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Zhang Y, Pan T, Ye Y, Wan Z, Liu B, Ding T. Multiscale-temporal Feature Extraction and boundary confusion alleviation for VAG-based fine-grained multi-grade osteoarthritis deterioration monitoring. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 255:108286. [PMID: 39029419 DOI: 10.1016/j.cmpb.2024.108286] [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: 05/06/2023] [Revised: 05/24/2024] [Accepted: 06/12/2024] [Indexed: 07/21/2024]
Abstract
BACKGROUND AND OBJECTIVE Multi-grade osteoarthritis (OA) deterioration monitoring in the daily paradigm using Vibroarthrography (VAG) is very challenging due to two difficulties: (1) the composition of VAG signals is complex in the daily paradigm where friction is intensified because of weight-bearing movements. (2) VAG signal samples near the decision boundary of adjacent deterioration grades are easy to be misclassified. The majority of existing works only focus on the binary classification of OA, providing inadequate assistance in instructing physicians to develop treatment plans based on the presence or absence of OA. Thus, we propose a novel framework for fine-grained multi-grade OA deterioration monitoring in the daily paradigm. METHODS We propose an end-to-end deep learning framework termed Fine-grained Multi-grade OA Deterioration Monitor (FMOADM), which consists of Multiscale-temporal Feature Extraction (MTFE) and Confusion-Free Master-Slave (CF-MS) Classification. Specifically, MTFE is adopted to extract multiscale-temporal discriminative features from the complicated VAG signals. And center loss is introduced by CF-MS to alleviate confusion at the boundary of adjacent deterioration grades in the feature space. Meanwhile, a master-slave structure is proposed for further fine-grained classification, where the master classifier integrates a channel attention mechanism and the slave classifier is designed to update MTFE parameters. As a result, the proposed method ensures fine-grained multi-grade OA monitoring performance via multiscale-temporal discriminative features and boundary confusion alleviation. RESULTS Experimental results on the VAG-OA dataset demonstrate that our framework outperforms counterpart methods in the daily paradigm. The proposed framework achieved 78% in precision, obtaining an 8% improvement over the state-of-the-art method. CONCLUSION The proposed framework benefits efficient multi-grade OA deterioration monitoring, empowering physicians to develop treatment plans based on fine-grained monitoring results. It takes knee joint health monitoring in daily activities a step further toward feasible.
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Affiliation(s)
- Yangwuyong Zhang
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Tongjie Pan
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yalan Ye
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
| | - Zhengyi Wan
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Benyuan Liu
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, Shaanxi, China
| | - Tan Ding
- Xijing Orthopaedics Hospital, Fourth Military Medical University, Xi'an, 710032, Shaanxi, China.
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Peng Y, Lv B, Yang Q, Peng Y, Jiang L, He M, Yao D, Xu W, Li F, Xu P. Evaluating the depression state during perinatal period by non-invasive scalp EEG. Cereb Cortex 2024; 34:bhae034. [PMID: 38342685 DOI: 10.1093/cercor/bhae034] [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: 12/12/2023] [Revised: 01/15/2024] [Accepted: 01/17/2024] [Indexed: 02/13/2024] Open
Abstract
Perinatal depression, with a prevalence of 10 to 20% in United States, is usually missed as multiple symptoms of perinatal depression are common in pregnant women. Worse, the diagnosis of perinatal depression still largely relies on questionnaires, leaving the objective biomarker being unveiled yet. This study suggested a safe and non-invasive technique to diagnose perinatal depression and further explore its underlying mechanism. Considering the non-invasiveness and clinical convenience of electroencephalogram for mothers-to-be and fetuses, we collected the resting-state electroencephalogram of pregnant women at the 38th week of gestation. Subsequently, the difference in network topology between perinatal depression patients and healthy mothers-to-be was explored, with related spatial patterns being adopted to achieve the classification of pregnant women with perinatal depression from those healthy ones. We found that the perinatal depression patients had decreased brain network connectivity, which indexed impaired efficiency of information processing. By adopting the spatial patterns, the perinatal depression could be accurately recognized with an accuracy of 87.88%; meanwhile, the depression severity at the individual level was effectively predicted, as well. These findings consistently illustrated that the resting-state electroencephalogram network could be a reliable tool for investigating the depression state across pregnant women, and will further facilitate the clinical diagnosis of perinatal depression.
