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Parsa M, Rad HY, Vaezi H, Hossein-Zadeh GA, Setarehdan SK, Rostami R, Rostami H, Vahabie AH. EEG-based classification of individuals with neuropsychiatric disorders using deep neural networks: A systematic review of current status and future directions. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107683. [PMID: 37406421 DOI: 10.1016/j.cmpb.2023.107683] [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: 06/05/2022] [Revised: 05/23/2023] [Accepted: 06/18/2023] [Indexed: 07/07/2023]
Abstract
The use of deep neural networks for electroencephalogram (EEG) classification has rapidly progressed and gained popularity in recent years, but automatic feature extraction from EEG signals remains a challenging task. The classification of neuropsychiatric disorders demands the extraction of neuro-markers for use in automated EEG classification. Numerous advanced deep learning algorithms can be used for this purpose. In this article, we present a comprehensive review of the main factors and parameters that affect the performance of deep neural networks in classifying different neuropsychiatric disorders using EEG signals. We also analyze the EEG features used for improving classification performance. Our analysis includes 82 scientific journal papers that applied deep neural networks for subject-wise classification based on EEG signals. We extracted information on the EEG dataset and types of disorders, deep neural network structures, performance, and hyperparameters. The results show that most studies have focused on clinical classification, achieving an average accuracy of 91.83 ± 7.34, with convolutional neural networks (CNNs) being the most frequently used network architecture and resting-state EEG signals being the most commonly used data type. Additionally, the review reveals that depression (N = 18), Alzheimer's (N = 11), and schizophrenia (N = 11) were studied more frequently than other types of neuropsychiatric disorders. Our review provides insight into the performance of deep neural networks in EEG classification and highlights the importance of EEG feature extraction in improving classification accuracy. By identifying the main factors and parameters that affect deep neural network performance in EEG classification, our review can guide future research in this area. We hope that our findings will encourage further exploration of deep learning methods for EEG classification and contribute to the development of more accurate and effective methods for diagnosing and monitoring neuropsychiatric disorders using EEG signals.
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Affiliation(s)
- Mohsen Parsa
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, North Kargar St., P.O. Box 14395/515, Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences, Artesh Highway, P.O. Box 19568-36484, Tehran, Iran
| | - Habib Yousefi Rad
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, North Kargar St., P.O. Box 14395/515, Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences, Artesh Highway, P.O. Box 19568-36484, Tehran, Iran
| | - Hadi Vaezi
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, North Kargar St., P.O. Box 14395/515, Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences, Artesh Highway, P.O. Box 19568-36484, Tehran, Iran
| | - Gholam-Ali Hossein-Zadeh
- Control and Intelligent Processing Center of Excellence, Faculty of Electrical and Computer Engineering, University of Tehran, North Kargar St., P.O. Box 14395/515, Tehran, Iran
| | - Seyed Kamaledin Setarehdan
- Control and Intelligent Processing Center of Excellence, Faculty of Electrical and Computer Engineering, University of Tehran, North Kargar St., P.O. Box 14395/515, Tehran, Iran
| | - Reza Rostami
- Faculty of Psychology and Education, University of Tehran, Jalal-Al-e-Ahmed, P.O. Box 14155-6456, Tehran, Iran
| | - Hana Rostami
- ACNC, Atieh Clinical Neuroscience Center, Valiasr St., P.O. Box 19697-13663, Tehran, Iran
| | - Abdol-Hossein Vahabie
- Faculty of Psychology and Education, University of Tehran, Jalal-Al-e-Ahmed, P.O. Box 14155-6456, Tehran, Iran; Cognitive Systems Laboratory, Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, P.O. Box 14395/515, Tehran, Iran; Pasargad Institute for Advanced Innovative Solutions (PIAIS), Tehran, Iran.
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Paranormal believers show reduced resting EEG beta band oscillations and inhibitory control than skeptics. Sci Rep 2023; 13:3258. [PMID: 36828909 PMCID: PMC9958009 DOI: 10.1038/s41598-023-30457-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 02/23/2023] [Indexed: 02/26/2023] Open
Abstract
Paranormal believers' thinking is frequently biased by intuitive beliefs. Lack of inhibition of these tempting beliefs is considered a key element in paranormal believers' thinking. However, the brain activity related to inhibitory control in paranormal believers is poorly understood. We examined EEG activities at resting state in alpha, beta, and gamma bands with inhibitory control in paranormal believers and skeptics. The present study shows that paranormal belief is related to the reduced power of the alpha, beta, and gamma frequency bands, and reduced inhibitory control. This study may contribute to understanding the differences between believers and skeptics in brain activity related to inhibitory control in paranormal believers.
