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Raveendran S, Kenchaiah R, Kumar S, Sahoo J, Farsana MK, Chowdary Mundlamuri R, Bansal S, Binu VS, Ramakrishnan AG, Ramakrishnan S, Kala S. Variational mode decomposition-based EEG analysis for the classification of disorders of consciousness. Front Neurosci 2024; 18:1340528. [PMID: 38379759 PMCID: PMC10876804 DOI: 10.3389/fnins.2024.1340528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Accepted: 01/22/2024] [Indexed: 02/22/2024] Open
Abstract
Aberrant alterations in any of the two dimensions of consciousness, namely awareness and arousal, can lead to the emergence of disorders of consciousness (DOC). The development of DOC may arise from more severe or targeted lesions in the brain, resulting in widespread functional abnormalities. However, when it comes to classifying patients with disorders of consciousness, particularly utilizing resting-state electroencephalogram (EEG) signals through machine learning methods, several challenges surface. The non-stationarity and intricacy of EEG data present obstacles in understanding neuronal activities and achieving precise classification. To address these challenges, this study proposes variational mode decomposition (VMD) of EEG before feature extraction along with machine learning models. By decomposing preprocessed EEG signals into specified modes using VMD, features such as sample entropy, spectral entropy, kurtosis, and skewness are extracted across these modes. The study compares the performance of the features extracted from VMD-based approach with the frequency band-based approach and also the approach with features extracted from raw-EEG. The classification process involves binary classification between unresponsive wakefulness syndrome (UWS) and the minimally conscious state (MCS), as well as multi-class classification (coma vs. UWS vs. MCS). Kruskal-Wallis test was applied to determine the statistical significance of the features and features with a significance of p < 0.05 were chosen for a second round of classification experiments. Results indicate that the VMD-based features outperform the features of other two approaches, with the ensemble bagged tree (EBT) achieving the highest accuracy of 80.5% for multi-class classification (the best in the literature) and 86.7% for binary classification. This approach underscores the potential of integrating advanced signal processing techniques and machine learning in improving the classification of patients with disorders of consciousness, thereby enhancing patient care and facilitating informed treatment decision-making.
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Affiliation(s)
- Sreelakshmi Raveendran
- Department of Electronics and Communication Engineering, Indian Institute of Information Technology, Kottayam, Kerala, India
| | | | - Santhos Kumar
- Department of Electronics and Communication Engineering, Indian Institute of Information Technology, Kottayam, Kerala, India
| | - Jayakrushna Sahoo
- Department of Computer Science and Engineering, Indian Institute of Information Technology, Kottayam, Kerala, India
| | - M. K. Farsana
- Department of Neurology, NIMHANS, Bangalore, Karnataka, India
| | | | - Sonia Bansal
- Department of Neuroanaesthesia and Neurocritical Care, NIMHANS, Bangalore, Karnataka, India
| | - V. S. Binu
- Department of Biostatistics, NIMHANS, Bangalore, Karnataka, India
| | - A. G. Ramakrishnan
- Department of Electrical Engineering and Centre for Neuroscience, Indian Institute of Science, Bangalore, Karnataka, India
| | | | - S. Kala
- Department of Electronics and Communication Engineering, Indian Institute of Information Technology, Kottayam, Kerala, India
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Chen YH, Yang J, Wu H, Beier KT, Sawan M. Challenges and future trends in wearable closed-loop neuromodulation to efficiently treat methamphetamine addiction. Front Psychiatry 2023; 14:1085036. [PMID: 36911117 PMCID: PMC9995819 DOI: 10.3389/fpsyt.2023.1085036] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 02/03/2023] [Indexed: 02/25/2023] Open
Abstract
Achieving abstinence from drugs is a long journey and can be particularly challenging in the case of methamphetamine, which has a higher relapse rate than other drugs. Therefore, real-time monitoring of patients' physiological conditions before and when cravings arise to reduce the chance of relapse might help to improve clinical outcomes. Conventional treatments, such as behavior therapy and peer support, often cannot provide timely intervention, reducing the efficiency of these therapies. To more effectively treat methamphetamine addiction in real-time, we propose an intelligent closed-loop transcranial magnetic stimulation (TMS) neuromodulation system based on multimodal electroencephalogram-functional near-infrared spectroscopy (EEG-fNIRS) measurements. This review summarizes the essential modules required for a wearable system to treat addiction efficiently. First, the advantages of neuroimaging over conventional techniques such as analysis of sweat, saliva, or urine for addiction detection are discussed. The knowledge to implement wearable, compact, and user-friendly closed-loop systems with EEG and fNIRS are reviewed. The features of EEG and fNIRS signals in patients with methamphetamine use disorder are summarized. EEG biomarkers are categorized into frequency and time domain and topography-related parameters, whereas for fNIRS, hemoglobin concentration variation and functional connectivity of cortices are described. Following this, the applications of two commonly used neuromodulation technologies, transcranial direct current stimulation and TMS, in patients with methamphetamine use disorder are introduced. The challenges of implementing intelligent closed-loop TMS modulation based on multimodal EEG-fNIRS are summarized, followed by a discussion of potential research directions and the promising future of this approach, including potential applications to other substance use disorders.
