1
|
Liu J, Zhu Y, Cong F, Björkman A, Malesevic N, Antfolk C. Analysis of modulations of mental fatigue on intra-individual variability from single-trial event related potentials. J Neurosci Methods 2024; 406:110110. [PMID: 38499275 DOI: 10.1016/j.jneumeth.2024.110110] [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: 09/29/2023] [Revised: 01/13/2024] [Accepted: 03/15/2024] [Indexed: 03/20/2024]
Abstract
BACKGROUND Intra-individual variability (IIV), a measure of variance within an individual's performance, has been demonstrated as metrics of brain responses for neural functionality. However, how mental fatigue modulates IIV remains unclear. Consequently, the development of robust mental fatigue detection methods at the single-trial level is challenging. NEW METHODS Based on a long-duration flanker task EEG dataset, the modulations of mental fatigue on IIV were explored in terms of response time (RT) and trial-to-trial latency variations of event-related potentials (ERPs). Specifically, latency variations were quantified using residue iteration decomposition (RIDE) to reconstruct latency-corrected ERPs. We compared reconstructed ERPs with raw ERPs by means of temporal principal component analysis (PCA). Furthermore, a single-trial classification pipeline was developed to detect the changes of mental fatigue levels. RESULTS We found an increased IIV in the RT metric in the fatigue state compared to the alert state. The same sequence of ERPs (N1, P2, N2, P3a, P3b, and slow wave, or SW) was separated from both raw and reconstructed ERPs using PCA, whereas differences between raw and reconstructed ERPs in explained variances for separated ERPs were found owing to IIV. Particularly, a stronger N2 was detected in the fatigue than alert state after RIDE. The single-trial fatigue detection pipeline yielded an acceptable accuracy of 73.3%. COMPARISON WITH EXISTING METHODS The IIV has been linked to aging and brain disorders, and as an extension, our finding demonstrates IIV as an efficient indicator of mental fatigue. CONCLUSIONS This study reveals significant modulations of mental fatigue on IIV at the behavioral and neural levels and establishes a robust mental fatigue detection pipeline.
Collapse
Affiliation(s)
- Jia Liu
- Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund 22100, Sweden.
| | - Yongjie Zhu
- Department of Computer Science, University of Helsinki, Helsinki 00560, Finland
| | - Fengyu Cong
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä 40014, Finland; School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian 116024, China; School of Artificial Intelligence, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China; Key Laboratory of Integrated Circuit and Biomedical Electronic System, Liaoning Province. Dalian University of Technology, Dalian 116024, China
| | - Anders Björkman
- Department of Hand Surgery, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Nebojsa Malesevic
- Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund 22100, Sweden
| | - Christian Antfolk
- Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund 22100, Sweden
| |
Collapse
|
2
|
Wang J, Guo X. Automated detection of myocardial infarction based on an improved state refinement module for LSTM/GRU. Artif Intell Med 2024; 152:102865. [PMID: 38640703 DOI: 10.1016/j.artmed.2024.102865] [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: 04/06/2023] [Revised: 02/10/2024] [Accepted: 03/30/2024] [Indexed: 04/21/2024]
Abstract
Myocardial infarction (MI) is a common cardiovascular disease caused by the blockages of coronary arteries. The visual inspection of electrocardiogram (ECG) is the main diagnosis pattern, while it is taxing and time-consuming. Motivated from state refinement module for long short term memory (SRM-LSTM), we proposed two improved state refinement frameworks based on LSTM and gated recurrent unit (GRU) called ISRM-LSTM and ISRM-GRU. Both are capable of adaptively refining current states of sample points in ECG with a message passing mechanism than existing LSTM. To evaluate the validity, both are installed into convolutional network architecture and standard LSTM, GRU and Residual networks are employed as control groups across the Physikalisch-Technische Bundesanstalt database. Empirical results confirm noticeable performance improvements than control groups and several existing algorithms with an accuracy of 99.1%. To our knowledge, both modules are the first attempt to consider the interaction characteristics into deep network and improve interpretability exhibiting considerable potentials on lightweight devices thanks to only utilization of three channel ECGs.
Collapse
Affiliation(s)
- Jibin Wang
- Department of Network Engineering, Anhui Science and Technology University, Fengyang 233100, China; School of Mathematics, Tianjin University, Tianjin 300354, China.