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Artificial Intelligence Algorithm-Based Computed Tomography Image in Assessment of Acute Renal Insufficiency of Patients Undergoing Percutaneous Coronary Intervention. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:2214583. [PMID: 35291424 PMCID: PMC8901312 DOI: 10.1155/2022/2214583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Revised: 01/28/2022] [Accepted: 01/31/2022] [Indexed: 11/18/2022]
Abstract
This study was aimed to analyze the changes in renal function of patients undergoing percutaneous coronary intervention (PCI) surgery and the characteristics of their computed tomography (CT) image based on artificial intelligence algorithms. In this study, 104 patients with coronary atherosclerotic heart disease (CAHD) were treated as the research objects. They were divided into an experimental group (patients who underwent CAG and PCI within 1 week after enhanced coronary CT (ECCT)) and the control group (patients who underwent CAG and PCI within 1-3 weeks after ECCT). Renal imaging scans of patients were performed by CT based on discrete inseparable shear transform (DNST) optimized algorithm, which was named as O-DNST. The results showed that the serum creatinine (Scr), blood urea nitrogen (BUN), and urine protein (UP) levels of patients in the experimental group were significantly higher than those of the control group 24-72 hours after surgery, while the levels of endogenous creatinine clearance (Ccr) and estimated glomerular filtration rate (eGFR) were significantly lower than those of the control group (P < 0.05). The levels of β2 microglobulin (β2-MG), C-reactive protein (CRP), interleukin 6 (IL-6), and tumor necrosis factor (TNF-α) in the experimental group were significantly higher than those in the control group 24-72 hours after surgery (P < 0.05). The incidence of contrast-induced nephropathy (CIN) in the experimental group (15.38%) was significantly higher than that in the control group (5.8%), and the difference was statistically significant (P < 0.05). The results showed that repeated application of contrast agent in a short period of time can promote the increase of serum inflammation levels in PCI patients, which may be a risk factor for CIN in PCI patients.
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Fiani B, Pasko KBD, Sarhadi K, Covarrubias C. Current uses, emerging applications, and clinical integration of artificial intelligence in neuroradiology. Rev Neurosci 2021; 33:383-395. [PMID: 34506699 DOI: 10.1515/revneuro-2021-0101] [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: 07/30/2021] [Accepted: 08/18/2021] [Indexed: 11/15/2022]
Abstract
Artificial intelligence (AI) is a branch of computer science with a variety of subfields and techniques, exploited to serve as a deductive tool that performs tasks originally requiring human cognition. AI tools and its subdomains are being incorporated into healthcare delivery for the improvement of medical data interpretation encompassing clinical management, diagnostics, and prognostic outcomes. In the field of neuroradiology, AI manifested through deep machine learning and connected neural networks (CNNs) has demonstrated incredible accuracy in identifying pathology and aiding in diagnosis and prognostication in several areas of neurology and neurosurgery. In this literature review, we survey the available clinical data highlighting the utilization of AI in the field of neuroradiology across multiple neurological and neurosurgical subspecialties. In addition, we discuss the emerging role of AI in neuroradiology, its strengths and limitations, as well as future needs in strengthening its role in clinical practice. Our review evaluated data across several subspecialties of neurology and neurosurgery including vascular neurology, spinal pathology, traumatic brain injury (TBI), neuro-oncology, multiple sclerosis, Alzheimer's disease, and epilepsy. AI has established a strong presence within the realm of neuroradiology as a successful and largely supportive technology aiding in the interpretation, diagnosis, and even prognostication of various pathologies. More research is warranted to establish its full scientific validity and determine its maximum potential to aid in optimizing and providing the most accurate imaging interpretation.
