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Chen D, Zhou X, Yao W, Wang F. Causal brain network analysis of driving fatigue based on generalized orthogonalized partially directed coherence. Neurosci Lett 2025; 844:138057. [PMID: 39566651 DOI: 10.1016/j.neulet.2024.138057] [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: 02/23/2024] [Revised: 09/20/2024] [Accepted: 11/15/2024] [Indexed: 11/22/2024]
Abstract
Driving fatigue is a serious threat to driving safety. Therefore, it is of great significance to accurately detect driving fatigue. In this study, the generalized orthogonal partial directed coherence (gOPDC) algorithm, which measures the time-frequency domain interaction of electroencephalogram (EEG) signals, was used to accurately estimate the connectivity between cortical channels. The causal brain network of driver continuous driving is constructed. The results show that the clustering coefficient and global efficiency tend to decrease with the increase in driving time. Causal information flow in the left prefrontal, parietal, occipital regions and the right posterior frontal region increased significantly when subjects transitioned from awake to fatigued, while causal information flow in the right prefrontal, parietal, occipital regions and the left posterior frontal region decreased mutually significantly. Compared with the traditional driving fatigue algorithm, the accuracy of the method used in this paper is higher than the traditional methods.
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Affiliation(s)
- Daping Chen
- Northeast Electric Power University, School of Mechanic Engineering, Jilin 132012, China
| | - Xin Zhou
- Market Supervision Administration of Tianmen Municipality, Tianmen 431700, China
| | - Wanchao Yao
- Northeast Electric Power University, School of Mechanic Engineering, Jilin 132012, China
| | - Fuwang Wang
- Northeast Electric Power University, School of Mechanic Engineering, Jilin 132012, China.
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Singh M, Prakash P, Kaur R, Sowers R, Brašić JR, Hernandez ME. A Deep Learning Approach for Automatic and Objective Grading of the Motor Impairment Severity in Parkinson's Disease for Use in Tele-Assessments. SENSORS (BASEL, SWITZERLAND) 2023; 23:9004. [PMID: 37960703 PMCID: PMC10650884 DOI: 10.3390/s23219004] [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: 09/20/2023] [Revised: 10/18/2023] [Accepted: 10/25/2023] [Indexed: 11/15/2023]
Abstract
Wearable sensors provide a tool for at-home monitoring of motor impairment progression in neurological conditions such as Parkinson's disease (PD). This study examined the ability of deep learning approaches to grade the motor impairment severity in a modified version of the Movement Disorders Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) using low-cost wearable sensors. We hypothesized that expanding training datasets with motion data from healthy older adults (HOAs) and initializing classifiers with weights learned from unsupervised pre-training would lead to an improvement in performance when classifying lower vs. higher motor impairment relative to a baseline deep learning model (XceptionTime). This study evaluated the change in classification performance after using expanded training datasets with HOAs and transferring weights from unsupervised pre-training compared to a baseline deep learning model (XceptionTime) using both upper extremity (finger tapping, hand movements, and pronation-supination movements of the hands) and lower extremity (toe tapping and leg agility) tasks consistent with the MDS-UPDRS. Overall, we found a 12.2% improvement in accuracy after expanding the training dataset and pre-training using max-vote inference on hand movement tasks. Moreover, we found that the classification performance improves for every task except toe tapping after the addition of HOA training data. These findings suggest that learning from HOA motion data can implicitly improve the representations of PD motion data for the purposes of motor impairment classification. Further, our results suggest that unsupervised pre-training can improve the performance of motor impairment classifiers without any additional annotated PD data, which may provide a viable solution for a widely deployable telemedicine solution.