| | - Xingtian Guo
- Clinical College, Anhui Medical University, Hefei 230031, China
| |
Collapse
|
3
|
Sahid MA, Babar MUH, Uddin MP. Predictive modeling of multi-class diabetes mellitus using machine learning and filtering iraqi diabetes data dynamics. PLoS One 2024; 19:e0300785. [PMID: 38753669 PMCID: PMC11098411 DOI: 10.1371/journal.pone.0300785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 03/05/2024] [Indexed: 05/18/2024] Open
Abstract
Diabetes is a persistent metabolic disorder linked to elevated levels of blood glucose, commonly referred to as blood sugar. This condition can have detrimental effects on the heart, blood vessels, eyes, kidneys, and nerves as time passes. It is a chronic ailment that arises when the body fails to produce enough insulin or is unable to effectively use the insulin it produces. When diabetes is not properly managed, it often leads to hyperglycemia, a condition characterized by elevated blood sugar levels or impaired glucose tolerance. This can result in significant harm to various body systems, including the nerves and blood vessels. In this paper, we propose a multiclass diabetes mellitus detection and classification approach using an extremely imbalanced Laboratory of Medical City Hospital data dynamics. We also formulate a new dataset that is moderately imbalanced based on the Laboratory of Medical City Hospital data dynamics. To correctly identify the multiclass diabetes mellitus, we employ three machine learning classifiers namely support vector machine, logistic regression, and k-nearest neighbor. We also focus on dimensionality reduction (feature selection-filter, wrapper, and embedded method) to prune the unnecessary features and to scale up the classification performance. To optimize the classification performance of classifiers, we tune the model by hyperparameter optimization with 10-fold grid search cross-validation. In the case of the original extremely imbalanced dataset with 70:30 partition and support vector machine classifier, we achieved maximum accuracy of 0.964, precision of 0.968, recall of 0.964, F1-score of 0.962, Cohen kappa of 0.835, and AUC of 0.99 by using top 4 feature according to filter method. By using the top 9 features according to wrapper-based sequential feature selection, the k-nearest neighbor provides an accuracy of 0.935 and 1.0 for the other performance metrics. For our created moderately imbalanced dataset with an 80:20 partition, the SVM classifier achieves a maximum accuracy of 0.938, and 1.0 for other performance metrics. For the multiclass diabetes mellitus detection and classification, our experiments outperformed conducted research based on the Laboratory of Medical City Hospital data dynamics.
Collapse
Affiliation(s)
- Md Abdus Sahid
- Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh
| | - Mozaddid Ul Hoque Babar
- Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh
| | - Md Palash Uddin
- Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh
| |
Collapse
|
4
|
Xiong P, Yang L, Zhang J, Xu J, Yang J, Wang H, Liu X. Detection of inferior myocardial infarction based on multi branch hybrid network. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2023]
|
5
|
Sun Q, Xu Z, Liang C, Zhang F, Li J, Liu R, Chen T, Ji B, Chen Y, Wang C. A dynamic learning-based ECG feature extraction method for myocardial infarction detection. Physiol Meas 2023; 43. [PMID: 36595315 DOI: 10.1088/1361-6579/acaa1a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 12/08/2022] [Indexed: 12/13/2022]
Abstract
Objective.Myocardial infarction (MI) is one of the leading causes of human mortality in all cardiovascular diseases globally. Currently, the 12-lead electrocardiogram (ECG) is widely used as a first-line diagnostic tool for MI. However, visual inspection of pathological ECG variations induced by MI remains a great challenge for cardiologists, since pathological changes are usually complex and slight.Approach.To have an accuracy of the MI detection, the prominent features extracted from in-depth mining of ECG signals need to be explored. In this study, a dynamic learning algorithm is applied to discover prominent features for identifying MI patients via mining the hidden inherent dynamics in ECG signals. Firstly, the distinctive dynamic features extracted from the multi-scale decomposition of dynamic modeling of the ECG signals effectively and comprehensibly represent the pathological ECG changes. Secondly, a few most important dynamic features are filtered through a hybrid feature selection algorithm based on filter and wrapper to form a representative reduced feature set. Finally, different classifiers based on the reduced feature set are trained and tested on the public PTB dataset and an independent clinical data set.Main results.Our proposed method achieves a significant improvement in detecting MI patients under the inter-patient paradigm, with an accuracy of 94.75%, sensitivity of 94.18%, and specificity of 96.33% on the PTB dataset. Furthermore, classifiers trained on PTB are verified on the test data set collected from 200 patients, yielding a maximum accuracy of 84.96%, sensitivity of 85.04%, and specificity of 84.80%.Significance.The experimental results demonstrate that our method performs distinctive dynamic feature extraction and may be used as an effective auxiliary tool to diagnose MI patients.
Collapse
Affiliation(s)
- Qinghua Sun
- School of Control Science and Engineering, Shandong University, Jinan 250061, People's Republic of China.,Center for Intelligent Medical Engineering, Shandong University, Jinan 250061, People's Republic of China
| | - Zhanfei Xu
- School of Control Science and Engineering, Shandong University, Jinan 250061, People's Republic of China
| | - Chunmiao Liang
- School of Control Science and Engineering, Shandong University, Jinan 250061, People's Republic of China
| | - Fukai Zhang
- School of Control Science and Engineering, Shandong University, Jinan 250061, People's Republic of China.,Center for Intelligent Medical Engineering, Shandong University, Jinan 250061, People's Republic of China
| | - Jiali Li
- School of Control Science and Engineering, Shandong University, Jinan 250061, People's Republic of China
| | - Rugang Liu
- Department of Emergency, Qilu Hospital of Shandong University, Jinan 250012, People's Republic of China
| | - Tianrui Chen
- School of Control Science and Engineering, Shandong University, Jinan 250061, People's Republic of China.,Center for Intelligent Medical Engineering, Shandong University, Jinan 250061, People's Republic of China
| | - Bing Ji
- School of Control Science and Engineering, Shandong University, Jinan 250061, People's Republic of China.,Center for Intelligent Medical Engineering, Shandong University, Jinan 250061, People's Republic of China
| | - Yuguo Chen
- Department of Emergency, Qilu Hospital of Shandong University, Jinan 250012, People's Republic of China
| | - Cong Wang
- School of Control Science and Engineering, Shandong University, Jinan 250061, People's Republic of China.,Center for Intelligent Medical Engineering, Shandong University, Jinan 250061, People's Republic of China
| |
Collapse
|
6
|
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.
