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Tsereteli A, Okujava N, Malashkhia N, Liluashvili K, de Weerd A. The ENCEVIS algorithm in the EMU and the factors affecting its performance: Our experience. Epilepsy Behav Rep 2024; 26:100656. [PMID: 38495403 PMCID: PMC10937301 DOI: 10.1016/j.ebr.2024.100656] [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: 12/27/2023] [Revised: 02/26/2024] [Accepted: 03/03/2024] [Indexed: 03/19/2024] Open
Abstract
The study's purpose was to assess the seizure detection performance of ENCEVIS 1.7, identify factors that may influence algorithm performance, and explore its potential for implementation and application in long-term video EEG monitoring units. The study included video-EEG recordings containing at least one epileptic seizure. Forty-three recordings, encompassing 112 seizures, were included in the analysis. True positive, false negative, and false positive seizure detections were defined. Factors that may influence algorithm performance were studied. ENCEVIS demonstrated an overall sensitivity of 71.2%, significantly higher (75.1%) in focal compared to generalized seizures (62%). Ictal patterns rhythmicity (rhythmic 59.4 %, arrhythmic 41.7 %), seizure duration (<10 sec 6.3 %, >60 sec. 63.9 % (p < 0.05)) and patient age (<18 years 39.5 %, >18 years 58.1 % (P < 0.05)) influenced ENCEVIS sensitivity. The coexistence of extracerebral signal changes did not influence sensitivity. ENCEVIS with 79.1% accuracy annotates at least one seizure in those recordings containing epileptic seizures. ENCEVIS seizure detection performance was reasonable for generalized/focal to bilateral tonic-clonic seizures and seizures with temporal lobe onset. Rhythmic ictal patterns, longer seizure duration, and adult age positively influenced algorithm performance. ENCEVIS can be a valuable tool for identifying recordings containing seizures and can potentially reduce the workload of neurophysiologists.
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Affiliation(s)
- Aleksandre Tsereteli
- Epilepsy and Sleep Centre, S. Khechinashvili University Hospital (SKUH), Georgia
| | - Natela Okujava
- Epilepsy and Sleep Centre, S. Khechinashvili University Hospital (SKUH), Georgia
- Department of Clinical Neurology, Tbilisi State Medical University (TSMU), Georgia
| | - Nikoloz Malashkhia
- Epilepsy and Sleep Centre, S. Khechinashvili University Hospital (SKUH), Georgia
| | | | - Al de Weerd
- Stichting Epilepsie Instellingen Nederland (SEIN), Zwolle, Netherland
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2
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Castillo Rodriguez MDLA, Brandt A, Schulze-Bonhage A. Differentiation of subclinical and clinical electrographic events in long-term electroencephalographic recordings. Epilepsia 2023; 64 Suppl 4:S47-S58. [PMID: 36008142 DOI: 10.1111/epi.17401] [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: 02/18/2022] [Revised: 08/22/2022] [Accepted: 08/22/2022] [Indexed: 11/28/2022]
Abstract
OBJECTIVE With the advent of ultra-long-term recordings for monitoring of epilepsies, the interpretation of results of isolated electroencephalographic (EEG) recordings covering only selected brain regions attracts considerable interest. In this context, the question arises of whether detected ictal EEG patterns correspond to clinically manifest seizures or rather to purely electrographic events, that is, subclinical events. METHODS EEG patterns from 268 clinical seizures and 252 subclinical electrographic events from 50 patients undergoing video-EEG monitoring were analyzed. Features extracted included predominant frequency band, duration, association with rhythmic muscle artifacts, spatial extent, and propagation patterns. Classification using logistic regression was performed based on data from the whole dataset of 10-20 system EEG recordings and from a subset of two temporal electrode contacts. RESULTS Correct separation of clinically manifest and purely electrographic events based on 10-20 system EEG recordings was possible in up to 83.8% of events, depending on the combination of features included. Correct classification based on two-channel recordings was only slightly inferior, achieving 78.6% accuracy; 74.4% and 74.8%, respectively, of events could be correctly classified when using duration alone with either electrode set, although classification accuracies were lower for some subgroups of seizures, particularly focal aware seizures and epileptic arousals. SIGNIFICANCE A correct classification of subclinical versus clinical EEG events was possible in 74%-83% of events based on full EEG recordings, and in 74%-78% when considering only a subset of two electrodes, matching the channel number available from new implantable diagnostic devices. This is a promising outcome, suggesting that ultra-long-term low-channel EEG recordings may provide sufficient information for objective seizure diaries. Intraindividual optimization using high numbers of ictal events may further improve separation, provided that supervised learning with external validation is feasible.
