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Wehrheim MH, Faskowitz J, Schubert A, Fiebach CJ. Reliability of variability and complexity measures for task and task-free BOLD fMRI. Hum Brain Mapp 2024; 45:e26778. [PMID: 38980175 PMCID: PMC11232465 DOI: 10.1002/hbm.26778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 05/06/2024] [Accepted: 06/24/2024] [Indexed: 07/10/2024] Open
Abstract
Brain activity continuously fluctuates over time, even if the brain is in controlled (e.g., experimentally induced) states. Recent years have seen an increasing interest in understanding the complexity of these temporal variations, for example with respect to developmental changes in brain function or between-person differences in healthy and clinical populations. However, the psychometric reliability of brain signal variability and complexity measures-which is an important precondition for robust individual differences as well as longitudinal research-is not yet sufficiently studied. We examined reliability (split-half correlations) and test-retest correlations for task-free (resting-state) BOLD fMRI as well as split-half correlations for seven functional task data sets from the Human Connectome Project to evaluate their reliability. We observed good to excellent split-half reliability for temporal variability measures derived from rest and task fMRI activation time series (standard deviation, mean absolute successive difference, mean squared successive difference), and moderate test-retest correlations for the same variability measures under rest conditions. Brain signal complexity estimates (several entropy and dimensionality measures) showed moderate to good reliabilities under both, rest and task activation conditions. We calculated the same measures also for time-resolved (dynamic) functional connectivity time series and observed moderate to good reliabilities for variability measures, but poor reliabilities for complexity measures derived from functional connectivity time series. Global (i.e., mean across cortical regions) measures tended to show higher reliability than region-specific variability or complexity estimates. Larger subcortical regions showed similar reliability as cortical regions, but small regions showed lower reliability, especially for complexity measures. Lastly, we also show that reliability scores are only minorly dependent on differences in scan length and replicate our results across different parcellation and denoising strategies. These results suggest that the variability and complexity of BOLD activation time series are robust measures well-suited for individual differences research. Temporal variability of global functional connectivity over time provides an important novel approach to robustly quantifying the dynamics of brain function. PRACTITIONER POINTS: Variability and complexity measures of BOLD activation show good split-half reliability and moderate test-retest reliability. Measures of variability of global functional connectivity over time can robustly quantify neural dynamics. Length of fMRI data has only a minor effect on reliability.
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Affiliation(s)
- Maren H. Wehrheim
- Department of PsychologyGoethe University FrankfurtFrankfurtGermany
- Department of Computer Science and MathematicsGoethe University FrankfurtFrankfurtGermany
- Frankfurt Institute for Advanced Studies (FIAS)FrankfurtGermany
| | - Joshua Faskowitz
- Department of Psychological and Brain SciencesIndiana UniversityBloomingtonUSA
| | - Anna‐Lena Schubert
- Department of PsychologyJohannes Gutenberg‐Universität MainzMainzGermany
| | - Christian J. Fiebach
- Department of PsychologyGoethe University FrankfurtFrankfurtGermany
- Brain Imaging CenterGoethe University FrankfurtFrankfurtGermany
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2
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Efe E, Yavsan E. AttBiLFNet: A novel hybrid network for accurate and efficient arrhythmia detection in imbalanced ECG signals. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:5863-5880. [PMID: 38872562 DOI: 10.3934/mbe.2024259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
Within the domain of cardiovascular diseases, arrhythmia is one of the leading anomalies causing sudden deaths. These anomalies, including arrhythmia, are detectable through the electrocardiogram, a pivotal component in the analysis of heart diseases. However, conventional methods like electrocardiography encounter challenges such as subjective analysis and limited monitoring duration. In this work, a novel hybrid model, AttBiLFNet, was proposed for precise arrhythmia detection in ECG signals, including imbalanced class distributions. AttBiLFNet integrates a Bidirectional Long Short-Term Memory (BiLSTM) network with a convolutional neural network (CNN) and incorporates an attention mechanism using the focal loss function. This architecture is capable of autonomously extracting features by harnessing BiLSTM's bidirectional information flow, which proves advantageous in capturing long-range dependencies. The attention mechanism enhances the model's focus on pertinent segments of the input sequence, which is particularly beneficial in class imbalance classification scenarios where minority class samples tend to be overshadowed. The focal loss function effectively addresses the impact of class imbalance, thereby improving overall classification performance. The proposed AttBiLFNet model achieved 99.55% accuracy and 98.52% precision. Moreover, performance metrics such as MF1, K score, and sensitivity were calculated, and the model was compared with various methods in the literature. Empirical evidence showed that AttBiLFNet outperformed other methods in terms of both accuracy and computational efficiency. The introduced model serves as a reliable tool for the timely identification of arrhythmias.
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Affiliation(s)
- Enes Efe
- Department of Electrical and Electronics Engineering, Hitit University, Corum 19030, Turkey
| | - Emrehan Yavsan
- Department of Electronics and Automation, Tekirdag Namik Kemal University, Tekirdag 59030, Turkey
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3
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Cabanas AM, Fuentes-Guajardo M, Sáez N, Catalán DD, Collao-Caiconte PO, Martín-Escudero P. Exploring the Hidden Complexity: Entropy Analysis in Pulse Oximetry of Female Athletes. BIOSENSORS 2024; 14:52. [PMID: 38275305 PMCID: PMC11154467 DOI: 10.3390/bios14010052] [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: 11/20/2023] [Revised: 01/06/2024] [Accepted: 01/12/2024] [Indexed: 01/27/2024]
Abstract
This study examines the relationship between physiological complexity, as measured by Approximate Entropy (ApEn) and Sample Entropy (SampEn), and fitness levels in female athletes. Our focus is on their association with maximal oxygen consumption (VO2,max). Our findings reveal a complex relationship between entropy metrics and fitness levels, indicating that higher fitness typically, though not invariably, correlates with greater entropy in physiological time series data; however, this is not consistent for all individuals. For Heart Rate (HR), entropy measures suggest stable patterns across fitness categories, while pulse oximetry (SpO2) data shows greater variability. For instance, the medium fitness group displayed an ApEn(HR) = 0.57±0.13 with a coefficient of variation (CV) of 22.17 and ApEn(SpO2) = 0.96±0.49 with a CV of 46.08%, compared to the excellent fitness group with ApEn(HR) = 0.60±0.09 with a CV of 15.19% and ApEn(SpO2) =0.85±0.42 with a CV of 49.46%, suggesting broader physiological responses among more fit individuals. The larger standard deviations and CVs for SpO2 entropy may indicate the body's proficient oxygen utilization at higher levels of physical demand. Our findings advocate for combining entropy metrics with wearable sensor technology for improved biomedical analysis and personalized healthcare.
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Affiliation(s)
- Ana M. Cabanas
- Departamento de Física, Universidad de Tarapacá, Arica 1010069, Chile; (N.S.); (D.D.C.)
| | | | - Nicolas Sáez
- Departamento de Física, Universidad de Tarapacá, Arica 1010069, Chile; (N.S.); (D.D.C.)
| | - Davidson D. Catalán
- Departamento de Física, Universidad de Tarapacá, Arica 1010069, Chile; (N.S.); (D.D.C.)
| | | | - Pilar Martín-Escudero
- Medical School of Sport Medicine, Faculty of Medicine, Universidad Complutense de Madrid, 28040 Madrid, Spain;
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4
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Wang Z, Zhang W, Li S, Chen X, Wu D. Unsupervised domain adaptation for cross-patient seizure classification. J Neural Eng 2023; 20:066002. [PMID: 37906968 DOI: 10.1088/1741-2552/ad0859] [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: 06/09/2023] [Accepted: 10/31/2023] [Indexed: 11/02/2023]
Abstract
Objective. Epileptic seizure is a chronic neurological disease affecting millions of patients. Electroencephalogram (EEG) is the gold standard in epileptic seizure classification. However, its low signal-to-noise ratio, strong non-stationarity, and large individual difference nature make it difficult to directly extend the seizure classification model from one patient to another. This paper considers multi-source unsupervised domain adaptation for cross-patient EEG-based seizure classification, i.e. there are multiple source patients with labeled EEG data, which are used to label the EEG trials of a new patient.Approach. We propose an source domain selection (SDS)-global domain adaptation (GDA)-target agent subdomain adaptation (TASA) approach, which includes SDS to filter out dissimilar source domains, GDA to align the overall distributions of the selected source domains and the target domain, and TASA to identify the most similar source domain to the target domain so that its labels can be utilized.Main results. Experiments on two public seizure datasets demonstrated that SDS-GDA-TASA outperformed 13 existing approaches in unsupervised cross-patient seizure classification.Significance. Our approach could save clinicians plenty of time in labeling EEG data for epilepsy patients, greatly increasing the efficiency of seizure diagnostics.