Collapse
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.
| |
Collapse
|
7
|
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.
Collapse
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.
| |
Collapse
|
8
|
Hassannataj Joloudari J, Mojrian S, Nodehi I, Mashmool A, Kiani Zadegan Z, Khanjani Shirkharkolaie S, Alizadehsani R, Tamadon T, Khosravi S, Akbari Kohnehshari M, Hassannatajjeloudari E, Sharifrazi D, Mosavi A, Loh HW, Tan RS, Acharya UR. Application of artificial intelligence techniques for automated detection of myocardial infarction: a review. Physiol Meas 2022; 43. [PMID: 35803247 DOI: 10.1088/1361-6579/ac7fd9] [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: 02/21/2022] [Accepted: 07/08/2022] [Indexed: 11/11/2022]
Abstract
Myocardial infarction (MI) results in heart muscle injury due to receiving insufficient blood flow. MI is the most common cause of mortality in middle-aged and elderly individuals worldwide. To diagnose MI, clinicians need to interpret electrocardiography (ECG) signals, which requires expertise and is subject to observer bias. Artificial intelligence-based methods can be utilized to screen for or diagnose MI automatically using ECG signals. In this work, we conducted a comprehensive assessment of artificial intelligence-based approaches for MI detection based on ECG and some other biophysical signals, including machine learning (ML) and deep learning (DL) models. The performance of traditional ML methods relies on handcrafted features and manual selection of ECG signals, whereas DL models can automate these tasks. The review observed that deep convolutional neural networks (DCNNs) yielded excellent classification performance for MI diagnosis, which explains why they have become prevalent in recent years. To our knowledge, this is the first comprehensive survey of artificial intelligence techniques employed for MI diagnosis using ECG and some other biophysical signals.
Collapse
Affiliation(s)
- Javad Hassannataj Joloudari
- Computer Engineering, University of Birjand, South Khorasan Province, Birjand, Iran, Birjand, South Khorasan, 9717434765, Iran (the Islamic Republic of)
| | - Sanaz Mojrian
- Mazandaran University of Science and Technology, Mazandaran Province, Babol, Danesh 5, No. Sheykh Tabarasi, Iran, Babol, 47166-85635, Iran (the Islamic Republic of)
| | - Issa Nodehi
- University of Qom, Qom, shahid khodakaram blvd، Iran, Qom, Qom, 1519-37195, Iran (the Islamic Republic of)
| | - Amir Mashmool
- University of Geneva, Via del Molo, 65, 16128 Genova GE, Italy, Geneva, Geneva, 16121, ITALY
| | - Zeynab Kiani Zadegan
- University of Birjand, South Khorasan Province, Birjand, Iran, Birjand, 9717434765, Iran (the Islamic Republic of)
| | - Sahar Khanjani Shirkharkolaie
- Mazandaran University of Science and Technology, Mazandaran Province, Babol, Danesh 5, No. Sheykh Tabarasi, Iran, Babol, 47166-85635, Iran (the Islamic Republic of)
| | - Roohallah Alizadehsani
- Deakin University - Geelong Waterfront Campus, IISRI, Geelong, Victoria, 3220, AUSTRALIA
| | - Tahereh Tamadon
- University of Birjand, South Khorasan Province, Birjand, Iran, Birjand, 9717434765, Iran (the Islamic Republic of)
| | - Samiyeh Khosravi
- University of Birjand, South Khorasan Province, Birjand, Iran, Birjand, 9717434765, Iran (the Islamic Republic of)
| | - Mitra Akbari Kohnehshari
- Bu Ali Sina University, QFRQ+V8H District 2, Hamedan, Iran, Hamedan, Hamedan, 6516738695, Iran (the Islamic Republic of)
| | - Edris Hassannatajjeloudari
- Maragheh University of Medical Sciences, 87VG+9J6, Maragheh, East Azerbaijan Province, Iran, Maragheh, East Azerbaijan, 55158-78151, Iran (the Islamic Republic of)
| | - Danial Sharifrazi
- Islamic Azad University Shiraz, Shiraz University, Iran, Shiraz, Fars, 74731-71987, Iran (the Islamic Republic of)
| | - Amir Mosavi
- Faculty of Informatics, Obuda University, Faculty of Informatics, Obuda University, Budapest, Hungary, Budapest, 1034, HUNGARY
| | - Hui Wen Loh
- Singapore University of Social Sciences, SG, Clementi Rd, 463, Singapore 599494, Singapore, 599491, SINGAPORE
| | - Ru-San Tan
- Department of Cardiology, National Heart Centre Singapore, 5 Hospital Dr, Singapore 169609, Singapore, 168752, SINGAPORE
| | - U Rajendra Acharya
- Electronic Computer Engineering Division, Ngee Ann Polytechnic, 535 Clementi Road, Singapore 599489, Singapore, 599489, SINGAPORE
| |
Collapse
|
9
|
Zhao X, Zhang J, Gong Y, Xu L, Liu H, Wei S, Wu Y, Cha G, Wei H, Mao J, Xia L. Reliable Detection of Myocardial Ischemia Using Machine Learning Based on Temporal-Spatial Characteristics of Electrocardiogram and Vectorcardiogram. Front Physiol 2022; 13:854191. [PMID: 35707012 PMCID: PMC9192098 DOI: 10.3389/fphys.2022.854191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 04/12/2022] [Indexed: 11/15/2022] Open
Abstract
