1
|
Lemoine É, Neves Briard J, Rioux B, Gharbi O, Podbielski R, Nauche B, Toffa D, Keezer M, Lesage F, Nguyen DK, Bou Assi E. Computer-assisted analysis of routine EEG to identify hidden biomarkers of epilepsy: A systematic review. Comput Struct Biotechnol J 2024; 24:66-86. [PMID: 38204455 PMCID: PMC10776381 DOI: 10.1016/j.csbj.2023.12.006] [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: 09/26/2023] [Revised: 12/05/2023] [Accepted: 12/05/2023] [Indexed: 01/12/2024] Open
Abstract
Background Computational analysis of routine electroencephalogram (rEEG) could improve the accuracy of epilepsy diagnosis. We aim to systematically assess the diagnostic performances of computed biomarkers for epilepsy in individuals undergoing rEEG. Methods We searched MEDLINE, EMBASE, EBM reviews, IEEE Explore and the grey literature for studies published between January 1961 and December 2022. We included studies reporting a computational method to diagnose epilepsy based on rEEG without relying on the identification of interictal epileptiform discharges or seizures. Diagnosis of epilepsy as per a treating physician was the reference standard. We assessed the risk of bias using an adapted QUADAS-2 tool. Results We screened 10 166 studies, and 37 were included. The sample size ranged from 8 to 192 (mean=54). The computed biomarkers were based on linear (43%), non-linear (27%), connectivity (38%), and convolutional neural networks (10%) models. The risk of bias was high or unclear in all studies, more commonly from spectrum effect and data leakage. Diagnostic accuracy ranged between 64% and 100%. We observed high methodological heterogeneity, preventing pooling of accuracy measures. Conclusion The current literature provides insufficient evidence to reliably assess the diagnostic yield of computational analysis of rEEG. Significance We provide guidelines regarding patient selection, reference standard, algorithms, and performance validation.
Collapse
Affiliation(s)
- Émile Lemoine
- Department of Neurosciences, University of Montreal, Canada
- Institute of biomedical engineering, Polytechnique Montreal, Canada
- University of Montreal Hospital Center’s Research Center, Canada
| | - Joel Neves Briard
- Department of Neurosciences, University of Montreal, Canada
- University of Montreal Hospital Center’s Research Center, Canada
| | - Bastien Rioux
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Oumayma Gharbi
- Department of Neurosciences, University of Montreal, Canada
- University of Montreal Hospital Center’s Research Center, Canada
| | | | - Bénédicte Nauche
- University of Montreal Hospital Center’s Research Center, Canada
| | - Denahin Toffa
- Department of Neurosciences, University of Montreal, Canada
- University of Montreal Hospital Center’s Research Center, Canada
| | - Mark Keezer
- Department of Neurosciences, University of Montreal, Canada
- School of Public Health, University of Montreal, Canada
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, the Netherlands
| | - Frédéric Lesage
- Institute of biomedical engineering, Polytechnique Montreal, Canada
| | - Dang K. Nguyen
- Department of Neurosciences, University of Montreal, Canada
- University of Montreal Hospital Center’s Research Center, Canada
| | - Elie Bou Assi
- Department of Neurosciences, University of Montreal, Canada
- University of Montreal Hospital Center’s Research Center, Canada
| |
Collapse
|
2
|
Mercier M, Pepi C, Carfi-Pavia G, De Benedictis A, Espagnet MCR, Pirani G, Vigevano F, Marras CE, Specchio N, De Palma L. The value of linear and non-linear quantitative EEG analysis in paediatric epilepsy surgery: a machine learning approach. Sci Rep 2024; 14:10887. [PMID: 38740844 PMCID: PMC11091060 DOI: 10.1038/s41598-024-60622-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 04/25/2024] [Indexed: 05/16/2024] Open
Abstract
Epilepsy surgery is effective for patients with medication-resistant seizures, however 20-40% of them are not seizure free after surgery. Aim of this study is to evaluate the role of linear and non-linear EEG features to predict post-surgical outcome. We included 123 paediatric patients who underwent epilepsy surgery at Bambino Gesù Children Hospital (January 2009-April 2020). All patients had long term video-EEG monitoring. We analysed 1-min scalp interictal EEG (wakefulness and sleep) and extracted 13 linear and non-linear EEG features (power spectral density (PSD), Hjorth, approximate entropy, permutation entropy, Lyapunov and Hurst value). We used a logistic regression (LR) as feature selection process. To quantify the correlation between EEG features and surgical outcome we used an artificial neural network (ANN) model with 18 architectures. LR revealed a significant correlation between PSD of alpha band (sleep), Mobility index (sleep) and the Hurst value (sleep and awake) with outcome. The fifty-four ANN models gave a range of accuracy (46-65%) in predicting outcome. Within the fifty-four ANN models, we found a higher accuracy (64.8% ± 7.6%) in seizure outcome prediction, using features selected by LR. The combination of PSD of alpha band, mobility and the Hurst value positively correlate with good surgical outcome.
