricular arrhythmia (VA) precipitating sudden cardiac arrest (SCD) is among the most frequent causes of death and pose a high burden on public health systems worldwide. The increasing availability of electrophysiological signals collected through conventional methods (e.g. electrocardiography (ECG)) and digital health technologies (e.g. wearable devices) in combination with novel predictive analytics using machine learning (ML) and deep learning (DL) hold potential for personalised predictions of arrhythmic events. METHODS This systematic review and exploratory meta-analysis assesses the state-of-the-art of ML/DL models of electrophysiological signals for personalised prediction of malignant VA or SCD, and studies potential causes of bias (PROSPERO, reference: CRD42021283464). Five electronic databases were searched to identify eligible studies. Pooled estimates of the diagnostic odds ratio (DOR) and summary area under the curve (AUROC) were calculated. Meta-analyses were performed separately for studies using publicly available, ad-hoc datasets, versus targeted clinical data acquisition. Studies were scored on risk of bias by the PROBAST tool. FINDINGS 2194 studies were identified of which 46 were included in the systematic review and 32 in the meta-analysis. Pooling of individual models demonstrated a summary AUROC of 0.856 (95% CI 0.755-0.909) for short-term (time-to-event up to 72 h) prediction and AUROC of 0.876 (95% CI 0.642-0.980) for long-term prediction (time-to-event up to years). While models developed on ad-hoc sets had higher pooled performance (AUROC 0.919, 95% CI 0.867-0.952), they had a high risk of bias related to the re-use and overlap of small ad-hoc datasets, choices of ML tool and a lack of external model validation. INTERPRETATION ML and DL models appear to accurately predict malignant VA and SCD. However, wide heterogeneity between studies, in part due to small ad-hoc datasets and choice of ML model, may reduce the ability to generalise and should be addressed in future studies. FUNDING This publication is part of the project DEEP RISK ICD (with project number 452019308) of the research programme Rubicon which is (partly) financed by the Dutch Research Council (NWO). This research is partly funded by the Amsterdam Cardiovascular Sciences (personal grant F.V.Y.T).
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Affiliation(s)
- Maarten Z H Kolk
- Amsterdam UMC Location University of Amsterdam, Heart Center, Department of Clinical and Experimental Cardiology, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Cardiovascular Sciences, Heart failure & arrhythmias, Amsterdam, The Netherlands
| | - Brototo Deb
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | | | - Neil K Bhatia
- Department of Cardiology, Emory University, Atlanta, GA, USA
| | - Paul Clopton
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Arthur A M Wilde
- Amsterdam UMC Location University of Amsterdam, Heart Center, Department of Clinical and Experimental Cardiology, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Cardiovascular Sciences, Heart failure & arrhythmias, Amsterdam, The Netherlands
| | - Sanjiv M Narayan
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Reinoud E Knops
- Amsterdam UMC Location University of Amsterdam, Heart Center, Department of Clinical and Experimental Cardiology, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Cardiovascular Sciences, Heart failure & arrhythmias, Amsterdam, The Netherlands
| | - Fleur V Y Tjong
- Amsterdam UMC Location University of Amsterdam, Heart Center, Department of Clinical and Experimental Cardiology, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Cardiovascular Sciences, Heart failure & arrhythmias, Amsterdam, The Netherlands.
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Automated Detection of Sudden Cardiac Death by Discrete Wavelet Transform of Electrocardiogram Signal. Symmetry (Basel) 2022. [DOI: 10.3390/sym14030571] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Sudden cardiac death (SCD) results in millions of deaths annually; as it is a fatal heart abnormality, early prediction of SCD could save peoples’ lives to the greatest extent. Symmetry and asymmetry play an important role in many fields. Electrocardiograms (ECG) as a noninvasive process for acquiring the electrical activity of the heart, has both asymmetric and non-stationary characteristics; it is frequently employed to diagnose and evaluate the heart’s condition. In this work, we have detected SCD 14 min (separately for each one-minute interval) prior to its occurrence by analyzing ECG signals using discrete wavelet transform (DWT) and locality preserving projection (LPP). In the experiment, we have performed DWT on ECG signals to obtain coefficients, then LPP as a reduction methodology was used to cut down these obtained coefficients. Then, the acquired LPP features were ranked using various methods, including the T-test, Bhattacharyya, Wilcoxon, and entropy. At last, the highly ranked LPP features were subjected to decision tree, k-nearest neighbor (KNN), and support vector machine classifiers for distinguishing normal from SCD ECG signals. Our proposed technique has achieved a highest accuracy of 97.6% for the detection of SCD 14 min prior using the KNN classifier, compared to the existing works. Our proposed method is capable of predicting the people at risk of developing SCD 14 min before its onset, and, hence, clinicians would have enough time to provide treatment in intensive care units (ICU) for a subject at risk of SCD. Thus, this proposed technique as a useful tool can increase the survival rate of many cardiac patients.