Background: Myocardial ischemia is a common early symptom of cardiovascular disease (CVD). Reliable detection of myocardial ischemia using computer-aided analysis of electrocardiograms (ECG) provides an important reference for early diagnosis of CVD. The vectorcardiogram (VCG) could improve the performance of ECG-based myocardial ischemia detection by affording temporal-spatial characteristics related to myocardial ischemia and capturing subtle changes in ST-T segment in continuous cardiac cycles. We aim to investigate if the combination of ECG and VCG could improve the performance of machine learning algorithms in automatic myocardial ischemia detection. Methods: The ST-T segments of 20-second, 12-lead ECGs, and VCGs were extracted from 377 patients with myocardial ischemia and 52 healthy controls. Then, sample entropy (SampEn, of 12 ECG leads and of three VCG leads), spatial heterogeneity index (SHI, of VCG) and temporal heterogeneity index (THI, of VCG) are calculated. Using a grid search, four SampEn and two features are selected as input signal features for ECG-only and VCG-only models based on support vector machine (SVM), respectively. Similarly, three features (SI, THI, and SHI, where SI is the SampEn of lead I) are further selected for the ECG + VCG model. 5-fold cross validation was used to assess the performance of ECG-only, VCG-only, and ECG + VCG models. To fully evaluate the algorithmic generalization ability, the model with the best performance was selected and tested on a third independent dataset of 148 patients with myocardial ischemia and 52 healthy controls. Results: The ECG + VCG model with three features (SI,THI, and SHI) yields better classifying results than ECG-only and VCG-only models with the average accuracy of 0.903, sensitivity of 0.903, specificity of 0.905, F1 score of 0.942, and AUC of 0.904, which shows better performance with fewer features compared with existing works. On the third independent dataset, the testing showed an AUC of 0.814. Conclusion: The SVM algorithm based on the ECG + VCG model could reliably detect myocardial ischemia, providing a potential tool to assist cardiologists in the early diagnosis of CVD in routine screening during primary care services.
Collapse
Affiliation(s)
- Xiaoye Zhao
- School of Instrument Science and Opto-Electronic Engineering, Hefei University of Technology, Hefei, China.,School of Electrical and Information Engineering, North Minzu University, Yinchuan, China.,Key Laboratory of Atmospheric Environment Remote Sensing of Ningxia, Yinchuan, China
| | - Jucheng Zhang
- Department of Clinical Engineering, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yinglan Gong
- Hangzhou Maixin Technology Co., Ltd., Hangzhou, China.,Institute of Wenzhou, Zhejiang University, Wenzhou, China
| | - Lihua Xu
- Hangzhou Linghua Biotech Ltd., Hangzhou, China
| | - Haipeng Liu
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, United Kingdom
| | - Shujun Wei
- Department of Cardiology, Ningxia Hui Autonomous Region People's Hospital, Yinchuan, China
| | - Yuan Wu
- Department of Cardiology, Ningxia Hui Autonomous Region People's Hospital, Yinchuan, China
| | - Ganhua Cha
- School of Electrical and Information Engineering, North Minzu University, Yinchuan, China
| | - Haicheng Wei
- School of Electrical and Information Engineering, North Minzu University, Yinchuan, China
| | - Jiandong Mao
- School of Instrument Science and Opto-Electronic Engineering, Hefei University of Technology, Hefei, China.,School of Electrical and Information Engineering, North Minzu University, Yinchuan, China.,Key Laboratory of Atmospheric Environment Remote Sensing of Ningxia, Yinchuan, China
| | - Ling Xia
- Key Laboratory for Biomedical Engineering of Ministry of Education, Institute of Biomedical Engineering, Zhejiang University, Hangzhou, China
| |
Collapse
|
10
|
Fang R, Lu CC, Chuang CT, Chang WH. A visually interpretable detection method combines 3-D ECG with a multi-VGG neural network for myocardial infarction identification. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 219:106762. [PMID: 35378394 DOI: 10.1016/j.cmpb.2022.106762] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 03/04/2022] [Accepted: 03/17/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE The automatic recognition of myocardial infarction (MI) by artificial intelligence (AI) has been an emerging topic of academic research and an existing classification method that can recognize conventional electrocardiogram (ECG) signals with high accuracy. However, they are employed to classify one-dimensional (1-D) ECG signals rather than three-dimensional (3-D) ECG images, and it is limited to provide physicians with significant recommendations to aid in diagnosis like highlighting abnormal leads. Other studies on 3-D ECG images either did not achieve high accuracy or did not employ an inter-patient classification scheme. By proposing a multi-VGG deep neural network, this study aims to develop an automatic classification method for identifying myocardial infarction with inter-patient high accuracy and proper interpretability using 3-D ECG image and a Grad-CAM++ method. METHODS We apply a multi-VGG deep convolutional neural network to top-view images of 3-D ECG, which are generated from simply denoised standard 12 leads ECG signals for classification. The multi-network method, which separately classifies QRS areas, ST areas, and whole heartbeats, was applied to improve classification performance. Furthermore, the Grad-CAM++ method was used to provide visually interpretable heatmaps for user's attention to improve network interpretability and assist physicians in MI diagnosis RESULTS: The proposed method achieved 95.65% inter-patient accuracy and exactly perfect inner-patient accuracy in the Physikalisch-Technische Bundesanstalt (PTB) diagnostic ECG database experiment. In the PTB-XL diagnostic ECG database, the proposed method achieved 97.23% inter-patient accuracy. The Grad-CAM++ experiment results also showed that the highlighted areas matched the medical diagnosis criteria for myocardial infarction. CONCLUSIONS Our method demonstrates that 3-D ECG images with AI classification can be efficiently employed for heart disease diagnosis with both high accuracy and visual interpretability.