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Affiliation(s)
- Mehar Singh
- Computer Science and Engineering Division, University of Michigan, Ann-Arbor, MI 48109, USA;
- Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - Prithvi Prakash
- School of Information Sciences, University of Illinois Urbana-Champaign, Champaign, IL 61820, USA;
| | - Rachneet Kaur
- Department of Industrial and Enterprise Systems Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA; (R.K.); (R.S.)
| | - Richard Sowers
- Department of Industrial and Enterprise Systems Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA; (R.K.); (R.S.)
| | - James Robert Brašić
- Section of High Resolution Brain Positron Emission Tomography Imaging, Division of Nuclear Medicine and Molecular Imaging, Russell H. Morgan Department of Radiology and Radiological Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Behavioral Health, New York City Health + Hospitals/Bellevue, 462 First Avenue, New York, NY 10016, USA
- Department of Psychiatry, New York University Grossman School of Medicine, New York University Langone Health, New York University, 550 First Avenue, New York, NY 10016, USA
| | - Manuel Enrique Hernandez
- Neuroscience Program, Beckman Institute, College of Liberal Arts & Sciences, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
- Department of Biomedical and Translational Sciences, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
- Department of Kinesiology and Community Health, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
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Arasteh E, Veldhoen ES, Long X, van Poppel M, van der Linden M, Alderliesten T, Nijman J, de Goederen R, Dudink J. Ultra-Wideband Radar for Simultaneous and Unobtrusive Monitoring of Respiratory and Heart Rates in Early Childhood: A Deep Transfer Learning Approach. SENSORS (BASEL, SWITZERLAND) 2023; 23:7665. [PMID: 37765721 PMCID: PMC10535330 DOI: 10.3390/s23187665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 08/31/2023] [Accepted: 09/02/2023] [Indexed: 09/29/2023]
Abstract
Unobtrusive monitoring of children's heart rate (HR) and respiratory rate (RR) can be valuable for promoting the early detection of potential health issues, improving communication with healthcare providers and reducing unnecessary hospital visits. A promising solution for wireless vital sign monitoring is radar technology. This paper presents a novel approach for the simultaneous estimation of children's RR and HR utilizing ultra-wideband (UWB) radar using a deep transfer learning algorithm in a cohort of 55 children. The HR and RR are calculated by processing radar signals via spectrogram from time epochs of 10 s (25 sample length of hamming window with 90% overlap) and then transforming the resultant representation into 2-dimensional images. These images were fed into a pre-trained Visual Geometry Group-16 (VGG-16) model (trained on ImageNet dataset), with weights of five added layers fine-tuned using the proposed data. The prediction on the test data achieved a mean absolute error (MAE) of 7.3 beats per minute (BPM < 6.5% of average HR) and 2.63 breaths per minute (BPM < 7% of average RR). We also achieved a significant Pearson's correlation of 77% and 81% between true and extracted for HR and RR, respectively. HR and RR samples are extracted every 10 s.
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Affiliation(s)
- Emad Arasteh
- Department of Neonatology, University Medical Center Utrecht, Wilhelmina Children’s Hospital, 3508 EA Utrecht, The Netherlands; (E.A.); (M.v.d.L.); (T.A.); (R.d.G.)
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, 3001 Leuven, Belgium
| | - Esther S. Veldhoen
- Pediatric Intensive Care Unit and Center of Home Mechanical Ventilation, University Medical Center Utrecht, Wilhelmina Children’s Hospital, 3508 EA Utrecht, The Netherlands; (E.S.V.); (M.v.P.); (J.N.)
| | - Xi Long
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AE Eindhoven, The Netherlands;
| | - Maartje van Poppel
- Pediatric Intensive Care Unit and Center of Home Mechanical Ventilation, University Medical Center Utrecht, Wilhelmina Children’s Hospital, 3508 EA Utrecht, The Netherlands; (E.S.V.); (M.v.P.); (J.N.)
| | - Marjolein van der Linden
- Department of Neonatology, University Medical Center Utrecht, Wilhelmina Children’s Hospital, 3508 EA Utrecht, The Netherlands; (E.A.); (M.v.d.L.); (T.A.); (R.d.G.)
| | - Thomas Alderliesten
- Department of Neonatology, University Medical Center Utrecht, Wilhelmina Children’s Hospital, 3508 EA Utrecht, The Netherlands; (E.A.); (M.v.d.L.); (T.A.); (R.d.G.)