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Lou J, Chen H, Huang S, Chen P, Yu Y, Chen F. Update on risk factors and biomarkers of sudden unexplained cardiac death. J Forensic Leg Med 2022; 87:102332. [DOI: 10.1016/j.jflm.2022.102332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 02/21/2022] [Accepted: 03/02/2022] [Indexed: 02/01/2023]
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Alamgir A, Mousa O, Shah Z. Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review. JMIR Med Inform 2021; 9:e30798. [PMID: 34927595 PMCID: PMC8726033 DOI: 10.2196/30798] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 10/07/2021] [Accepted: 10/10/2021] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Cardiac arrest is a life-threatening cessation of activity in the heart. Early prediction of cardiac arrest is important, as it allows for the necessary measures to be taken to prevent or intervene during the onset. Artificial intelligence (AI) technologies and big data have been increasingly used to enhance the ability to predict and prepare for the patients at risk. OBJECTIVE This study aims to explore the use of AI technology in predicting cardiac arrest as reported in the literature. METHODS A scoping review was conducted in line with the guidelines of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping reviews. Scopus, ScienceDirect, Embase, the Institute of Electrical and Electronics Engineers, and Google Scholar were searched to identify relevant studies. Backward reference list checks of the included studies were also conducted. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively. RESULTS Out of 697 citations retrieved, 41 studies were included in the review, and 6 were added after backward citation checking. The included studies reported the use of AI in the prediction of cardiac arrest. Of the 47 studies, we were able to classify the approaches taken by the studies into 3 different categories: 26 (55%) studies predicted cardiac arrest by analyzing specific parameters or variables of the patients, whereas 16 (34%) studies developed an AI-based warning system. The remaining 11% (5/47) of studies focused on distinguishing patients at high risk of cardiac arrest from patients who were not at risk. Two studies focused on the pediatric population, and the rest focused on adults (45/47, 96%). Most of the studies used data sets with a size of <10,000 samples (32/47, 68%). Machine learning models were the most prominent branch of AI used in the prediction of cardiac arrest in the studies (38/47, 81%), and the most used algorithm was the neural network (23/47, 49%). K-fold cross-validation was the most used algorithm evaluation tool reported in the studies (24/47, 51%). CONCLUSIONS AI is extensively used to predict cardiac arrest in different patient settings. Technology is expected to play an integral role in improving cardiac medicine. There is a need for more reviews to learn the obstacles to the implementation of AI technologies in clinical settings. Moreover, research focusing on how to best provide clinicians with support to understand, adapt, and implement this technology in their practice is also necessary.
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Affiliation(s)
- Asma Alamgir
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Osama Mousa
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Zubair Shah
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
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Ebrahimzadeh E, Asgarinejad M, Saliminia S, Ashoori S, Seraji M. PREDICTING CLINICAL RESPONSE TO TRANSCRANIAL MAGNETIC STIMULATION IN MAJOR DEPRESSION USING TIME-FREQUENCY EEG SIGNAL PROCESSING. BIOMEDICAL ENGINEERING: APPLICATIONS, BASIS AND COMMUNICATIONS 2021. [DOI: 10.4015/s1016237221500484] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is defined as a noninvasive technique of brain stimulation conducted for both diagnostic and therapeutic purposes. rTMS can effectively excite the brain neurons and increase brain plasticity, which becomes particularly useful in psychiatric and neurological fields. Biomarkers that predict clinical outcomes in depression are essential for increasing the precision of treatments and clinical outcomes. The electroencephalogram (EEG) is a noninvasive neurophysiological test that is promising as a biomarker sensitive to treatment effects. The aim of our study was to investigate a novel nonlinear index of the resting state EEG activity as a predictor of clinical outcome and compare its predictive capacity to traditional frequency-based indices. EEG was recorded from 50 patients with treatment resistant depression (TRD) and 24 healthy comparison (HC) subjects. TRD patients were treated with excitatory rTMS to the dorsolateral prefrontal cortex (DLPFC) for 4–6 weeks. EEG signals were first decomposed using the ICA algorithm and the extracted components were then processed by time-frequency analysis. We then go on to compare the participants’ depression severity before, after, and 2 months after finishing the last treatment session using the proposed rTMS therapy. Absolute powers (APs), band powers (BPs), and theta and beta band entropies (BAs), which were extracted from the EEG, are used as features for the classification of changes in patients and normal cases after applying rTMS. Accordingly, we can go beyond the Beck score and clinically classify the EEG signal into two classes: depression and normal. The results demonstrated 78.37%, 74.32%, and 82.43% accuracy for artificial neural network (ANN), [Formula: see text]-nearest neighbor (KNN), and support vector machine (SVM) classifiers, respectively, indicating the superiority of the proposed method to those mentioned in similar studies. Also, the electrophysiological changes are shown to be evident in patients with major depression. Our data show that the time-frequency index yields superior outcome prediction performance compared to the traditional frequency band indices. Our findings warrant further investigation of EEG-based biomarkers in depression.
