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Xu Y, Zhong H, Ying S, Liu W, Chen G, Luo X, Li G. Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:8639. [PMID: 37896732 PMCID: PMC10611358 DOI: 10.3390/s23208639] [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: 09/07/2023] [Revised: 10/10/2023] [Accepted: 10/18/2023] [Indexed: 10/29/2023]
Abstract
Depressive disorder (DD) has become one of the most common mental diseases, seriously endangering both the affected person's psychological and physical health. Nowadays, a DD diagnosis mainly relies on the experience of clinical psychiatrists and subjective scales, lacking objective, accurate, practical, and automatic diagnosis technologies. Recently, electroencephalogram (EEG) signals have been widely applied for DD diagnosis, but mainly with high-density EEG, which can severely limit the efficiency of the EEG data acquisition and reduce the practicability of diagnostic techniques. The current study attempts to achieve accurate and practical DD diagnoses based on combining frontal six-channel electroencephalogram (EEG) signals and deep learning models. To this end, 10 min clinical resting-state EEG signals were collected from 41 DD patients and 34 healthy controls (HCs). Two deep learning models, multi-resolution convolutional neural network (MRCNN) combined with long short-term memory (LSTM) (named MRCNN-LSTM) and MRCNN combined with residual squeeze and excitation (RSE) (named MRCNN-RSE), were proposed for DD recognition. The results of this study showed that the higher EEG frequency band obtained the better classification performance for DD diagnosis. The MRCNN-RSE model achieved the highest classification accuracy of 98.48 ± 0.22% with 8-30 Hz EEG signals. These findings indicated that the proposed analytical framework can provide an accurate and practical strategy for DD diagnosis, as well as essential theoretical and technical support for the treatment and efficacy evaluation of DD.
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Affiliation(s)
- Yanting Xu
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China; (Y.X.); (S.Y.)
| | - Hongyang Zhong
- College of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China; (H.Z.); (W.L.); (G.C.)
| | - Shangyan Ying
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China; (Y.X.); (S.Y.)
| | - Wei Liu
- College of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China; (H.Z.); (W.L.); (G.C.)
| | - Guibin Chen
- College of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China; (H.Z.); (W.L.); (G.C.)
| | - Xiaodong Luo
- The Second Hospital of Jinhua, Jinhua 321016, China
| | - Gang Li
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321004, China
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2
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Ju U. Task and Resting-State Functional Connectivity Predict Driving Violations. Brain Sci 2023; 13:1236. [PMID: 37759837 PMCID: PMC10526865 DOI: 10.3390/brainsci13091236] [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: 07/26/2023] [Revised: 08/17/2023] [Accepted: 08/22/2023] [Indexed: 09/29/2023] Open
Abstract
Aberrant driving behaviors cause accidents; however, there is a lack of understanding of the neural mechanisms underlying these behaviors. To address this issue, a task and resting-state functional connectivity was used to predict aberrant driving behavior and associated personality traits. The study included 29 right-handed participants with driving licenses issued for more than 1 year. During the functional magnetic resonance imaging experiment, participants first recorded their resting state and then watched a driving video while continuously rating the risk and speed on each block. Functional connectome-based predictive modeling was employed for whole brain tasks and resting-state functional connectivity to predict driving behavior (violation, error, and lapses), sensation-seeking, and impulsivity. Resting state and task-based functional connectivity were found to significantly predict driving violations, with resting state significantly predicting lapses and task-based functional connectivity showing a tendency to predict errors. Conversely, neither impulsivity nor sensation-seeking was associated with functional connectivity. The results suggest a significant association between aberrant driving behavior, but a nonsignificant association between impulsivity and sensation-seeking, and task-based or resting state functional connectivity. This could provide a deeper understanding of the neural processing underlying reckless driving that may ultimately be used to prevent accidents.
