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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.
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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
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Ming Z, Chen D, Gao T, Tang Y, Tu W, Chen J. V2IED: Dual-view learning framework for detecting events of interictal epileptiform discharges. Neural Netw 2024; 172:106136. [PMID: 38266472 DOI: 10.1016/j.neunet.2024.106136] [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: 05/06/2023] [Revised: 11/20/2023] [Accepted: 01/16/2024] [Indexed: 01/26/2024]
Abstract
Interictal epileptiform discharges (IED) as large intermittent electrophysiological events are associated with various severe brain disorders. Automated IED detection has long been a challenging task, and mainstream methods largely focus on singling out IEDs from backgrounds from the perspective of waveform, leaving normal sharp transients/artifacts with similar waveforms almost unattended. An open issue still remains to accurately detect IED events that directly reflect the abnormalities in brain electrophysiological activities, minimizing the interference from irrelevant sharp transients with similar waveforms only. This study then proposes a dual-view learning framework (namely V2IED) to detect IED events from multi-channel EEG via aggregating features from the two phases: (1) Morphological Feature Learning: directly treating the EEG as a sequence with multiple channels, a 1D-CNN (Convolutional Neural Network) is applied to explicitly learning the deep morphological features; and (2) Spatial Feature Learning: viewing the EEG as a 3D tensor embedding channel topology, a CNN captures the spatial features at each sampling point followed by an LSTM (Long Short-Term Memories) to learn the evolution of these features. Experimental results from a public EEG dataset against the state-of-the-art counterparts indicate that: (1) compared with the existing optimal models, V2IED achieves a larger area under the receiver operating characteristic (ROC) curve in detecting IEDs from normal sharp transients with a 5.25% improvement in accuracy; (2) the introduction of spatial features improves performance by 2.4% in accuracy; and (3) V2IED also performs excellently in distinguishing IEDs from background signals especially benign variants.
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Affiliation(s)
- Zhekai Ming
- School of Computer Science, the Hubei Key Laboratory of Multimedia and Network Communication Engineering, the National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, 430072, China
| | - Dan Chen
- School of Computer Science, the Hubei Key Laboratory of Multimedia and Network Communication Engineering, the National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, 430072, China.
| | - Tengfei Gao
- School of Computer Science, the Hubei Key Laboratory of Multimedia and Network Communication Engineering, the National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, 430072, China
| | - Yunbo Tang
- College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China
| | - Weiping Tu
- School of Computer Science, the Hubei Key Laboratory of Multimedia and Network Communication Engineering, the National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, 430072, China
| | - Jingying Chen
- National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, China
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Konomatsu K, Kakisaka Y, Jin K, Ukishiro K, Sakata A, Shimogawa T, Morioka T, Kubota T, Soga T, Aoki M, Nakasato N. Dynamic electro-clinical changes corresponding to immediate recovery after glucose administration from insulinoma-induced hypoglycemia: Report of two cases. Epileptic Disord 2023; 25:900-903. [PMID: 37632394 DOI: 10.1002/epd2.20155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 08/07/2023] [Accepted: 08/21/2023] [Indexed: 08/28/2023]
Affiliation(s)
- Kazutoshi Konomatsu
- Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
- Department of Neurology, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
| | - Yosuke Kakisaka
- Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
| | - Kazutaka Jin
- Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
| | - Kazushi Ukishiro
- Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
| | - Ayumi Sakata
- Department of Clinical Chemistry and Laboratory Medicine, Kyushu University Hospital, Fukuoka, Japan
| | - Takafumi Shimogawa
- Department of Neurosurgery, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Takato Morioka
- Department of Neurosurgery, Hachisuga Hospital, Munakata, Japan
| | - Takafumi Kubota
- Department of Neurology, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
| | - Temma Soga
- Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
- Department of Neurology, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
| | - Masashi Aoki
- Department of Neurology, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
| | - Nobukazu Nakasato
- Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
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Greenblatt AS, Beniczky S, Nascimento FA. Pitfalls in scalp EEG: Current obstacles and future directions. Epilepsy Behav 2023; 149:109500. [PMID: 37931388 DOI: 10.1016/j.yebeh.2023.109500] [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: 09/02/2023] [Revised: 10/15/2023] [Accepted: 10/17/2023] [Indexed: 11/08/2023]
Abstract
Although electroencephalography (EEG) serves a critical role in the evaluation and management of seizure disorders, it is commonly misinterpreted, resulting in avoidable medical, social, and financial burdens to patients and health care systems. Overinterpretation of sharply contoured transient waveforms as being representative of interictal epileptiform abnormalities lies at the core of this problem. However, the magnitude of these errors is amplified by the high prevalence of paroxysmal events exhibited in clinical practice that compel investigation with EEG. Neurology training programs, which vary considerably both in the degree of exposure to EEG and the composition of EEG didactics, have not effectively addressed this widespread issue. Implementation of competency-based curricula in lieu of traditional educational approaches may enhance proficiency in EEG interpretation amongst general neurologists in the absence of formal subspecialty training. Efforts in this regard have led to the development of a systematic, high-fidelity approach to the interpretation of epileptiform discharges that is readily employable across medical centers. Additionally, machine learning techniques hold promise for accelerating accurate and reliable EEG interpretation, particularly in settings where subspecialty interpretive EEG services are not readily available. This review highlights common diagnostic errors in EEG interpretation, limitations in current educational paradigms, and initiatives aimed at resolving these challenges.
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Affiliation(s)
- Adam S Greenblatt
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund and Aarhus University Hospital, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Fábio A Nascimento
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.