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Affiliation(s)
- Ziwei Wang
- Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, People's Republic of China
| | - Wen Zhang
- Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, People's Republic of China
| | - Siyang Li
- Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, People's Republic of China
| | - Xinru Chen
- Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, People's Republic of China
| | - Dongrui Wu
- Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, People's Republic of China
- Belt and Road Joint Laboratory on Measurement and Control Technology, Huazhong University of Science and Technology, Wuhan 430074, People's Republic of China
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Santos DOC, Trindade MAS, da Silva AJ. Nonextensive realizations in interacting ion channels: Implications for mechano-electrical transducer mechanisms. Biosystems 2023; 232:105005. [PMID: 37611860 DOI: 10.1016/j.biosystems.2023.105005] [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: 05/09/2023] [Revised: 08/12/2023] [Accepted: 08/19/2023] [Indexed: 08/25/2023]
Abstract
We propose a theoretical model to investigate the thermodynamics of single and coupled two-state ion channels, associated with mechanoelectrical transduction (MET) and hair cell biophysics. The modeling was based on the Tsallis nonextensive statistical mechanics. The choice for a nonextensive framework in modeling ion channels is encouraged on the fact that we take into account the presence of interactions or long-range correlations in the dynamics of single and coupled ion channels. However, the basic assumptions that support Boltzmann-Gibbs statistics, traditionally used to model ion channel dynamics, state that the system is formed by independent or weakly interacting elements. Despite being well studied in many biological systems, the literature has not yet addressed the study of both entropy and mutual information related to isolated or physically interacting pairs of MET channels. Inspired by hair cell biophysics, we show how the presence of nonextensivity, or subadditivity and superadditivity modulates the nonextensive entropy and mutual information as functions of stereocilia displacements. We also observe that the magnitude of the interaction between the two channels, given by a nonextensive parameter, influences the amplitude of the nonextensive joint entropy and mutual information as functions of the hair cell displacements. Finally, we show how nonextensivity regulates the current versus displacement curve for a single and a pair of interacting two-state channels. The present findings shed light on the thermodynamic process involved in the molecular mechanisms of the auditory system.
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Affiliation(s)
- D O C Santos
- Universidade Federal do Sul da Bahia, CEP 45600-923, Itabuna, Bahia, Brazil
| | - M A S Trindade
- Colegiado de Física, Departamento de Ciências Exatas e da Terra, Universidade do Estado da Bahia, CEP 41150-000, Salvador, Bahia, Brazil
| | - A J da Silva
- Universidade Federal do Sul da Bahia, CEP 45600-923, Itabuna, Bahia, Brazil.
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6
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Zeng W, Yuan C. Myocardial infarction detection using ITD, DWT and deterministic learning based on ECG signals. Cogn Neurodyn 2023; 17:941-964. [PMID: 37522048 PMCID: PMC10374507 DOI: 10.1007/s11571-022-09870-7] [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: 02/09/2022] [Revised: 07/16/2022] [Accepted: 08/05/2022] [Indexed: 11/03/2022] Open
Abstract
Nowadays, cardiovascular diseases (CVD) is one of the prime causes of human mortality, which has received tremendous and elaborative research interests regarding the prevention issue. Myocardial ischemia is a kind of CVD which will lead to myocardial infarction (MI). The diagnostic criterion of MI is supplemented with clinical judgement and several electrocardiographic (ECG) or vectorcardiographic (VCG) programs. However the visual inspection of ECG or VCG signals by cardiologists is tedious, laborious and subjective. To overcome such disadvantages, numerous MI detection techniques including signal processing and artificial intelligence tools have been developed. In this study, we propose a novel technique for automatic detection of MI based on disparity of cardiac system dynamics and synthesis of the standard 12-lead and Frank XYZ leads. First, 12-lead ECG signals are synthesized with Frank XYZ leads to build a hybrid 4-dimensional cardiac vector, which is decomposed into a series of proper rotation components (PRCs) by using the intrinsic time-scale decomposition (ITD) method. The novel cardiac vector may fully reflect the pathological alterations provoked by MI and may be correlated to the disparity of cardiac system dynamics between healthy and MI subjects. ITD is employed to measure the variability of cardiac vector and the first PRCs are extracted as predominant PRCs which contain most of the cardiac vector's energy. Second, four levels discrete wavelet transform with third-order Daubechies (db3) wavelet function is employed to decompose the predominant PRCs into different frequency bands, which combines with three-dimensional phase space reconstruction to derive features. The properties associated with the cardiac system dynamics are preserved. Since the frequency components above 40 Hz are lack of use in ECG analysis, in order to reduce the feature dimension, the advisable sub-band (D4) is selected for feature acquisition. Third, neural networks are then used to model, identify and classify cardiac system dynamics between normal (healthy) and MI cardiac vector signals. The difference of cardiac system dynamics between healthy control and MI cardiac vector is computed and used for the detection of MI based on a bank of estimators. Finally, experiments are carried out on the PhysioNet PTB database to assess the effectiveness of the proposed method, in which conventional 12-lead and Frank XYZ leads ECG signal fragments from 148 patients with MI and 52 healthy controls were extracted. By using the tenfold cross-validation style, the achieved average classification accuracy is reported to be 98.20%. Results verify the effectiveness of the proposed method which can serve as a potential candidate for the automatic detection of MI in the clinical application.
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Affiliation(s)
- Wei Zeng
- School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan, 364012 People’s Republic of China
| | - Chengzhi Yuan
- Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI 02881 USA
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7
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Jahnavi D, Dash A, Bs R, Ghosh N, Patra A, Mandana KM, Khandelwal S. Non-invasive Coronary Artery Disease Screening Based on Electrocardiogram Characteristics and Clinical Risk Factors. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083194 DOI: 10.1109/embc40787.2023.10340892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Coronary artery disease (CAD), an acute and life-threatening cardiovascular disease, is a leading cause of mortality and morbidity worldwide. Coronary angiography, the principal diagnostic tool for CAD, is invasive, expensive, and requires a lot of skilled effort. The current study aims to develop an automated and non-invasive CAD detection model and improve its performance as closely as possible to clinically acceptable diagnostic sensitivity. Electrocardiogram (ECG) characteristics are observed to be altered due to CAD and can be studied to develop a screening tool for its detection. The subject's clinical information can help broadly identify the high-cardiac-risk population and serve as a primary step in diagnosing CAD. This paper presents an approach to automatically detect CAD based on clinical data, morphological ECG features, and heart rate variability (HRV) features extracted from short-duration Lead-II ECG recordings. A few popular machine-learning classifiers, including support vector machine (SVM), random forest (RF), K-nearest neighbours (KNN), Gaussian Naïve Bayes (GNB), and multi-layer perceptron (MLP), are trained on the extracted feature space, and their performance is evaluated. Classifiers built by integrating clinical data and features extracted from ECG recordings demonstrated better performance than those built on each feature set separately, and the RF classifier outperforms other considered machine learners and reports an average testing accuracy of 94% and a G-mean score of 92% with a 5-fold cross-validation training accuracy of 95(± 0.04)%.Clinical relevance- The proposed method uses a brief, single-lead ECG recording and performs similarly to current clinical practices in an explainable manner. This makes it suitable for deployment via wearable technology (like smart watch gadgets) and telemonitoring, which may facilitate an earlier and more widespread CAD diagnosis.