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Affiliation(s)
- Uijong Ju
- Department of Information Display, Kyung Hee University, Seoul 02447, Republic of Korea
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3
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Alalayah KM, Senan EM, Atlam HF, Ahmed IA, Shatnawi HSA. Automatic and Early Detection of Parkinson's Disease by Analyzing Acoustic Signals Using Classification Algorithms Based on Recursive Feature Elimination Method. Diagnostics (Basel) 2023; 13:diagnostics13111924. [PMID: 37296776 DOI: 10.3390/diagnostics13111924] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 05/23/2023] [Accepted: 05/27/2023] [Indexed: 06/12/2023] Open
Abstract
Parkinson's disease (PD) is a neurodegenerative condition generated by the dysfunction of brain cells and their 60-80% inability to produce dopamine, an organic chemical responsible for controlling a person's movement. This condition causes PD symptoms to appear. Diagnosis involves many physical and psychological tests and specialist examinations of the patient's nervous system, which causes several issues. The methodology method of early diagnosis of PD is based on analysing voice disorders. This method extracts a set of features from a recording of the person's voice. Then machine-learning (ML) methods are used to analyse and diagnose the recorded voice to distinguish Parkinson's cases from healthy ones. This paper proposes novel techniques to optimize the techniques for early diagnosis of PD by evaluating selected features and hyperparameter tuning of ML algorithms for diagnosing PD based on voice disorders. The dataset was balanced by the synthetic minority oversampling technique (SMOTE) and features were arranged according to their contribution to the target characteristic by the recursive feature elimination (RFE) algorithm. We applied two algorithms, t-distributed stochastic neighbour embedding (t-SNE) and principal component analysis (PCA), to reduce the dimensions of the dataset. Both t-SNE and PCA finally fed the resulting features into the classifiers support-vector machine (SVM), K-nearest neighbours (KNN), decision tree (DT), random forest (RF), and multilayer perception (MLP). Experimental results proved that the proposed techniques were superior to existing studies in which RF with the t-SNE algorithm yielded an accuracy of 97%, precision of 96.50%, recall of 94%, and F1-score of 95%. In addition, MLP with the PCA algorithm yielded an accuracy of 98%, precision of 97.66%, recall of 96%, and F1-score of 96.66%.
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Affiliation(s)
- Khaled M Alalayah
- Department of Computer Science, Faculty of Science and Arts, Najran University, Sharurah 68341, Saudi Arabia
| | - Ebrahim Mohammed Senan
- Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Alrazi University, Sana'a, Yemen
| | - Hany F Atlam
- Cyber Security Centre, WMG, University of Warwick, Coventry CV4 7AL, UK
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Beni NH, Jiang N. Heartbeat detection from single-lead ECG contaminated with simulated EMG at different intensity levels: A comparative study. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
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5
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Nguyen KH, Ebbatson M, Tran Y, Craig A, Nguyen H, Chai R. Source-Space Brain Functional Connectivity Features in Electroencephalogram-Based Driver Fatigue Classification. SENSORS (BASEL, SWITZERLAND) 2023; 23:2383. [PMID: 36904587 PMCID: PMC10007183 DOI: 10.3390/s23052383] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 02/10/2023] [Accepted: 02/19/2023] [Indexed: 06/18/2023]
Abstract
This study examined the brain source space functional connectivity from the electroencephalogram (EEG) activity of 48 participants during a driving simulation experiment where they drove until fatigue developed. Source-space functional connectivity (FC) analysis is a state-of-the-art method for understanding connections between brain regions that may indicate psychological differences. Multi-band FC in the brain source space was constructed using the phased lag index (PLI) method and used as features to train an SVM classification model to classify driver fatigue and alert conditions. With a subset of critical connections in the beta band, a classification accuracy of 93% was achieved. Additionally, the source-space FC feature extractor demonstrated superiority over other methods, such as PSD and sensor-space FC, in classifying fatigue. The results suggested that source-space FC is a discriminative biomarker for detecting driving fatigue.