Collapse
Affiliation(s)
- Rui Fang
- Graduate Institute of Manufacturing Technology, National Taipei University of Technology, Taipei 10608, Taiwan
| | - Chih-Cheng Lu
- Graduate Institute of Manufacturing Technology, National Taipei University of Technology, Taipei 10608, Taiwan; Department of Intelligent Automation Engineering, National Taipei University of Technology, Taipei 10608, Taiwan; Department of Mechanical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
| | - Cheng-Ta Chuang
- Department of Intelligent Automation Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
| | - Wen-Han Chang
- Emergency Department, Mackay Memorial Hospital, Taipei 10449, Taiwan
| |
Collapse
|
11
|
Tomas B, Grabovac M, Tomas K. Application of the R-peak detection algorithm for locating noise in ECG signals. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
12
|
Martin H, Morar U, Izquierdo W, Cabrerizo M, Cabrera A, Adjouadi M. Real-time frequency-independent single-Lead and single-beat myocardial infarction detection. Artif Intell Med 2021; 121:102179. [PMID: 34763801 DOI: 10.1016/j.artmed.2021.102179] [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: 02/17/2021] [Revised: 07/29/2021] [Accepted: 09/21/2021] [Indexed: 11/26/2022]
Abstract
This study proposes a novel real-time frequency-independent myocardial infarction detector for Lead II electrocardiograms. The underlying Deep-LSTM network is trained using the PTB-XL database, the largest to date publicly available electrocardiography dataset, and is tested over the same and the older PTB database. By testing the model over distinct datasets, collected under different conditions and from different patients, a more realistic measure of the performance can be gauged from the deployed system. The detector is trained over 3589 myocardial infarction (MI) patients and 7115 healthy controls (HC) while it is evaluated on 1076 MIs and 1840 HCs. The proposed algorithm, achieved an accuracy of 77.12%, recall/sensitivity of 75.85%, and a specificity of 83.02% over the entire PTB database; 85.07%, 81.54%, 87.31% over the PTB-XL validation set (fold 9), and 84.17%, 78.37%, 87.55% over the PTB-XL test set (fold 10). The model also achieves stable performance metrics over the frequency range of 202 Hz to 2.8 kHz. The processing time is dependent on the sampling frequency, ranging from 130 ms at 202 Hz to 1.8 s at 2.8 kHz. Such outcome is within the time required for real-time processing (less than 300 ms for fast heartbeats), between 202 Hz and 500 Hz making the algorithm practically real-time. Therefore, the proposed MI detector could be readily deployed onto existing wearable and/or portable devices and test instruments; potentially having significant societal and clinical impact in the lives of patients at risk for myocardial infarction.
Collapse
Affiliation(s)
- Harold Martin
- CATE, Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA.
| | - Ulyana Morar
- CATE, Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Walter Izquierdo
- CATE, Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Mercedes Cabrerizo
- CATE, Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | | | - Malek Adjouadi
- CATE, Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| |
Collapse
|
13
|
Rahul J, Sharma LD, Bohat VK. Short duration Vectorcardiogram based inferior myocardial infarction detection: class and subject-oriented approach. BIOMED ENG-BIOMED TE 2021; 66:489-501. [PMID: 33939896 DOI: 10.1515/bmt-2020-0329] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 03/30/2021] [Indexed: 12/17/2022]
Abstract
Myocardial infarction (MI) happens when blood stops circulating to an explicit segment of the heart causing harm to the heart muscles. Vectorcardiography (VCG) is a technique of recording direction and magnitude of the signals that are produced by the heart in a 3-lead representation. In this work, we present a technique for detection of MI in the inferior portion of heart using short duration VCG signals. The raw signal was pre-processed using the median and Savitzky-Golay (SG) filter. The Stationary Wavelet Transform (SWT) was used for time-invariant decomposition of the signal followed by feature extraction. The selected features using minimum-redundancy-maximum-relevance (mRMR) based feature selection method were applied to the supervised classification methods. The efficacy of the proposed method was assessed under both class-oriented and a more real-life subject-oriented approach. An accuracy of 99.14 and 89.37% were achieved respectively. Results of the proposed technique are better than existing state-of-art methods and used VCG segment is shorter. Thus, a shorter segment and a high accuracy can be helpful in the automation of timely and reliable detection of MI. The satisfactory performance achieved in the subject-oriented approach shows reliability and applicability of the proposed technique.