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Affiliation(s)
- Yueheng Peng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Bin Lv
- Department of Gynecology and Obstetrics, West China Second University Hospital, Sichuan University, Chengdu 610040, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu 610040, Sichuan, China
| | - Qingqing Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yan Peng
- Department of Gynecology and Obstetrics, West China Second University Hospital, Sichuan University, Chengdu 610040, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu 610040, Sichuan, China
| | - Lin Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Mengling He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu 610054, China
- School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Wenming Xu
- Department of Obstetrics/Gynecology, Joint Laboratory of Reproductive Medicine (SCU-CUHK), Key Laboratory of Obstetric, Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu 610041, China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu 610054, China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu 610054, China
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Mirzabagherian H, Menhaj MB, Suratgar AA, Talebi N, Abbasi Sardari MR, Sajedin A. Temporal-spatial convolutional residual network for decoding attempted movement related EEG signals of subjects with spinal cord injury. Comput Biol Med 2023; 164:107159. [PMID: 37531857 DOI: 10.1016/j.compbiomed.2023.107159] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 05/19/2023] [Accepted: 06/07/2023] [Indexed: 08/04/2023]
Abstract
Brain Computer Interface (BCI) offers a promising approach to restoring hand functionality for people with cervical spinal cord injury (SCI). A reliable classification of brain activities based on appropriate flexibility in feature extraction could enhance BCI systems performance. In the present study, based on convolutional layers with temporal-spatial, Separable and Depthwise structures, we develop Temporal-Spatial Convolutional Residual Network)TSCR-Net(and Temporal-Spatial Convolutional Iterative Residual Network)TSCIR-Net(structures to classify electroencephalogram (EEG) signals. Using EEG signals in five different hand movement classes of SCI people, we compare the effectiveness of TSCIR-Net and TSCR-Net models with some competitive methods. We use the bayesian hyperparameter optimization algorithm to tune the hyperparameters of compact convolutional neural networks. In order to show the high generalizability of the proposed models, we compare the results of the models in different frequency ranges. Our proposed models decoded distinctive characteristics of different movement efforts and obtained higher classification accuracy than previous deep neural networks. Our findings indicate that TSCIR-Net and TSCR-Net models fulfills a better classification accuracy of 71.11%, and 64.55% for EEG_All and 57.74%, and 67.87% for EEG_Low frequency data sets than the compared methods in the literature.
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Affiliation(s)
- Hamed Mirzabagherian
- Department of Electrical Engineering, Amirkabir University of Technology, Hafez Ave. 15875-4413, Tehran, Iran.
| | - Mohammad Bagher Menhaj
- Department of Electrical Engineering, Amirkabir University of Technology, Hafez Ave. 15875-4413, Tehran, Iran.
| | - Amir Abolfazl Suratgar
- Department of Electrical Engineering, Amirkabir University of Technology, Hafez Ave. 15875-4413, Tehran, Iran.
| | - Nasibeh Talebi
- Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran.
| | | | - Atena Sajedin
- Department of Electrical Engineering, Amirkabir University of Technology, Hafez Ave. 15875-4413, Tehran, Iran.
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Lal U, Mathavu Vasanthsena S, Hoblidar A. Temporal Feature Extraction and Machine Learning for Classification of Sleep Stages Using Telemetry Polysomnography. Brain Sci 2023; 13:1201. [PMID: 37626557 PMCID: PMC10452545 DOI: 10.3390/brainsci13081201] [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: 06/29/2023] [Revised: 08/09/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023] Open
Abstract
Accurate sleep stage detection is crucial for diagnosing sleep disorders and tailoring treatment plans. Polysomnography (PSG) is considered the gold standard for sleep assessment since it captures a diverse set of physiological signals. While various studies have employed complex neural networks for sleep staging using PSG, our research emphasises the efficacy of a simpler and more efficient architecture. We aimed to integrate a diverse set of feature extraction measures with straightforward machine learning, potentially offering a more efficient avenue for sleep staging. We also aimed to conduct a comprehensive comparative analysis of feature extraction measures, including the power spectral density, Higuchi fractal dimension, singular value decomposition entropy, permutation entropy, and detrended fluctuation analysis, coupled with several machine-learning models, including XGBoost, Extra Trees, Random Forest, and LightGBM. Furthermore, data augmentation methods like the Synthetic Minority Oversampling Technique were also employed to rectify the inherent class imbalance in sleep data. The subsequent results highlighted that the XGBoost classifier, when used with a combination of all feature extraction measures as an ensemble, achieved the highest performance, with accuracies of 87%, 90%, 93%, 96%, and 97% and average F1-scores of 84.6%, 89%, 90.33%, 93.5%, and 93.5% for distinguishing between five-stage, four-stage, three-stage, and two distinct two-stage sleep configurations, respectively. This combined feature extraction technique represents a novel addition to the body of research since it achieves higher performance than many recently developed deep neural networks by utilising simpler machine-learning models.