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Wang X, Wang X, Wang C, Wang Z, Liu X, Lv X, Tang Y. A Two-Stage Automatic System for Detection of Interictal Epileptiform Discharges from Scalp Electroencephalograms. eNeuro 2023; 10:ENEURO.0111-23.2023. [PMID: 37914407 PMCID: PMC10668214 DOI: 10.1523/eneuro.0111-23.2023] [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: 04/04/2023] [Revised: 08/22/2023] [Accepted: 09/15/2023] [Indexed: 11/03/2023] Open
Abstract
The objective of this work was to develop a deep learning-based automatic system with reliable performance in detecting interictal epileptiform discharges (IEDs) from scalp electroencephalograms (EEGs). For the present study, 484 raw scalp EEG recordings were included, standardized, and split into 406 for training and 78 for testing. Two neurophysiologists individually annotated the recordings for training in channel-wise manner. Annotations were divided into segments, on which nine deep neural networks (DNNs) were trained for the multiclassification of IED, artifact, and background. The fitted IED detectors were then evaluated on 78 EEG recordings with IED events fully annotated by three experts independently (majority agreement). A two montage-based decision mechanism (TMDM) was designed to determine whether an IED event occurred at a single time instant. Area under the precision-recall curve (AUPRC), as well as false-positive rates, F1 scores, and kappa agreement scores for sensitivity = 0.8 were estimated. In multitype classification, five DNNs provided one-versus-rest AUPRC mean value >0.993 using fivefold cross-validation. In IED detection, the system that had integrated the temporal convolutional network (TCN)-based IED detector and the TMDM rule achieved an AUPRC of 0.811. The false positive was 0.194/min (11.64/h), and the F1 score was 0.745. The agreement score between the system and the experts was 0.905. The proposed framework provides a TCN-based IED detector and a novel two montage-based determining mechanism that combined to make an automatic IED detection system. The system would be useful in aiding clinic EEG interpretation.
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Affiliation(s)
- Xiaoyun Wang
- Department of Neurology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, People's Republic of China
| | - Xing Wang
- Department of Signal Processing Research, Beijing Solar Electronic Technologies Company Ltd, Beijing 100044, People's Republic of China
| | - Chong Wang
- Department of Neurology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, People's Republic of China
| | - Zhongyuan Wang
- Department of Neurology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, People's Republic of China
| | - Xiangyu Liu
- Department of Neurosurgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, People's Republic of China
| | - Xiaoling Lv
- Geriatrics Research Institute of Zhejiang Province, Zhejiang Provincial Key Lab of Geriatrics, Zhejiang Hospital, Hangzhou 310013, People's Republic of China
| | - Ying Tang
- Geriatrics Research Institute of Zhejiang Province, Zhejiang Provincial Key Lab of Geriatrics, Zhejiang Hospital, Hangzhou 310013, People's Republic of China
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Aanestad E, Gilhus NE, Olberg HK, Kural MA, Beniczky S, Brogger J. Spike count and morphology in the classification of epileptiform discharges. Front Neurol 2023; 14:1165592. [PMID: 37288067 PMCID: PMC10242725 DOI: 10.3389/fneur.2023.1165592] [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: 02/14/2023] [Accepted: 04/24/2023] [Indexed: 06/09/2023] Open
Abstract
Purpose The purpose of this study is to investigate the impact of Bergen Epileptiform Morphology Score (BEMS) and interictal epileptiform discharge (IED) candidate count in EEG classification. Methods We included 400 consecutive patients from a clinical SCORE EEG database during 2013-2017 who had focal sharp discharges in their EEG, but no previous diagnosis of epilepsy. Three blinded EEG readers marked all IED candidates. BEMS and IED candidate counts were combined to classify EEGs as epileptiform or non-epileptiform. Diagnostic performance was assessed and then validated in an external dataset. Results Interictal epileptiform discharge (IED) candidate count and BEMS were moderately correlated. The optimal criteria to classify an EEG as epileptiform were either one spike at BEMS > = 58, two at > = 47, or seven at > = 36. These criteria had almost perfect inter-rater reliability (Gwet's AC1 0.96), reasonable sensitivity of 56-64%, and high specificity of 98-99%. The sensitivity was 27-37%, and the specificity was 93-97% for a follow-up diagnosis of epilepsy. In the external dataset, the sensitivity for an epileptiform EEG was 60-70%, and the specificity was 90-93%. Conclusion Quantified EEG spike morphology (BEMS) and IED candidate count can be combined to classify an EEG as epileptiform with high reliability but with lower sensitivity than regular visual EEG review.
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Affiliation(s)
- Eivind Aanestad
- Department of Clinical Neurophysiology, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Nils Erik Gilhus
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Department of Neurology, Haukeland University Hospital, Bergen, Norway
| | - Henning Kristian Olberg
- Department of Clinical Neurophysiology, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Mustafa Aykut Kural
- Department of Clinical Neurophysiology, Danish Epilepsy Centre Filadelfia, Dianalund, Denmark
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Centre Filadelfia, Dianalund, Denmark
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Jan Brogger
- Department of Clinical Neurophysiology, Haukeland University Hospital, Bergen, Norway
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Chung YG, Lee WJ, Na SM, Kim H, Hwang H, Yun CH, Kim KJ. Deep learning-based automated detection and multiclass classification of focal interictal epileptiform discharges in scalp electroencephalograms. Sci Rep 2023; 13:6755. [PMID: 37185941 PMCID: PMC10130023 DOI: 10.1038/s41598-023-33906-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Accepted: 04/20/2023] [Indexed: 05/17/2023] Open
Abstract
Detection and spatial distribution analyses of interictal epileptiform discharges (IEDs) are important for diagnosing, classifying, and treating focal epilepsy. This study proposes deep learning-based models to detect focal IEDs in electroencephalography (EEG) recordings of the frontal, temporal, and occipital scalp regions. This study included 38 patients with frontal (n = 15), temporal (n = 13), and occipital (n = 10) IEDs and 232 controls without IEDs from a single tertiary center. All the EEG recordings were segmented into 1.5-s epochs and fed into 1- or 2-dimensional convolutional neural networks to construct binary classification models to detect IEDs in each focal region and multiclass classification models to categorize IEDs into frontal, temporal, and occipital regions. The binary classification models exhibited accuracies of 79.3-86.4%, 93.3-94.2%, and 95.5-97.2% for frontal, temporal, and occipital IEDs, respectively. The three- and four-class models exhibited accuracies of 87.0-88.7% and 74.6-74.9%, respectively, with temporal, occipital, and non-IEDs F1-scores of 89.9-92.3%, 84.9-90.6%, and 84.3-86.0%; and 86.6-86.7%, 86.8-87.2%, and 67.8-69.2% for the three- and four-class (frontal, 50.3-58.2%) models, respectively. The deep learning-based models could help enhance EEG interpretation. Although they performed well, the resolution of region-specific focal IED misinterpretations and further model improvement are needed.