Collapse
Affiliation(s)
- Mattia Mercier
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
- Department of Physiology, Behavioural Neuroscience PhD Program, Sapienza University, Rome, Italy
| | - Chiara Pepi
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
| | - Giusy Carfi-Pavia
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
| | - Alessandro De Benedictis
- Neurosurgery Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, 00165, Rome, Italy
| | | | - Greta Pirani
- Department of Mechanical and Aerospace Engineering - DIMA, Sapienza University of Rome, Rome, Italy
| | - Federico Vigevano
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
| | - Carlo Efisio Marras
- Neurosurgery Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, 00165, Rome, Italy
| | - Nicola Specchio
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy.
| | - Luca De Palma
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
| |
Collapse
|
3
|
Shin Y, Hwang S, Lee SB, Son H, Chu K, Jung KY, Lee SK, Park KI, Kim YG. Using spectral and temporal filters with EEG signal to predict the temporal lobe epilepsy outcome after antiseizure medication via machine learning. Sci Rep 2023; 13:22532. [PMID: 38110465 PMCID: PMC10728218 DOI: 10.1038/s41598-023-49255-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 12/06/2023] [Indexed: 12/20/2023] Open
Abstract
Epilepsy is a neurological disorder in which the brain is transiently altered. Predicting outcomes in epilepsy is essential for providing feedback that can foster improved outcomes in the future. This study aimed to investigate whether applying spectral and temporal filters to resting-state electroencephalography (EEG) signals could improve the prediction of outcomes for patients taking antiseizure medication to treat temporal lobe epilepsy (TLE). We collected EEG data from a total of 46 patients (divided into a seizure-free group (SF, n = 22) and a non-seizure-free group (NSF, n = 24)) with TLE and retrospectively reviewed their clinical data. We segmented spectral and temporal ranges with various time-domain features (Hjorth parameters, statistical parameters, energy, zero-crossing rate, inter-channel correlation, inter-channel phase locking value and spectral information derived from Fourier transform, Stockwell transform, and wavelet transform) and compared their performance by applying an optimal frequency strategy, an optimal duration strategy, and a combination strategy. For all time-domain features, the optimal frequency and time combination strategy showed the highest performance in distinguishing SF patients from NSF patients (area under the curve (AUC) = 0.790 ± 0.159). Furthermore, optimal performance was achieved by utilizing a feature vector derived from statistical parameters within the 39- to 41-Hz frequency band with a window length of 210 s, as evidenced by an AUC of 0.748. By identifying the optimal parameters, we improved the performance of the prediction model. These parameters can serve as standard parameters for predicting outcomes based on resting-state EEG signals.
Collapse
Affiliation(s)
- Youmin Shin
- Department of Transdisciplinary Medicine, Seoul National University Hospital, Seoul, Republic of Korea
- Interdisciplinary Program in Bio-Engineering, Seoul National University, Seoul, Korea
| | - Sungeun Hwang
- Department of Neurology, Ewha Womans University Mokdong Hospital, Seoul, Republic of Korea
| | - Seung-Bo Lee
- Department of Medical Informatics, Keimyung University School of Medicine, Daegu, Republic of Korea
| | - Hyoshin Son
- Department of Neurosurgery, Seoul National University Hospital, Seoul, Republic of Korea
| | - Kon Chu
- Department of Neurology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Neurology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Ki-Young Jung
- Department of Neurology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Neurology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Sang Kun Lee
- Department of Neurology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Neurology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Kyung-Il Park
- Department of Neurology, Seoul National University College of Medicine, Seoul, Republic of Korea.