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Affiliation(s)
- Elias Ebrahimzadeh
- CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | | | - Sarah Saliminia
- Biomedical Engineering Department, School of Electrical Engineering, Payame Noor University of North Tehran, Tehran, Iran
| | - Sarvenaz Ashoori
- Biomedical Engineering Department, School of Electrical Engineering, Payame Noor University of North Tehran, Tehran, Iran
| | - Masoud Seraji
- Center for Molecular and Behavioral Neuroscience, Rutgers, The State University of New Jersey, Newark, NJ, USA
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Personalized Body Constitution Inquiry Based on Machine Learning. JOURNAL OF HEALTHCARE ENGINEERING 2020; 2020:8834465. [PMID: 33274038 PMCID: PMC7676967 DOI: 10.1155/2020/8834465] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Accepted: 10/30/2020] [Indexed: 02/01/2023]
Abstract
Background Body constitution (BC) is the abstract concept indicating the state of a person's health in Traditional Chinese Medicine (TCM). The doctor identifies the body constitution of the patient through inspection and inquiry. Previous research simulates doctors to identify BC types according to a patient's objective physical indicators. However, the lack of subjective feeling information can reduce the accuracy of the machine to imitate the doctor's diagnosis. The Constitution in Chinese Medicine Questionnaire (CCMQ) is used to collect subjective information but suffers from low acquisition efficiency. Methods This paper presents a personalized body constitution inquiry method based on a machine learning technique. It employs a random generator, a feature extractor, and a classifier to simulate the doctor inquiry and generate a personalized questionnaire. Specifically, the feature extractor evaluates and sorts the question of the constitution in the CCMQ based on the recognition results of the tongue coating image of patients. The sorted questions and relevant BC label are inputted into the classifier; the best questions are screened out for patients. Results The experimental results show that our method can select personalized questions from the CCMQ for the patients, significantly reducing the time and the number of questions to answer. It also improves the accuracy of recognizing BC. Compared with the CCMQ, patients had 68.3% fewer questions to answer and the time occupied by answering is reduced by 80.3%. Conclusions The proposed method can simulate the doctor's inquiry and pick out personalized questions for patients. It can act as auxiliary diagnosis tools to collect subjective patient feelings and help make further judgments on the patient's BC types.
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Lai D, Zhang Y, Zhang X. Single Lead ECG-based Ventricular Repolarization Classification for Early Identification of Unexpected Ventricular Fibrillation .. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:5567-5570. [PMID: 33019239 DOI: 10.1109/embc44109.2020.9176355] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Malignant ventricular arrhythmia (especially ventricular fibrillation (VF)) is the main reason which causes sudden cardiac death (SCD). This paper presents an automatic SCD-patient classifier we developed to identify patients with unexpected VF using 60-minutes continuous single-lead electrocardiograms (ECG) signals before that. Patients are classified as having SCD if the majority of their recorded ventricular repolarization (VR) is recognized as characteristic of unexpected VF. Thus, the classifier's underlying task is to recognize individual VR delineated from single-lead ECG signals as SCD VR, where VR from non-SCD patients are used as controls. With the reported clinical practices of SCD, we extracted five morphological and temporal features (both commonly used and newly developed ones) from ECG signals for VR classification. To evaluate classification performance, we trained and tested k nearest neighbor classifier, a decision tree classifier, and a Naïve Bayes classifier using five-fold cross validation on 36 one-hour ECG signals (18 from patients at risk of SCD and 18 from control people). We compared the performance of these three classifiers, and the patient-classification sensitivity is approximately 98.02-99.51%. Moreover, the k nearest neighbor with a higher accuracy (98.89%) and specificity (98.27%) performed better than the other two. Importantly, the results show obvious superiorities of performance over that in the same duration and of usefulness over several minutes given by related works.Clinical Relevance- This could be integrated into a real-time, long-term out-of-hospital SCD predictor to improve the warning veracity and bring forward the warning time, especially for patients with implantable cardiac defibrillators or pacemakers, etc..
