1
|
Gefferie SR, Ossenblok PPW, Dietze CS, Sargsyan A, Bourez-Swart M, van den Maagdenberg AMJM, Thijs RD. Detection of short-lasting and ictal spike-and-wave discharges in around-the-ears EEG recordings in children with absence epilepsy. Epilepsy Res 2024; 204:107385. [PMID: 38851173 DOI: 10.1016/j.eplepsyres.2024.107385] [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: 03/29/2024] [Revised: 05/23/2024] [Accepted: 05/29/2024] [Indexed: 06/10/2024]
Abstract
PURPOSE Long-term ambulatory EEG recordings can improve the monitoring of absence epilepsy in children, but signal quality and increased review workload are a concern. We evaluated the feasibility of around-the-ears EEG arrays (cEEGrids) to capture 3-Hz short-lasting and ictal spike-and-wave discharges and assessed the performance of automated detection software in cEEGrids data. We compared patterns of bilateral synchronisation between short-lasting and ictal spike-and-wave discharges. METHODS We recruited children with suspected generalised epilepsy undergoing routine video-EEG monitoring and performed simultaneous cEEGrids recordings. We used ASSYST software to detect short-lasting 3-Hz spike-and-wave discharges (1-3 s) and ictal spike-and-wave discharges in the cEEGrids data. We assessed data quality and sensitivity of cEEGrids for spike-and-wave discharges in routine EEG. We determined the sensitivity and false detection rate for automated spike-and-wave discharge detection in cEEGrids data. We compared bihemispheric synchrony across the onset of short-lasting and ictal spike-and-wave discharges using the mean phase coherence in the 2-4 Hz frequency band. RESULTS We included nine children with absence epilepsy (median age = 11 y, range 8-15 y, nine females) and recorded 4 h and 27 min of cEEGrids data. The recordings from seven participants were suitable for quantitative analysis, containing 82 spike-and-wave discharges. The cEEGrids captured 58 % of all spike-and-wave discharges (median individual sensitivity: 100 %, range: 47-100 %). ASSYST detected 82 % of all spike-and-wave discharges (median: 100 %, range: 41-100 %) with a false detection rate of 48/h (median: 6/h, range: 0-154/h). The mean phase coherence significantly increased during short-lasting and ictal spike-and-wave discharges in the 500-ms pre-onset to 1-s post-onset interval. CONCLUSIONS cEEGrids are of variable quality for monitoring spike-and-wave discharges in children with absence epilepsy. ASSYST could facilitate the detection of short-lasting and ictal spike-and-wave discharges with clear periodic structures but with low specificity. A similar course of bihemispheric synchrony between short-lasting and ictal spike-and-wave discharges indicates that cortico-thalamic driving may be relevant for both types of spike-and-wave discharges.
Collapse
Affiliation(s)
- Silvano R Gefferie
- Department of Clinical Neurophysiology (location Zwolle & Heemstede), Stichting Epilepsie Instellingen Nederland (SEIN), Achterweg 5, Heemstede, SW 2103, the Netherlands; Department of Neurology, Leiden University Medical Centre, Albinusdreef 2, Leiden, RC 2300, the Netherlands
| | - Pauly P W Ossenblok
- Department of Clinical Neurophysiology (location Zwolle & Heemstede), Stichting Epilepsie Instellingen Nederland (SEIN), Achterweg 5, Heemstede, SW 2103, the Netherlands; Clinical Neuro-Science projects, De Wittenkade 283, Amsterdam, DD 1052, the Netherlands
| | - Christoph S Dietze
- Department of Clinical Neurophysiology (location Zwolle & Heemstede), Stichting Epilepsie Instellingen Nederland (SEIN), Achterweg 5, Heemstede, SW 2103, the Netherlands
| | - Armen Sargsyan
- Orbeli Institute of Physiology, 22 Orbeli Bros. str 0028, Yerevan, Armenia; Kaoskey Pty. Ltd., Unit 6, 3 Central Ave, Sydney, Australia
| | - Mireille Bourez-Swart
- Department of Clinical Neurophysiology (location Zwolle & Heemstede), Stichting Epilepsie Instellingen Nederland (SEIN), Achterweg 5, Heemstede, SW 2103, the Netherlands
| | - Arn M J M van den Maagdenberg
- Department of Neurology, Leiden University Medical Centre, Albinusdreef 2, Leiden, RC 2300, the Netherlands; Department of Human Genetics, Leiden University Medical Centre, Albinusdreef 2, Leiden, RC 2300, the Netherlands
| | - Roland D Thijs
- Department of Clinical Neurophysiology (location Zwolle & Heemstede), Stichting Epilepsie Instellingen Nederland (SEIN), Achterweg 5, Heemstede, SW 2103, the Netherlands; Department of Neurology, Leiden University Medical Centre, Albinusdreef 2, Leiden, RC 2300, the Netherlands; Department of Clinical & Experimental Epilepsy, UCL Queen Square Institute of Neurology, London WC1N 3BG, United Kingdom.