- Department of Neurology, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Republic of Korea.
| | - Young-Gon Kim
- Department of Transdisciplinary Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
- Department of Medicine, Seoul National University College of Medicine, Seoul, Korea.
| |
Collapse
|
4
|
Li Z, Zhao B, Hu W, Zhang C, Wang X, Zhang J, Zhang K. Machine learning-based classification of physiological and pathological high-frequency oscillations recorded by stereoelectroencephalography. Seizure 2023; 113:58-65. [PMID: 37984126 DOI: 10.1016/j.seizure.2023.11.005] [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: 05/25/2023] [Revised: 11/07/2023] [Accepted: 11/09/2023] [Indexed: 11/22/2023] Open
Abstract
OBJECTIVE High-frequency oscillations (HFOs) are an efficient indicator to locate the epileptogenic zone (EZ). However, physiological HFOs produced in the normal brain region may interfere with EZ localization. The present study aimed to build a machine learning-based classifier to distinguish the properties of each HFO event based on features in different domains. METHODS HFOs were detected in focal epilepsy patients from two different hospitals who underwent stereoelectroencephalography and subsequent resection surgery. Subsequently, 37 features in four different domains (time, frequency and time-frequency, entropy-based and nonlinear) were extracted for each HFO. After extraction, a fast correlation-based filter (FCBF) algorithm was applied for feature selection. The machine learning classifier was trained on the feature matrix with and without FCBF and then tested on the data set from patients in another hospital. RESULTS A dataset was compiled, consisting of 89,844 pathological HFOs and 23,613 physiological HFOs from 17 patients assigned to the training dataset. Additionally, 12,695 pathological HFOs and 5,599 physiological HFOs from 9 patients were assigned to the testing dataset. Four features (ripple band power, arithmetic mean, Petrosian fractal dimension and zero crossings) were obtained for classifier training after FCBF. The classifier showed an area under the curve (AUC) of 0.95/0.98 for FCBF/no FCBF features in the training dataset and AUC of 0.82/0.90 for FCBF/no FCBF features in the testing dataset. Our findings indicated that the classifier utilizing all features demonstrated superior performance compared to the one relying on FCBF-processed features. CONCLUSION Our classifier could reliably differentiate pathological HFOs from physiological ones, which could promote the development of HFOs in EZ localization.
Collapse
Affiliation(s)
- Zilin Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Baotian Zhao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wenhan Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China; Beijing Key Laboratory of Neurostimulation, Beijing, China
| | - Chao Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiu Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China; Beijing Key Laboratory of Neurostimulation, Beijing, China
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China; Beijing Key Laboratory of Neurostimulation, Beijing, China.
| |
Collapse
|
5
|
Lemoine É, Toffa D, Pelletier-Mc Duff G, Xu AQ, Jemel M, Tessier JD, Lesage F, Nguyen DK, Bou Assi E. Machine-learning for the prediction of one-year seizure recurrence based on routine electroencephalography. Sci Rep 2023; 13:12650. [PMID: 37542101 PMCID: PMC10403587 DOI: 10.1038/s41598-023-39799-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 07/31/2023] [Indexed: 08/06/2023] Open
Abstract
Predicting seizure recurrence risk is critical to the diagnosis and management of epilepsy. Routine electroencephalography (EEG) is a cornerstone of the estimation of seizure recurrence risk. However, EEG interpretation relies on the visual identification of interictal epileptiform discharges (IEDs) by neurologists, with limited sensitivity. Automated processing of EEG could increase its diagnostic yield and accessibility. The main objective was to develop a prediction model based on automated EEG processing to predict one-year seizure recurrence in patients undergoing routine EEG. We retrospectively selected a consecutive cohort of 517 patients undergoing routine EEG at our institution (training set) and a separate, temporally shifted cohort of 261 patients (testing set). We developed an automated processing pipeline to extract linear and non-linear features from the EEGs. We trained machine learning algorithms on multichannel EEG segments to predict one-year seizure recurrence. We evaluated the impact of IEDs and clinical confounders on performances and validated the performances on the testing set. The receiver operating characteristic area-under-the-curve for seizure recurrence after EEG in the testing set was 0.63 (95% CI 0.55-0.71). Predictions were still significantly above chance in EEGs with no IEDs. Our findings suggest that there are changes other than IEDs in the EEG signal embodying seizure propensity.