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Affiliation(s)
- Yoon Gi Chung
- Division of Pediatric Neurology, Department of Pediatrics, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Republic of Korea
| | - Woo-Jin Lee
- Department of Neurology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Republic of Korea
| | - Sung Min Na
- Division of Pediatric Neurology, Department of Pediatrics, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Republic of Korea
| | - Hunmin Kim
- Division of Pediatric Neurology, Department of Pediatrics, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Republic of Korea.
| | - Hee Hwang
- Division of Pediatric Neurology, Department of Pediatrics, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Republic of Korea
- Kakao Healthcare, Seongnam-si, Republic of Korea
| | - Chang-Ho Yun
- Department of Neurology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Republic of Korea
| | - Ki Joong Kim
- Department of Pediatrics, Seoul National University Children's Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
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Hoffman C, Cheng J, Ji D, Dabaghian Y. Pattern dynamics and stochasticity of the brain rhythms. Proc Natl Acad Sci U S A 2023; 120:e2218245120. [PMID: 36976768 PMCID: PMC10083604 DOI: 10.1073/pnas.2218245120] [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: 10/29/2022] [Accepted: 02/07/2023] [Indexed: 03/29/2023] Open
Abstract
Our current understanding of brain rhythms is based on quantifying their instantaneous or time-averaged characteristics. What remains unexplored is the actual structure of the waves-their shapes and patterns over finite timescales. Here, we study brain wave patterning in different physiological contexts using two independent approaches: The first is based on quantifying stochasticity relative to the underlying mean behavior, and the second assesses "orderliness" of the waves' features. The corresponding measures capture the waves' characteristics and abnormal behaviors, such as atypical periodicity or excessive clustering, and demonstrate coupling between the patterns' dynamics and the animal's location, speed, and acceleration. Specifically, we studied patterns of θ, γ, and ripple waves recorded in mice hippocampi and observed speed-modulated changes of the wave's cadence, an antiphase relationship between orderliness and acceleration, as well as spatial selectiveness of patterns. Taken together, our results offer a complementary-mesoscale-perspective on brain wave structure, dynamics, and functionality.
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Affiliation(s)
- Clarissa Hoffman
- Department of Neurology, McGovern Medical School, The University of Texas, Houston, TX77030
| | - Jingheng Cheng
- Department of Neuroscience, Baylor College of Medicine, Houston, TX77030
| | - Daoyun Ji
- Department of Neuroscience, Baylor College of Medicine, Houston, TX77030
- Department of Molecular and Cell Biology, Baylor College of Medicine, Houston, TX77030
| | - Yuri Dabaghian
- Department of Neurology, McGovern Medical School, The University of Texas, Houston, TX77030
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A quantitative approach to evaluating interictal epileptiform discharges based on interpretable quantitative criteria. Clin Neurophysiol 2023; 146:10-17. [PMID: 36473334 DOI: 10.1016/j.clinph.2022.10.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 10/01/2022] [Accepted: 10/31/2022] [Indexed: 11/18/2022]
Abstract
OBJECTIVE To provide quantitative measures of the six International Federation of Clinical Neurophysiology (IFCN) criteria for interictal epileptiform discharge (IED) identification and estimate the likelihood of a candidate IED being epileptiform. METHODS We designed an algorithm to identify five fiducial landmarks (onset, peak, trough, slow-wave peak, offset) of a candidate IED, and from these to quantify the six IFCN features of IEDs. Another model was trained with these features to quantify the probability that the waveform is epileptiform and incorporated into a user-friendly interface. RESULTS The model's performance is excellent (area under the receiver operating characteristic curve (AUROC) = 0.88; calibration error 0.03) but lower than human experts (receiver operating characteristic (ROC) curve is below experts' operating points) or a deep neural-network model (SpikeNet; AUCROC = 0.97; calibration error 0.04). The six features were all significant (p<0.001), but not equally important when determining potential epileptiform nature of candidate IEDs: waveform asymmetry was the most (coefficient 0.64) and duration the least discriminative (coefficient 0.09). CONCLUSIONS Our approach quantifies the six IFCN criteria for IED identification and combines them in an easily interpretable, accessible fashion that accurately captures the likelihood that a candidate waveform is epileptiform. SIGNIFICANCE This model may assist clinical electroencephalographers decide whether candidate waveforms are epileptiform and may assist trainees learn to identify IEDs.