| |
Collapse
|
2
|
Koren J, Lang C, Gritsch G, Mayer L, Hartmann M, Hafner S, Kluge T, Baumgartner C. Idiopathic generalized epilepsies in the epilepsy monitoring unit: Systematic quantification of focal EEG and semiological signs. Clin Neurophysiol 2024; 162:82-90. [PMID: 38603948 DOI: 10.1016/j.clinph.2024.03.025] [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: 12/10/2023] [Revised: 03/04/2024] [Accepted: 03/23/2024] [Indexed: 04/13/2024]
Abstract
OBJECTIVE Focal seizure symptoms (FSS) and focal interictal epileptiform discharges (IEDs) are common in patients with idiopathic generalized epilepsies (IGEs), but dedicated studies systematically quantifying them both are lacking. We used automatic IED detection and localization algorithms and correlated these EEG findings with clinical FSS for the first time in IGE patients. METHODS 32 patients with IGEs undergoing long-term video EEG monitoring were systematically analyzed regarding focal vs. generalized IEDs using automatic IED detection and localization algorithms. Quantitative EEG findings were correlated with FSS. RESULTS We observed FSS in 75% of patients, without significant differences between IGE subgroups. Mostly varying/shifting lateralizations of FSS across successive recorded seizures were seen. We detected a total of 81,949 IEDs, whereof 19,513 IEDs were focal (23.8%). Focal IEDs occurred in all patients (median 13% focal IEDs per patient, range 1.1 - 51.1%). Focal IED lateralization and localization predominance had no significant effect on FSS. CONCLUSIONS All included patients with IGE showed focal IEDs and three-quarter had focal seizure symptoms irrespective of the specific IGE subgroup. Focal IED localization had no significant effect on lateralization and localization of FSS. SIGNIFICANCE Our findings may facilitate diagnostic and treatment decisions in patients with suspected IGE and focal signs.
Collapse
Affiliation(s)
- Johannes Koren
- Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology, Vienna, Austria; Department of Neurology, Clinic Hietzing, Vienna, Austria.
| | - Clemens Lang
- Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology, Vienna, Austria; Department of Neurology, Clinic Hietzing, Vienna, Austria
| | - Gerhard Gritsch
- Center for Health & Bioresources, AIT Austrian Institute of Technology GmbH, Vienna, Austria
| | - Lisa Mayer
- Department of Neurology, Clinic Hietzing, Vienna, Austria
| | - Manfred Hartmann
- Center for Health & Bioresources, AIT Austrian Institute of Technology GmbH, Vienna, Austria
| | | | - Tilmann Kluge
- Center for Health & Bioresources, AIT Austrian Institute of Technology GmbH, Vienna, Austria
| | - Christoph Baumgartner
- Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology, Vienna, Austria; Department of Neurology, Clinic Hietzing, Vienna, Austria; Medical Faculty, Sigmund Freud University, Vienna, Austria
| |
Collapse
|
3
|
Busia P, Cossettini A, Ingolfsson TM, Benatti S, Burrello A, Jung VJB, Scherer M, Scrugli MA, Bernini A, Ducouret P, Ryvlin P, Meloni P, Benini L. Reducing False Alarms in Wearable Seizure Detection With EEGformer: A Compact Transformer Model for MCUs. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2024; 18:608-621. [PMID: 38261487 DOI: 10.1109/tbcas.2024.3357509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2024]
Abstract
The long-term, continuous analysis of electroencephalography (EEG) signals on wearable devices to automatically detect seizures in epileptic patients is a high-potential application field for deep neural networks, and specifically for transformers, which are highly suited for end-to-end time series processing without handcrafted feature extraction. In this work, we propose a small-scale transformer detector, the EEGformer, compatible with unobtrusive acquisition setups that use only the temporal channels. EEGformer is the result of a hardware-oriented design exploration, aiming for efficient execution on tiny low-power micro-controller units (MCUs) and low latency and false alarm rate to increase patient and caregiver acceptance.Tests conducted on the CHB-MIT dataset show a 20% reduction of the onset detection latency with respect to the state-of-the-art model for temporal acquisition, with a competitive 73% seizure detection probability and 0.15 false-positive-per-hour (FP/h). Further investigations on a novel and challenging scalp EEG dataset result in the successful detection of 88% of the annotated seizure events, with 0.45 FP/h.We evaluate the deployment of the EEGformer on three commercial low-power computing platforms: the single-core Apollo4 MCU and the GAP8 and GAP9 parallel MCUs. The most efficient implementation (on GAP9) results in as low as 13.7 ms and 0.31 mJ per inference, demonstrating the feasibility of deploying the EEGformer on wearable seizure detection systems with reduced channel count and multi-day battery duration.
Collapse
|
4
|
Vakilna YS, Li X, Hampson JS, Huang Y, Mosher JC, Dabaghian Y, Luo X, Talavera B, Pati S, Todd M, Hays R, Szabo CA, Zhang GQ, Lhatoo SD. Reliable detection of generalized convulsive seizures using an off-the-shelf digital watch: A multisite phase 2 study. Epilepsia 2024. [PMID: 38738972 DOI: 10.1111/epi.17974] [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: 12/13/2023] [Revised: 03/19/2024] [Accepted: 03/25/2024] [Indexed: 05/14/2024]
Abstract
OBJECTIVE The aim of this study was to develop a machine learning algorithm using an off-the-shelf digital watch, the Samsung watch (SM-R800), and evaluate its effectiveness for the detection of generalized convulsive seizures (GCS) in persons with epilepsy. METHODS This multisite epilepsy monitoring unit (EMU) phase 2 study included 36 adult patients. Each patient wore a Samsung watch that contained accelerometer, gyroscope, and photoplethysmographic sensors. Sixty-eight time and frequency domain features were extracted from the sensor data and were used to train a random forest algorithm. A testing framework was developed that would better reflect the EMU setting, consisting of (1) leave-one-patient-out cross-validation (LOPO CV) on GCS patients, (2) false alarm rate (FAR) testing on nonseizure patients, and (3) "fixed-and-frozen" prospective testing on a prospective patient cohort. Balanced accuracy, precision, sensitivity, and FAR were used to quantify the performance of the algorithm. Seizure onsets and offsets were determined by using video-electroencephalographic (EEG) monitoring. Feature importance was calculated as the mean decrease in Gini impurity during the LOPO CV testing. RESULTS LOPO CV results showed balanced accuracy of .93 (95% confidence interval [CI] = .8-.98), precision of .68 (95% CI = .46-.85), sensitivity of .87 (95% CI = .62-.96), and FAR of .21/24 h (interquartile range [IQR] = 0-.90). Testing the algorithm on patients without seizure resulted in an FAR of .28/24 h (IQR = 0-.61). During the "fixed-and-frozen" prospective testing, two patients had three GCS, which were detected by the algorithm, while generating an FAR of .25/24 h (IQR = 0-.89). Feature importance showed that heart rate-based features outperformed accelerometer/gyroscope-based features. SIGNIFICANCE Commercially available wearable digital watches that reliably detect GCS, with minimum false alarm rates, may overcome usage adoption and other limitations of custom-built devices. Contingent on the outcomes of a prospective phase 3 study, such devices have the potential to provide non-EEG-based seizure surveillance and forecasting in the clinical setting.