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8
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Khan MU, Aziz S, Iqtidar K, Fernandez-Rojas R. Computer-aided diagnosis system for cardiac disorders using variational mode decomposition and novel cepstral quinary patterns. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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9
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Chawla P, Rana SB, Kaur H, Singh K. Computer-aided diagnosis of autism spectrum disorder from EEG signals using deep learning with FAWT and multiscale permutation entropy features. Proc Inst Mech Eng H 2023; 237:282-294. [PMID: 36515392 DOI: 10.1177/09544119221141751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Autism spectrum disorder (ASD), a neurodevelopment disorder, is characterized by significant difficulties in social interaction and emerges as a major threat to children. Its computer-aided diagnosis used by neurologists improves the detection process and has a favorable impact on patients' health. Currently, a biomarker termed electroencephalography (EEG) is considered as vital tool to detect abnormal electrical activity in the brain. In this context, the present paper brings forth a novel approach for automated diagnosis of ASD from multichannel EEG signals using flexible analytic wavelet transform (FAWT). Firstly, this approach processes the acquired EEG signals with filtering and segmentation into short-duration EEG segments in the range of 5-20 s. These segmented EEG signals are decomposed into five levels using FAWT technique to obtain various sub-bands. Further, multiscale permutation entropy values are extracted from decomposed sub-bands which are used as feature vectors in the present work. Afterwards, these feature vectors are evaluated by traditional machine learning algorithms viz., k-nearest neighbor, logistic regression, support vector machine, and random forest, as well as convolutional neural network (CNN) as deep learning algorithm with different segment durations. The analysis of results reveals that CNN provides maximum accuracy, sensitivity, specificity, and area under the curve of 99.19%, 99.34%, 99.21%, and 0.9997, respectively, for 10 s duration EEG segment to identify ASD patients among healthy individuals. Thus, the proposed CNN architecture would be extremely helpful during diagnostic process of autism disease for neurologists.
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Affiliation(s)
- Parikha Chawla
- Department of Engineering & Technology, Guru Nanak Dev University Regional Campus, Gurdaspur, Punjab, India
| | - Shashi B Rana
- Department of Engineering & Technology, Guru Nanak Dev University Regional Campus, Gurdaspur, Punjab, India
| | - Hardeep Kaur
- Department of Electronics Technology, Guru Nanak Dev University, Amritsar, Punjab, India
| | - Kuldeep Singh
- Department of Electronics Technology, Guru Nanak Dev University, Amritsar, Punjab, India
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10
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Chawla P, Rana SB, Kaur H, Singh K, Yuvaraj R, Murugappan M. A decision support system for automated diagnosis of Parkinson’s disease from EEG using FAWT and entropy features. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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11
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Nesaragi N, Sharma A, Patidar S, Acharya UR. Automated diagnosis of coronary artery disease using scalogram-based tensor decomposition with heart rate signals. Med Eng Phys 2022; 110:103811. [PMID: 35525698 DOI: 10.1016/j.medengphy.2022.103811] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 03/31/2022] [Accepted: 04/25/2022] [Indexed: 01/18/2023]
Abstract
Early identification of coronary artery disease (CAD) can facilitate timely clinical intervention and save lives. This study aims to develop a machine learning framework that uses tensor analysis on heart rate (HR) signals to automate the CAD detection task. A third-order tensor representing a time-frequency relationship is constructed by fusing scalograms as vertical slices of the tensor. Each scalogram is computed from the considered time frame of a given HR signal. The derived scalogram represents the heterogeneity of data as a two-dimensional map. These two-dimensional maps are stacked one after the other horizontally along the z-axis to form a 3-way tensor for each HR signal. Each two-dimensional map is represented as a vertical slice in the xy - plane. Tensor factorization of such a fused tensor for every HR signal is performed using canonical polyadic (CP) decomposition. Only the core factor is retained later, excluding the three unitary matrices to provide the latent feature set for the detection task. The resultant latent features are then fed to machine learning classifiers for binary classification. Bayesian optimization is performed in a five-fold cross-validation strategy in search of the optimal machine learning classifier. The experimental results yielded the accuracy, sensitivity, and specificity of 96.62%, 96.53%, and 96.67%, respectively, with the bagged trees ensemble method. The proposed tensor decomposition deciphered higher-order interrelations among the considered time-frequency representations of HR signals.
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Affiliation(s)
- Naimahmed Nesaragi
- Dept. of Electronics & Communication Engineering, National Institute of Technology, Goa, India
| | - Ashish Sharma
- Dept. of Electronics & Communication Engineering, National Institute of Technology, Goa, India
| | - Shivnarayan Patidar
- Dept. of Electronics & Communication Engineering, National Institute of Technology, Goa, India.
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan; School of Science and Technology, Singapore University of Social Sciences, Singapore.
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12
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Schizophrenia Diagnosis by Weighting the Entropy Measures of the Selected EEG Channel. J Med Biol Eng 2022. [DOI: 10.1007/s40846-022-00762-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
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13
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Sun Q, Liang C, Chen T, Ji B, Liu R, Wang L, Tang M, Chen Y, Wang C. Early detection of myocardial ischemia in 12-lead ECG using deterministic learning and ensemble learning. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107124. [PMID: 36156437 DOI: 10.1016/j.cmpb.2022.107124] [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: 11/25/2021] [Revised: 08/18/2022] [Accepted: 09/09/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVE Early detection of myocardial ischemia is a necessary but difficult problem in cardiovascular diseases. Approaches that exclusively rely on classical ST and T wave changes on the standard 12-lead electrocardiogram (ECG) lack sufficient accuracy in detecting myocardial ischemia. This study aims to construct generalizable models for the detection of myocardial ischemia in patients with subtle ECG waveform changes (namely non-diagnostic ECG) using ensemble learning to integrate ECG dynamic features acquired via deterministic learning. METHODS First, cardiodynamicsgram (CDG), a noninvasive spatiotemporal electrocardiographic method, is generated through dynamic modeling of ECG signals using the deterministic learning algorithm. Then, the spectral fitting exponent, Lyapunov exponent, and Lempel-Ziv complexity are extracted from CDG. Subsequently, the bagging-based heterogeneous ensemble algorithm is applied on CDG features to generate diverse base classifiers and aggregate them with weighted voting to obtain an ensemble model for myocardial ischemia detection. Finally, we train and test the proposed heterogeneous ensemble model on a real-world clinical dataset. This dataset consists of 499 non-diagnostic 12-lead ECG records from 499 patients collected from three independent medical centers, including 383 patients with myocardial ischemia and 116 patients without ischemia. RESULTS With 10-times 5-fold cross-validation technology, our proposed method achieves an average accuracy of 89.10%, sensitivity of 91.72%, and specificity of 82.69% using the heterogeneous ensemble algorithm on the real-world clinical dataset. On three independent medical centers, our ensemble model also achieves accuracy performance over 82% for patients with non-diagnostic ECG. Furthermore, our ensemble model trained with real-world clinical data yields promising results of 91.11% accuracy, 90.49% sensitivity, and 92.88% specificity on the external test set of the public PTB dataset. CONCLUSION The experimental results demonstrate that the proposed model combining ensemble learning and deterministic learning presents excellent diagnostic accuracy and generalization in clinical practice, and could be implemented as a complement to the standard ECG in the clinical diagnosis of myocardial ischemia.
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Affiliation(s)
- Qinghua Sun
- Center for Intelligent Medical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China
| | - Chunmiao Liang
- Center for Intelligent Medical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China
| | - Tianrui Chen
- Center for Intelligent Medical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China
| | - Bing Ji
- Center for Intelligent Medical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China
| | - Rugang Liu
- Department of Emergency, Qilu Hospital of Shandong University, Jinan, China
| | - Lei Wang
- Department of Cardiology, Shihezi People's Hospital, Shihezi, China
| | - Min Tang
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuguo Chen
- Department of Emergency, Qilu Hospital of Shandong University, Jinan, China
| | - Cong Wang
- Center for Intelligent Medical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China.
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14
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Can statistical and entropy-based features extracted from ECG signals efficiently differentiate the cannabis-consuming women population from the non-consumer? Med Hypotheses 2022. [DOI: 10.1016/j.mehy.2022.110952] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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15
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A novel technique for the detection of myocardial dysfunction using ECG signals based on CEEMD, DWT, PSR and neural networks. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10262-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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16
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ECG Heartbeat Classification Using CONVXGB Model. ELECTRONICS 2022. [DOI: 10.3390/electronics11152280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
ELECTROCARDIOGRAM (ECG) signals are reliable in identifying and monitoring patients with various cardiac diseases and severe cardiovascular syndromes, including arrhythmia and myocardial infarction (MI). Thus, cardiologists use ECG signals in diagnosing cardiac diseases. Machine learning (ML) has also proven its usefulness in the medical field and in signal classification. However, current ML approaches rely on hand-crafted feature extraction methods or very complicated deep learning networks. This paper presents a novel method for feature extraction from ECG signals and ECG classification using a convolutional neural network (CNN) with eXtreme Gradient Boosting (XBoost), ConvXGB. This model was established by stacking two convolutional layers for automatic feature extraction from ECG signals, followed by XGBoost as the last layer, which is used for classification. This technique simplified ECG classification in comparison to other methods by minimizing the number of required parameters and eliminating the need for weight readjustment throughout the backpropagation phase. Furthermore, experiments on two famous ECG datasets–the Massachusetts Institute of Technology–Beth Israel Hospital (MIT-BIH) and Physikalisch-Technische Bundesanstalt (PTB) datasets–demonstrated that this technique handled the ECG signal classification issue better than either CNN or XGBoost alone. In addition, a comparison showed that this model outperformed state-of-the-art models, with scores of 0.9938, 0.9839, 0.9836, 0.9837, and 0.9911 for accuracy, precision, recall, F1-score, and specificity, respectively.