Collapse
Affiliation(s)
- Jagdeep Rahul
- Department of Electronics & Communication Engineering, Rajiv Gandhi University, Itanagar, Arunachal Pradesh, India
| | - Lakhan Dev Sharma
- School of Electronics Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India
| | - Vijay Kumar Bohat
- Department of Computer Science & Engineering, Bennett University, Greater Noida, Uttar Pradesh, India
| |
Collapse
|
14
|
Interpretable Detection and Location of Myocardial Infarction Based on Ventricular Fusion Rule Features. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:4123471. [PMID: 34676061 PMCID: PMC8526260 DOI: 10.1155/2021/4123471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 08/22/2021] [Accepted: 09/24/2021] [Indexed: 11/24/2022]
Abstract
Myocardial infarction (MI) is one of the most common cardiovascular diseases threatening human life. In order to accurately distinguish myocardial infarction and have a good interpretability, the classification method that combines rule features and ventricular activity features is proposed in this paper. Specifically, according to the clinical diagnosis rule and the pathological changes of myocardial infarction on the electrocardiogram, the local information extracted from the Q wave, ST segment, and T wave is computed as the rule feature. All samples of the QT segment are extracted as ventricular activity features. Then, in order to reduce the computational complexity of the ventricular activity features, the effects of Discrete Wavelet Transform (DWT), Principal Component Analysis (PCA), and Locality Preserving Projections (LPP) on the extracted ventricular activity features are compared. Combining rule features and ventricular activity features, all the 12 leads features are fused as the ultimate feature vector. Finally, eXtreme Gradient Boosting (XGBoost) is used to identify myocardial infarction, and the overall accuracy rate of 99.86% is obtained on the Physikalisch-Technische Bundesanstalt (PTB) database. This method has a good medical diagnosis basis while improving the accuracy, which is very important for clinical decision-making.
Collapse
|
15
|
Aslan Z. Migraine detection from EEG signals using tunable Q-factor wavelet transform and ensemble learning techniques. Phys Eng Sci Med 2021; 44:1201-1212. [PMID: 34505992 DOI: 10.1007/s13246-021-01055-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 08/31/2021] [Indexed: 10/20/2022]
Abstract
Migraine is one of the major neurovascular diseases that recur, can persist for a long time, cripple or weaken the brain. This study uses electroencephalogram (EEG) signals for the diagnosis of migraine, and a computer-aided diagnosis system is presented to support expert opinion. A tunable Q-factor wavelet transform (TQWT) based method is proposed for the analysis of the oscillatory structure of EEG signals. With TQWT, EEG signals are decomposed into sub bands. Then, the features are statistically calculated from these bands. The success of the obtained features in distinguishing between migraine patients and healthy control subjects was performed using the Kruskal Wallis test. Feature values obtained from each sub band were classified using well-known ensemble learning techniques and their classification performances were tested. Among the evaluated classifiers, the highest classification performance was achieved as 89.6% by using the Rotation Forest algorithm with the features obtained with Sub band 2. These results reveal the potential of the study as a tool that will support expert opinion in the diagnosis of migraine.
Collapse
Affiliation(s)
- Zülfikar Aslan
- Technical Sciences Vocational School, Gaziantep University, 27310, Gaziantep, Turkey.
| |
Collapse
|
16
|
Near real-time single-beat myocardial infarction detection from single-lead electrocardiogram using Long Short-Term Memory Neural Network. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102683] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
|
17
|
Xiong P, Xue Y, Zhang J, Liu M, Du H, Zhang H, Hou Z, Wang H, Liu X. Localization of myocardial infarction with multi-lead ECG based on DenseNet. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 203:106024. [PMID: 33743488 DOI: 10.1016/j.cmpb.2021.106024] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 02/22/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Myocardial infarction (MI) is a critical acute ischemic heart disease, which can be early diagnosed by electrocardiogram (ECG). However, the most research of MI localization pay more attention on the specific changes in every ECG lead independent. In our study, the research envisages the development of a novel multi-lead MI localization approach based on the densely connected convolutional network (DenseNet). METHODS Considering the correlation of the multi-lead ECG, the method using parallel 12-lead ECG, systematically exploited the correlation of the inter-lead signals. In addition, the dense connection of DenseNet enhanced the reuse of the feature information between the inter-lead and intra-lead signals. The proposed method automatically captured the effective pathological features, which improved the identification of MI. RESULTS The experimental results based on PTB diagnostic ECG database showed that the accuracy, sensitivity and specificity of the proposed method was 99.87%, 99.84% and 99.98% for 11 types of MI localization. CONCLUSIONS The proposed method has achieved superior results compared to other localization methods, which can be introduced into the clinical practice to assist the diagnosis of MI.