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Localizing confined epileptic foci in patients with an unclear focus or presumed multifocality using a component-based EEG-fMRI method. Cogn Neurodyn 2020; 15:207-222. [PMID: 33854640 DOI: 10.1007/s11571-020-09614-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 06/07/2020] [Accepted: 06/23/2020] [Indexed: 02/07/2023] Open
Abstract
Precise localization of epileptic foci is an unavoidable prerequisite in epilepsy surgery. Simultaneous EEG-fMRI recording has recently created new horizons to locate foci in patients with epilepsy and, in comparison with single-modality methods, has yielded more promising results although it is still subject to limitations such as lack of access to information between interictal events. This study assesses its potential added value in the presurgical evaluation of patients with complex source localization. Adult candidates considered ineligible for surgery on account of an unclear focus and/or presumed multifocality on the basis of EEG underwent EEG-fMRI. Adopting a component-based approach, this study attempts to identify the neural behavior of the epileptic generators and detect the components-of-interest which will later be used as input in the GLM model, substituting the classical linear regressor. Twenty-eight sets interictal epileptiform discharges (IED) from nine patients were analyzed. In eight patients, at least one BOLD response was significant, positive and topographically related to the IEDs. These patients were rejected for surgery because of an unclear focus in four, presumed multifocality in three, and a combination of the two conditions in two. Component-based EEG-fMRI improved localization in five out of six patients with unclear foci. In patients with presumed multifocality, component-based EEG-fMRI advocated one of the foci in five patients and confirmed multifocality in one of the patients. In seven patients, component-based EEG-fMRI opened new prospects for surgery and in two of these patients, intracranial EEG supported the EEG-fMRI results. In these complex cases, component-based EEG-fMRI either improved source localization or corroborated a negative decision regarding surgical candidacy. As supported by the statistical findings, the developed EEG-fMRI method leads to a more realistic estimation of localization compared to the conventional EEG-fMRI approach, making it a tool of high value in pre-surgical evaluation of patients with refractory epilepsy. To ensure proper implementation, we have included guidelines for the application of component-based EEG-fMRI in clinical practice.
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Detection of sudden cardiac death by a comparative study of heart rate variability in normal and abnormal heart conditions. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.06.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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Hakimi N, Jodeiri A, Mirbagheri M, Setarehdan SK. Proposing a convolutional neural network for stress assessment by means of derived heart rate from functional near infrared spectroscopy. Comput Biol Med 2020; 121:103810. [PMID: 32568682 DOI: 10.1016/j.compbiomed.2020.103810] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 05/03/2020] [Accepted: 05/03/2020] [Indexed: 02/01/2023]
Abstract
BACKGROUND Stress is known as one of the major factors threatening human health. A large number of studies have been performed in order to either assess or relieve stress by analyzing the brain and heart-related signals. METHOD In this study, a method based on the Convolutional Neural Network (CNN) approach is proposed to assess stress induced by the Montreal Imaging Stress Task. The proposed model is trained on the heart rate signal derived from functional Near-Infrared Spectroscopy (fNIRS), which is referred to as HRF. In this regard, fNIRS signals of 20 healthy volunteers were recorded using a configuration of 23 channels located on the prefrontal cortex. The proposed deep learning system consists of two main parts where in the first part, the one-dimensional convolutional neural network is employed to build informative activation maps, and then in the second part, a stack of deep fully connected layers is used to predict the stress existence probability. Thereafter, the employed CNN method is compared with the Dense Neural Network, Support Vector Machine, and Random Forest regarding various classification metrics. RESULTS Results clearly showed the superiority of CNN over all other methods. Additionally, the trained HRF model significantly outperforms the model trained on the filtered fNIRS signals, where the HRF model could achieve 98.69 ± 0.45% accuracy, which is 10.09% greater than the accuracy obtained by the fNIRS model. CONCLUSIONS Employment of the proposed deep learning system trained on the HRF measurements leads to higher stress classification accuracy than the accuracy reported in the existing studies where the same experimental procedure has been done. Besides, the proposed method suggests better stability with lower variation in prediction. Furthermore, its low computational cost opens up the possibility to be applied in real-time monitoring of stress assessment.