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Montoya A, Habtour E, Moreu F. Detecting hidden transient events in noisy nonlinear time-series. CHAOS (WOODBURY, N.Y.) 2022; 32:073131. [PMID: 35907744 DOI: 10.1063/5.0097973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 07/05/2022] [Indexed: 06/15/2023]
Abstract
The information impulse function (IIF), running Variance, and local Hölder Exponent are three conceptually different time-series evaluation techniques. These techniques examine time-series for local changes in information content, statistical variation, and point-wise smoothness, respectively. Using simulated data emulating a randomly excited nonlinear dynamical system, this study interrogates the utility of each method to correctly differentiate a transient event from the background while simultaneously locating it in time. Computational experiments are designed and conducted to evaluate the efficacy of each technique by varying pulse size, time location, and noise level in time-series. Our findings reveal that, in most cases, the first instance of a transient event is more easily observed with the information-based approach of IIF than with the Variance and local Hölder Exponent methods. While our study highlights the unique strengths of each technique, the results suggest that very robust and reliable event detection for nonlinear systems producing noisy time-series data can be obtained by incorporating the IIF into the analysis.
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Affiliation(s)
- A Montoya
- Sandia National Laboratories, Albuquerque, New Mexico 87185, USA
| | - E Habtour
- Department of Aeronautics and Astronautics, University of Washington, Seattle, Washington 98195, USA
| | - F Moreu
- Department of Civil, Construction, and Environmental Engineering, University of New Mexico, Albuquerque, New Mexico 87131, USA
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18
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Investigating the effect of sound in horror clip on the cardiac electrophysiology of young adults using wavelet packet decomposition and machine learning classifiers. BIOMEDICAL ENGINEERING ADVANCES 2022. [DOI: 10.1016/j.bea.2022.100037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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19
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Li H, Wang X, Liu C, Li P, Jiao Y. Integrating multi-domain deep features of electrocardiogram and phonocardiogram for coronary artery disease detection. Comput Biol Med 2021; 138:104914. [PMID: 34638021 DOI: 10.1016/j.compbiomed.2021.104914] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 09/17/2021] [Accepted: 09/28/2021] [Indexed: 11/30/2022]
Abstract
Electrocardiogram (ECG) and phonocardiogram (PCG) are both noninvasive and convenient tools that can capture abnormal heart states caused by coronary artery disease (CAD). However, it is very challenging to detect CAD relying on ECG or PCG alone due to low diagnostic sensitivity. Recently, several studies have attempted to combine ECG and PCG signals for diagnosing heart abnormalities, but only conventional manual features have been used. Considering the strong feature extraction capabilities of deep learning, this paper develops a multi-input convolutional neural network (CNN) framework that integrates time, frequency, and time-frequency domain deep features of ECG and PCG for CAD detection. Simultaneously recorded ECG and PCG signals from 195 subjects are used. The proposed framework consists of 1-D and 2-D CNN models and uses signals, spectrum images, and time-frequency images of ECG and PCG as inputs. The framework combining multi-domain deep features of two-modal signals is very effective in classifying non-CAD and CAD subjects, achieving an accuracy, sensitivity, and specificity of 96.51%, 99.37%, and 90.08%, respectively. The comparison with existing studies demonstrates that our method is very competitive in CAD detection. The proposed approach is very promising in assisting the real-world CAD diagnosis, especially under general medical conditions.
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Affiliation(s)
- Han Li
- School of Control Science and Engineering, Shandong University, Jinan, 250061, China
| | - Xinpei Wang
- School of Control Science and Engineering, Shandong University, Jinan, 250061, China.
| | - Changchun Liu
- School of Control Science and Engineering, Shandong University, Jinan, 250061, China.
| | - Peng Li
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Yu Jiao
- School of Control Science and Engineering, Shandong University, Jinan, 250061, China
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20
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Chen J, Wu Z, Liu Y, Wang L, Li T, Dong Y, Qin Q, Ding S. Prevalence, Association Relation, and Dynamic Evolution Analysis of Critical Values in Health Checkup in China: A Retrospective Study. Front Public Health 2021; 9:630356. [PMID: 34368036 PMCID: PMC8339420 DOI: 10.3389/fpubh.2021.630356] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 06/16/2021] [Indexed: 01/06/2023] Open
Abstract
Objective: The critical values in health checkup play a key role in preventing chronic diseases and different types of cancer. This study aimed to analyze the prevalence, association relation, and dynamic evolution of critical values in health checkups at a large physical examination center in China. Methods: Herein, we chose 33,639 samples of physical examiners from January 2017 to December 2019. After strict exclusion processes, combined with the critical values in health checkup reporting data, 4,721 participants with at least one critical value were included. We first defined a critical value list for laboratory test, imaging, cervical cancer screening, electrocardiogram, and health checkup informed on site, and then performed a cross-sectional study to analyze the distribution and significance of critical values of 4,721 participants from different views and the association relation of 628 participants with more than one critical value and a retrospective cohort study to analyze the incidence and dynamic evolution of critical values based on 2,813 participants attending the physical examination from 2017 to 2019. Results: A total of 4,721 participants were included in the retrospective study. The prevalence of 10 critical values from 33,639 participants was over 0.6%. The critical values of obesity, hypertension, Glucose_T, Liver_T, Kidney_T, Lipid_T, Urine_T, and Head_CT were significantly increased in men (P < 0.05), whereas the results were the opposite for the Blood_T and Thyroid_US (P < 0.01). The prevalence trend of critical values increased along with age, where the prevalence of men was higher than that of women under 60 years old (P < 0.01), while the prevalence of women increased by four times and exceeded the prevalence of men above 70 years old. Association relation analysis identified 16 and 6 effective rules for men and women, respectively, where the critical values of Urine_T and Glucose_T played the central roles. Furthermore, a retrospective dynamic evolution analysis found that the incidence of new critical values was about 10%, the incidence of persistent critical values was about 50%, and that most of the effective evolution paths tended to no critical values for men and women. Conclusion: In conclusion, this study provides a new perspective to explore the population health status using the critical value reporting data in a physical examination center, which can assist in decision-making by health management at the population level and in the prevention and treatment of various types of cancer and chronic diseases at the individual level.
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Affiliation(s)
- Jingfeng Chen
- Health Management Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhuoqing Wu
- Institute of Systems Engineering, Dalian University of Technology, Dalian, China
| | - Yanan Liu
- Health Management Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Lin Wang
- Health Management Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Tiantian Li
- Health Management Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yihan Dong
- Health Management Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Qian Qin
- Health Management Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Suying Ding
- Health Management Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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21
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Jahmunah V, Ng EYK, San TR, Acharya UR. Automated detection of coronary artery disease, myocardial infarction and congestive heart failure using GaborCNN model with ECG signals. Comput Biol Med 2021; 134:104457. [PMID: 33991857 DOI: 10.1016/j.compbiomed.2021.104457] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 04/22/2021] [Accepted: 04/26/2021] [Indexed: 01/02/2023]
Abstract
Cardiovascular diseases (CVDs) are main causes of death globally with coronary artery disease (CAD) being the most important. Timely diagnosis and treatment of CAD is crucial to reduce the incidence of CAD complications like myocardial infarction (MI) and ischemia-induced congestive heart failure (CHF). Electrocardiogram (ECG) signals are most commonly employed as the diagnostic screening tool to detect CAD. In this study, an automated system (AS) was developed for the automated categorization of electrocardiogram signals into normal, CAD, myocardial infarction (MI) and congestive heart failure (CHF) classes using convolutional neural network (CNN) and unique GaborCNN models. Weight balancing was used to balance the imbalanced dataset. High classification accuracies of more than 98.5% were obtained by the CNN and GaborCNN models respectively, for the 4-class classification of normal, coronary artery disease, myocardial infarction and congestive heart failure classes. GaborCNN is a more preferred model due to its good performance and reduced computational complexity as compared to the CNN model. To the best of our knowledge, this is the first study to propose GaborCNN model for automated categorizing of normal, coronary artery disease, myocardial infarction and congestive heart failure classes using ECG signals. Our proposed system is equipped to be validated with bigger database and has the potential to aid the clinicians to screen for CVDs using ECG signals.