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Affiliation(s)
- Khanh Ha Nguyen
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, Australia
| | - Matthew Ebbatson
- School of Engineering, Swinburne University of Technology, Melbourne, VIC 3122, Australia
| | - Yvonne Tran
- Department of Linguistics, Macquarie University Hearing, Macquarie University, Sydney, NSW 2109, Australia
| | - Ashley Craig
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
- John Walsh Centre for Rehabilitation Research, Kolling Institute, Northern Sydney Local Health District, St Leonards, Sydney, NSW 2065, Australia
| | - Hung Nguyen
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, Australia
| | - Rifai Chai
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, Australia
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L-Tetrolet Pattern-Based Sleep Stage Classification Model Using Balanced EEG Datasets. Diagnostics (Basel) 2022; 12:diagnostics12102510. [PMID: 36292199 PMCID: PMC9600064 DOI: 10.3390/diagnostics12102510] [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: 09/09/2022] [Revised: 10/10/2022] [Accepted: 10/13/2022] [Indexed: 11/24/2022] Open
Abstract
Background: Sleep stage classification is a crucial process for the diagnosis of sleep or sleep-related diseases. Currently, this process is based on manual electroencephalogram (EEG) analysis, which is resource-intensive and error-prone. Various machine learning models have been recommended to standardize and automate the analysis process to address these problems. Materials and methods: The well-known cyclic alternating pattern (CAP) sleep dataset is used to train and test an L-tetrolet pattern-based sleep stage classification model in this research. By using this dataset, the following three cases are created, and they are: Insomnia, Normal, and Fused cases. For each of these cases, the machine learning model is tasked with identifying six sleep stages. The model is structured in terms of feature generation, feature selection, and classification. Feature generation is established with a new L-tetrolet (Tetris letter) function and multiple pooling decomposition for level creation. We fuse ReliefF and iterative neighborhood component analysis (INCA) feature selection using a threshold value. The hybrid and iterative feature selectors are named threshold selection-based ReliefF and INCA (TSRFINCA). The selected features are classified using a cubic support vector machine. Results: The presented L-tetrolet pattern and TSRFINCA-based sleep stage classification model yield 95.43%, 91.05%, and 92.31% accuracies for Insomnia, Normal dataset, and Fused cases, respectively. Conclusion: The recommended L-tetrolet pattern and TSRFINCA-based model push the envelope of current knowledge engineering by accurately classifying sleep stages even in the presence of sleep disorders.
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Bin Heyat MB, Akhtar F, Abbas SJ, Al-Sarem M, Alqarafi A, Stalin A, Abbasi R, Muaad AY, Lai D, Wu K. Wearable Flexible Electronics Based Cardiac Electrode for Researcher Mental Stress Detection System Using Machine Learning Models on Single Lead Electrocardiogram Signal. BIOSENSORS 2022; 12:bios12060427. [PMID: 35735574 PMCID: PMC9221208 DOI: 10.3390/bios12060427] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 06/06/2022] [Accepted: 06/14/2022] [Indexed: 05/02/2023]
Abstract
In the modern world, wearable smart devices are continuously used to monitor people's health. This study aims to develop an automatic mental stress detection system for researchers based on Electrocardiogram (ECG) signals from smart T-shirts using machine learning classifiers. We used 20 subjects, including 10 from mental stress (after twelve hours of continuous work in the laboratory) and 10 from normal (after completing the sleep or without any work). We also applied three scoring techniques: Chalder Fatigue Scale (CFS), Specific Fatigue Scale (SFS), Depression, Anxiety, and Stress Scale (DASS), to confirm the mental stress. The total duration of ECG recording was 1800 min, including 1200 min during mental stress and 600 min during normal. We calculated two types of features, such as demographic and extracted by ECG signal. In addition, we used Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), and Logistic Regression (LR) to classify the intra-subject (mental stress and normal) and inter-subject classification. The DT leave-one-out model has better performance in terms of recall (93.30%), specificity (96.70%), precision (94.40%), accuracy (93.30%), and F1 (93.50%) in the intra-subject classification. Additionally, The classification accuracy of the system in classifying inter-subjects is 94.10% when using a DT classifier. However, our findings suggest that the wearable smart T-shirt based on the DT classifier may be used in big data applications and health monitoring. Mental stress can lead to mitochondrial dysfunction, oxidative stress, blood pressure, cardiovascular disease, and various health problems. Therefore, real-time ECG signals help assess cardiovascular and related risk factors in the initial stage based on machine learning techniques.