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Affiliation(s)
- Yun-Hsuan Chen
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, China.,Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, China
| | - Jie Yang
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, China.,Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, China
| | - Hemmings Wu
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Kevin T Beier
- Department of Physiology and Biophysics, University of California, Irvine, Irvine, CA, United States.,Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, United States.,Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States.,Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, CA, United States.,Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA, United States
| | - Mohamad Sawan
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, China.,Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, China
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Chen CC, Chung CR, Tsai MC, Wu EHK, Chiu PR, Tsai PY, Yeh SC. Impaired Brain-Heart Relation in Patients With Methamphetamine Use Disorder During VR Induction of Drug Cue Reactivity. IEEE J Transl Eng Health Med 2022; 12:1-9. [PMID: 38059128 PMCID: PMC10697298 DOI: 10.1109/jtehm.2022.3206333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 07/12/2022] [Accepted: 09/08/2022] [Indexed: 12/08/2023]
Abstract
Methamphetamine use disorder (MUD) is an illness associated with severe health consequences. Virtual reality (VR) is used to induce the drug-cue reactivity and significant EEG and ECG abnormalities were found in MUD patients. However, whether a link exists between EEG and ECG abnormalities in patients with MUD during exposure to drug cues remains unknown. This is important from the therapeutic viewpoint because different treatment strategies may be applied when EEG abnormalities and ECG irregularities are complications of MUD. We designed a VR system with drug cues and EEG and ECG were recorded during VR exposure. Sixteen patients with MUD and sixteen healthy subjects were recruited. Statistical tests and Pearson correlation were employed to analyze the EEG and ECG. The results showed that, during VR induction, the patients with MUD but not healthy controls showed significant [Formula: see text] and [Formula: see text] power increases when the stimulus materials were most intense. This finding indicated that the stimuli are indiscriminate to healthy controls but meaningful to patients with MUD. Five heart rate variability (HRV) indexes significantly differed between patients and controls, suggesting abnormalities in the reaction of patient's autonomic nervous system. Importantly, significant relations between EEG and HRV indexes changes were only identified in the controls, but not in MUD patients, signifying a disruption of brain-heart relations in patients. Our findings of stimulus-specific EEG changes and the impaired brain-heart relations in patients with MUD shed light on the understanding of drug-cue reactivity and may be used to design diagnostic and/or therapeutic strategies for MUD.
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Affiliation(s)
- Chun-Chuan Chen
- Department of Biomedical Sciences and EngineeringNational Central UniversityTaoyuan City320317Taiwan
| | - Chia-Ru Chung
- Computer Science and Information Engineering DepartmentNational Central UniversityTaoyuan City320317Taiwan
| | - Meng-Chang Tsai
- Department of PsychiatryKaohsiung Chang Gung Memorial HospitalKaohsiung City83301Taiwan
- Department of PsychiatryChang Gung University College of MedicineKaohsiung City83301Taiwan
| | - Eric Hsiao-Kuang Wu
- Computer Science and Information Engineering DepartmentNational Central UniversityTaoyuan City320317Taiwan
| | - Po-Ru Chiu
- Department of Biomedical Sciences and EngineeringNational Central UniversityTaoyuan City320317Taiwan
| | - Po-Yi Tsai
- Department of Physical Medicine and RehabilitationTaipei Veterans General HospitalTaipei112201Taiwan
| | - Shih-Ching Yeh
- Computer Science and Information Engineering DepartmentNational Central UniversityTaoyuan City320317Taiwan
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