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Affiliation(s)
- Naser Hakimi
- Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran; Department of Neonatology, Wilhelmina Children's Hospital, University Medical Center Utrecht, the Netherlands; Artinis Medical Systems B.V., Elst, the Netherlands.
| | - Ata Jodeiri
- Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Mahya Mirbagheri
- Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - S Kamaledin Setarehdan
- Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
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Shi M, He H, Geng W, Wu R, Zhan C, Jin Y, Zhu F, Ren S, Shen B. Early Detection of Sudden Cardiac Death by Using Ensemble Empirical Mode Decomposition-Based Entropy and Classical Linear Features From Heart Rate Variability Signals. Front Physiol 2020; 11:118. [PMID: 32158399 PMCID: PMC7052183 DOI: 10.3389/fphys.2020.00118] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2019] [Accepted: 02/03/2020] [Indexed: 02/05/2023] Open
Abstract
Sudden cardiac death (SCD), which can deprive a person of life within minutes, is a destructive heart abnormality. Thus, providing early warning information for patients at risk of SCD, especially those outside hospitals, is essential. In this study, we investigated the performances of ensemble empirical mode decomposition (EEMD)-based entropy features on SCD identification. EEMD-based entropy features were obtained by using the following technology: (1) EEMD was performed on HRV beats to decompose them into intrinsic mode functions (IMFs), (2) five entropy parameters, namely Rényi entropy (RenEn), fuzzy entropy (FuEn), dispersion Entropy (DisEn), improved multiscale permutation entropy (IMPE), and Renyi distribution entropy(RdisEn), were computed from the first four IMFs obtained, which were named EEMD-based entropy features. Additionally, an automated scheme combining EEMD-based entropy and classical linear (time and frequency domains) features was proposed with the intention of detecting SCD early by analyzing 14 min (at seven successive intervals of 2 min) heart rate variability (HRV) in signals from a normal population and subjects at risk of SCD. Firstly, EEMD-based entropy and classical linear measurements were extracted from HRV beats, and then the integrated measurements were ranked by various methodologies, i.e., t-test, entropy, receiver-operating characteristics (ROC), Wilcoxon, and Bhattacharyya. Finally, these ranked features were fed into a k-Nearest Neighbor algorithm for classification. Compared with several state-of-the-art methods, the proposed scheme firstly predicted subjects at risk of SCD up to 14 min earlier with an accuracy of 96.1%, a sensitivity of 97.5%, and a specificity of 94.4% 14 min before SCD onset. The simulation results exhibited that EEMD-based entropy estimators showed significant difference between SCD patients and normal individuals and outperformed the classical linear estimators in SCD detection, the EEMD-based FuEn and IMPE indexes were particularly useful assessments for identification of patients at risk of SCD and can be used as novel indices to reveal the disorders of rhythm variations of the autonomic nervous system when affected by SCD.
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Affiliation(s)
- Manhong Shi
- Center for Systems Biology, Soochow University, Suzhou, China.,College of Information and Network Engineering, Anhui Science and Technology University, Fengyang, China
| | - Hongxin He
- Center for Systems Biology, Soochow University, Suzhou, China
| | - Wanchen Geng
- Applied Mathematical Sciences, University of Connecticut, Storrs, CT, United States
| | - Rongrong Wu
- Center for Systems Biology, Soochow University, Suzhou, China
| | - Chaoying Zhan
- Center for Systems Biology, Soochow University, Suzhou, China
| | - Yanwen Jin
- Center for Systems Biology, Soochow University, Suzhou, China
| | - Fei Zhu
- School of Computer Science & Technology, Soochow University, Suzhou, China
| | - Shumin Ren
- Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, China
| | - Bairong Shen
- Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, China
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Mirbagheri M, Hakimi N, Ebrahimzadeh E, Setarehdan SK. Quality analysis of heart rate derived from functional near-infrared spectroscopy in stress assessment. INFORMATICS IN MEDICINE UNLOCKED 2020. [DOI: 10.1016/j.imu.2019.100286] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
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Amoozegar S, Pooyan M, Roughani M. Toward a closed-loop deep brain stimulation in Parkinson's disease using local field potential in parkinsonian rat model. Med Hypotheses 2019; 132:109360. [PMID: 31442919 DOI: 10.1016/j.mehy.2019.109360] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 08/04/2019] [Accepted: 08/11/2019] [Indexed: 02/06/2023]
Abstract
Deep brain stimulation (DBS) is an invasive method used for treating Parkinson's disease in its advanced stages. Nowadays, the initial adjustment of DBS parameters and their automatic matching proportion to the progression of the disease is viewed as one of the research areas discussed by the researchers, which is called closed-loop DBS. Various studies were conducted regarding finding the signal(s) which reflects different symptoms of the disease. Local Field Potential (LFP) is one of the signals that is suitable for using as feedback, because it can be recorded by the same implemented electrodes for stimulation. The present study aimed to identify the distinguishing features of patients from healthy individuals using LFP signals. METHODS In the present study, LFP was recorded from the rats in sham and parkinsonian model groups. After evaluating the signals in the frequency domain, sixty-six features were extracted from power spectral density of LFPs. The features were classified by Support Vector Machine (SVM) to determine the ability of features for separating parkinsonian rats from healthy ones. Finally, the most effective features were selected for distinguishing between the sham and parkinsonian model groups using a genetic algorithm. RESULTS The results indicated that the frequency domain features of LFP signals from rats have capacity of using them as a feedback for closed-loop DBS. The accuracy of the Support Vector Machine classification using all 66 features was 80.42% which increased to 84.41% using 38 features selected by genetic algorithm. The proposed method not only increase the accuracy, but it also reduce computation by decreasing the number of the effective features. The results indicate the significant capacity of the proposed method for identifying the effective high-frequency features to control the closed-loop DBS. CONCLUSIONS The ability of using LFP signals as feedback in closed-loop DBS was shown by extracting useful information in frequency bands below and above 100 Hz regarding LFP signals of parkinsonian rats and sham ones. Based on the results, features at frequencies above 100 Hz were more powerful and robust than below 100 Hz. The genetic algorithm was used for optimizing the classification problem.