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Affiliation(s)
- Brian Fiani
- Department of Neurosurgery, Desert Regional Medical Center, 1150 N Indian Canyon Dr, Palm Springs, CA, 92262, USA
| | - Kory B Dylan Pasko
- School of Medicine, Georgetown University, 3900 Reservoir Rd NW, Washington, DC, 20007, USA
| | - Kasra Sarhadi
- Department of Neurology, University of Washington, Main Hospital, 325 9th Ave, Seattle, WA, 98104, USA
| | - Claudia Covarrubias
- School of Medicine, Universidad Anáhuac Querétaro, Cto. Universidades I, Fracción 2, 76246 Qro., Querétaro, Mexico
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Obukhov YV, Kershner IA, Tolmacheva RA, Sinkin MV, Zhavoronkova LA. Wavelet Ridges in EEG Diagnostic Features Extraction: Epilepsy Long-Time Monitoring and Rehabilitation after Traumatic Brain Injury. SENSORS (BASEL, SWITZERLAND) 2021; 21:5989. [PMID: 34577198 PMCID: PMC8468146 DOI: 10.3390/s21185989] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 08/29/2021] [Accepted: 09/03/2021] [Indexed: 01/06/2023]
Abstract
Interchannel EEG synchronization, as well as its violation, is an important diagnostic sign of a number of diseases. In particular, during an epileptic seizure, such synchronization occurs starting from some pairs of channels up to many pairs in a generalized seizure. Additionally, for example, after traumatic brain injury, the destruction of interneuronal connections occurs, which leads to a violation of interchannel synchronization when performing motor or cognitive tests. Within the framework of a unified approach to the analysis of interchannel EEG synchronization using the ridges of wavelet spectra, two problems were solved. First, the segmentation of the initial data of long-term monitoring of scalp EEG with various artifacts into fragments suspicious of epileptic seizures in order to reduce the total duration of the fragments analyzed by the doctor. Second, assessments of recovery after rehabilitation of cognitive functions in patients with moderate traumatic brain injury. In the first task, the initial EEG was segmented into fragments in which at least two channels were synchronized, and by the adaptive threshold method into fragments with a high value of the EEG power spectral density. Overlapping in time synchronized fragments with fragments of high spectral power density was determined. As a result, the total duration of the fragments for analysis by the doctor was reduced by more than 60 times. In the second task, the network of phase-related EEG channels was determined during the cognitive test before and after rehabilitation. Calculation-logical and spatial-pattern cognitive tests were used. The positive dynamics of rehabilitation was determined during the initialization of interhemispheric connections and connections in the frontal cortex of the brain.
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Affiliation(s)
- Yury Vladimirovich Obukhov
- Kotelnikov Institute of Radio Engineering and Electronics of RAS, Mokhovaya St. 11-7, 125009 Moscow, Russia; (Y.V.O.); (R.A.T.)
| | - Ivan Andreevich Kershner
- Kotelnikov Institute of Radio Engineering and Electronics of RAS, Mokhovaya St. 11-7, 125009 Moscow, Russia; (Y.V.O.); (R.A.T.)
| | - Renata Alekseevna Tolmacheva
- Kotelnikov Institute of Radio Engineering and Electronics of RAS, Mokhovaya St. 11-7, 125009 Moscow, Russia; (Y.V.O.); (R.A.T.)
| | - Mikhail Vladimirovich Sinkin
- Department of Neurosurgery of the Sklifosovsky Research Institute for Emergency Medicine of Moscow Healthcare Department, Bolshaya Sukharevskaya Square 3, 129090 Moscow, Russia; or
- Laboratory of Invasive Neurointerfaces of the Research Institute TechnoBioMed, A.I. Yevdokimov Moscow State University of Medicine and Dentistry, Delegatskaya St. 20 p.1, 127473 Moscow, Russia
| | - Ludmila Alekseevna Zhavoronkova
- Laboratory of General and Clinical Neurophysiology of the Institute of Higher Nervous Activity and Neurophysiology of RAS, Butlerova St. 5a, 117485 Moscow, Russia; or
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Schmid W, Fan Y, Chi T, Golanov E, Regnier-Golanov AS, Austerman RJ, Podell K, Cherukuri P, Bentley T, Steele CT, Schodrof S, Aazhang B, Britz GW. Review of wearable technologies and machine learning methodologies for systematic detection of mild traumatic brain injuries. J Neural Eng 2021; 18. [PMID: 34330120 DOI: 10.1088/1741-2552/ac1982] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 07/30/2021] [Indexed: 12/16/2022]
Abstract
Mild traumatic brain injuries (mTBIs) are the most common type of brain injury. Timely diagnosis of mTBI is crucial in making 'go/no-go' decision in order to prevent repeated injury, avoid strenuous activities which may prolong recovery, and assure capabilities of high-level performance of the subject. If undiagnosed, mTBI may lead to various short- and long-term abnormalities, which include, but are not limited to impaired cognitive function, fatigue, depression, irritability, and headaches. Existing screening and diagnostic tools to detect acute andearly-stagemTBIs have insufficient sensitivity and specificity. This results in uncertainty in clinical decision-making regarding diagnosis and returning to activity or requiring further medical treatment. Therefore, it is important to identify relevant physiological biomarkers that can be integrated into a mutually complementary set and provide a combination of data modalities for improved on-site diagnostic sensitivity of mTBI. In recent years, the processing power, signal fidelity, and the number of recording channels and modalities of wearable healthcare devices have improved tremendously and generated an enormous amount of data. During the same period, there have been incredible advances in machine learning tools and data processing methodologies. These achievements are enabling clinicians and engineers to develop and implement multiparametric high-precision diagnostic tools for mTBI. In this review, we first assess clinical challenges in the diagnosis of acute mTBI, and then consider recording modalities and hardware implementation of various sensing technologies used to assess physiological biomarkers that may be related to mTBI. Finally, we discuss the state of the art in machine learning-based detection of mTBI and consider how a more diverse list of quantitative physiological biomarker features may improve current data-driven approaches in providing mTBI patients timely diagnosis and treatment.