Collapse
Affiliation(s)
- Yash Shashank Vakilna
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
- Texas Institute of Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Xiaojin Li
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
- Texas Institute of Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Jaison S Hampson
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
- Texas Institute of Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Yan Huang
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
- Texas Institute of Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - John C Mosher
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
- Texas Institute of Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Yuri Dabaghian
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
- Texas Institute of Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Xi Luo
- Department of Biostatistics and Data Science, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Blanca Talavera
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
- Texas Institute of Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Sandipan Pati
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
- Texas Institute of Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Masel Todd
- Department of Neurology, University of Texas Medical Branch, Galveston, Texas, USA
| | - Ryan Hays
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Charles Akos Szabo
- Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
| | - Guo-Qiang Zhang
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
- Texas Institute of Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Samden D Lhatoo
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
- Texas Institute of Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, Texas, USA
| |
Collapse
|
5
|
Donner E, Devinsky O, Friedman D. Wearable Digital Health Technology for Epilepsy. N Engl J Med 2024; 390:736-745. [PMID: 38381676 DOI: 10.1056/nejmra2301913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Affiliation(s)
- Elizabeth Donner
- From the Division of Neurology, Hospital for Sick Children, and the Department of Paediatrics, University of Toronto - both in Toronto (E.D.); and the Epilepsy Center, Department of Neurology, New York University Grossman School of Medicine, New York (O.D., D.F.)
| | - Orrin Devinsky
- From the Division of Neurology, Hospital for Sick Children, and the Department of Paediatrics, University of Toronto - both in Toronto (E.D.); and the Epilepsy Center, Department of Neurology, New York University Grossman School of Medicine, New York (O.D., D.F.)
| | - Daniel Friedman
- From the Division of Neurology, Hospital for Sick Children, and the Department of Paediatrics, University of Toronto - both in Toronto (E.D.); and the Epilepsy Center, Department of Neurology, New York University Grossman School of Medicine, New York (O.D., D.F.)
| |
Collapse
|
6
|
Beniczky S, Ryvlin P. Mobile health and digital technology in epilepsy: The dawn of a new era. Epilepsia 2023; 64 Suppl 4:S1-S3. [PMID: 37921045 DOI: 10.1111/epi.17813] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 10/31/2023] [Indexed: 11/04/2023]
Affiliation(s)
- Sándor Beniczky
- Department of Neurophysiology, Danish Epilepsy Center, Dianalund, Denmark
- Department of Clinical Medicine, Aarhus University and Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
- Department of Neurology, University of Szeged, Szeged, Hungary
| | - Philippe Ryvlin
- Department of Clinical Neurosciences, Lausanne University Hospital, Lausanne, Switzerland
| |
Collapse
|
7
|
Kerr WT, McFarlane KN. Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist. Curr Neurol Neurosci Rep 2023; 23:869-879. [PMID: 38060133 DOI: 10.1007/s11910-023-01318-7] [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] [Accepted: 10/24/2023] [Indexed: 12/08/2023]
Abstract
PURPOSE OF REVIEW Machine Learning (ML) and Artificial Intelligence (AI) are data-driven techniques to translate raw data into applicable and interpretable insights that can assist in clinical decision making. Some of these tools have extremely promising initial results, earning both great excitement and creating hype. This non-technical article reviews recent developments in ML/AI in epilepsy to assist the current practicing epileptologist in understanding both the benefits and limitations of integrating ML/AI tools into their clinical practice. RECENT FINDINGS ML/AI tools have been developed to assist clinicians in almost every clinical decision including (1) predicting future epilepsy in people at risk, (2) detecting and monitoring for seizures, (3) differentiating epilepsy from mimics, (4) using data to improve neuroanatomic localization and lateralization, and (5) tracking and predicting response to medical and surgical treatments. We also discuss practical, ethical, and equity considerations in the development and application of ML/AI tools including chatbots based on Large Language Models (e.g., ChatGPT). ML/AI tools will change how clinical medicine is practiced, but, with rare exceptions, the transferability to other centers, effectiveness, and safety of these approaches have not yet been established rigorously. In the future, ML/AI will not replace epileptologists, but epileptologists with ML/AI will replace epileptologists without ML/AI.