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Aanestad E, Gilhus NE, Brogger J. A New Score for Sharp Discharges in the EEG Predicts Epilepsy. J Clin Neurophysiol 2023; 40:9-16. [PMID: 33935218 PMCID: PMC9799053 DOI: 10.1097/wnp.0000000000000849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
PURPOSE A challenge in EEG interpretation is to correctly classify suspicious focal sharp activity as epileptiform or not. A predictive score was developed from morphologic features of the first focal sharp discharge, which can help in this decision. METHODS From a clinical standard EEG database, the authors identified 2,063 patients without a previous epilepsy diagnosis who had a focal sharp discharge in their EEG. Morphologic features (amplitude, area of slow wave, etc.) were extracted using an open source one-click algorithm in EEGLAB, masked to clinical classification. A score was developed from these features and validated with the clinical diagnosis of epilepsy over 2 to 6 years of follow-up. Independent external validation was performed in Kural long-term video-EEG monitoring dataset. RESULTS The score for the first focal sharp discharge had a moderate predictive performance for the clinical designation as the EEG being epileptiform (area under the receiver operating characteristics curve = 0.86). Best specificity was 91% and sensitivity 55%. The score also predicted a future epilepsy diagnosis (area under the receiver operating characteristics curve = 0.70). Best specificity was 86% and sensitivity 38%. Validation on the external dataset had an area under the receiver operating characteristics curve = 0.80. Clinical EEG identification of focal interictal epileptiform discharges had an area under the receiver operating characteristics curve = 0.73 for prediction of epilepsy. The score was based on amplitude, slope, difference from background, slow after-wave area, and age. Interrater reproducibility was high (ICC = 0.91). CONCLUSIONS The designation of the first focal sharp discharge as epileptiform depends on reproducible morphologic features. Characteristic features were amplitude, slope, slow after-wave area, and difference from background. The score was predictive of future epilepsy. Halford semiquantitative scale had similar diagnostic performance but lower reproducibility.
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Affiliation(s)
- Eivind Aanestad
- Section for Clinical Neurophysiology, Department of Neurology, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Nils E. Gilhus
- Department of Neurology, Haukeland University Hospital, Bergen, Norway; and
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Jan Brogger
- Section for Clinical Neurophysiology, Department of Neurology, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
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Mattioli P, Cleeren E, Hadady L, Cossu A, Cloppenborg T, Arnaldi D, Beniczky S. Electric Source Imaging in Presurgical Evaluation of Epilepsy: An Inter-Analyser Agreement Study. Diagnostics (Basel) 2022; 12:diagnostics12102303. [PMID: 36291992 PMCID: PMC9601236 DOI: 10.3390/diagnostics12102303] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 09/13/2022] [Accepted: 09/21/2022] [Indexed: 11/16/2022] Open
Abstract
Electric source imaging (ESI) estimates the cortical generator of the electroencephalography (EEG) signals recorded with scalp electrodes. ESI has gained increasing interest for the presurgical evaluation of patients with drug-resistant focal epilepsy. In spite of a standardised analysis pipeline, several aspects tailored to the individual patient involve subjective decisions of the expert performing the analysis, such as the selection of the analysed signals (interictal epileptiform discharges and seizures, identification of the onset epoch and time-point of the analysis). Our goal was to investigate the inter-analyser agreement of ESI in presurgical evaluations of epilepsy, using the same software and analysis pipeline. Six experts, of whom five had no previous experience in ESI, independently performed interictal and ictal ESI of 25 consecutive patients (17 temporal, 8 extratemporal) who underwent presurgical evaluation. The overall agreement among experts for the ESI methods was substantial (AC1 = 0.65; 95% CI: 0.59–0.71), and there was no significant difference between the methods. Our results suggest that using a standardised analysis pipeline, newly trained experts reach similar ESI solutions, calling for more standardisation in this emerging clinical application in neuroimaging.
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Affiliation(s)
- Pietro Mattioli
- Department of Neuroscience (DINOGMI), University of Genoa, 16132 Genoa, Italy
- Danish Epilepsy Center, 4293 Dianalund, Denmark
| | - Evy Cleeren
- Danish Epilepsy Center, 4293 Dianalund, Denmark
- Department of Neurology, University Hospital Leuven, 3000 Leuven, Belgium
| | - Levente Hadady
- Danish Epilepsy Center, 4293 Dianalund, Denmark
- Department of Neurology, Albert Szent-Györgyi Medical School, University of Szeged, 6720 Szeged, Hungary
| | - Alberto Cossu
- Danish Epilepsy Center, 4293 Dianalund, Denmark
- Child Neuropsychiatry, Department of Surgical Sciences, Dentistry, Gynecology and Pediatrics, University of Verona, 37126 Verona, Italy
| | - Thomas Cloppenborg
- Department of Epileptology, Krankenhaus Mara, Medical School, Bielefeld University, 33615 Bielefeld, Germany
| | - Dario Arnaldi
- Department of Neuroscience (DINOGMI), University of Genoa, 16132 Genoa, Italy
- IRCCS San Martino Hospital, 16132 Genoa, Italy
| | - Sándor Beniczky
- Danish Epilepsy Center, 4293 Dianalund, Denmark
- Department of Neurology, Albert Szent-Györgyi Medical School, University of Szeged, 6720 Szeged, Hungary
- Department of Clinical Neurophysiology, Aarhus University Hospital, 8200 Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, 8000 Aarhus, Denmark
- Correspondence: ; Tel.: +45-26-981536
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12
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McLaren JR, Jing J, Westover MB, Nascimento FA. Journal Club: Criteria for Defining Interictal Epileptiform Discharges in EEG. Neurology 2022; 99:430-432. [PMID: 35853743 PMCID: PMC9519249 DOI: 10.1212/wnl.0000000000200991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 06/03/2022] [Indexed: 11/15/2022] Open
Affiliation(s)
- John R McLaren
- From the Department of Neurology (J.R.M., J.J., M.B.W., F.A.N.), Massachusetts General Hospital, Harvard Medical School, Boston, and Department of Neurology (F.A.B.), Washington University School of Medicine, St. Louis, MO.