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Affiliation(s)
- William Schmid
- Department of Electrical and Computer Engineering and Neuroengineering Initiative (NEI), Rice University, Houston, TX 77005, United States of America
| | - Yingying Fan
- Department of Electrical and Computer Engineering and Neuroengineering Initiative (NEI), Rice University, Houston, TX 77005, United States of America
| | - Taiyun Chi
- Department of Electrical and Computer Engineering and Neuroengineering Initiative (NEI), Rice University, Houston, TX 77005, United States of America
| | - Eugene Golanov
- Department of Neurosurgery, Houston Methodist Hospital, Houston, TX 77030, United States of America
| | | | - Ryan J Austerman
- Department of Neurosurgery, Houston Methodist Hospital, Houston, TX 77030, United States of America
| | - Kenneth Podell
- Department of Neurology, Houston Methodist Hospital, Houston, TX 77030, United States of America
| | - Paul Cherukuri
- Institute of Biosciences and Bioengineering (IBB), Rice University, Houston, TX 77005, United States of America
| | - Timothy Bentley
- Office of Naval Research, Arlington, VA 22203, United States of America
| | - Christopher T Steele
- Military Operational Medicine Research Program, US Army Medical Research and Development Command, Fort Detrick, MD 21702, United States of America
| | - Sarah Schodrof
- Department of Athletics-Sports Medicine, Rice University, Houston, TX 77005, United States of America
| | - Behnaam Aazhang
- Department of Electrical and Computer Engineering and Neuroengineering Initiative (NEI), Rice University, Houston, TX 77005, United States of America
| | - Gavin W Britz
- Department of Neurosurgery, Houston Methodist Hospital, Houston, TX 77030, United States of America
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Neurodevelopmental effects of childhood malnutrition: A neuroimaging perspective. Neuroimage 2021; 231:117828. [PMID: 33549754 DOI: 10.1016/j.neuroimage.2021.117828] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 01/28/2021] [Accepted: 01/31/2021] [Indexed: 02/08/2023] Open
Abstract
Approximately one in five children worldwide suffers from childhood malnutrition and its complications, including increased susceptibility to inflammation and infectious diseases. Due to improved early interventions, most of these children now survive early malnutrition, even in low-resource settings (LRS). However, many continue to exhibit neurodevelopmental deficits, including low IQ, poor school performance, and behavioral problems over their lifetimes. Most studies have relied on neuropsychological tests, school performance, and mental health and behavioral measures. Few studies, in contrast, have assessed brain structure and function, and to date, these have mainly relied on low-cost techniques, including electroencephalography (EEG) and evoked potentials (ERP). The use of more advanced methods of neuroimaging, including magnetic resonance imaging (MRI) and functional near-infrared spectroscopy (fNIRS), has been limited by cost factors and lack of availability of these technologies in developing countries, where malnutrition is nearly ubiquitous. This report summarizes the current state of knowledge and evidence gaps regarding childhood malnutrition and the study of its impact on neurodevelopment. It may help to inform the development of new strategies to improve the identification, classification, and treatment of neurodevelopmental disabilities in underserved populations at the highest risk for childhood malnutrition.
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