Collapse
Affiliation(s)
- Wesley T Kerr
- Department of Neurology, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA.
- Department of Biomedical Informatics, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA.
- Department of Neurology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
| | - Katherine N McFarlane
- Department of Neurology, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA
| |
Collapse
|
8
|
Meritam Larsen P, Beniczky S. Non-electroencephalogram-based seizure detection devices: State of the art and future perspectives. Epilepsy Behav 2023; 148:109486. [PMID: 37857030 DOI: 10.1016/j.yebeh.2023.109486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 10/06/2023] [Accepted: 10/07/2023] [Indexed: 10/21/2023]
Abstract
INTRODUCTION AND PURPOSE The continuously expanding research and development of wearable devices for automated seizure detection in epilepsy uses mostly non-invasive technology. Real-time alarms, triggered by seizure detection devices, are needed for safety and prevention to decrease seizure-related morbidity and mortality, as well as objective quantification of seizure frequency and severity. Our review strives to provide a state-of-the-art on automated seizure detection using non-invasive wearable devices in an ambulatory (home) environment and to highlight the prospects for future research. METHODS A joint working group of the International League Against Epilepsy (ILAE) and the International Federation of Clinical Neurophysiology (IFCN) recently published a clinical practice guideline on automated seizure detection using wearable devices. We updated the systematic literature search for the period since the last search by the joint working group. We selected studies qualifying minimally as phase-2 clinical validation trials, in accordance with standards for testing and validation of seizure detection devices. RESULTS High-level evidence (phases 3 and 4) is available only for the detection of tonic-clonic seizures and major motor seizures when using wearable devices based on accelerometry, surface electromyography (EMG), or a multimodal device combining accelerometry and heart rate. The reported sensitivity of these devices is 79.4-96%, with a false alarm rate of 0.20-1.92 per 24 hours (0-0.03 per night). A single phase-3 study validated the detection of absence seizures using a single-channel wearable EEG device. Two phase-4 studies showed overall user satisfaction with wearable seizure detection devices, which helped decrease injuries related to tonic-clonic seizures. Overall satisfaction, perceived sensitivity, and improvement in quality-of-life were significantly higher for validated devices. CONCLUSIONS Among the vast number of studies published on seizure detection devices, most are strongly affected by potential bias, providing a too-optimistic perspective. By applying the standards for clinical validation studies, potential bias can be reduced, and the quality of a continuously growing number of studies in this field can be assessed and compared. The ILAE-IFCN clinical practice guideline on automated seizure detection using wearable devices recommends using clinically validated wearable devices for automated detection of tonic-clonic seizures when significant safety concerns exist. The studies published after the guideline was issued only provide incremental knowledge and would not change the current recommendations.
Collapse
Affiliation(s)
- Pirgit Meritam Larsen
- Department of Clinical Neurophysiology, Danish Epilepsy Centre, Visbys Allé 5, 4293 Dianalund, Denmark.
| | - Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Centre, Visbys Allé 5, 4293 Dianalund, Denmark; Department of Clinical Neurophysiology, Aarhus University Hospital, and Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 165, 8200 Aarhus, Denmark.
| |
Collapse
|
9
|
Petrossian G, Kateb P, Miquet-Westphal F, Cicoira F. Advances in Electrode Materials for Scalp, Forehead, and Ear EEG: A Mini-Review. ACS APPLIED BIO MATERIALS 2023; 6:3019-3032. [PMID: 37493408 DOI: 10.1021/acsabm.3c00322] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
Electroencephalogram (EEG) records the electrical activity of neurons in the cerebral cortex and is used extensively to diagnose, treat, and monitor psychiatric and neurological conditions. Reliable contact between the skin and the electrodes is essential for achieving consistency and for obtaining electroencephalographic information. There has been an increasing demand for effective equipment and electrodes to overcome the time-consuming and cumbersome application of traditional systems. Recently, ear-centered EEG has met with growing interest since it can provide good signal quality due to the proximity of the ear to the brain. In addition, it can facilitate mobile and unobtrusive usage due to its smaller size and ease of use, since it can be used without interfering with the patient's daily activities. The purpose of this mini-review is to first introduce the broad range of electrodes used in conventional (scalp) EEG and subsequently discuss the state-of-the-art literature about around- and in-the-ear EEG.