| | - Jin Jing
- From the Department of Neurology (J.R.M., J.J., M.B.W., F.A.N.), Massachusetts General Hospital, Harvard Medical School, Boston, and Department of Neurology (F.A.B.), Washington University School of Medicine, St. Louis, MO
| | - M Brandon Westover
- From the Department of Neurology (J.R.M., J.J., M.B.W., F.A.N.), Massachusetts General Hospital, Harvard Medical School, Boston, and Department of Neurology (F.A.B.), Washington University School of Medicine, St. Louis, MO
| | - Fábio A Nascimento
- From the Department of Neurology (J.R.M., J.J., M.B.W., F.A.N.), Massachusetts General Hospital, Harvard Medical School, Boston, and Department of Neurology (F.A.B.), Washington University School of Medicine, St. Louis, MO
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13
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Papadelis C, Conrad SE, Song Y, Shandley S, Hansen D, Bosemani M, Malik S, Keator C, Perry MS. Case Report: Laser Ablation Guided by State of the Art Source Imaging Ends an Adolescent's 16-Year Quest for Seizure Freedom. Front Hum Neurosci 2022; 16:826139. [PMID: 35145387 PMCID: PMC8821813 DOI: 10.3389/fnhum.2022.826139] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/03/2022] [Indexed: 01/14/2023] Open
Abstract
Epilepsy surgery is the most effective therapeutic approach for children with drug resistant epilepsy (DRE). Recent advances in neurosurgery, such as the Laser Interstitial Thermal Therapy (LITT), improved the safety and non-invasiveness of this method. Electric and magnetic source imaging (ESI/MSI) plays critical role in the delineation of the epileptogenic focus during the presurgical evaluation of children with DRE. Yet, they are currently underutilized even in tertiary epilepsy centers. Here, we present a case of an adolescent who suffered from DRE for 16 years and underwent surgery at Cook Children's Medical Center (CCMC). The patient was previously evaluated in a level 4 epilepsy center and treated with multiple antiseizure medications for several years. Presurgical evaluation at CCMC included long-term video electroencephalography (EEG), magnetoencephalography (MEG) with simultaneous conventional EEG (19 channels) and high-density EEG (256 channels) in two consecutive sessions, MRI, and fluorodeoxyglucose - positron emission tomography (FDG-PET). Video long-term EEG captured nine focal-onset clinical seizures with a maximal evolution over the right frontal/frontal midline areas. MRI was initially interpreted as non-lesional. FDG-PET revealed a small region of hypometabolism at the anterior right superior temporal gyrus. ESI and MSI performed with dipole clustering showed a tight cluster of dipoles in the right anterior insula. The patient underwent intracranial EEG which indicated the right anterior insular as seizure onset zone. Eventually LITT rendered the patient seizure free (Engel 1; 12 months after surgery). Retrospective analysis of ESI and MSI clustered dipoles found a mean distance of dipoles from the ablated volume ranging from 10 to 25 mm. Our findings highlight the importance of recent technological advances in the presurgical evaluation and surgical treatment of children with DRE, and the underutilization of epilepsy surgery in children with DRE.
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Affiliation(s)
- Christos Papadelis
- Jane and John Justin Neuroscience Center, Cook Children's Health Care System, Fort Worth, TX, United States
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX, United States
- School of Medicine, Texas Christian University, University of North Texas Health Science Center, Fort Worth, TX, United States
- *Correspondence: Christos Papadelis orcid.org/0000-0001-6125-9217
| | - Shannon E. Conrad
- Jane and John Justin Neuroscience Center, Cook Children's Health Care System, Fort Worth, TX, United States
| | - Yanlong Song
- Jane and John Justin Neuroscience Center, Cook Children's Health Care System, Fort Worth, TX, United States
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX, United States
| | - Sabrina Shandley
- Jane and John Justin Neuroscience Center, Cook Children's Health Care System, Fort Worth, TX, United States
| | - Daniel Hansen
- Jane and John Justin Neuroscience Center, Cook Children's Health Care System, Fort Worth, TX, United States
| | - Madhan Bosemani
- Department of Radiology, Cook Children's Medical Center, Fort Worth, TX, United States
| | - Saleem Malik
- Jane and John Justin Neuroscience Center, Cook Children's Health Care System, Fort Worth, TX, United States
| | - Cynthia Keator
- Jane and John Justin Neuroscience Center, Cook Children's Health Care System, Fort Worth, TX, United States
| | - M. Scott Perry
- Jane and John Justin Neuroscience Center, Cook Children's Health Care System, Fort Worth, TX, United States
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14
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Sultana S, Hitomi T, Kobayashi MD, Shimotake A, Matsuhashi M, Takahashi R, Ikeda A. Long Time Constant May Endorses Sharp Waves and Spikes Than Sharp Transients in Scalp Electroencephalography: A Comparison of Both After-Slow Among Different Time Constant and High-Frequency Activity Analysis. Front Hum Neurosci 2021; 15:748893. [PMID: 34744663 PMCID: PMC8569184 DOI: 10.3389/fnhum.2021.748893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 09/28/2021] [Indexed: 11/29/2022] Open
Abstract
Objective: To clarify whether long time constant (TC) is useful for detecting the after-slow activity of epileptiform discharges (EDs): sharp waves and spikes and for differentiating EDs from sharp transients (Sts). Methods: We employed 68 after-slow activities preceded by 32 EDs (26 sharp waves and six spikes) and 36 Sts from 52 patients with partial and generalized epilepsy (22 men, 30 women; mean age 39.08 ± 13.13 years) defined by visual inspection. High-frequency activity (HFA) associated with the apical component of EDs and Sts was also investigated to endorse two groups. After separating nine Sts that were labeled by visual inspection but did not fulfill the amplitude criteria for after-slow of Sts, 59 activities (32 EDs and 27 Sts) were analyzed about the total area of after-slow under three TCs (long: 2 s; conventional: 0.3 s; and short: 0.1 s). Results: Compared to Sts, HFA was found significantly more with the apical component of EDs (p < 0.05). The total area of after-slow in all 32 EDs under TC 2 s was significantly larger than those under TC 0.3 s and 0.1 s (p < 0.001). Conversely, no significant differences were observed in the same parameter of 27 Sts among the three different TCs. Regarding separated nine Sts, the total area of after-slow showed a similar tendency to that of 27 Sts under three different TCs. Significance: These results suggest that long TC could be useful for selectively endorsing after-slow of EDs and differentiating EDs from Sts. These findings are concordant with the results of the HFA analysis. Visual inspection is also equally good as the total area of after-slow analysis.