Collapse
Affiliation(s)
- Émile Lemoine
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada
- Institute of Biomedical Engineering, École Polytechnique de Montréal, Montréal, Qc, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - Denahin Toffa
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - Geneviève Pelletier-Mc Duff
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - An Qi Xu
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - Mezen Jemel
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - Jean-Daniel Tessier
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - Frédéric Lesage
- Institute of Biomedical Engineering, École Polytechnique de Montréal, Montréal, Qc, Canada
- Centre de Recherche de l'institut de Cardiologie de Montréal, Montréal, Qc, Canada
| | - Dang K Nguyen
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - Elie Bou Assi
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada.
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada.
| |
Collapse
|
6
|
Peh WY, Thangavel P, Yao Y, Thomas J, Tan YL, Dauwels J. Six-Center Assessment of CNN-Transformer with Belief Matching Loss for Patient-Independent Seizure Detection in EEG. Int J Neural Syst 2023; 33:2350012. [PMID: 36809996 DOI: 10.1142/s0129065723500120] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Neurologists typically identify epileptic seizures from electroencephalograms (EEGs) by visual inspection. This process is often time-consuming, especially for EEG recordings that last hours or days. To expedite the process, a reliable, automated, and patient-independent seizure detector is essential. However, developing a patient-independent seizure detector is challenging as seizures exhibit diverse characteristics across patients and recording devices. In this study, we propose a patient-independent seizure detector to automatically detect seizures in both scalp EEG and intracranial EEG (iEEG). First, we deploy a convolutional neural network with transformers and belief matching loss to detect seizures in single-channel EEG segments. Next, we extract regional features from the channel-level outputs to detect seizures in multi-channel EEG segments. At last, we apply post-processing filters to the segment-level outputs to determine seizures' start and end points in multi-channel EEGs. Finally, we introduce the minimum overlap evaluation scoring as an evaluation metric that accounts for minimum overlap between the detection and seizure, improving upon existing assessment metrics. We trained the seizure detector on the Temple University Hospital Seizure (TUH-SZ) dataset and evaluated it on five independent EEG datasets. We evaluate the systems with the following metrics: sensitivity (SEN), precision (PRE), and average and median false positive rate per hour (aFPR/h and mFPR/h). Across four adult scalp EEG and iEEG datasets, we obtained SEN of 0.617-1.00, PRE of 0.534-1.00, aFPR/h of 0.425-2.002, and mFPR/h of 0-1.003. The proposed seizure detector can detect seizures in adult EEGs and takes less than 15[Formula: see text]s for a 30[Formula: see text]min EEG. Hence, this system could aid clinicians in reliably identifying seizures expeditiously, allocating more time for devising proper treatment.
Collapse
Affiliation(s)
- Wei Yan Peh
- Interdisciplinary Graduate School (IGS), Nanyang Technological University, Singapore 639798, Singapore
| | - Prasanth Thangavel
- Interdisciplinary Graduate School (IGS), Nanyang Technological University, Singapore 639798, Singapore
| | - Yuanyuan Yao
- Katholieke Universiteit Leuven, Oude Markt 13, 3000 Leuven, Belgium
| | - John Thomas
- Montreal Neurological Institute, McGill University, Montreal QC H3A 2B4, Canada
| | - Yee-Leng Tan
- National Neuroscience Institute, Singapore 308433, Singapore
| | - Justin Dauwels
- Department of Microelectronics, Delft, University of Technology, 2628 CD Delft, Netherlands
| |
Collapse
|
7
|
Talantseva OI, Portnova GV, Romanova RS, Martynova DA, Sysoeva OV, Grigorenko EL. Does the Potocki–Lupski Syndrome Convey the Autism Spectrum Disorder Phenotype? Case Report and Scoping Review. J Pers Med 2023; 13:jpm13030439. [PMID: 36983620 PMCID: PMC10053863 DOI: 10.3390/jpm13030439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 02/17/2023] [Accepted: 02/23/2023] [Indexed: 03/06/2023] Open
Abstract
Potocki–Lupski Syndrome (PTLS) is a rare condition associated with a duplication of 17p11.2 that may underlie a wide range of congenital abnormalities and heterogeneous behavioral phenotypes. Along with developmental delay and intellectual disability, autism-specific traits are often reported to be the most common among patients with PTLS. To contribute to the discussion of the role of autism spectrum disorder (ASD) in the PTLS phenotype, we present a case of a female adolescent with a de novo dup(17) (p11.2p11.2) without ASD features, focusing on in-depth clinical, behavioral, and electrophysiological (EEG) evaluations. Among EEG features, we found the atypical peak–slow wave patterns and a unique saw-like sharp wave of 13 Hz that was not previously described in any other patient. The power spectral density of the resting state EEG was typical in our patient with only the values of non-linear EEG dynamics: Hjorth complexity and fractal dimension were drastically attenuated compared with the patient’s neurotypical peers. Here we also summarize results from previously published reports of PTLS that point to the approximately 21% occurrence of ASD in PTLS that might be biased, taking into account methodological limitations. More consistent among PTLS patients were intellectual disability and speech and language disorders.