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Affiliation(s)
- Utkarsh Lal
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India;
| | - Suhas Mathavu Vasanthsena
- Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India;
| | - Anitha Hoblidar
- Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India;
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5
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Gaiduk M, Serrano Alarcón Á, Seepold R, Martínez Madrid N. Current status and prospects of automatic sleep stages scoring: Review. Biomed Eng Lett 2023; 13:247-272. [PMID: 37519865 PMCID: PMC10382458 DOI: 10.1007/s13534-023-00299-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 06/07/2023] [Accepted: 06/18/2023] [Indexed: 08/01/2023] Open
Abstract
The scoring of sleep stages is one of the essential tasks in sleep analysis. Since a manual procedure requires considerable human and financial resources, and incorporates some subjectivity, an automated approach could result in several advantages. There have been many developments in this area, and in order to provide a comprehensive overview, it is essential to review relevant recent works and summarise the characteristics of the approaches, which is the main aim of this article. To achieve it, we examined articles published between 2018 and 2022 that dealt with the automated scoring of sleep stages. In the final selection for in-depth analysis, 125 articles were included after reviewing a total of 515 publications. The results revealed that automatic scoring demonstrates good quality (with Cohen's kappa up to over 0.80 and accuracy up to over 90%) in analysing EEG/EEG + EOG + EMG signals. At the same time, it should be noted that there has been no breakthrough in the quality of results using these signals in recent years. Systems involving other signals that could potentially be acquired more conveniently for the user (e.g. respiratory, cardiac or movement signals) remain more challenging in the implementation with a high level of reliability but have considerable innovation capability. In general, automatic sleep stage scoring has excellent potential to assist medical professionals while providing an objective assessment.
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Affiliation(s)
- Maksym Gaiduk
- HTWG Konstanz – University of Applied Sciences, Alfred-Wachtel-Str.8, 78462 Konstanz, Germany
| | | | - Ralf Seepold
- HTWG Konstanz – University of Applied Sciences, Alfred-Wachtel-Str.8, 78462 Konstanz, Germany
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Podder P, Das SR, Mondal MRH, Bharati S, Maliha A, Hasan MJ, Piltan F. LDDNet: A Deep Learning Framework for the Diagnosis of Infectious Lung Diseases. SENSORS (BASEL, SWITZERLAND) 2023; 23:480. [PMID: 36617076 PMCID: PMC9824583 DOI: 10.3390/s23010480] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/25/2022] [Accepted: 12/27/2022] [Indexed: 06/17/2023]
Abstract
This paper proposes a new deep learning (DL) framework for the analysis of lung diseases, including COVID-19 and pneumonia, from chest CT scans and X-ray (CXR) images. This framework is termed optimized DenseNet201 for lung diseases (LDDNet). The proposed LDDNet was developed using additional layers of 2D global average pooling, dense and dropout layers, and batch normalization to the base DenseNet201 model. There are 1024 Relu-activated dense layers and 256 dense layers using the sigmoid activation method. The hyper-parameters of the model, including the learning rate, batch size, epochs, and dropout rate, were tuned for the model. Next, three datasets of lung diseases were formed from separate open-access sources. One was a CT scan dataset containing 1043 images. Two X-ray datasets comprising images of COVID-19-affected lungs, pneumonia-affected lungs, and healthy lungs exist, with one being an imbalanced dataset with 5935 images and the other being a balanced dataset with 5002 images. The performance of each model was analyzed using the Adam, Nadam, and SGD optimizers. The best results have been obtained for both the CT scan and CXR datasets using the Nadam optimizer. For the CT scan images, LDDNet showed a COVID-19-positive classification accuracy of 99.36%, a 100% precision recall of 98%, and an F1 score of 99%. For the X-ray dataset of 5935 images, LDDNet provides a 99.55% accuracy, 73% recall, 100% precision, and 85% F1 score using the Nadam optimizer in detecting COVID-19-affected patients. For the balanced X-ray dataset, LDDNet provides a 97.07% classification accuracy. For a given set of parameters, the performance results of LDDNet are better than the existing algorithms of ResNet152V2 and XceptionNet.