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Affiliation(s)
| | - Armin Brandt
- Epilepsy Center, University Medical Center Freiburg, Freiburg, Germany
| | - Andreas Schulze-Bonhage
- Epilepsy Center, University Medical Center Freiburg, Freiburg, Germany
- Spemann Graduate School of Biology and Medicine, Freiburg, Germany
- European Reference Network EpiCare, Freiburg, Germany
- NeuroModulBasic, Freiburg, Germany
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3
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Remvig LS, Duun-Henriksen J, Fürbass F, Hartmann M, Viana PF, Kappel Overby AM, Weisdorf S, Richardson MP, Beniczky S, Kjaer TW. Detecting temporal lobe seizures in ultra long-term subcutaneous EEG using algorithm-based data reduction. Clin Neurophysiol 2022; 142:86-93. [PMID: 35987094 DOI: 10.1016/j.clinph.2022.07.504] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 06/10/2022] [Accepted: 07/03/2022] [Indexed: 11/03/2022]
Abstract
OBJECTIVE Ultra long-term monitoring with subcutaneous EEG (sqEEG) offers objective outpatient recording of electrographic seizures as an alternative to self-reported epileptic seizure diaries. This methodology requires an algorithm-based automatic seizure detection to indicate periods of potential seizure activity to reduce the time spent on visual review. The objective of this study was to evaluate the performance of a sqEEG-based automatic seizure detection algorithm. METHODS A multicenter cohort of subjects using sqEEG were analyzed, including nine people with epilepsy (PWE) and 12 healthy subjects, recording a total of 965 days. The automatic seizure detections of a deep-neural-network algorithm were compared to annotations from three human experts. RESULTS Data reduction ratios were 99.6% in PWE and 99.9% in the control group. The cross-PWE sensitivity was 86% (median 80%, range 69-100% when PWE were evaluated individually), and the corresponding median false detection rate was 2.4 detections per 24 hours (range: 2.0-13.0). CONCLUSIONS Our findings demonstrated that step one in a sqEEG-based semi-automatic seizure detection/review process can be performed with high sensitivity and clinically applicable specificity. SIGNIFICANCE Ultra long-term sqEEG bears the potential of improving objective seizure quantification.
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Affiliation(s)
- Line S Remvig
- UNEEG Medical A/S, Borupvang 2, DK-3450 Allerød, Denmark.
| | | | - Franz Fürbass
- Center for Health & Bioresources, AIT Austrian Institute of Technology GmbH, Giefinggasse 4, 1210 Vienna, Austria
| | - Manfred Hartmann
- Center for Health & Bioresources, AIT Austrian Institute of Technology GmbH, Giefinggasse 4, 1210 Vienna, Austria
| | - Pedro F Viana
- Department of Basic & Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King's College London, Denmark Hill, London, UK; Faculty of Medicine, University of Lisbon, Av. Prof. Egas Moniz MB, 1649-028 Lisboa, Portugal
| | | | - Sigge Weisdorf
- Center of Neurophysiology, Department Neurology, Zealand University Hospital, Sygehusvej 10, DK-4000 Roskilde, Denmark
| | - Mark P Richardson
- Department of Basic & Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King's College London, Denmark Hill, London, UK
| | - Sándor Beniczky
- The Danish Epilepsy Centre, Filadelfia, Kolonivej 1, 4293 Dianalund, Denmark; Aarhus University and Aarhus University Hospital, Palle Juul-Jensens Blvd 99, DK-8200 Aarhus, Denmark
| | - Troels W Kjaer
- Center of Neurophysiology, Department Neurology, Zealand University Hospital, Sygehusvej 10, DK-4000 Roskilde, Denmark; Department of Clinical Medicine, University of Copenhagen, Blegdamsvej 3b, DK.2200 Copenhagen, Denmark
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Safety Evaluation of Employing Temporal Interference Transcranial Alternating Current Stimulation in Human Studies. Brain Sci 2022; 12:brainsci12091194. [PMID: 36138930 PMCID: PMC9496688 DOI: 10.3390/brainsci12091194] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 08/31/2022] [Accepted: 09/02/2022] [Indexed: 01/20/2023] Open
Abstract
Temporal interference transcranial alternating current stimulation (TI-tACS) is a new technique of noninvasive brain stimulation. Previous studies have shown the effectiveness of TI-tACS in stimulating brain areas in a selective manner. However, its safety in modulating human brain neurons is still untested. In this study, 38 healthy adults were recruited to undergo a series of neurological and neuropsychological measurements regarding safety concerns before and after active (2 mA, 20/70 Hz, 30 min) or sham (0 mA, 0 Hz, 30 min) TI-tACS. The neurological and neuropsychological measurements included electroencephalography (EEG), serum neuron-specific enolase (NSE), the Montreal Cognitive Assessment (MoCA), the Purdue Pegboard Test (PPT), an abbreviated version of the California Computerized Assessment Package (A-CalCAP), a revised version of the Visual Analog Mood Scale (VAMS-R), a self-assessment scale (SAS), and a questionnaire about adverse effects (AEs). We found no significant difference between the measurements of the active and sham TI-tACS groups. Meanwhile, no serious or intolerable adverse effects were reported or observed in the active stimulation group of 19 participants. These results support that TI-tACS is safe and tolerable in terms of neurological and neuropsychological functions and adverse effects for use in human brain stimulation studies under typical transcranial electric stimulation (TES) conditions (2 mA, 20/70 Hz, 30 min).