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Affiliation(s)
- Sana Amoozegar
- Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
| | - Mohammad Pooyan
- Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran.
| | - Mehrdad Roughani
- Department of Physiology, Faculty of Medical Sciences, Shahed University, Tehran, Iran
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Ebrahimzadeh E, Shams M, Fayaz F, Rajabion L, Mirbagheri M, Nadjar Araabi B, Soltanian-Zadeh H. Quantitative determination of concordance in localizing epileptic focus by component-based EEG-fMRI. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 177:231-241. [PMID: 31319952 DOI: 10.1016/j.cmpb.2019.06.003] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2018] [Revised: 05/07/2019] [Accepted: 06/04/2019] [Indexed: 02/01/2023]
Abstract
BACKGROUND AND OBJECTIVE Accurate seizure onset zone (SOZ) localization is an essential step in pre-surgical assessment of patients with refractory focal epilepsy. Complex pathophysiology of epileptic cerebral structures, seizure types and frequencies have not been considered as influential features for accurate identification of SOZ using EEG-fMRI. There is a crucial need to quantitatively measure concordance between presumed SOZ and IED-related BOLD response in different brain regions to improve SOZ delineation. METHODS A novel component-based EEG-fMRI approach is proposed to measure physical distance between BOLD clusters and selected component dipole location using patient-specific high resolution anatomical images. The method is applied on 18 patients with refractory focal epilepsy to localize epileptic focus and determine concordance quantitatively and compare between maximum BOLD cluster with identified component dipole. To measure concordance, distance from a voxel with maximal z-score of maximum BOLD to center of extracted component dipole is measured. RESULTS BOLD clusters to spikes distances for concordant (<25 mm), partially concordant (25-50 mm), and discordant (>50 mm) groups were significantly different (p < 0.0001). The results showed full concordance in 17 IED types (17.85 ± 4.69 mm), partial concordance in 4 (36.47 ± 8.84 mm), and nodiscordance, which is a significant rise compared to the existing literature. The proposed method is premised on the cross-correlation between the spike template outside the scanner and the highly-ranked extracted components. It successfully surpasses the limitations of conventional EEG-fMRI studies which are largely dependent on inside-scanner spikes. More significantly, the proposed method improves localization accuracy to 97% which marks a dramatic rise compared to conventional works. CONCLUSIONS This study demonstrated that BOLD changes were related to epileptic spikes in different brain regions in patients with refractory focal epilepsy. In a systematic quantitative approach, concordance levels based on the distance between center of maximum BOLD cluster and dipole were determined by component-based EEG-fMRI method. Therefore, component-based EEG-fMRI can be considered as a reliable predictor of SOZ in patients with focal epilepsy and included as part of clinical evaluation for patients with medically resistant epilepsy.