Collapse
Affiliation(s)
- Oksana I. Talantseva
- Center for Cognitive Sciences, Sirius University of Science and Technology, 354340 Sirius, Russia
| | - Galina V. Portnova
- Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, 117485 Moscow, Russia
| | - Raisa S. Romanova
- Center for Cognitive Sciences, Sirius University of Science and Technology, 354340 Sirius, Russia
| | - Daria A. Martynova
- Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, 117485 Moscow, Russia
| | - Olga V. Sysoeva
- Center for Cognitive Sciences, Sirius University of Science and Technology, 354340 Sirius, Russia
- Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, 117485 Moscow, Russia
| | - Elena L. Grigorenko
- Center for Cognitive Sciences, Sirius University of Science and Technology, 354340 Sirius, Russia
- Department of Psychology, University of Houston, Houston, TX 77204, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
- Child Study Center, Yale University, New Haven, CT 06519, USA
- Correspondence:
| |
Collapse
|
8
|
Lemoine É, Neves Briard J, Rioux B, Podbielski R, Nauche B, Toffa D, Keezer M, Lesage F, Nguyen DK, Bou Assi E. Computer-assisted analysis of routine electroencephalogram to identify hidden biomarkers of epilepsy: protocol for a systematic review. BMJ Open 2023; 13:e066932. [PMID: 36693684 PMCID: PMC9884857 DOI: 10.1136/bmjopen-2022-066932] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
INTRODUCTION The diagnosis of epilepsy frequently relies on the visual interpretation of the electroencephalogram (EEG) by a neurologist. The hallmark of epilepsy on EEG is the interictal epileptiform discharge (IED). This marker lacks sensitivity: it is only captured in a small percentage of 30 min routine EEGs in patients with epilepsy. In the past three decades, there has been growing interest in the use of computational methods to analyse the EEG without relying on the detection of IEDs, but none have made it to the clinical practice. We aim to review the diagnostic accuracy of quantitative methods applied to ambulatory EEG analysis to guide the diagnosis and management of epilepsy. METHODS AND ANALYSIS The protocol complies with the recommendations for systematic reviews of diagnostic test accuracy by Cochrane. We will search MEDLINE, EMBASE, EBM reviews, IEEE Explore along with grey literature for articles, conference papers and conference abstracts published after 1961. We will include observational studies that present a computational method to analyse the EEG for the diagnosis of epilepsy in adults or children without relying on the identification of IEDs or seizures. The reference standard is the diagnosis of epilepsy by a physician. We will report the estimated pooled sensitivity and specificity, and receiver operating characteristic area under the curve (ROC AUC) for each marker. If possible, we will perform a meta-analysis of the sensitivity and specificity and ROC AUC for each individual marker. We will assess the risk of bias using an adapted QUADAS-2 tool. We will also describe the algorithms used for signal processing, feature extraction and predictive modelling, and comment on the reproducibility of the different studies. ETHICS AND DISSEMINATION Ethical approval was not required. Findings will be disseminated through peer-reviewed publication and presented at conferences related to this field. PROSPERO REGISTRATION NUMBER CRD42022292261.