Collapse
Affiliation(s)
- Peng Xiong
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071000, China; College of Electronic and Information Engineering, Hebei University, Baoding 071002, China
| | - Yanping Xue
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071000, China; College of Electronic and Information Engineering, Hebei University, Baoding 071002, China
| | - Jieshuo Zhang
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071000, China; College of Physics Science and Technology, Hebei University, Baoding 071002, China
| | - Ming Liu
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071000, China; College of Electronic and Information Engineering, Hebei University, Baoding 071002, China
| | - Haiman Du
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071000, China; College of Electronic and Information Engineering, Hebei University, Baoding 071002, China
| | - Hong Zhang
- Affiliated Hospital of Hebei University, Baoding 071002, China
| | - Zengguang Hou
- Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Hongrui Wang
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071000, China; College of Electronic and Information Engineering, Hebei University, Baoding 071002, China
| | - Xiuling Liu
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071000, China; College of Electronic and Information Engineering, Hebei University, Baoding 071002, China.
| |
Collapse
|
18
|
Guo Y, Xu H, Li X, Zhou Z. Effect of Parecoxib on Hippocampus and Hypothalamic Orexin Neurons in Rats with Cerebral Infarction. J BIOMATER TISS ENG 2021. [DOI: 10.1166/jbt.2021.2564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Cerebral infarction has seriously threatened human life and health. Parecoxib is the first nonsteroidal analgesic for surgical analgesia. However, its effect on orexin neurons during cerebral infarction treatment is unclear. In this study, a rat model of cerebral infarction was established
by suture method. The experiment was assigned into sham operation group, cerebral infarction model group (MCAO), high and low dose group of parecoxib. Western blotting and immunofluorescence staining was used to evaluate the activity of orexin neurons. The infarct size was evaluated by TTC
staining. The apoptosis of neurons in hypothalamus and hippocampus was determined by AV-PI staining. TTC staining suggested that parecoxib treatment significantly reduced cerebral infarct size, increased orexin neuronal activity, and decreased neuronal apoptosis in hypothalamus and hippocampus,
which were significantly different from sham-operated groups. This study demonstrates that parecoxib has a protective effect on cerebral infarction rats, which can inhibit the apoptosis of hypothalamic and hippocampal neurons through the orexin neuron pathway. It provides a theoretical basis
for the protective effect of parecoxib, indicating that it might be a new target for the treatment of cerebral infarction.
Collapse
Affiliation(s)
- Yapeng Guo
- Department of Neurology, Lu’an Affiliated Hospital of Anhui Medical University, Lu’an People’s Hospital, Lu’an, Anhui, 237000, China
| | - Heng Xu
- Department of Neurology, The First Affiliated Hospital of Wannan Medical College, Wuhu, Anhui, 241001, China
| | - Xuyi Li
- Department of Pharmacy, The First Affiliated Hospital of Wannan Medical College, Wuhu, Anhui, 241001, China
| | - Zhiming Zhou
- Department of Neurology, The First Affiliated Hospital of Wannan Medical College, Wuhu, Anhui, 241001, China
| |
Collapse
|
19
|
Xie L, Li Z, Zhou Y, He Y, Zhu J. Computational Diagnostic Techniques for Electrocardiogram Signal Analysis. SENSORS (BASEL, SWITZERLAND) 2020; 20:E6318. [PMID: 33167558 PMCID: PMC7664289 DOI: 10.3390/s20216318] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 10/27/2020] [Accepted: 11/04/2020] [Indexed: 12/25/2022]
Abstract
Cardiovascular diseases (CVDs), including asymptomatic myocardial ischemia, angina, myocardial infarction, and ischemic heart failure, are the leading cause of death globally. Early detection and treatment of CVDs significantly contribute to the prevention or delay of cardiovascular death. Electrocardiogram (ECG) records the electrical impulses generated by heart muscles, which reflect regular or irregular beating activity. Computer-aided techniques provide fast and accurate tools to identify CVDs using a patient's ECG signal, which have achieved great success in recent years. Latest computational diagnostic techniques based on ECG signals for estimating CVDs conditions are summarized here. The procedure of ECG signals analysis is discussed in several subsections, including data preprocessing, feature engineering, classification, and application. In particular, the End-to-End models integrate feature extraction and classification into learning algorithms, which not only greatly simplifies the process of data analysis, but also shows excellent accuracy and robustness. Portable devices enable users to monitor their cardiovascular status at any time, bringing new scenarios as well as challenges to the application of ECG algorithms. Computational diagnostic techniques for ECG signal analysis show great potential for helping health care professionals, and their application in daily life benefits both patients and sub-healthy people.
Collapse
Affiliation(s)
- Liping Xie
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China; (Z.L.); (Y.Z.); (Y.H.); (J.Z.)