Collapse
Affiliation(s)
- Gayaneh Petrossian
- Department of Chemical Engineering, Polytechnique Montréal, Montréal, Québec H3C 3A7, Canada
| | - Pierre Kateb
- Department of Chemical Engineering, Polytechnique Montréal, Montréal, Québec H3C 3A7, Canada
| | | | - Fabio Cicoira
- Department of Chemical Engineering, Polytechnique Montréal, Montréal, Québec H3C 3A7, Canada
| |
Collapse
|
10
|
Tveit J, Aurlien H, Plis S, Calhoun VD, Tatum WO, Schomer DL, Arntsen V, Cox F, Fahoum F, Gallentine WB, Gardella E, Hahn CD, Husain AM, Kessler S, Kural MA, Nascimento FA, Tankisi H, Ulvin LB, Wennberg R, Beniczky S. Automated Interpretation of Clinical Electroencephalograms Using Artificial Intelligence. JAMA Neurol 2023; 80:805-812. [PMID: 37338864 PMCID: PMC10282956 DOI: 10.1001/jamaneurol.2023.1645] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 03/23/2023] [Indexed: 06/21/2023]
Abstract
Importance Electroencephalograms (EEGs) are a fundamental evaluation in neurology but require special expertise unavailable in many regions of the world. Artificial intelligence (AI) has a potential for addressing these unmet needs. Previous AI models address only limited aspects of EEG interpretation such as distinguishing abnormal from normal or identifying epileptiform activity. A comprehensive, fully automated interpretation of routine EEG based on AI suitable for clinical practice is needed. Objective To develop and validate an AI model (Standardized Computer-based Organized Reporting of EEG-Artificial Intelligence [SCORE-AI]) with the ability to distinguish abnormal from normal EEG recordings and to classify abnormal EEG recordings into categories relevant for clinical decision-making: epileptiform-focal, epileptiform-generalized, nonepileptiform-focal, and nonepileptiform-diffuse. Design, Setting, and Participants In this multicenter diagnostic accuracy study, a convolutional neural network model, SCORE-AI, was developed and validated using EEGs recorded between 2014 and 2020. Data were analyzed from January 17, 2022, until November 14, 2022. A total of 30 493 recordings of patients referred for EEG were included into the development data set annotated by 17 experts. Patients aged more than 3 months and not critically ill were eligible. The SCORE-AI was validated using 3 independent test data sets: a multicenter data set of 100 representative EEGs evaluated by 11 experts, a single-center data set of 9785 EEGs evaluated by 14 experts, and for benchmarking with previously published AI models, a data set of 60 EEGs with external reference standard. No patients who met eligibility criteria were excluded. Main Outcomes and Measures Diagnostic accuracy, sensitivity, and specificity compared with the experts and the external reference standard of patients' habitual clinical episodes obtained during video-EEG recording. Results The characteristics of the EEG data sets include development data set (N = 30 493; 14 980 men; median age, 25.3 years [95% CI, 1.3-76.2 years]), multicenter test data set (N = 100; 61 men, median age, 25.8 years [95% CI, 4.1-85.5 years]), single-center test data set (N = 9785; 5168 men; median age, 35.4 years [95% CI, 0.6-87.4 years]), and test data set with external reference standard (N = 60; 27 men; median age, 36 years [95% CI, 3-75 years]). The SCORE-AI achieved high accuracy, with an area under the receiver operating characteristic curve between 0.89 and 0.96 for the different categories of EEG abnormalities, and performance similar to human experts. Benchmarking against 3 previously published AI models was limited to comparing detection of epileptiform abnormalities. The accuracy of SCORE-AI (88.3%; 95% CI, 79.2%-94.9%) was significantly higher than the 3 previously published models (P < .001) and similar to human experts. Conclusions and Relevance In this study, SCORE-AI achieved human expert level performance in fully automated interpretation of routine EEGs. Application of SCORE-AI may improve diagnosis and patient care in underserved areas and improve efficiency and consistency in specialized epilepsy centers.
Collapse
Affiliation(s)
| | - Harald Aurlien
- Holberg EEG, Bergen, Norway
- Department of Clinical Neurophysiology, Haukeland University Hospital, Bergen, Norway
| | - Sergey Plis
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta
| | - Vince D. Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta
| | | | - Donald L. Schomer
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Vibeke Arntsen
- Department of Neurology and Clinical Neurophysiology, St Olavs Hospital, Trondheim University Hospital, Norway
| | - Fieke Cox
- Department of Clinical Neurophysiology, Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, the Netherlands
| | - Firas Fahoum
- Department of Neurology, Tel Aviv Sourasky Medical Center and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - William B. Gallentine
- Department of Neurology and Pediatrics, Stanford University Lucile Packard Children’s Hospital, Palo Alto, California
| | - Elena Gardella
- Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund, Denmark
- Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Cecil D. Hahn
- Division of Neurology, The Hospital for Sick Children, Toronto, Canada
- Department of Paediatrics, University of Toronto, Toronto, Canada
| | - Aatif M. Husain
- Department of Neurology, Duke University Medical Center, Durham, North Carolina
- Neurodiagnostic Center, Veterans Affairs Medical Center, Durham, North Carolina
| | - Sudha Kessler
- Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Mustafa Aykut Kural
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Fábio A. Nascimento
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Hatice Tankisi
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Line B. Ulvin
- Department of Neurology, Oslo University Hospital, Norway
| | - Richard Wennberg
- Division of Neurology, Department of Medicine, Krembil Brain Institute, University Health Network, Toronto Western Hospital, University of Toronto, Toronto, Canada
| | - Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund, Denmark
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| |
Collapse
|
11
|
Glaba P, Latka M, Krause MJ, Kroczka S, Kuryło M, Kaczorowska-Frontczak M, Walas W, Jernajczyk W, Sebzda T, West BJ. EEG phase synchronization during absence seizures. Front Neuroinform 2023; 17:1169584. [PMID: 37404335 PMCID: PMC10317177 DOI: 10.3389/fninf.2023.1169584] [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: 02/19/2023] [Accepted: 05/25/2023] [Indexed: 07/06/2023] Open
Abstract
Absence seizures-generalized rhythmic spike-and-wave discharges (SWDs) are the defining property of childhood (CAE) and juvenile (JAE) absence epilepsies. Such seizures are the most compelling examples of pathological neuronal hypersynchrony. All the absence detection algorithms proposed so far have been derived from the properties of individual SWDs. In this work, we investigate EEG phase synchronization in patients with CAE/JAE and healthy subjects to explore the possibility of using the wavelet phase synchronization index to detect seizures and quantify their disorganization (fragmentation). The overlap of the ictal and interictal probability density functions was high enough to preclude effective seizure detection based solely on changes in EEG synchronization. We used a machine learning classifier with the phase synchronization index (calculated for 1 s data segments with 0.5 s overlap) and the normalized amplitude as features to detect generalized SWDs. Using 19 channels (10-20 setup), we identified 99.2% of absences. However, the overlap of the segments classified as ictal with seizures was only 83%. The analysis showed that seizures were disorganized in approximately half of the 65 subjects. On average, generalized SWDs lasted about 80% of the duration of abnormal EEG activity. The disruption of the ictal rhythm can manifest itself as the disappearance of epileptic spikes (with high-amplitude delta waves persisting), transient cessation of epileptic discharges, or loss of global synchronization. The detector can analyze a real-time data stream. Its performance is good for a six-channel setup (Fp1, Fp2, F7, F8, O1, O2), which can be implemented as an unobtrusive EEG headband. False detections are rare for controls and young adults (0.03% and 0.02%, respectively). In patients, they are more frequent (0.5%), but in approximately 82% cases, classification errors are caused by short epileptiform discharges. Most importantly, the proposed detector can be applied to parts of EEG with abnormal EEG activity to quantitatively determine seizure fragmentation. This property is important because a previous study reported that the probability of disorganized discharges is eight times higher in JAE than in CAE. Future research must establish whether seizure properties (frequency, length, fragmentation, etc.) and clinical characteristics can help distinguish CAE and JAE.