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Affiliation(s)
- Prajoy Podder
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka 1205, Bangladesh
| | - Sanchita Rani Das
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka 1205, Bangladesh
| | - M. Rubaiyat Hossain Mondal
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka 1205, Bangladesh
| | - Subrato Bharati
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka 1205, Bangladesh
| | - Azra Maliha
- Faculty of Engineering and IT, The British University in Dubai, Dubai P.O. Box 345015, United Arab Emirates
| | - Md Junayed Hasan
- National Subsea Centre, Robert Gordon University, Aberdeen AB10 7AQ, UK
| | - Farzin Piltan
- Ulsan Industrial Artificial Intelligence (UIAI) Lab, Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea
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Recognising situation awareness associated with different workloads using EEG and eye-tracking features in air traffic control tasks. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.110179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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8
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Tasci G, Loh HW, Barua PD, Baygin M, Tasci B, Dogan S, Tuncer T, Palmer EE, Tan RS, Acharya UR. Automated accurate detection of depression using twin Pascal’s triangles lattice pattern with EEG Signals. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.110190] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Sohaib M, Ghaffar A, Shin J, Hasan MJ, Suleman MT. Automated Analysis of Sleep Study Parameters Using Signal Processing and Artificial Intelligence. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:13256. [PMID: 36293844 PMCID: PMC9603486 DOI: 10.3390/ijerph192013256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 09/27/2022] [Accepted: 10/12/2022] [Indexed: 06/16/2023]
Abstract
An automated sleep stage categorization can readily face noise-contaminated EEG recordings, just as other signal processing applications. Therefore, the denoising of the contaminated signals is inevitable to ensure a reliable analysis of the EEG signals. In this research work, an empirical mode decomposition is used in combination with stacked autoencoders to conduct automatic sleep stage classification with reliable analytical performance. Due to the decomposition of the composite signal into several intrinsic mode functions, empirical mode decomposition offers an effective solution for denoising non-stationary signals such as EEG. Preliminary results showed that through these intrinsic modes, a signal with a high signal-to-noise ratio can be obtained, which can be used for further analysis with confidence. Therefore, later, when statistical features were extracted from the denoised signals and were classified using stacked autoencoders, improved results were obtained for Stage 1, Stage 2, Stage 3, Stage 4, and REM stage EEG signals using this combination.
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Affiliation(s)
- Muhammad Sohaib
- Department of Software Engineering, Lahore Garrison University, Lahore 54000, Pakistan
| | - Ayesha Ghaffar
- Department of Software Engineering, Lahore Garrison University, Lahore 54000, Pakistan
| | - Jungpil Shin
- School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Fukushima, Japan
| | - Md Junayed Hasan
- National Subsea Centre, Robert Gordon University, Scotland AB10 7AQ, UK
| | - Muhammad Taseer Suleman
- Digital Forensics Research and Service Centre, Lahore Garrison University, Lahore 54000, Pakistan or
- Department of Computer Science, School of Systems and Technology, University of Management and Technology Lahore, Lahore 54770, Pakistan
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Wang Z, Ma Z, Liu W, An Z, Huang F. A Depression Diagnosis Method Based on the Hybrid Neural Network and Attention Mechanism. Brain Sci 2022; 12:834. [PMID: 35884641 PMCID: PMC9313113 DOI: 10.3390/brainsci12070834] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 06/23/2022] [Accepted: 06/24/2022] [Indexed: 02/01/2023] Open
Abstract
Depression is a common but easily misdiagnosed disease when using a self-assessment scale. Electroencephalograms (EEGs) provide an important reference and objective basis for the identification and diagnosis of depression. In order to improve the accuracy of the diagnosis of depression by using mainstream algorithms, a high-performance hybrid neural network depression detection method is proposed in this paper combined with deep learning technology. Firstly, a concatenating one-dimensional convolutional neural network (1D-CNN) and gated recurrent unit (GRU) are employed to extract the local features and to determine the global features of the EEG signal. Secondly, the attention mechanism is introduced to form the hybrid neural network. The attention mechanism assigns different weights to the multi-dimensional features extracted by the network, so as to screen out more representative features, which can reduce the computational complexity of the network and save the training time of the model while ensuring high precision. Moreover, dropout is applied to accelerate network training and address the over-fitting problem. Experiments reveal that the 1D-CNN-GRU-ATTN model has more effectiveness and a better generalization ability compared with traditional algorithms. The accuracy of the proposed method in this paper reaches 99.33% in a public dataset and 97.98% in a private dataset, respectively.