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Affiliation(s)
- V Jahmunah
- Department of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
| | - E Y K Ng
- Department of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore.
| | | | - U Rajendra Acharya
- School of Engineering, Ngee Ann Polytechnic, Singapore; Biomedical Engineering, School of Social Science and Technology, Singapore University of Social Sciences, Singapore; International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan; Department Bioinformatics and Medical Engineering, Asia University, Taiwan; School of Management and Enterprise, University of Southern Queensland, Australia.
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22
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Detection of subthalamic nucleus using novel higher-order spectra features in microelectrode recordings signals. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.04.016] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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23
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Zeng W, Yuan J, Yuan C, Wang Q, Liu F, Wang Y. A novel technique for the detection of myocardial dysfunction using ECG signals based on hybrid signal processing and neural networks. Soft comput 2021. [DOI: 10.1007/s00500-020-05465-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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24
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ZHANG HUAN, WANG XINPEI, LIU CHANGCHUN, LI YUANYANG, LIU YUANYUAN, LI PENG, YAO LIANKE, WANG JIKUO, JIAO YU. A METHOD FOR DETECTING CORONARY ARTERY STENOSIS BASED ON ECG SIGNALS. J MECH MED BIOL 2021. [DOI: 10.1142/s0219519421500032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Coronary heart disease (CHD) is a typical cardiovascular disease whose occurrence and development is a long process. Timely and accurate diagnosis of patients with varying degrees of coronary artery stenosis (VDCAS) is conducive to accurate treatment and prognosis assessment. This study aims to correctly classify VDCAS patients by utilizing multi-domain features fusion of single-lead 5-min ECG signals and machine learning methods, so as to provide reference for doctors to judge the CHD development process. ECG signals were collected from 206 subjects with CHD, mild CHD, thoracalgia and normal coronary angiograms (TNCA), and healthy. Then, the time, frequency, time–frequency, and nonlinear domain features of ECG signals were extracted to establish a multi-domain feature set. To get the optimum subset of features, the recursive feature elimination (RFE) and information gain (IG) were selected. Subsequently, eXtreme Gradient Boosting (XGBoost) and random forest (RF) were adopted for classification. Results indicated that RFE combined with XGBoost was significantly effective in classifying VDCAS patients. When the four categories of subjects (CHD, mild CHD, TNCA, and healthy) were classified, the average accuracy, sensitivity, specificity, and F1-score of the proposed method were 91.74%, 89.39%, 96.80%, and 90.09%, respectively. Besides, three categories of subjects (no stenosis, luminal narrowing [Formula: see text] 50%, and luminal narrowing [Formula: see text] 50%) and two categories of subjects (CHD and healthy) were also analyzed, and the average accuracy was 91.27% and 98.46%, respectively. The results suggest that the proposed method can provide reference for doctors to judge VDCAS patients.
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Affiliation(s)
- HUAN ZHANG
- School of Control Science and Engineering, Shandong University Jinan, Shandong 250061, P. R. China
| | - XINPEI WANG
- School of Control Science and Engineering, Shandong University Jinan, Shandong 250061, P. R. China
| | - CHANGCHUN LIU
- School of Control Science and Engineering, Shandong University Jinan, Shandong 250061, P. R. China
| | - YUANYANG LI
- Department of Medical Engineering, Shandong Provincial Hospital, Affiliated to Shandong First, Medical University Jinan, Shandong 250061, P. R. China
| | - YUANYUAN LIU
- School of Control Science and Engineering, Shandong University Jinan, Shandong 250061, P. R. China
| | - PENG LI
- Division of Sleep and CirCHDian Disorders, Brigham and Women’s Hospital, Division of Sleep Medicine Harvard, Medical School Boston, MA 02115, USA
| | - LIANKE YAO
- School of Control Science and Engineering, Shandong University Jinan, Shandong 250061, P. R. China
| | - JIKUO WANG
- School of Control Science and Engineering, Shandong University Jinan, Shandong 250061, P. R. China
| | - YU JIAO
- School of Control Science and Engineering, Shandong University Jinan, Shandong 250061, P. R. China
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25
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Zhang H, Wang X, Liu C, Liu Y, Li P, Yao L, Li H, Wang J, Jiao Y. Detection of coronary artery disease using multi-modal feature fusion and hybrid feature selection. Physiol Meas 2020; 41. [PMID: 33080588 DOI: 10.1088/1361-6579/abc323] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 10/20/2020] [Indexed: 11/11/2022]
Abstract
Objective: Coronary artery disease (CAD) is a common fatal disease. At present, an accurate method to screen CAD is urgently needed. This study aims to provide optimal detection models for suspected CAD detection according to the differences in medical conditions, so as to assist physicians to make accurate judgments on suspected CAD patients.Approach: Electrocardiogram (ECG) and phonocardiogram (PCG) signals of 32 CAD patients and 30 patients with chest pain and normal coronary angiograms (CPNCA) were simultaneously collected for this paper. For each subject, the ECG and PCG multi-domain features were extracted, and the results of Holter monitoring, echocardiography (ECHO), and biomarker levels (BIO) were obtained to construct a multi-modal feature set. Then, a hybrid feature selection (HFS) method was developed using mutual information, recursive feature elimination, random forest, and weight of support vector machine to obtain the optimal feature subset. A support vector machine with nested cross-validation was used for classification.Main results: Results showed that the Holter model achieved the best performance as a single-modal feature model with an accuracy of 82.67%. In terms of multi-modal feature models, PCG-Holter, PCG-Holter-ECHO, PCG-Holter-ECHO-BIO, and ECG-PCG-Holter-ECHO-BIO were the optimal bimodal, three-modal, four-modal, and five-modal models, with accuracies of 90.38%, 91.92%, 95.25%, and 96.67%, respectively. Among them, the ECG-PCG-Holter-ECHO-BIO model, which was constructed by combining ECG and PCG signals features with Holter, ECHO, and BIO examination results, achieved the best classification results with an average accuracy, sensitivity, specificity, and F1-measure of 96.67%, 96.67%, 96.67%, and 96.64%, respectively.Significance: The study indicated that multi-modal feature fusion and HFS can obtain more effective information for CAD detection and provide a reference for physicians to diagnose CAD patients.
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Affiliation(s)
- Huan Zhang
- Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, People's Republic of China
| | - Xinpei Wang
- Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, People's Republic of China
| | - Changchun Liu
- Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, People's Republic of China
| | - Yuanyuan Liu
- Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, People's Republic of China
| | - Peng Li
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA 02115, United States of America.,Division of Sleep Medicine, Harvard Medical School, Boston, MA 02115, United States of America
| | - Lianke Yao
- Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, People's Republic of China
| | - Han Li
- Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, People's Republic of China
| | - Jikuo Wang
- Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, People's Republic of China
| | - Yu Jiao
- Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, People's Republic of China
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26
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Alizadehsani R, Khosravi A, Roshanzamir M, Abdar M, Sarrafzadegan N, Shafie D, Khozeimeh F, Shoeibi A, Nahavandi S, Panahiazar M, Bishara A, Beygui RE, Puri R, Kapadia S, Tan RS, Acharya UR. Coronary artery disease detection using artificial intelligence techniques: A survey of trends, geographical differences and diagnostic features 1991-2020. Comput Biol Med 2020; 128:104095. [PMID: 33217660 DOI: 10.1016/j.compbiomed.2020.104095] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 10/24/2020] [Accepted: 10/24/2020] [Indexed: 02/06/2023]
Abstract
While coronary angiography is the gold standard diagnostic tool for coronary artery disease (CAD), but it is associated with procedural risk, it is an invasive technique requiring arterial puncture, and it subjects the patient to radiation and iodinated contrast exposure. Artificial intelligence (AI) can provide a pretest probability of disease that can be used to triage patients for angiography. This review comprehensively investigates published papers in the domain of CAD detection using different AI techniques from 1991 to 2020, in order to discern broad trends and geographical differences. Moreover, key decision factors affecting CAD diagnosis are identified for different parts of the world by aggregating the results from different studies. In this study, all datasets that have been used for the studies for CAD detection, their properties, and achieved performances using various AI techniques, are presented, compared, and analyzed. In particular, the effectiveness of machine learning (ML) and deep learning (DL) techniques to diagnose and predict CAD are reviewed. From PubMed, Scopus, Ovid MEDLINE, and Google Scholar search, 500 papers were selected to be investigated. Among these selected papers, 256 papers met our criteria and hence were included in this study. Our findings demonstrate that AI-based techniques have been increasingly applied for the detection of CAD since 2008. AI-based techniques that utilized electrocardiography (ECG), demographic characteristics, symptoms, physical examination findings, and heart rate signals, reported high accuracy for the detection of CAD. In these papers, the authors ranked the features based on their assessed clinical importance with ML techniques. The results demonstrate that the attribution of the relative importance of ML features for CAD diagnosis is different among countries. More recently, DL methods have yielded high CAD detection performance using ECG signals, which drives its burgeoning adoption.