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Affiliation(s)
- Shamima Sultana
- Department of Neurology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Takefumi Hitomi
- Department of Neurology, Graduate School of Medicine, Kyoto University, Kyoto, Japan.,Department of Clinical Laboratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | | | - Akihiro Shimotake
- Department of Neurology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Masao Matsuhashi
- Department of Epilepsy, Movement Disorders and Physiology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Ryosuke Takahashi
- Department of Neurology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Akio Ikeda
- Department of Epilepsy, Movement Disorders and Physiology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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15
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Li P, Piliouras E, Poghosyan V, AlHameed M, Laleg-Kirati TM. Automatic Detection of Epileptiform EEG Discharges based on the Semi-Classical Signal Analysis (SCSA) method. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:928-931. [PMID: 34891442 DOI: 10.1109/embc46164.2021.9631028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
In this paper we utilize a signal processing tool, which can help physicians and clinical researchers to automate the process of EEG epileptiform spike detection. The semi-classical signal analysis method (SCSA) is a data-driven signal decomposition method developed for pulse-shaped signal characterization. We present an algorithm framework to process and extract features from the patient's EEG recording by deriving the mathematical motivation behind SCSA and quantifying existing spike diagnosis criterion with it. The proposed method can help reduce the amount of data to manually analyse. We have tested our proposed algorithm framework with real data, which guarantees the method's statistical reliability and robustness.
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16
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Vaudano AE, Mirandola L, Talami F, Giovannini G, Monti G, Riguzzi P, Volpi L, Michelucci R, Bisulli F, Pasini E, Tinuper P, Di Vito L, Gessaroli G, Malagoli M, Pavesi G, Cardinale F, Tassi L, Lemieux L, Meletti S. fMRI-Based Effective Connectivity in Surgical Remediable Epilepsies: A Pilot Study. Brain Topogr 2021; 34:632-650. [PMID: 34152513 DOI: 10.1007/s10548-021-00857-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 06/13/2021] [Indexed: 11/24/2022]
Abstract
Simultaneous EEG-fMRI can contribute to identify the epileptogenic zone (EZ) in focal epilepsies. However, fMRI maps related to Interictal Epileptiform Discharges (IED) commonly show multiple regions of signal change rather than focal ones. Dynamic causal modeling (DCM) can estimate effective connectivity, i.e. the causal effects exerted by one brain region over another, based on fMRI data. Here, we employed DCM on fMRI data in 10 focal epilepsy patients with multiple IED-related regions of BOLD signal change, to test whether this approach can help the localization process of EZ. For each subject, a family of competing deterministic, plausible DCM models were constructed using IED as autonomous input at each node, one at time. The DCM findings were compared to the presurgical evaluation results and classified as: "Concordant" if the node identified by DCM matches the presumed focus, "Discordant" if the node is distant from the presumed focus, or "Inconclusive" (no statistically significant result). Furthermore, patients who subsequently underwent intracranial EEG recordings or surgery were considered as having an independent validation of DCM results. The effective connectivity focus identified using DCM was Concordant in 7 patients, Discordant in two cases and Inconclusive in one. In four of the 6 patients operated, the DCM findings were validated. Notably, the two Discordant and Invalidated results were found in patients with poor surgical outcome. Our findings provide preliminary evidence to support the applicability of DCM on fMRI data to investigate the epileptic networks in focal epilepsy and, particularly, to identify the EZ in complex cases.
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Affiliation(s)
- A E Vaudano
- Neurology Unit, OCB Hospital, Azienda Ospedaliero-Universitaria of Modena, Via Giardini 1355, 41100, Modena, Italy. .,Center for Neuroscience and Neurotechnology, Department of Biomedical, Metabolic, and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy.