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Affiliation(s)
- Md Belal Bin Heyat
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China;
| | - Faijan Akhtar
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China;
| | - Syed Jafar Abbas
- Faculty of Management, Vancouver Island University, Nanaimo, BC V9R5S5, Canada;
| | - Mohammed Al-Sarem
- College of Computer Science and Engineering, Taibah University, Medina 42353, Saudi Arabia;
- Department of Computer Science, University of Sheba Province, Marib, Yemen
- Correspondence: (M.A.-S.); (D.L.); (K.W.)
| | - Abdulrahman Alqarafi
- College of Computer Science and Engineering, Taibah University, Medina 42353, Saudi Arabia;
| | - Antony Stalin
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China;
| | - Rashid Abbasi
- School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China;
| | - Abdullah Y. Muaad
- Department of Studies in Computer Science, University of Mysore, Mysore 570005, Karnataka, India;
- IT Department, Sana’a Community College, Sana’a 5695, Yemen
| | - Dakun Lai
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
- Correspondence: (M.A.-S.); (D.L.); (K.W.)
| | - Kaishun Wu
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China;
- Correspondence: (M.A.-S.); (D.L.); (K.W.)
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8
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Developing a Deep Neural Network for Driver Fatigue Detection Using EEG Signals Based on Compressed Sensing. SUSTAINABILITY 2022. [DOI: 10.3390/su14052941] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
In recent years, driver fatigue has become one of the main causes of road accidents. As a result, fatigue detection systems have been developed to warn drivers, and, among the available methods, EEG signal analysis is recognized as the most reliable method for detecting driver fatigue. This study presents an automated system for a two-stage classification of driver fatigue, using a combination of compressed sensing (CS) theory and deep neural networks (DNNs), that is based on EEG signals. First, CS theory is used to compress the recorded EEG data in order to reduce the computational load. Then, the compressed EEG data is fed into the proposed deep convolutional neural network for automatic feature extraction/selection and classification purposes. The proposed network architecture includes seven convolutional layers together with three long short-term memory (LSTM) layers. For compression rates of 40, 50, 60, 70, 80, and 90, the simulation results for a single-channel recording show accuracies of 95, 94.8, 94.6, 94.4, 94.4, and 92%, respectively. Furthermore, by comparing the results to previous methods, the accuracy of the proposed method for the two-stage classification of driver fatigue has been improved and can be used to effectively detect driver fatigue.
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9
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Wang A, Liu H, Yang J, Chen G. Ensemble feature selection for stable biomarker identification and cancer classification from microarray expression data. Comput Biol Med 2022; 142:105208. [PMID: 35016102 DOI: 10.1016/j.compbiomed.2021.105208] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 12/19/2021] [Accepted: 12/31/2021] [Indexed: 01/31/2023]
Abstract
Microarray technology facilitates the simultaneous measurement of expression of tens of thousands of genes and enables us to study cancers and tumors at the molecular level. Because microarray data are typically characterized by small sample size and high dimensionality, accurate and stable feature selection is thus of fundamental importance to the diagnostic accuracy and deep understanding of disease mechanism. Hence, we in this study present an ensemble feature selection framework to improve the discrimination and stability of finally selected features. Specifically, we utilize sampling techniques to obtain multiple sampled datasets, from each of which we use a base feature selector to select a subset of features. Afterwards, we develop two aggregation strategies to combine multiple feature subsets into one set. Finally, comparative experiments are conducted on four publicly available microarray datasets covering both binary and multi-class cases in terms of classification accuracy and three stability metrics. Results show that the proposed method obtains better stability scores and achieves comparable to and even better classification performance than its competitors.
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Affiliation(s)
- Aiguo Wang
- School of Electronic Information Engineering, Foshan University, Foshan, China.
| | - Huancheng Liu
- School of Electronic Information Engineering, Foshan University, Foshan, China.
| | - Jing Yang
- School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China.
| | - Guilin Chen
- School of Computer and Information Engineering, Chuzhou University, Chuzhou, China.
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