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Yang Y, Truong ND, Eshraghian JK, Maher C, Nikpour A, Kavehei O. A multimodal AI system for out-of-distribution generalization of seizure identification. IEEE J Biomed Health Inform 2022; 26:3529-3538. [PMID: 35263265 DOI: 10.1109/jbhi.2022.3157877] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Artificial intelligence (AI) and health sensory data-fusion hold the potential to automate many laborious and time-consuming processes in hospitals or ambulatory settings, e.g. home monitoring and telehealth. One such unmet challenge is rapid and accurate epileptic seizure annotation. An accurate and automatic approach can provide an alternative way to label seizures in epilepsy or deliver a substitute for inaccurate patient self-reports. Multimodal sensory fusion is believed to provide an avenue to improve the performance of AI systems in seizure identification. We propose a state-of-the-art performing AI system that combines electroencephalogram (EEG) and electrocardiogram (ECG) for seizure identification, tested on clinical data with early evidence demonstrating generalization across hospitals. The model was trained and validated on the publicly available Temple University Hospital (TUH) dataset. To evaluate performance in a clinical setting, we conducted non-patient-specific pseudo-prospective inference tests on three out-of-distribution datasets, including EPILEPSIAE (30 patients) and the Royal Prince Alfred Hospital (RPAH) in Sydney, Australia (31 neurologists-shortlisted patients and 30 randomly selected). Our multimodal approach improves the area under the receiver operating characteristic curve (AUC-ROC) by an average margin of 6.71% and 14.42% for deep learning techniques using EEG-only and ECG-only, respectively. Our model's state-of-the-art performance and robustness to out-of-distribution datasets show the accuracy and efficiency necessary to improve epilepsy diagnoses. To the best of our knowledge, this is the first pseudo-prospective study of an AI system combining EEG and ECG modalities for automatic seizure annotation achieved with fusion of two deep learning networks.
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Elezi L, Koren JP, Pirker S, Baumgartner C. Automatic seizure detection and seizure pattern morphology. Clin Neurophysiol 2022; 138:214-220. [DOI: 10.1016/j.clinph.2022.02.027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 02/09/2022] [Accepted: 02/18/2022] [Indexed: 11/28/2022]
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Munch Nielsen J, Zibrandtsen IC, Masulli P, Lykke Sørensen T, Andersen TS, Wesenberg Kjær T. Towards a wearable multi-modal seizure detection system in epilepsy: a pilot study. Clin Neurophysiol 2022; 136:40-48. [DOI: 10.1016/j.clinph.2022.01.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 01/11/2022] [Accepted: 01/13/2022] [Indexed: 11/30/2022]
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Nielsen JM, Rades D, Kjaer TW. Wearable electroencephalography for ultra-long-term seizure monitoring: a systematic review and future prospects. Expert Rev Med Devices 2021; 18:57-67. [PMID: 34836477 DOI: 10.1080/17434440.2021.2012152] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
INTRODUCTION : Wearable electroencephalography (EEG) for objective seizure counting might transform the clinical management of epilepsy. Non-EEG modalities have been validated for the detection of convulsive seizures, but there is still an unmet need for the detection of non-convulsive seizures. AREAS COVERED : The main objective of this systematic review was to explore the current status on wearable surface- and subcutaneous EEG for long-term seizure monitoring in epilepsy. We included 17 studies and evaluated the progress on the field, including device specifications, intended populations, and main results on the published studies including diagnostic accuracy measures. Furthermore, we examine the hurdles for widespread clinical implementation. This systematic review and expert opinion both consults the PRISMA guidelines and reflects on the future perspectives of this emerging field. EXPERT OPINION : Wearable EEG for long-term seizure monitoring is an emerging field, with plenty of proposed devices and proof-of-concept clinical validation studies. The possible implications of these devices are immense including objective seizure counting and possibly forecasting. However, the true clinical value of the devices, including effects on patient important outcomes and clinical decision making is yet to be unveiled and large-scale clinical validation trials are called for.