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Affiliation(s)
- Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, Australia
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, Australia
| | - Mohamad Roshanzamir
- Department of Engineering, Fasa Branch, Islamic Azad University, Post Box No 364, Fasa, Fars, 7461789818, Iran
| | - Moloud Abdar
- Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, Australia
| | - Nizal Sarrafzadegan
- Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Khorram Ave, Isfahan, Iran; Faculty of Medicine, SPPH, University of British Columbia, Vancouver, BC, Canada.
| | - Davood Shafie
- Heart Failure Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Fahime Khozeimeh
- Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, Australia
| | - Afshin Shoeibi
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran; Faculty of Electrical and Computer Engineering, Biomedical Data Acquisition Lab, K. N. Toosi University of Technology, Tehran, Iran
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, Australia
| | - Maryam Panahiazar
- Institute for Computational Health Sciences, University of California, San Francisco, USA
| | - Andrew Bishara
- Department of Anesthesia and Perioperative Care, University of California, San Francisco, USA
| | - Ramin E Beygui
- Cardiovascular Surgery Division, Department of Surgery, University of California, San Francisco, CA, USA
| | - Rishi Puri
- Department of Cardiovascular Medicine, Cleveland Clinic, OH, USA
| | - Samir Kapadia
- Department of Cardiovascular Medicine, Cleveland Clinic, OH, USA
| | - Ru-San Tan
- Department of Cardiology, National Heart Centre Singapore, Singapore
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan
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27
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Kang X, Handayani DOD, Chong PP, Acharya UR. Profiling of pornography addiction among children using EEG signals: A systematic literature review. Comput Biol Med 2020; 125:103970. [PMID: 32892114 DOI: 10.1016/j.compbiomed.2020.103970] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 08/09/2020] [Accepted: 08/09/2020] [Indexed: 01/15/2023]
Abstract
Nowadays human behavior has been affected with the advent of new digital technologies. Due to the rampant use of the Internet by children, many have been addicted to pornography. This addiction has negatively affected the behaviors of children including increased impulsiveness, learning ability to attention, poor decision-making, memory problems, and deficit in emotion regulation. The children with porn addiction can be identified by parents and medical practitioners as third-party observers. This systematic literature review (SLR) is conducted to increase the understanding of porn addiction using electroencephalogram (EEG) signals. We have searched five different databases namely IEEE, ACM, Science Direct, Springer and National Center for Biotechnology Information (NCBI) using addiction, porn, and EEG as keywords along with 'OR 'operation in between the expressions. We have selected 46 studies in this work by screening 815,554 papers from five databases. Our results show that it is possible to identify children with porn addiction using EEG signals.
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Affiliation(s)
- Xiaoxi Kang
- Master of Computer Science, Taylor's University, 1, Jalan Taylors, 47500, Subang Jaya, Selangor, Malaysia.
| | - Dini Oktarina Dwi Handayani
- School of Computer Science & Engineering, Faculty of Innovation & Technology, Taylor's University, 1, Jalan Taylors, 47500, Subang Jaya, Selangor, Malaysia.
| | - Pei Pei Chong
- School of Biosciences, Faculty of Health and Medical Sciences, Taylor's University, 1 Jalan Taylors, 47500, Subang Jaya, Selangor, Malaysia.
| | - U Rajendra Acharya
- Ngee Ann, Singapore University of Social Science, University of Malaya, Malaysia; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan.
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28
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Swain SS, Patra D, Singh YO. Automated detection of myocardial infarction in ECG using modified Stockwell transform and phase distribution pattern from time-frequency analysis. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.06.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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29
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Zeng W, Yuan J, Yuan C, Wang Q, Liu F, Wang Y. Classification of myocardial infarction based on hybrid feature extraction and artificial intelligence tools by adopting tunable-Q wavelet transform (TQWT), variational mode decomposition (VMD) and neural networks. Artif Intell Med 2020; 106:101848. [PMID: 32593387 DOI: 10.1016/j.artmed.2020.101848] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 02/16/2020] [Accepted: 03/20/2020] [Indexed: 12/18/2022]
Abstract
Cardiovascular diseases (CVD) is the leading cause of human mortality and morbidity around the world, in which myocardial infarction (MI) is a silent condition that irreversibly damages the heart muscles. Currently, electrocardiogram (ECG) is widely used by the clinicians to diagnose MI patients due to its inexpensiveness and non-invasive nature. Pathological alterations provoked by MI cause slow conduction by increasing axial resistance on coupling between cells. This issue may cause abnormal patterns in the dynamics of the tip of the cardiac vector in the ECG signals. However, manual interpretation of the pathological alternations induced by MI is a time-consuming, tedious and subjective task. To overcome such disadvantages, computer-aided diagnosis techniques including signal processing and artificial intelligence tools have been developed. In this study we propose a novel technique for automatic detection of MI based on hybrid feature extraction and artificial intelligence tools. Tunable quality factor (Q-factor) wavelet transform (TQWT), variational mode decomposition (VMD) and phase space reconstruction (PSR) are utilized to extract representative features to form cardiac vectors with synthesis of the standard 12-lead and Frank XYZ leads. They are combined with neural networks to model, identify and detect abnormal patterns in the dynamics of cardiac system caused by MI. First, 12-lead ECG signals are reduced to 3-dimensional VCG signals, which are synthesized with Frank XYZ leads to build a hybrid 4-dimensional cardiac vector. Second, this vector is decomposed into a set of frequency subbands with a number of decomposition levels by using the TQWT method. Third, VMD is employed to decompose the subband of the 4-dimensional cardiac vector into different intrinsic modes, in which the first intrinsic mode contains the majority of the cardiac vector's energy and is considered to be the predominant intrinsic mode. It is selected to construct the reference variable for analysis. Fourth, phase space of the reference variable is reconstructed, in which the properties associated with the nonlinear cardiac system dynamics are preserved. Three-dimensional (3D) PSR together with Euclidean distance (ED) has been utilized to derive features, which demonstrate significant difference in cardiac system dynamics between normal (healthy) and MI cardiac vector signals. Fifth, cardiac system dynamics can be modeled and identified using neural networks, which employ the ED of 3D PSR of the reference variable as the input features. The difference of cardiac system dynamics between healthy control and MI cardiac vector is computed and used for the detection of MI based on a bank of estimators. Finally, data sets, which include conventional 12-lead and Frank XYZ leads ECG signal fragments from 148 patients with MI and 52 healthy controls from PTB diagnostic ECG database, are used for evaluation. By using the 10-fold cross-validation style, the achieved average classification accuracy is reported to be 97.98%. Currently, ST segment evaluation is one of the major and traditional ways for the MI detection. However, there exist weak or even undetectable ST segments in many ECG signals. Since the proposed method does not rely on the information of ST waves, it can serve as a complementary MI detection algorithm in the intensive care unit (ICU) of hospitals to assist the clinicians in confirming their diagnosis. Overall, our results verify that the proposed features may satisfactorily reflect cardiac system dynamics, and are complementary to the existing ECG features for automatic cardiac function analysis.