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Affiliation(s)
- Zhuozheng Wang
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; (Z.M.); (W.L.)
| | - Zhuo Ma
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; (Z.M.); (W.L.)
| | - Wei Liu
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; (Z.M.); (W.L.)
| | - Zhefeng An
- Advising Center for Student Development, Beijing University of Technology, Beijing 100124, China;
| | - Fubiao Huang
- Department of Occupational Therapy, China Rehabilitation Research Center, Beijing 100068, China;
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Chaabene S, Bouaziz B, Boudaya A, Hökelmann A, Ammar A, Chaari L. Convolutional Neural Network for Drowsiness Detection Using EEG Signals. SENSORS (BASEL, SWITZERLAND) 2021; 21:1734. [PMID: 33802357 PMCID: PMC7959292 DOI: 10.3390/s21051734] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Revised: 02/04/2021] [Accepted: 02/24/2021] [Indexed: 12/18/2022]
Abstract
Drowsiness detection (DD) has become a relevant area of active research in biomedical signal processing. Recently, various deep learning (DL) researches based on the EEG signals have been proposed to detect fatigue conditions. The research presented in this paper proposes an EEG classification system for DD based on DL networks. However, the proposed DD system is mainly realized into two procedures; (i) data acquisition and (ii) model analysis. For the data acquisition procedure, two key steps are considered, which are the signal collection using a wearable Emotiv EPOC+ headset to record 14 channels of EEG, and the signal annotation. Furthermore, a data augmentation (DA) step has been added to the proposed system to overcome the problem of over-fitting and to improve accuracy. As regards the model analysis, a comparative study is also introduced in this paper to argue the choice of DL architecture and frameworks used in our DD system. In this sense, The proposed DD protocol makes use of a convolutional neural network (CNN) architecture implemented using the Keras library. The results showed a high accuracy value (90.42%) in drowsy/awake discrimination and revealed the efficiency of the proposed DD system compared to other research works.
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Affiliation(s)
- Siwar Chaabene
- Multimedia InfoRmation Systems and Advanced Computing Laboratory (MIRACL), University of Sfax, Sfax 3021, Tunisia; (S.C.); (B.B.); (A.B.)
- Digital Research Center of Sfax, B.P. 275, Sakiet Ezzit, Sfax 3021, Tunisia
| | - Bassem Bouaziz
- Multimedia InfoRmation Systems and Advanced Computing Laboratory (MIRACL), University of Sfax, Sfax 3021, Tunisia; (S.C.); (B.B.); (A.B.)
- Digital Research Center of Sfax, B.P. 275, Sakiet Ezzit, Sfax 3021, Tunisia
| | - Amal Boudaya
- Multimedia InfoRmation Systems and Advanced Computing Laboratory (MIRACL), University of Sfax, Sfax 3021, Tunisia; (S.C.); (B.B.); (A.B.)
- Digital Research Center of Sfax, B.P. 275, Sakiet Ezzit, Sfax 3021, Tunisia
| | - Anita Hökelmann
- Institute of Sport Science, Otto-von-Guericke University Magdeburg, 39104 Magdeburg, Germany;
| | - Achraf Ammar
- Institute of Sport Science, Otto-von-Guericke University Magdeburg, 39104 Magdeburg, Germany;
- Interdisciplinary Laboratory in Neurosciences, Physiology and Psychology: Physical Activity, Health and Learning (LINP2), UFR STAPS, UPL, Paris Nanterre University, 92000 Nanterre, France
| | - Lotfi Chaari
- IRIT-ENSEEIHT, University of Toulouse, 31013 Toulouse, France;
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