Collapse
Affiliation(s)
- Pawel Glaba
- Department of Biomedical Engineering, Wroclaw University of Science and Technology, Wrocław, Poland
| | - Miroslaw Latka
- Department of Biomedical Engineering, Wroclaw University of Science and Technology, Wrocław, Poland
| | | | - Sławomir Kroczka
- Department of Child Neurology, Jagiellonian University Medical College, Kraków, Poland
| | - Marta Kuryło
- Department of Pediatric Neurology, T. Marciniak Hospital, Wrocław, Poland
| | | | - Wojciech Walas
- Department of Anesthesiology, Intensive Care and Regional Extracorporeal Membrane Oxygenation (ECMO) Center, Institute of Medical Sciences, University of Opole, Opole, Poland
| | - Wojciech Jernajczyk
- Clinical Neurophysiology, Institute of Psychiatry and Neurology, Warszawa, Poland
| | - Tadeusz Sebzda
- Department of Physiology and Pathophysiology, Medical University of Wroclaw, Wrocław, Poland
| | - Bruce J. West
- Center for Nonlinear Science, University of North Texas, Denton, TX, United States
| |
Collapse
|
12
|
Djemal A, Bouchaala D, Fakhfakh A, Kanoun O. Wearable Electromyography Classification of Epileptic Seizures: A Feasibility Study. Bioengineering (Basel) 2023; 10:703. [PMID: 37370634 DOI: 10.3390/bioengineering10060703] [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: 05/04/2023] [Revised: 05/29/2023] [Accepted: 06/07/2023] [Indexed: 06/29/2023] Open
Abstract
Accurate diagnosis and classification of epileptic seizures can greatly support patient treatments. As many epileptic seizures are convulsive and have a motor component, the analysis of muscle activity can provide valuable information for seizure classification. Therefore, this paper present a feasibility study conducted on healthy volunteers, focusing on tracking epileptic seizures movements using surface electromyography signals (sEMG) measured on human limb muscles. For the experimental studies, first, compact wireless sensor nodes were developed for real-time measurement of sEMG on the gastrocnemius, flexor carpi ulnaris, biceps brachii, and quadriceps muscles on the right side and the left side. For the classification of the seizure, a machine learning model has been elaborated. The 16 common sEMG time-domain features were first extracted and examined with respect to discrimination and redundancy. This allowed the features to be classified into irrelevant features, important features, and redundant features. Redundant features were examined with the Big-O notation method and with the average execution time method to select the feature that leads to lower complexity and reduced processing time. The finally selected six features were explored using different machine learning classifiers to compare the resulting classification accuracy. The results show that the artificial neural network (ANN) model with the six features: IEMG, WAMP, MYOP, SE, SKEW, and WL, had the highest classification accuracy (99.95%). A further study confirms that all the chosen eight sensors are necessary to reach this high classification accuracy.
Collapse
Affiliation(s)
- Achraf Djemal
- Measurement and Sensor Technology, Chemnitz University of Technology, Reichenhainer Straße 70, 09126 Chemnitz, Germany
- Laboratory of Signals, Systems, Artificial Intelligence and Networks, Digital Research Centre of Sfax, National School of Electronics and Telecommunications of Sfax, Technopole of Sfax, Ons City 3021, Tunisia
| | - Dhouha Bouchaala
- National Engineering School of Sfax, University of Sfax, Route de la Soukra km 4, Sfax 3038, Tunisia
| | - Ahmed Fakhfakh
- Laboratory of Signals, Systems, Artificial Intelligence and Networks, Digital Research Centre of Sfax, National School of Electronics and Telecommunications of Sfax, Technopole of Sfax, Ons City 3021, Tunisia
| | - Olfa Kanoun
- Measurement and Sensor Technology, Chemnitz University of Technology, Reichenhainer Straße 70, 09126 Chemnitz, Germany
| |
Collapse
|
13
|
Yang S, Li M, Wang J, Shi Z, He B, Xie J, Xu G. A low-cost and portable wrist exoskeleton using EEG-sEMG combined strategy for prolonged active rehabilitation. Front Neurorobot 2023; 17:1161187. [PMID: 37292117 PMCID: PMC10244749 DOI: 10.3389/fnbot.2023.1161187] [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: 02/08/2023] [Accepted: 05/03/2023] [Indexed: 06/10/2023] Open
Abstract
Introduction Hemiparesis is a common consequence of stroke that severely impacts the life quality of the patients. Active training is a key factor in achieving optimal neural recovery, but current systems for wrist rehabilitation present challenges in terms of portability, cost, and the potential for muscle fatigue during prolonged use. Methods To address these challenges, this paper proposes a low-cost, portable wrist rehabilitation system with a control strategy that combines surface electromyogram (sEMG) and electroencephalogram (EEG) signals to encourage patients to engage in consecutive, spontaneous rehabilitation sessions. In addition, a detection method for muscle fatigue based on the Boruta algorithm and a post-processing layer are proposed, allowing for the switch between sEMG and EEG modes when muscle fatigue occurs. Results This method significantly improves accuracy of fatigue detection from 4.90 to 10.49% for four distinct wrist motions, while the Boruta algorithm selects the most essential features and stabilizes the effects of post-processing. The paper also presents an alternative control mode that employs EEG signals to maintain active control, achieving an accuracy of approximately 80% in detecting motion intention. Discussion For the occurrence of muscle fatigue during long term rehabilitation training, the proposed system presents a promising approach to addressing the limitations of existing wrist rehabilitation systems.