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Affiliation(s)
- Wei Zeng
- School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan 364012, PR China.
| | - Jian Yuan
- School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan 364012, PR China
| | - Chengzhi Yuan
- Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI 02881, USA
| | - Qinghui Wang
- School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan 364012, PR China
| | - Fenglin Liu
- School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan 364012, PR China
| | - Ying Wang
- School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan 364012, PR China
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Nasarian E, Abdar M, Fahami MA, Alizadehsani R, Hussain S, Basiri ME, Zomorodi-Moghadam M, Zhou X, Pławiak P, Acharya UR, Tan RS, Sarrafzadegan N. Association between work-related features and coronary artery disease: A heterogeneous hybrid feature selection integrated with balancing approach. Pattern Recognit Lett 2020. [DOI: 10.1016/j.patrec.2020.02.010] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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31
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Asgharzadeh-Bonab A, Amirani MC, Mehri A. Spectral entropy and deep convolutional neural network for ECG beat classification. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.02.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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32
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1D-CADCapsNet: One dimensional deep capsule networks for coronary artery disease detection using ECG signals. Phys Med 2020; 70:39-48. [DOI: 10.1016/j.ejmp.2020.01.007] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Revised: 11/27/2019] [Accepted: 01/07/2020] [Indexed: 12/14/2022] Open
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Lih OS, Jahmunah V, San TR, Ciaccio EJ, Yamakawa T, Tanabe M, Kobayashi M, Faust O, Acharya UR. Comprehensive electrocardiographic diagnosis based on deep learning. Artif Intell Med 2020; 103:101789. [PMID: 32143796 DOI: 10.1016/j.artmed.2019.101789] [Citation(s) in RCA: 82] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 11/06/2019] [Accepted: 12/31/2019] [Indexed: 11/15/2022]
Abstract
Cardiovascular disease (CVD) is the leading cause of death worldwide, and coronary artery disease (CAD) is a major contributor. Early-stage CAD can progress if undiagnosed and left untreated, leading to myocardial infarction (MI) that may induce irreversible heart muscle damage, resulting in heart chamber remodeling and eventual congestive heart failure (CHF). Electrocardiography (ECG) signals can be useful to detect established MI, and may also be helpful for early diagnosis of CAD. For the latter especially, the ECG perturbations can be subtle and potentially misclassified during manual interpretation and/or when analyzed by traditional algorithms found in ECG instrumentation. For automated diagnostic systems (ADS), deep learning techniques are favored over conventional machine learning techniques, due to the automatic feature extraction and selection processes involved. This paper highlights various deep learning algorithms exploited for the classification of ECG signals into CAD, MI, and CHF conditions. The Convolutional Neural Network (CNN), followed by combined CNN and Long Short-Term Memory (LSTM) models, appear to be the most useful architectures for classification. A 16-layer LSTM model was developed in our study and validated using 10-fold cross-validation. A classification accuracy of 98.5% was achieved. Our proposed model has the potential to be a useful diagnostic tool in hospitals for the classification of abnormal ECG signals.
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Affiliation(s)
- Oh Shu Lih
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - V Jahmunah
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | | | | | - Toshitaka Yamakawa
- Department of Computer Science and Electrical Engineering, Kumamoto University, Japan
| | - Masayuki Tanabe
- Department of Computer Science and Electrical Engineering, Kumamoto University, Japan; International Research Organization for Advanced Science and Technology (IROAST) Kumamoto University, Kumamoto, Japan
| | - Makiko Kobayashi
- Department of Computer Science and Electrical Engineering, Kumamoto University, Japan
| | - Oliver Faust
- Department of Engineering and Mathematics, Sheffield Hallam University, United Kingdom
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan; International Research Organization for Advanced Science and Technology (IROAST) Kumamoto University, Kumamoto, Japan.
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Novel Methodology for Cardiac Arrhythmias Classification Based on Long-Duration ECG Signal Fragments Analysis. SERIES IN BIOENGINEERING 2020. [DOI: 10.1007/978-981-13-9097-5_11] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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35
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Sharma M, Patel S, Choudhary S, Acharya UR. Automated Detection of Sleep Stages Using Energy-Localized Orthogonal Wavelet Filter Banks. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2019. [DOI: 10.1007/s13369-019-04197-8] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
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Fu Y, Zhao S, Wang L, Zhu R. A Wearable Sensor Using Structured Silver-Particle Reinforced PDMS for Radial Arterial Pulse Wave Monitoring. Adv Healthc Mater 2019; 8:e1900633. [PMID: 31293071 DOI: 10.1002/adhm.201900633] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2019] [Revised: 06/27/2019] [Indexed: 01/31/2023]
Abstract
Human pulse signals contain important and useful physiological information for the auxiliary diagnosis of cardiovascular disease. Here, a wearable pulse sensor based on piezo-thermic transduction is reported using a structured silver-particle reinforced polydimethylsiloxane (PDMS) membrane, for monitoring radial arterial pulse waves. The structured silver-particle reinforced PDMS membrane is optimally designed to meet the specific requirements on sensitivity, linearity, and effective preload measuring range for pulse detection by adjusting the air gap volume fraction and silver particle volume fraction of the structured material. The sensor is endowed with high sensitivity, good linearity in preload measuring range, allowing to detect the subtle pulse waveforms of subjects at different ages under different contact pressures, such as superficial (Fu), medium (Zhong) and deep (Chen). The developed pulse device provides a promising approach for homecare pulse monitoring.
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Affiliation(s)
- Yu Fu
- State Key Laboratory of Precision Measurement Technology and InstrumentsDepartment of Precision InstrumentTsinghua University Beijing 100084 China
| | - Shuai Zhao
- State Key Laboratory of Precision Measurement Technology and InstrumentsDepartment of Precision InstrumentTsinghua University Beijing 100084 China
| | - Liangqi Wang
- State Key Laboratory of Precision Measurement Technology and InstrumentsDepartment of Precision InstrumentTsinghua University Beijing 100084 China
| | - Rong Zhu
- State Key Laboratory of Precision Measurement Technology and InstrumentsDepartment of Precision InstrumentTsinghua University Beijing 100084 China
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Tsallis C. Beyond Boltzmann-Gibbs-Shannon in Physics and Elsewhere. ENTROPY 2019; 21:e21070696. [PMID: 33267410 PMCID: PMC7515208 DOI: 10.3390/e21070696] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Accepted: 06/28/2019] [Indexed: 01/15/2023]
Abstract
The pillars of contemporary theoretical physics are classical mechanics, Maxwell electromagnetism, relativity, quantum mechanics, and Boltzmann–Gibbs (BG) statistical mechanics –including its connection with thermodynamics. The BG theory describes amazingly well the thermal equilibrium of a plethora of so-called simple systems. However, BG statistical mechanics and its basic additive entropy SBG started, in recent decades, to exhibit failures or inadequacies in an increasing number of complex systems. The emergence of such intriguing features became apparent in quantum systems as well, such as black holes and other area-law-like scenarios for the von Neumann entropy. In a different arena, the efficiency of the Shannon entropy—as the BG functional is currently called in engineering and communication theory—started to be perceived as not necessarily optimal in the processing of images (e.g., medical ones) and time series (e.g., economic ones). Such is the case in the presence of generic long-range space correlations, long memory, sub-exponential sensitivity to the initial conditions (hence vanishing largest Lyapunov exponents), and similar features. Finally, we witnessed, during the last two decades, an explosion of asymptotically scale-free complex networks. This wide range of important systems eventually gave support, since 1988, to the generalization of the BG theory. Nonadditive entropies generalizing the BG one and their consequences have been introduced and intensively studied worldwide. The present review focuses on these concepts and their predictions, verifications, and applications in physics and elsewhere. Some selected examples (in quantum information, high- and low-energy physics, low-dimensional nonlinear dynamical systems, earthquakes, turbulence, long-range interacting systems, and scale-free networks) illustrate successful applications. The grounding thermodynamical framework is briefly described as well.