Collapse
Affiliation(s)
- Émile Lemoine
- Department of Neurosciences, University of Montreal, Montreal, Québec, Canada
- Institute of Biomedical Engineering, Ecole Polytechnique de Montreal, Montreal, Québec, Canada
| | - Joel Neves Briard
- Department of Neurosciences, University of Montreal, Montreal, Québec, Canada
- University of Montreal Hospital Centre Research Centre, Montreal, Québec, Canada
| | - Bastien Rioux
- Department of Neurosciences, University of Montreal, Montreal, Québec, Canada
- University of Montreal Hospital Centre Research Centre, Montreal, Québec, Canada
| | - Renata Podbielski
- University of Montreal Hospital Centre Research Centre, Montreal, Québec, Canada
| | - Bénédicte Nauche
- University of Montreal Hospital Centre Research Centre, Montreal, Québec, Canada
| | - Denahin Toffa
- Department of Neurosciences, University of Montreal, Montreal, Québec, Canada
- University of Montreal Hospital Centre Research Centre, Montreal, Québec, Canada
| | - Mark Keezer
- Department of Neurosciences, University of Montreal, Montreal, Québec, Canada
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, The Netherlands
| | - Frédéric Lesage
- Institute of Biomedical Engineering, Ecole Polytechnique de Montreal, Montreal, Québec, Canada
| | - Dang K Nguyen
- Department of Neurosciences, University of Montreal, Montreal, Québec, Canada
- University of Montreal Hospital Centre Research Centre, Montreal, Québec, Canada
| | - Elie Bou Assi
- Department of Neurosciences, University of Montreal, Montreal, Québec, Canada
- University of Montreal Hospital Centre Research Centre, Montreal, Québec, Canada
| |
Collapse
|
9
|
Zhou J, Liu L, Leng Y, Yang Y, Gao B, Jiang Z, Nie W, Yuan Q. Both Cross-Patient and Patient-Specific Seizure Detection Based on Self-Organizing Fuzzy Logic. Int J Neural Syst 2022; 32:2250017. [DOI: 10.1142/s0129065722500174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Automatic epilepsy detection is of great significance for the diagnosis and treatment of patients. Most detection methods are based on patient-specific models and have achieved good results. However, in practice, new patients do not have their own previous EEG data and therefore cannot be initially diagnosed. If the EEG data of other patients can be used to achieve cross-patient detection, and cross-patient and patient-specific experiments can be combined at the same time, this method will be more widely used. In this work, an EEG classification model based on a self-organizing fuzzy logic (SOF) classifier is proposed for both cross-patient and patient-specific seizure detection. After preprocessing, the features of the original EEG signal are extracted and sent to the SOF classifier. This classification model is free from predefined parameters or a prior assumption regarding the EEG data generation model and only stores the key meta-parameters in memory. Therefore, it is very suitable for large-scale EEG signals in cross-patient detection. Selecting different granularity and classification distance in two different experiments after post-processing will achieve the best results. Experiments were conducted using a long-term continuous scalp EEG database and the [Formula: see text]-mean of cross-patient and patient-specific detection reached 83.35% and 92.04%, respectively. A comparison with other methods shows that there is greater performance and generalizability with this method.