| | | | | | | | | |
Collapse
|
20
|
Liu J, Zhu Y, Sun H, Ristaniemi T, Cong F. Sustaining Attention for a Prolonged Duration Affects Dynamic Organizations of Frequency-Specific Functional Connectivity. Brain Topogr 2020; 33:677-692. [PMID: 32929555 PMCID: PMC7593315 DOI: 10.1007/s10548-020-00795-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 09/01/2020] [Indexed: 12/03/2022]
Abstract
Sustained attention encompasses a cascade of fundamental functions. The human ability to implement a sustained attention task is supported by brain networks that dynamically formed and dissolved through oscillatory synchronization. The decrement of vigilance induced by prolonged task engagement affects sustained attention. However, little is known about which stage or combinations are affected by vigilance decrement. Here, we applied an analysis framework composed of weighted phase lag index (wPLI) and tensor component analysis (TCA) to an EEG dataset collected during 80 min sustained attention task to examine the electrophysiological basis of such effect. We aimed to characterize the phase-coupling networks to untangle different phases involved in sustained attention and study how they are modulated by vigilance decrement. We computed the time–frequency domain wPLI from each block and subject and constructed a fourth-order tensor, containing the time, frequency, functional connectivity (FC), and blocks × subjects. This tensor was subjected to the TCA to identify the interacted and low-dimensional components representing the frequency-specific dynamic FC (fdFC). We extracted four types of neuromakers during a sustained attention task, namely the pre-stimulus alpha right-lateralized parieto-occipital FC, the post-stimulus theta fronto-parieto-occipital FC, delta fronto-parieto-occipital FC, and beta right/left sensorimotor FCs. All these fdFCs were impaired by vigilance decrement. These fdFCs, except for the beta left sensorimotor network, were restored by rewards, although the restoration by reward in the beta right sensorimotor network was transient. These findings provide implications for dissociable effects of vigilance decrement on sustained attention by utilizing the tensor-based framework.
Collapse
Affiliation(s)
- Jia Liu
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China. .,Faculty of Information Technology, University of Jyvaskyla, 40014, Jyvaskyla, Finland. .,Department of Psychology, Neuroscience and Behavior, MacMaster University, Hamilton, ON, L8S4K1, Canada.
| | - Yongjie Zhu
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China.,Faculty of Information Technology, University of Jyvaskyla, 40014, Jyvaskyla, Finland
| | - Hongjin Sun
- Department of Psychology, Neuroscience and Behavior, MacMaster University, Hamilton, ON, L8S4K1, Canada
| | - Tapani Ristaniemi
- Faculty of Information Technology, University of Jyvaskyla, 40014, Jyvaskyla, Finland
| | - Fengyu Cong
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China. .,Faculty of Information Technology, University of Jyvaskyla, 40014, Jyvaskyla, Finland. .,School of Artificial Intelligence, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China. .,Key Laboratory of Integrated Circuit and Biomedical Electronic System, Dalian University of Technology, Dalian, 116024, China.
| |
Collapse
|
21
|
A Novel Islanding Detection Technique for a Resilient Photovoltaic-Based Distributed Power Generation System Using a Tunable-Q Wavelet Transform and an Artificial Neural Network. ENERGIES 2020. [DOI: 10.3390/en13164238] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Finding an appropriate technique to detect an islanding issue is one of the major challenges associated with the design of a resilient grid-linked photovoltaic-based distributed power generation (PV-DPG) system. In general, the technique used for islanding detection must be able to sense the disruptions from the electric grid and quickly disconnect PV-DPG from the grid. The quick disconnection of PV-DPG mostly avoids power quality problems, damage to power assets, voltage stability issues, and frequency instability. In this paper, a new islanding detection technique that is based on tunable Q-factor wavelet transform (TQWT) and an artificial neural network (ANN) is proposed for PV-DPG. The proposed approach consists of two steps: in the first step, the vital detection parameters are computed by performing simulations considering all possible switching transients, islanding events, and faults from the grid side. Then, the decomposition of obtained signals is done using TQWT on different levels. Using the obtained coefficients, at each level, features such as range, minimum, mean, standard deviation, maximum, energy, and log energy entropy are computed. The optimal feature set was selected as the input for the second step. The classification of the non-islanding and islanding states for PV-DPG is made using the ANN classifier in the second step, which achieved an accuracy of 98%. The results representing the efficiency of the proposed approach in noisy and non-noisy environments are also explained. Overall, it is understood that the proposed islanding detection technique would provide suitable insights to detect an islanding issue.
Collapse
|
22
|
Vanus J, Fiedorova K, Kubicek J, Gorjani OM, Augustynek M. Wavelet-Based Filtration Procedure for Denoising the Predicted CO 2 Waveforms in Smart Home within the Internet of Things. SENSORS 2020; 20:s20030620. [PMID: 31979168 PMCID: PMC7038360 DOI: 10.3390/s20030620] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 01/13/2020] [Accepted: 01/17/2020] [Indexed: 01/30/2023]
Abstract
The operating cost minimization of smart homes can be achieved with the optimization of the management of the building’s technical functions by determination of the current occupancy status of the individual monitored spaces of a smart home. To respect the privacy of the smart home residents, indirect methods (without using cameras and microphones) are possible for occupancy recognition of space in smart homes. This article describes a newly proposed indirect method to increase the accuracy of the occupancy recognition of monitored spaces of smart homes. The proposed procedure uses the prediction of the course of CO2 concentration from operationally measured quantities (temperature indoor and relative humidity indoor) using artificial neural networks with a multilayer perceptron algorithm. The mathematical wavelet transformation method is used for additive noise canceling from the predicted course of the CO2 concentration signal with an objective increase accuracy of the prediction. The calculated accuracy of CO2 concentration waveform prediction in the additive noise-canceling application was higher than 98% in selected experiments.
Collapse
|