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Affiliation(s)
- Elias Ebrahimzadeh
- CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran; Seaman Family MR Research Centre, University of Calgary, Calgary, Alberta, Canada.
| | - Mohammad Shams
- Department of Biomedical Engineering, George Washington University, Washington D.C., USA
| | - Farahnaz Fayaz
- Biomedical Engineering Department, School of Electrical Engineering, Payame Noor University of North Tehran, Tehran, Iran
| | - Lila Rajabion
- Department of IT, College of Business, University of South Florida Sarasota-Manatee, USA
| | - Mahya Mirbagheri
- CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Babak Nadjar Araabi
- CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Hamid Soltanian-Zadeh
- CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran; Image Analysis Laboratory, Department of Radiology, Henry Ford Hospital, Detroit, MI, USA
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Meng F, Zhang Z, Hou X, Qian Z, Wang Y, Chen Y, Wang Y, Zhou Y, Chen Z, Zhang X, Yang J, Zhang J, Guo J, Li K, Chen L, Zhuang R, Jiang H, Zhou W, Tang S, Wei Y, Zou J. Machine learning for prediction of sudden cardiac death in heart failure patients with low left ventricular ejection fraction: study protocol for a retroprospective multicentre registry in China. BMJ Open 2019; 9:e023724. [PMID: 31101692 PMCID: PMC6530409 DOI: 10.1136/bmjopen-2018-023724] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
INTRODUCTION Left ventricular ejection fraction (LVEF) ≤35%, as current significant implantable cardioverter-defibrillator (ICD) indication for primary prevention of sudden cardiac death (SCD) in heart failure (HF) patients, has been widely recognised to be inefficient. Improvement of patient selection for low LVEF (≤35%) is needed to optimise deployment of ICD. Most of the existing prediction models are not appropriate to identify ICD candidates at high risk of SCD in HF patients with low LVEF. Compared with traditional statistical analysis, machine learning (ML) can employ computer algorithms to identify patterns in large datasets, analyse rules automatically and build both linear and non-linear models in order to make data-driven predictions. This study is aimed to develop and validate new models using ML to improve the prediction of SCD in HF patients with low LVEF. METHODS AND ANALYSIS We will conduct a retroprospective, multicentre, observational registry of Chinese HF patients with low LVEF. The HF patients with LVEF ≤35% after optimised medication at least 3 months will be enrolled in this study. The primary endpoints are all-cause death and SCD. The secondary endpoints are malignant arrhythmia, sudden cardiac arrest, cardiopulmonary resuscitation and rehospitalisation due to HF. The baseline demographic, clinical, biological, electrophysiological, social and psychological variables will be collected. Both ML and traditional multivariable Cox proportional hazards regression models will be developed and compared in the prediction of SCD. Moreover, the ML model will be validated in a prospective study. ETHICS AND DISSEMINATION The study protocol has been approved by the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University (2017-SR-06). All results of this study will be published in international peer-reviewed journals and presented at relevant conferences. TRIAL REGISTRATION NUMBER ChiCTR-POC-17011842; Pre-results.
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Affiliation(s)
- Fanqi Meng
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- Department of Cardiology, Xiamen Cardiovascular Hospital, Xiamen University, Xiamen, Fujian, China
| | - Zhihua Zhang
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- Department of Cardiology, Jiangning Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China
| | - Xiaofeng Hou
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Zhiyong Qian
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yao Wang
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yanhong Chen
- Department of Cardiology, Wuhan Asia Heart Hospital, Wuhan, Hubei, China
| | - Yilian Wang
- Department of Cardiology, The Second People’s Hospital of Lianyungang, Lianyungang, Jiangsu, China
| | - Ye Zhou
- Department of Cardiology, The Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu, China
| | - Zhen Chen
- Department of Cardiology, Taixing People’s Hospital, Taixing, Jiangsu, China
| | - Xiwen Zhang
- Department of Cardiology, The First People’s Hospital of Huaian, Huaian, Jiangsu, China
| | - Jing Yang
- Department of Cardiology, The First People’s Hospital of Huaian, Huaian, Jiangsu, China
| | - Jinlong Zhang
- Department of Cardiology, The First People’s Hospital of Yancheng, Yancheng, Jiangsu, China
| | - Jianghong Guo
- Department of Cardiology, Rugao People’s Hospital, Rugao, Jiangsu, China
| | - Kebei Li
- Department of Cardiology, The First People’s Hospital of Zhangjiagang, Zhangjiagang, Jiangsu, China
| | - Lu Chen
- Department of Cardiology, The Third People’s Hospital of Suzhou, Suzhou, Jiangsu, China
| | - Ruijuan Zhuang
- Department of Cardiology, The Third People’s Hospital of Wuxi, Wuxi, Jiangsu, China
| | - Hai Jiang
- Department of Cardiology, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Weihua Zhou
- School of Computing, University of Southern Mississippi, Hattiesburg, Mississippi, USA
| | - Shaowen Tang
- Department of Epidemiology, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yongyue Wei
- Department of Biostatistics, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Jiangang Zou
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- Key Laboratory of Targeted Intervention of Cardiovascular Disease, Collaborative Innovation Center for Cardiovascular Disease Translational Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