Collapse
Affiliation(s)
- Jiazheng Zhou
- Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong, Normal University, Jinan 250358, P. R. China
| | - Li Liu
- Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong, Normal University, Jinan 250358, P. R. China
| | - Yan Leng
- Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong, Normal University, Jinan 250358, P. R. China
| | - Yuying Yang
- Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong, Normal University, Jinan 250358, P. R. China
| | - Bin Gao
- Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong, Normal University, Jinan 250358, P. R. China
| | - Zonghong Jiang
- College of Resources and Environment Engineering, Guizhou University, Guiyang 550025, P. R. China
| | - Weiwei Nie
- The First Affiliated Hospital of Shandong, First Medical University, Jinan 250014, P. R. China
| | - Qi Yuan
- Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong, Normal University, Jinan 250358, P. R. China
| |
Collapse
|
10
|
Lee SB, Kim YJ, Hwang S, Son H, Lee SK, Park KI, Kim YG. Predicting Parkinson's disease using gradient boosting decision tree models with electroencephalography signals. Parkinsonism Relat Disord 2022; 95:77-85. [PMID: 35051896 DOI: 10.1016/j.parkreldis.2022.01.011] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 01/11/2022] [Accepted: 01/11/2022] [Indexed: 12/22/2022]
Abstract
INTRODUCTION Parkinson's disease (PD) is a neurodegenerative disorder with only symptomatic treatments currently available. Although correct, early diagnoses of PD are important, the existing diagnostic method based on pathologic examinations only has an accuracy of approximately 80.6%. Although electroencephalography (EEG)-based assistive technology has been introduced, it has been difficult to implement in practice due to the high computational complexity and low accuracy of the analysis methods. This study proposed a fast, accurate PD prediction method using the Hjorth parameter and the gradient boosting decision tree (GBDT) algorithm. METHOD We used an open EEG dataset with 41 PD patients and 41 healthy controls (HCs); EEG signals were recorded from participants at the University of New Mexico (PD: 27 vs. HC: 27) and University of Iowa (PD: 14 vs. HC: 14). We explored the analytic time segment and frequency range in which the Hjorth parameter best represents the EEG characteristics of PD patients. RESULTS Our best model (CatBoost-based) distinguished PD patients from controls with an accuracy of 89.3%, an area under the receiver operating characteristics curve (AUC) of 0.912, an F-score of 0.903, and an odds ratio of 115.5. These results showed that our models outperformed those of all other previous works and were even superior to previously known pathologic examination-based diagnoses with long-term follow-up (accuracy = 83.9%). CONCLUSION The proposed methods are expected to be utilized as an effective method for improving the diagnosis of PD.
Collapse
Affiliation(s)
- Seung-Bo Lee
- Office of Hospital Information, Seoul National University Hospital, Seoul, Republic of Korea.
| | - Yong-Jeong Kim
- Transdisciplinary Department of Medicine & Advanced Technology, Seoul National University Hospital, Seoul, Republic of Korea.
| | - Sungeun Hwang
- Department of Neurology, Ewha Womans University Mokdong Hospital, Seoul, Republic of Korea.
| | - Hyoshin Son
- Department of Neurology, Seoul National University Hospital, Seoul, Republic of Korea.
| | - Sang Kun Lee
- Department of Neurology, Seoul National University Hospital, Seoul, Republic of Korea.
| | - Kyung-Il Park
- Department of Neurology, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Republic of Korea; Department of Neurology, Seoul National University College of Medicine, Seoul, Republic of Korea.
| | - Young-Gon Kim
- Transdisciplinary Department of Medicine & Advanced Technology, Seoul National University Hospital, Seoul, Republic of Korea; AI Institute, Seoul National University, Seoul, Republic of Korea.
| |
Collapse
|
11
|
Contento M, Bertaccini B, Biggi M, Magliani M, Failli Y, Rosati E, Massacesi L, Paganini M. Prediction of seizure recurrence risk following discontinuation of antiepileptic drugs. Epilepsia 2021; 62:2159-2170. [PMID: 34250596 PMCID: PMC8457060 DOI: 10.1111/epi.16993] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 06/20/2021] [Accepted: 06/21/2021] [Indexed: 12/14/2022]
Abstract
OBJECTIVE Discontinuation of antiepileptic drugs (AEDs) in seizure-free patients is an important goal because of possible long-term side effects and the social stigma burden of epilepsy. The purpose of this work was to assess seizure recurrence risk after suspension of AEDs, to evaluate predictors for recurrence, and to investigate the recovery of seizure control after relapse. In addition, the accuracy of a previously published prediction model of seizure recurrence risk was estimated. METHODS Seizure-free patients with epilepsy who had discontinued AEDs were retrospectively enrolled. The frequency of seizure relapses after AED withdrawal as well as prognosis after recurrence were assessed and the predictive role of baseline clinical-demographic variables was evaluated. The aforementioned prediction model was also validated and its accuracy assessed at different seizure-relapse probability levels. RESULTS The enrolled patients (n = 133) had been followed for a median of 3 years (range 0.8-33 years) after AED discontinuation; 60 (45%) of them relapsed. Previous febrile seizures in childhood (hazard ratio [HR] 3.927; 95% confidence interval [CI] 1.403-10.988), a seizure-free period on therapy of less than 2 years (HR 2.313; 95% CI 1.193-4.486), and persistent motor deficits (HR 4.568; 95% CI 1.412-14.772) were the clinical features associated with relapse risk in univariate analysis. Among these variables, only a seizure-free period on therapy of less than 2 years was associated with seizure recurrence in multivariate analysis (HR 2.365; 95% CI 1.178-4.7444). Pharmacological control of epilepsy was restored in 82.4% of the patients who relapsed. In this population, the aforementioned prediction model showed an unsatisfactory accuracy. SIGNIFICANCE A period of freedom from seizure on therapy of less than 2 years was the main predictor of seizure recurrence. The accuracy of the previously described prediction tool was low in this cohort, thus suggesting its cautious use in real-world clinical practice.