| | - L Mirandola
- Center for Neuroscience and Neurotechnology, Department of Biomedical, Metabolic, and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - F Talami
- Center for Neuroscience and Neurotechnology, Department of Biomedical, Metabolic, and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - G Giovannini
- Neurology Unit, OCB Hospital, Azienda Ospedaliero-Universitaria of Modena, Via Giardini 1355, 41100, Modena, Italy.,Center for Neuroscience and Neurotechnology, Department of Biomedical, Metabolic, and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - G Monti
- Neurology Unit, AUSL Modena, Ospedale Ramazzini, Carpi, MO, Italy
| | - P Riguzzi
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Unit of Neurology, Bellaria Hospital, Bologna, Italy
| | - L Volpi
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Unit of Neurology, Bellaria Hospital, Bologna, Italy
| | - R Michelucci
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Unit of Neurology, Bellaria Hospital, Bologna, Italy
| | - F Bisulli
- Department of Biomedical and NeuroMotor Sciences (DIBINEM), University of Bologna, Bologna, Italy.,IRCCS Istituto delle Scienze Neurologiche di Bologna, Epilepsy Center (Reference Center for Rare and Complex Epilepsies - EpiCARE), Bologna, Italy
| | - E Pasini
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Unit of Neurology, Bellaria Hospital, Bologna, Italy
| | - P Tinuper
- Department of Biomedical and NeuroMotor Sciences (DIBINEM), University of Bologna, Bologna, Italy.,IRCCS Istituto delle Scienze Neurologiche di Bologna, Epilepsy Center (Reference Center for Rare and Complex Epilepsies - EpiCARE), Bologna, Italy
| | - L Di Vito
- Department of Biomedical and NeuroMotor Sciences (DIBINEM), University of Bologna, Bologna, Italy.,IRCCS Istituto delle Scienze Neurologiche di Bologna, Epilepsy Center (Reference Center for Rare and Complex Epilepsies - EpiCARE), Bologna, Italy
| | - G Gessaroli
- Neurology Unit, OCB Hospital, Azienda Ospedaliero-Universitaria of Modena, Via Giardini 1355, 41100, Modena, Italy
| | - M Malagoli
- Neuroradiology Unit, OCB Hospital, Azienda Ospedaliero-Universitaria of Modena, Modena, Italy
| | - G Pavesi
- Center for Neuroscience and Neurotechnology, Department of Biomedical, Metabolic, and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy.,Neurosurgery Unit, OCB Hospital, Azienda Ospedaliero-Universitaria of Modena, Modena, Italy
| | - F Cardinale
- "Claudio Munari" Epilepsy Surgery Center, Niguarda Hospital, Milan, Italy
| | - L Tassi
- "Claudio Munari" Epilepsy Surgery Center, Niguarda Hospital, Milan, Italy
| | - L Lemieux
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
| | - S Meletti
- Neurology Unit, OCB Hospital, Azienda Ospedaliero-Universitaria of Modena, Via Giardini 1355, 41100, Modena, Italy.,Center for Neuroscience and Neurotechnology, Department of Biomedical, Metabolic, and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
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17
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Fürbass F, Koren J, Hartmann M, Brandmayr G, Hafner S, Baumgartner C. Activation patterns of interictal epileptiform discharges in relation to sleep and seizures: An artificial intelligence driven data analysis. Clin Neurophysiol 2021; 132:1584-1592. [PMID: 34030056 DOI: 10.1016/j.clinph.2021.03.052] [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: 11/30/2020] [Revised: 03/11/2021] [Accepted: 03/26/2021] [Indexed: 10/21/2022]
Abstract
OBJECTIVE To quantify effects of sleep and seizures on the rate of interictal epileptiform discharges (IED) and to classify patients with epilepsy based on IED activation patterns. METHODS We analyzed long-term EEGs from 76 patients with at least one recorded epileptic seizure during monitoring. IEDs were detected with an AI-based algorithm and validated by visual inspection. We then used unsupervised clustering to characterize patient sub-cohorts with similar IED activation patterns regarding circadian rhythms, deep sleep activation, and seizure occurrence. RESULTS Five sub-cohorts with similar IED activation patterns were found: "Sporadic" (14%, n = 10) without or few IEDs, "Continuous" (32%, n = 23) with weak circadian/deep sleep or seizure modulation, "Nighttime & seizure activation" (23%, n = 17) with high IED rates during normal sleep times and after seizures but without deep sleep modulation, "Deep sleep" (19%, n = 14) with strong IED modulation during deep sleep, and "Seizure deactivation" (12%, n = 9) with deactivation of IEDs after seizures. Patients showing "Deep sleep" IED pattern were diagnosed with temporal lobe epilepsy in 86%, while 80% of the "Sporadic" cluster were extratemporal. CONCLUSIONS Patients with epilepsy can be characterized by using temporal relationships between rates of IEDs, circadian rhythms, deep sleep and seizures. SIGNIFICANCE This work presents the first approach to data-driven classification of epilepsy patients based on their fully validated temporal pattern of IEDs.
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Affiliation(s)
- Franz Fürbass
- Center for Health & Bioresources, AIT Austrian Institute of Technology GmbH, Vienna, Austria.
| | - Johannes Koren
- Department of Neurology, Clinic Hietzing, Vienna, Austria; Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology, Vienna, Austria
| | - Manfred Hartmann
- Center for Health & Bioresources, AIT Austrian Institute of Technology GmbH, Vienna, Austria
| | - Georg Brandmayr
- Center for Health & Bioresources, AIT Austrian Institute of Technology GmbH, Vienna, Austria; Institute of Artificial Intelligence & Decision Support, Medical University Vienna, Vienna, Austria
| | | | - Christoph Baumgartner
- Department of Neurology, Clinic Hietzing, Vienna, Austria; Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology, Vienna, Austria; Medical Faculty, Sigmund Freud University, Vienna, Austria
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18
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Kural MA, Qerama E, Johnsen B, Fuchs S, Beniczky S. The influence of the abundance and morphology of epileptiform discharges on diagnostic accuracy: How many spikes you need to spot in an EEG. Clin Neurophysiol 2021; 132:1543-1549. [PMID: 34030055 DOI: 10.1016/j.clinph.2021.03.045] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 02/04/2021] [Accepted: 03/04/2021] [Indexed: 11/16/2022]
Abstract
OBJECTIVE The operational definition of interictal epileptiform discharges (IEDs) of the International Federation of Clinical Neurophysiology (IFCN) described six morphological criteria. Our objective was to assess the impact of pattern-repetition in the EEG-recording, on the diagnostic accuracy of using the IFCN criteria. For clinical implementation, specificity over 95% was set as target. METHODS Interictal EEG-recordings of 20-minutes, containing sharp-transients, from 60 patients (30 with epilepsy and 30 with non-epileptic paroxysmal events) were evaluated by three experts, who first marked IEDs solely based on expert opinion, and then, independently from the first session evaluated the presence of the IFCN criteria for each sharp-transient. The gold standard was derived from long-term video-EEG recordings of the patientś habitual paroxysmal episodes. RESULTS Presence of at least one discharge fulfilling five criteria provided a specificity of 100% (sensitivity: 70%). For discharges fulfilling fewer criteria, a higher number of discharges was needed to keep the specificity over 95% (5 discharges, when only 3 criteria were fulfilled). A sequential combination of these sets of criteria and thresholds provided a specificity of 97% and sensitivity of 80%. CONCLUSIONS Pattern-repetition and IED morphology influence diagnostic accuracy. SIGNIFICANCE Systematic application of these criteria will improve quality of clinical EEG interpretation.