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Affiliation(s)
- Constantino Tsallis
- Centro Brasileiro de Pesquisas Físicas and National Institute of Science and Technology for Complex Systems–Rua Dr. Xavier Sigaud 150, Rio de Janeiro 22290-180, Brazil;
- Santa Fe Institute–1399 Hyde Park Road, Santa Fe, NM 87501, USA
- Complexity Science Hub Vienna–Josefstädter Strasse 39, 1080 Vienna, Austria
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Alizadehsani R, Abdar M, Roshanzamir M, Khosravi A, Kebria PM, Khozeimeh F, Nahavandi S, Sarrafzadegan N, Acharya UR. Machine learning-based coronary artery disease diagnosis: A comprehensive review. Comput Biol Med 2019; 111:103346. [PMID: 31288140 DOI: 10.1016/j.compbiomed.2019.103346] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 06/26/2019] [Accepted: 06/26/2019] [Indexed: 02/02/2023]
Abstract
Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) and often leads to a heart attack. It annually causes millions of deaths and billions of dollars in financial losses worldwide. Angiography, which is invasive and risky, is the standard procedure for diagnosing CAD. Alternatively, machine learning (ML) techniques have been widely used in the literature as fast, affordable, and noninvasive approaches for CAD detection. The results that have been published on ML-based CAD diagnosis differ substantially in terms of the analyzed datasets, sample sizes, features, location of data collection, performance metrics, and applied ML techniques. Due to these fundamental differences, achievements in the literature cannot be generalized. This paper conducts a comprehensive and multifaceted review of all relevant studies that were published between 1992 and 2019 for ML-based CAD diagnosis. The impacts of various factors, such as dataset characteristics (geographical location, sample size, features, and the stenosis of each coronary artery) and applied ML techniques (feature selection, performance metrics, and method) are investigated in detail. Finally, the important challenges and shortcomings of ML-based CAD diagnosis are discussed.
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Affiliation(s)
- Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Australia.
| | - Moloud Abdar
- Département d'informatique, Université du Québec à Montréal, Montréal, Québec, Canada
| | - Mohamad Roshanzamir
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Australia
| | - Parham M Kebria
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Australia
| | - Fahime Khozeimeh
- Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Australia
| | - Nizal Sarrafzadegan
- Faculty of Medicine, SPPH, University of British Columbia, Vancouver, BC, Canada; Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Khorram Ave, Isfahan, Iran
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia
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Sharma M, Rajendra Acharya U. A new method to identify coronary artery disease with ECG signals and time-Frequency concentrated antisymmetric biorthogonal wavelet filter bank. Pattern Recognit Lett 2019. [DOI: 10.1016/j.patrec.2019.04.014] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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40
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Shirole U, Joshi M, Bagul P. Cardiac, diabetic and normal subjects classification using decision tree and result confirmation through orthostatic stress index. INFORMATICS IN MEDICINE UNLOCKED 2019. [DOI: 10.1016/j.imu.2019.100252] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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41
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Magnetocardiography-Based Ischemic Heart Disease Detection and Localization Using Machine Learning Methods. IEEE Trans Biomed Eng 2018; 66:1658-1667. [PMID: 30369432 DOI: 10.1109/tbme.2018.2877649] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
OBJECTIVE This study focused on developing a fast and accurate automatic ischemic heart disease detection/localization methodology. METHODS T wave was segmented from averaged Magnetocardiography (MCG) recordings and 164 features were subsequently extracted. These features were categorized into three groups: time domain features, frequency domain features, and information theory features. Next, we compared different machine learning classifiers including: k-nearest neighbor, decision tree, support vector machine (SVM), and XGBoost. To identify ischemia heart disease (IHD) case, we selected three classifiers with best performance and applied model ensemble to average results. All 164 features were used in this stage. To localize ischemia, we classified IHD group according to stenosis locations, including left anterior descending (LAD), left circumflex artery (LCX), and right coronary artery (RCA). For this task, we used XGBoost classifier and 18 time domain features. RESULTS For IHD detection, the SVM-XGBoost model achieved best results with accuracy = 94.03%, precision = 86.56%, recall = 97.78%, F-score = 92.79%, AUC = 0.98, and average precision = 0.98. For ischemia localization, XGBoost model achieved accuracy = 0.74, 0.68, and 0.65, for LAD, LCX, and RCA, respectively. CONCLUSION we have developed an automatic IHD detection and localization system. We find that 1. T wave repolarization synchronicity is an important factor to distinguish IHD from normal subjects 2. Magnetic field pattern is associated with stenosis location. SIGNIFICANCE The proposed machine learning method provides the clinicians a fast and accurate diagnosis tool to interpret MCG data, boosting its acceptance into clinics. Furthermore, the magnetic pole characteristics revealed by the method shows to be related to ischemia location, presenting the opportunity to noninvasively locate ischemia.
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Hagiwara Y, Fujita H, Oh SL, Tan JH, Tan RS, Ciaccio EJ, Acharya UR. Computer-aided diagnosis of atrial fibrillation based on ECG Signals: A review. Inf Sci (N Y) 2018. [DOI: 10.1016/j.ins.2018.07.063] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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43
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Sharma M, Tan RS, Acharya UR. A novel automated diagnostic system for classification of myocardial infarction ECG signals using an optimal biorthogonal filter bank. Comput Biol Med 2018; 102:341-356. [PMID: 30049414 DOI: 10.1016/j.compbiomed.2018.07.005] [Citation(s) in RCA: 66] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2018] [Revised: 07/06/2018] [Accepted: 07/07/2018] [Indexed: 11/16/2022]
Abstract
Myocardial infarction (MI), also referred to as heart attack, occurs when there is an interruption of blood flow to parts of the heart, due to the acute rupture of atherosclerotic plaque, which leads to damage of heart muscle. The heart muscle damage produces changes in the recorded surface electrocardiogram (ECG). The identification of MI by visual inspection of the ECG requires expert interpretation, and is difficult as the ECG signal changes associated with MI can be short in duration and low in magnitude. Hence, errors in diagnosis can lead to delay the initiation of appropriate medical treatment. To lessen the burden on doctors, an automated ECG based system can be installed in hospitals to help identify MI changes on ECG. In the proposed study, we develop a single-channel single lead ECG based MI diagnostic system validated using noisy and clean datasets. The raw ECG signals are taken from the Physikalisch-Technische Bundesanstalt database. We design a novel two-band optimal biorthogonal filter bank (FB) for analysis of the ECG signals. We present a method to design a novel class of two-band optimal biorthogonal FB in which not only the product filter but the analysis lowpass filter is also a halfband filter. The filter design problem has been composed as a constrained convex optimization problem in which the objective function is a convex combination of multiple quadratic functions and the regularity and perfect reconstruction conditions are imposed in the form linear equalities. ECG signals are decomposed into six subbands (SBs) using the newly designed wavelet FB. Following to this, discriminating features namely, fuzzy entropy (FE), signal-fractal-dimensions (SFD), and renyi entropy (RE) are computed from all the six SBs. The features are fed to the k-nearest neighbor (KNN). The proposed system yields an accuracy of 99.62% for the noisy dataset and an accuracy of 99.74% for the clean dataset, using 10-fold cross validation (CV) technique. Our MI identification system is robust and highly accurate. It can thus be installed in clinics for detecting MI.
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Affiliation(s)
- Manish Sharma
- Department of Electrical Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad, India.
| | - Ru San Tan
- National Heart Centre Singapore, Singapore.
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, University of Social Sciences (SUSS), Singapore; School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, 47500, Subang Jaya, Malaysia.
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Yildirim Ö. A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification. Comput Biol Med 2018; 96:189-202. [PMID: 29614430 DOI: 10.1016/j.compbiomed.2018.03.016] [Citation(s) in RCA: 221] [Impact Index Per Article: 36.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Revised: 03/23/2018] [Accepted: 03/24/2018] [Indexed: 11/28/2022]
Abstract
Long-short term memory networks (LSTMs), which have recently emerged in sequential data analysis, are the most widely used type of recurrent neural networks (RNNs) architecture. Progress on the topic of deep learning includes successful adaptations of deep versions of these architectures. In this study, a new model for deep bidirectional LSTM network-based wavelet sequences called DBLSTM-WS was proposed for classifying electrocardiogram (ECG) signals. For this purpose, a new wavelet-based layer is implemented to generate ECG signal sequences. The ECG signals were decomposed into frequency sub-bands at different scales in this layer. These sub-bands are used as sequences for the input of LSTM networks. New network models that include unidirectional (ULSTM) and bidirectional (BLSTM) structures are designed for performance comparisons. Experimental studies have been performed for five different types of heartbeats obtained from the MIT-BIH arrhythmia database. These five types are Normal Sinus Rhythm (NSR), Ventricular Premature Contraction (VPC), Paced Beat (PB), Left Bundle Branch Block (LBBB), and Right Bundle Branch Block (RBBB). The results show that the DBLSTM-WS model gives a high recognition performance of 99.39%. It has been observed that the wavelet-based layer proposed in the study significantly improves the recognition performance of conventional networks. This proposed network structure is an important approach that can be applied to similar signal processing problems.
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Affiliation(s)
- Özal Yildirim
- Computer Engineering Department, Engineering Faculty, Munzur University, Tunceli, Turkey.
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