Collapse
|
14
|
Frauscher B, Bénar CG, Engel JJ, Grova C, Jacobs J, Kahane P, Wiebe S, Zjilmans M, Dubeau F. Neurophysiology, Neuropsychology, and Epilepsy, in 2022: Hills We Have Climbed and Hills Ahead. Neurophysiology in epilepsy. Epilepsy Behav 2023; 143:109221. [PMID: 37119580 DOI: 10.1016/j.yebeh.2023.109221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 05/01/2023]
Abstract
Since the discovery of the human electroencephalogram (EEG), neurophysiology techniques have become indispensable tools in our armamentarium to localize epileptic seizures. New signal analysis techniques and the prospects of artificial intelligence and big data will offer unprecedented opportunities to further advance the field in the near future, ultimately resulting in improved quality of life for many patients with drug-resistant epilepsy. This article summarizes selected presentations from Day 1 of the two-day symposium "Neurophysiology, Neuropsychology, Epilepsy, 2022: Hills We Have Climbed and the Hills Ahead". Day 1 was dedicated to highlighting and honoring the work of Dr. Jean Gotman, a pioneer in EEG, intracranial EEG, simultaneous EEG/ functional magnetic resonance imaging, and signal analysis of epilepsy. The program focused on two main research directions of Dr. Gotman, and was dedicated to "High-frequency oscillations, a new biomarker of epilepsy" and "Probing the epileptic focus from inside and outside". All talks were presented by colleagues and former trainees of Dr. Gotman. The extended summaries provide an overview of historical and current work in the neurophysiology of epilepsy with emphasis on novel EEG biomarkers of epilepsy and source imaging and concluded with an outlook on the future of epilepsy research, and what is needed to bring the field to the next level.
Collapse
Affiliation(s)
- B Frauscher
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
| | - C G Bénar
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - J Jr Engel
- David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
| | - C Grova
- Multimodal Functional Imaging Lab, PERFORM Centre, Department of Physics, Concordia University, Montreal, QC, Canada; Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, QC, Canada; Montreal Neurological Institute and Hospital, Neurology and Neurosurgery Department, McGill University, Montreal, QC, Canada
| | - J Jacobs
- Department of Pediatric and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - P Kahane
- Univ. Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble Institute Neurosciences, Department of Neurology, 38000 Grenoble, France
| | - S Wiebe
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - M Zjilmans
- Stichting Epilepsie Instellingen Nederland, The Netherlands; Brain Center, University Medical Center Utrecht, The Netherlands
| | - F Dubeau
- Montreal Neurological Institute and Hospital, Neurology and Neurosurgery Department, McGill University, Montreal, QC, Canada
| |
Collapse
|
15
|
Li Z, Chen L, Xu C, Chen Z, Wang Y. Non-invasive sensory neuromodulation in epilepsy: Updates and future perspectives. Neurobiol Dis 2023; 179:106049. [PMID: 36813206 DOI: 10.1016/j.nbd.2023.106049] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 02/15/2023] [Accepted: 02/16/2023] [Indexed: 02/22/2023] Open
Abstract
Epilepsy, one of the most common neurological disorders, often is not well controlled by current pharmacological and surgical treatments. Sensory neuromodulation, including multi-sensory stimulation, auditory stimulation, olfactory stimulation, is a kind of novel noninvasive mind-body intervention and receives continued attention as complementary safe treatment of epilepsy. In this review, we summarize the recent advances of sensory neuromodulation, including enriched environment therapy, music therapy, olfactory therapy, other mind-body interventions, for the treatment of epilepsy based on the evidence from both clinical and preclinical studies. We also discuss their possible anti-epileptic mechanisms on neural circuit level and propose perspectives on possible research directions for future studies.