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22
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Ebrahimzadeh E, Soltanian-Zadeh H, Araabi BN, Fesharaki SSH, Habibabadi JM. Component-related BOLD response to localize epileptic focus using simultaneous EEG-fMRI recordings at 3T. J Neurosci Methods 2019; 322:34-49. [PMID: 31026487 DOI: 10.1016/j.jneumeth.2019.04.010] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 04/18/2019] [Accepted: 04/21/2019] [Indexed: 02/01/2023]
Abstract
BACKGROUND Simultaneous EEG-fMRI experiments record spatiotemporal dynamics of epileptic activity. A shortcoming of spike-based EEG-fMRI studies is their inability to provide information about behavior of epileptic generators when no spikes are visible. NEW METHOD We extract time series of epileptic components identified on EEG and fit them with Generalized Linear Model (GLM) model. This allows a precise and reliable localization of epileptic foci in addition to predicting generator's behavior. The proposed method works in the source domain and delineates generators considering spatial correlation between spike template and candidate components in addition to patient's medical records. RESULTS The proposed method was applied on 20 patients with refractory epilepsy and 20 age- and gender-matched healthy controls. The identified components were examined statistically and threshold of localization accuracy was determined as 86% based on Receiver Operating Characteristic (ROC) curve analysis. Accuracy, sensitivity, and specificity were found to be 88%, 85%, and 95%, respectively. Contribution of EEG-fMRI and concordance between EEG and fMRI were also evaluated. Concordance was found in 19 patients and contribution in 17. COMPARISON WITH EXISTING METHODS We compared the proposed method with conventional methods. Our comparisons showed superiority of the proposed method. In particular, when epileptogenic zone was located deep in the brain, the method outperformed existing methods. CONCLUSIONS This study contributes substantially to increasing the yield of EEG-fMRI and presents a realistic estimate of the neural behavior of epileptic generators, to the best of our knowledge, for the first time in the literature.
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Affiliation(s)
- Elias Ebrahimzadeh
- CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran; Department of Clinical Neurosciences, Hotchkiss Brain Institute, Cumming School of Medicine, and Seaman Family MR Research Centre, University of Calgary, Calgary, Alberta, Canada
| | - Hamid Soltanian-Zadeh
- CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran; Image Analysis Laboratory, Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, MI, USA.
| | - Babak Nadjar Araabi
- CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | | | - Jafar Mehvari Habibabadi
- Isfahan Neurosciences Research Center, Department of Neurology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
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23
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Zangeneh Soroush M, Maghooli K, Setarehdan SK, Nasrabadi AM. Emotion recognition through EEG phase space dynamics and Dempster-Shafer theory. Med Hypotheses 2019; 127:34-45. [PMID: 31088645 DOI: 10.1016/j.mehy.2019.03.025] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Revised: 02/25/2019] [Accepted: 03/26/2019] [Indexed: 02/01/2023]
Abstract
Emotions play an important role in our life. Emotion recognition which is considered a subset of brain computer interface (BCI), has drawn a great deal of attention during recent years. Researchers from different fields have tried to classify emotions through physiological signals. Nonlinear analysis has been reported to be successful and effective in emotion classification due to the nonlinear and non-stationary behavior of biological signals. In this study, phase space reconstruction and Poincare planes are employed to describe the dynamics of electroencephalogram (EEG) in emotional states. EEG signals are taken from a reliable database and phase space is reconstructed. A new transformation is introduced in order to quantify the phase space. Dynamic characteristics of the new space are considered as features. Most significant features are selected and samples are classified into four groups including high arousal - high valence (HAHV), low arousal - high valence (LAHV), high arousal - low valence (HALV) and low arousal - low valence (LALV). Classification accuracy was about 90% on average. Results suggest that the proposed method is successful and classification performance is good in comparison with most studies in this field. Brain activity is also reported with respect to investigating brain function during emotion elicitation. We managed to introduce a new way to analyze EEG phase space. The proposed method is applied in a real world and challenging application (i.e. emotion classification). Not only does the proposed method describe EEG changes due to different emotional states but also it is able to represent new characteristics of complex systems. The suggested approach paves the way for researchers to analyze and understand more about chaotic signals and systems.
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Affiliation(s)
- Morteza Zangeneh Soroush
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Keivan Maghooli
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
| | - Seyed Kamaledin Setarehdan
- Control and Intelligent Processing Centre of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Ali Motie Nasrabadi
- Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
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