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Affiliation(s)
- Mustafa Aykut Kural
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
| | - Erisela Qerama
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
| | - Birger Johnsen
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
| | - Steffen Fuchs
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
| | - Sándor Beniczky
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund, Denmark; and Department of Clinical Medicine, Aarhus University, Denmark.
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19
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Mitsuhashi T, Sonoda M, Jeong JW, Silverstein BH, Iwaki H, Luat AF, Sood S, Asano E. Four-dimensional tractography animates propagations of neural activation via distinct interhemispheric pathways. Clin Neurophysiol 2020; 132:520-529. [PMID: 33450573 DOI: 10.1016/j.clinph.2020.11.030] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 11/16/2020] [Accepted: 11/27/2020] [Indexed: 12/23/2022]
Abstract
OBJECTIVE To visualize and validate the dynamics of interhemispheric neural propagations induced by single-pulse electrical stimulation (SPES). METHODS This methodological study included three patients with drug-resistant focal epilepsy who underwent measurement of cortico-cortical spectral responses (CCSRs) during bilateral stereo-electroencephalography recording. We delivered SPES to 83 electrode pairs and analyzed CCSRs recorded at 268 nonepileptic electrode sites. Diffusion-weighted imaging (DWI) tractography localized the interhemispheric white matter pathways as streamlines directly connecting two electrode sites. We localized and visualized the putative SPES-related fiber activation, at each 1-ms time window, based on the propagation velocity defined as the DWI-based streamline length divided by the early CCSR peak latency. RESULTS The resulting movie, herein referred to as four-dimensional tractography, delineated the spatiotemporal dynamics of fiber activation via the corpus callosum and anterior commissure. Longer streamline length was associated with delayed peak latency and smaller amplitude of CCSRs. The cortical regions adjacent to each fiber activation site indeed exhibited CCSRs at the same time window. CONCLUSIONS Our four-dimensional tractography successfully animated neural propagations via distinct interhemispheric pathways. SIGNIFICANCE Our novel animation method has the potential to help investigators in addressing the mechanistic significance of the interhemispheric network dynamics supporting physiological function.
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Affiliation(s)
- Takumi Mitsuhashi
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA; Department of Neurosurgery, Juntendo University, Tokyo, 1138421, Japan
| | - Masaki Sonoda
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA; Department of Neurosurgery, Yokohama City University, Yokohama, 2360004, Japan
| | - Jeong-Won Jeong
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA; Department of Neurology, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
| | - Brian H Silverstein
- Translational Neuroscience Program, Wayne State University, Detroit, MI 48202, USA
| | - Hirotaka Iwaki
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA; Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai, 9808575, Japan
| | - Aimee F Luat
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA; Department of Neurology, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
| | - Sandeep Sood
- Department of Neurosurgery, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
| | - Eishi Asano
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA; Department of Neurology, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA.
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20
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Mitsuhashi T, Sonoda M, Iwaki H, Luat AF, Sood S, Asano E. Effects of depth electrode montage and single-pulse electrical stimulation sites on neuronal responses and effective connectivity. Clin Neurophysiol 2020; 131:2781-2792. [PMID: 33130438 DOI: 10.1016/j.clinph.2020.09.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 08/05/2020] [Accepted: 09/08/2020] [Indexed: 10/23/2022]
Abstract
OBJECTIVE To determine the optimal depth electrode montages for the assessment of effective connectivity based on single-pulse electrical stimulation (SPES). To determine the effect of SPES locations on the extent of resulting neuronal propagations. METHODS We studied 14 epilepsy patients who underwent invasive monitoring with depth electrodes and measurement of cortico-cortical evoked potentials (CCEPs) and cortico-cortical spectral responses (CCSRs). We determined the effects of electrode montage and stimulus sites on the CCEP/CCSR amplitudes. RESULTS Bipolar and Laplacian montages effectively reduced the degree of SPES-related signal deflections at extra-cortical levels, including outside the brain, while maintaining those at the cortical level. SPES of structures more proximal to the deep white matter, compared to the cortical surface, elicited greater CCEPs and CCSRs. CONCLUSIONS On depth electrode recording, bipolar and Laplacian montages are suitable for measurement of near-field CCEPs and CCSRs. SPES of the white matter axons may induce neuronal propagations to extensive regions of the cerebral cortex. SIGNIFICANCE This study helps to establish the practical guidelines on the diagnostic use of CCEPs/CCSRs.
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Affiliation(s)
- Takumi Mitsuhashi
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA; Department of Neurosurgery, Juntendo University, Tokyo 1138421, Japan
| | - Masaki Sonoda
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA; Department of Neurosurgery, Yokohama City University, Yokohama 2360004, Japan
| | - Hirotaka Iwaki
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA; Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai 9808575, Japan
| | - Aimee F Luat
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA; Department of Neurology, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
| | - Sandeep Sood
- Department of Neurosurgery, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
| | - Eishi Asano
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA; Department of Neurology, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA; Translational Neuroscience Program, Wayne State University, Detroit, MI 48202, USA.
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