Collapse
Affiliation(s)
- Zhongxia Li
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China; Zhejiang Rehabilitation Medical Center Department, The Third Affiliated Hospital, Zhejiang Chinese Medical University, Hangzhou, China
| | - Liying Chen
- Department of Pharmacy, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Cenglin Xu
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Zhong Chen
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Yi Wang
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China; Zhejiang Rehabilitation Medical Center Department, The Third Affiliated Hospital, Zhejiang Chinese Medical University, Hangzhou, China.
| |
Collapse
|
16
|
Prieto-Avalos G, Sánchez-Morales LN, Alor-Hernández G, Sánchez-Cervantes JL. A Review of Commercial and Non-Commercial Wearables Devices for Monitoring Motor Impairments Caused by Neurodegenerative Diseases. BIOSENSORS 2022; 13:72. [PMID: 36671907 PMCID: PMC9856141 DOI: 10.3390/bios13010072] [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: 11/10/2022] [Revised: 12/24/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
Neurodegenerative diseases (NDDs) are among the 10 causes of death worldwide. The effects of NDDs, including irreversible motor impairments, have an impact not only on patients themselves but also on their families and social environments. One strategy to mitigate the pain of NDDs is to early identify and remotely monitor related motor impairments using wearable devices. Technological progress has contributed to reducing the hardware complexity of mobile devices while simultaneously improving their efficiency in terms of data collection and processing and energy consumption. However, perhaps the greatest challenges of current mobile devices are to successfully manage the security and privacy of patient medical data and maintain reasonable costs with respect to the traditional patient consultation scheme. In this work, we conclude: (1) Falls are most monitored for Parkinson's disease, while tremors predominate in epilepsy and Alzheimer's disease. These findings will provide guidance for wearable device manufacturers to strengthen areas of opportunity that need to be addressed, and (2) Of the total universe of commercial wearables devices that are available on the market, only a few have FDA approval, which means that there is a large number of devices that do not safeguard the integrity of the users who use them.
Collapse
Affiliation(s)
- Guillermo Prieto-Avalos
- Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
| | - Laura Nely Sánchez-Morales
- CONACYT-Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
| | - Giner Alor-Hernández
- Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
| | - José Luis Sánchez-Cervantes
- CONACYT-Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
| |
Collapse
|
17
|
Anders C, Arnrich B. Wearable electroencephalography and multi-modal mental state classification: A systematic literature review. Comput Biol Med 2022; 150:106088. [PMID: 36137314 DOI: 10.1016/j.compbiomed.2022.106088] [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: 05/10/2022] [Revised: 08/10/2022] [Accepted: 09/03/2022] [Indexed: 11/19/2022]
Abstract
BACKGROUND Wearable multi-modal time-series classification applications outperform their best uni-modal counterparts and hold great promise. A modality that directly measures electrical correlates from the brain is electroencephalography. Due to varying noise sources, different key brain regions, key frequency bands, and signal characteristics like non-stationarity, techniques for data pre-processing and classification algorithms are task-dependent. METHOD Here, a systematic literature review on mental state classification for wearable electroencephalography is presented. Four search terms in different combinations were used for an in-title search. The search was executed on the 29th of June 2022, across Google Scholar, PubMed, IEEEXplore, and ScienceDirect. 76 most relevant publications were set into context as the current state-of-the-art in mental state time-series classification. RESULTS Pre-processing techniques, features, and time-series classification models were analyzed. Across publications, a window length of one second was mainly chosen for classification and spectral features were utilized the most. The achieved performance per time-series classification model is analyzed, finding linear discriminant analysis, decision trees, and k-nearest neighbors models outperform support-vector machines by a factor of up to 1.5. A historical analysis depicts future trends while under-reported aspects relevant to practical applications are discussed. CONCLUSIONS Five main conclusions are given, covering utilization of available area for electrode placement on the head, most often or scarcely utilized features and time-series classification model architectures, baseline reporting practices, as well as explainability and interpretability of Deep Learning. The importance of a 'test battery' assessing the influence of data pre-processing and multi-modality on time-series classification performance is emphasized.
Collapse
Affiliation(s)
- Christoph Anders
- Hasso Plattner Institute, University of Potsdam, Potsdam, 14482, Brandenburg, Germany.
| | - Bert Arnrich
- Hasso Plattner Institute, University of Potsdam, Potsdam, 14482, Brandenburg, Germany.
| |
Collapse
|
18
|
Hsieh JC, Li Y, Wang H, Perz M, Tang Q, Tang KWK, Pyatnitskiy I, Reyes R, Ding H, Wang H. Design of hydrogel-based wearable EEG electrodes for medical applications. J Mater Chem B 2022; 10:7260-7280. [PMID: 35678148 DOI: 10.1039/d2tb00618a] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The electroencephalogram (EEG) is considered to be a promising method for studying brain disorders. Because of its non-invasive nature, subjects take a lower risk compared to some other invasive methods, while the systems record the brain signal. With the technological advancement of neural and material engineering, we are in the process of achieving continuous monitoring of neural activity through wearable EEG. In this article, we first give a brief introduction to EEG bands, circuits, wired/wireless EEG systems, and analysis algorithms. Then, we review the most recent advances in the interfaces used for EEG recordings, focusing on hydrogel-based EEG electrodes. Specifically, the advances for important figures of merit for EEG electrodes are reviewed. Finally, we summarize the potential medical application of wearable EEG systems.
Collapse
Affiliation(s)
- Ju-Chun Hsieh
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Yang Li
- Department of Chemical Engineering, Polytechnique Montréal, Montréal, Québec H3C3J7, Canada
| | - Huiqian Wang
- Department of Mathematics, The University of Texas at Austin, Austin, TX 78712, USA
| | - Matt Perz
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Qiong Tang
- Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin, Austin, TX 78712, USA
| | - Kai Wing Kevin Tang
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Ilya Pyatnitskiy
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Raymond Reyes
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Hong Ding
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Huiliang Wang
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| |
Collapse
|