Collapse
Affiliation(s)
- Margherita Contento
- Department of Neurosciences, Drug and Child Health, University of Florence, Florence, Italy
| | - Bruno Bertaccini
- Department of Statistics Informatics and Application "G.Parenti", University of Florence, Florence, Italy
| | - Martina Biggi
- Department of Neurosciences, Drug and Child Health, University of Florence, Florence, Italy
| | - Matteo Magliani
- Department of Neurosciences, Drug and Child Health, University of Florence, Florence, Italy
| | - Ylenia Failli
- Department of Neurosciences, Drug and Child Health, University of Florence, Florence, Italy
| | - Eleonora Rosati
- Department Neurology 2, Careggi University Hospital, Florence, Italy
| | - Luca Massacesi
- Department of Neurosciences, Drug and Child Health, University of Florence, Florence, Italy.,Department Neurology 2, Careggi University Hospital, Florence, Italy
| | - Marco Paganini
- Department Neurology 2, Careggi University Hospital, Florence, Italy
| |
Collapse
|
12
|
Peng P, Xie L, Wei H. A Deep Fourier Neural Network for Seizure Prediction Using Convolutional Neural Network and Ratios of Spectral Power. Int J Neural Syst 2021; 31:2150022. [PMID: 33970057 DOI: 10.1142/s0129065721500222] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Epileptic seizure prediction is one of the most used therapeutic adjuvant strategies for drug-resistant epilepsy. Conventional methods usually adopt handcrafted features and manual parameter setting. The over-reliance on the expertise of specialists may lead to weak exploitation of features and low popularization of clinical application. This paper proposes a novel parameterless patient-specific method based on Fourier Neural Network (FNN), where the Fourier transform and backpropagation learning are synthesized to make the predictor more efficient and practical. The employment of FNN is the first attempt in the field of seizure prediction due to its automatic extraction of immanent spectra in epileptic signals. Despite the self-adaptive superiority of FNN, we introduce Convolutional Neural Network (CNN) to further improve its search capability in high-dimensional feature spaces. The study also develops a multi-layer module to estimate spectral power ratios of raw recordings, which optimizes the prediction by enhancing feature diversity. Based on these modules, this paper proposes a two-channel deep neural network: Fourier Ratio Convolutional Neural Network (FRCNN). To demonstrate the reliability of the model, we explain the mathematical meaning of hidden-layer neurons in FRCNN theoretically. This approach is evaluated on both intracranial and scalp EEG datasets. It shows that the predictor achieved a sensitivity of 91.2% and a false prediction rate (FPR) of 0.06[Formula: see text]h[Formula: see text] across intracranial subjects and a sensitivity of 85.4% and an FPR of 0.14[Formula: see text]h[Formula: see text] over scalp subjects. The results indicate that FRCNN enables the convenience of epilepsy treatments while preserving a high degree of precision. In the end, a detailed comparison with the previous methods demonstrates that FRCNN has achieved higher performance and generalization ability.
Collapse
Affiliation(s)
- Peizhen Peng
- Key Laboratory of Measurement and Control of CSE, Ministry of Education, School of Automation, Southeast University, Nanjing 210096, P. R. China
| | - Liping Xie
- Key Laboratory of Measurement and Control of CSE, Ministry of Education, School of Automation, Southeast University, Nanjing 210096, P. R. China
| | - Haikun Wei
- Key Laboratory of Measurement and Control of CSE, Ministry of Education, School of Automation, Southeast University, Nanjing 210096, P. R. China
| |
Collapse
|