| | - Joppe Nijman
- Pediatric Intensive Care Unit and Center of Home Mechanical Ventilation, University Medical Center Utrecht, Wilhelmina Children’s Hospital, 3508 EA Utrecht, The Netherlands; (E.S.V.); (M.v.P.); (J.N.)
| | - Robbin de Goederen
- Department of Neonatology, University Medical Center Utrecht, Wilhelmina Children’s Hospital, 3508 EA Utrecht, The Netherlands; (E.A.); (M.v.d.L.); (T.A.); (R.d.G.)
| | - Jeroen Dudink
- Department of Neonatology, University Medical Center Utrecht, Wilhelmina Children’s Hospital, 3508 EA Utrecht, The Netherlands; (E.A.); (M.v.d.L.); (T.A.); (R.d.G.)
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Göker H. Automatic detection of Parkinson's disease from power spectral density of electroencephalography (EEG) signals using deep learning model. Phys Eng Sci Med 2023; 46:1163-1174. [PMID: 37245195 DOI: 10.1007/s13246-023-01284-x] [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: 07/26/2022] [Accepted: 05/18/2023] [Indexed: 05/29/2023]
Abstract
Parkinson's disease (PD) is characterized by slowed movements, speech disorders, an inability to control muscle movements, and tremors in the hands and feet. In the early stages of PD, the changes in these motor signs are very vague, so an objective and accurate diagnosis is difficult. The disease is complex, progressive, and very common. There are more than 10 million people worldwide suffering from PD. In this study, an EEG-based deep learning model was proposed for the automatic detection of PD to support experts. The EEG dataset comprises signals recorded by the University of Iowa from 14 PD patients and 14 healthy controls. First of all, the power spectral density values (PSDs) of the frequencies between 1 and 49 Hz of the EEG signals were calculated separately using periodogram, welch, and multitaper spectral analysis methods. 49 feature vectors were extracted for each of the three different experiments. Then, the performances of support vector machine, random forest, k-nearest neighbor, and bidirectional long-short-term memory (BiLSTM) algorithms were compared using the PSDs feature vectors. After the comparison, the model integrating welch spectral analysis and the BiLSTM algorithm showed the highest performance as a result of the experiments. The deep learning model achieved satisfactory performance with 0.965 specificity, 0.994 sensitivity, 0.964 precision, 0.978 f1-score, 0.958 Matthews correlation coefficient, and 97.92% accuracy. The study is a promising attempt to detect PD from EEG signals and it also provides evidence that deep learning algorithms are more effective than machine learning algorithms for EEG signal analysis.
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Affiliation(s)
- Hanife Göker
- Health Services Vocational College, Gazi University, 06830, Ankara, Turkey.
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Survey of Machine Learning Techniques in the Analysis of EEG Signals for Parkinson’s Disease: A Systematic Review. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12146967] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background: Parkinson’s disease (PD) affects 7–10 million people worldwide. Its diagnosis is clinical and can be supported by image-based tests, which are expensive and not always accessible. Electroencephalograms (EEG) are non-invasive, widely accessible, low-cost tests. However, the signals obtained are difficult to analyze visually, so advanced techniques, such as Machine Learning (ML), need to be used. In this article, we review those studies that consider ML techniques to study the EEG of patients with PD. Methods: The review process was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, which are used to provide quality standards for the objective evaluation of various studies. All publications before February 2022 were included, and their main characteristics and results were evaluated and documented through three key points associated with the development of ML techniques: dataset quality, data preprocessing, and model evaluation. Results: 59 studies were included. The predominating models were Support Vector Machine (SVM) and Artificial Neural Networks (ANNs). In total, 31 articles diagnosed PD with a mean accuracy of 97.35 ± 3.46%. There was no standard cleaning protocol for EEG and a great heterogeneity in EEG characteristics was shown, although spectral features predominated by 88.37%. Conclusions: Neither the cleaning protocol nor the number of EEG channels influenced the classification results. A baseline value was provided for the PD diagnostic problem, although recent studies focus on the identification of cognitive impairment.
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