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Affiliation(s)
- Jonas Munch Nielsen
- Department of Neurology, Zealand University Hospital, Region Sjælland. Vestermarksvej 11, 4000 Roskilde, Denmark.,Department of Clinical Medicine, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen N, Denmark
| | - Dirk Rades
- Department of Radiation Oncology, University of Lübeck, Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Troels Wesenberg Kjaer
- Department of Neurology, Zealand University Hospital, Region Sjælland. Vestermarksvej 11, 4000 Roskilde, Denmark.,Department of Clinical Medicine, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen N, Denmark
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Böttcher S, Bruno E, Manyakov NV, Epitashvili N, Claes K, Glasstetter M, Thorpe S, Lees S, Dümpelmann M, Van Laerhoven K, Richardson MP, Schulze-Bonhage A. Detecting Tonic-Clonic Seizures in Multimodal Biosignal Data From Wearables: Methodology Design and Validation. JMIR Mhealth Uhealth 2021; 9:e27674. [PMID: 34806993 PMCID: PMC8663471 DOI: 10.2196/27674] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 07/23/2021] [Accepted: 09/03/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Video electroencephalography recordings, routinely used in epilepsy monitoring units, are the gold standard for monitoring epileptic seizures. However, monitoring is also needed in the day-to-day lives of people with epilepsy, where video electroencephalography is not feasible. Wearables could fill this gap by providing patients with an accurate log of their seizures. OBJECTIVE Although there are already systems available that provide promising results for the detection of tonic-clonic seizures (TCSs), research in this area is often limited to detection from 1 biosignal modality or only during the night when the patient is in bed. The aim of this study is to provide evidence that supervised machine learning can detect TCSs from multimodal data in a new data set during daytime and nighttime. METHODS An extensive data set of biosignals from a multimodal watch worn by people with epilepsy was recorded during their stay in the epilepsy monitoring unit at 2 European clinical sites. From a larger data set of 243 enrolled participants, those who had data recorded during TCSs were selected, amounting to 10 participants with 21 TCSs. Accelerometry and electrodermal activity recorded by the wearable device were used for analysis, and seizure manifestation was annotated in detail by clinical experts. Ten accelerometry and 3 electrodermal activity features were calculated for sliding windows of variable size across the data. A gradient tree boosting algorithm was used for seizure detection, and the optimal parameter combination was determined in a leave-one-participant-out cross-validation on a training set of 10 seizures from 8 participants. The model was then evaluated on an out-of-sample test set of 11 seizures from the remaining 2 participants. To assess specificity, we additionally analyzed data from up to 29 participants without TCSs during the model evaluation. RESULTS In the leave-one-participant-out cross-validation, the model optimized for sensitivity could detect all 10 seizures with a false alarm rate of 0.46 per day in 17.3 days of data. In a test set of 11 out-of-sample TCSs, amounting to 8.3 days of data, the model could detect 10 seizures and produced no false positives. Increasing the test set to include data from 28 more participants without additional TCSs resulted in a false alarm rate of 0.19 per day in 78 days of wearable data. CONCLUSIONS We show that a gradient tree boosting machine can robustly detect TCSs from multimodal wearable data in an original data set and that even with very limited training data, supervised machine learning can achieve a high sensitivity and low false-positive rate. This methodology may offer a promising way to approach wearable-based nonconvulsive seizure detection.
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Affiliation(s)
- Sebastian Böttcher
- Epilepsy Center, Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany.,Ubiquitous Computing, Department of Electrical Engineering and Computer Science, University of Siegen, Siegen, Germany
| | - Elisa Bruno
- Division of Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Nikolay V Manyakov
- Data Science Analytics & Insights, Janssen Research & Development, Beerse, Belgium
| | - Nino Epitashvili
- Epilepsy Center, Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany
| | | | - Martin Glasstetter
- Epilepsy Center, Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany
| | - Sarah Thorpe
- The RADAR-CNS Patient Advisory Board, King's College London, London, United Kingdom
| | - Simon Lees
- The RADAR-CNS Patient Advisory Board, King's College London, London, United Kingdom
| | - Matthias Dümpelmann
- Epilepsy Center, Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany
| | - Kristof Van Laerhoven
- Ubiquitous Computing, Department of Electrical Engineering and Computer Science, University of Siegen, Siegen, Germany
| | - Mark P Richardson
- Division of Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.,National Institute of Health Research Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany
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- see Acknowledgements, London, United Kingdom
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10
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Zhou Y, You J, Zhu F, Bragin A, Engel J, Li L. Automatic Electrophysiological Noise Reduction and Epileptic Seizure Detection for Stereoelectroencephalography. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:107-112. [PMID: 34891250 DOI: 10.1109/embc46164.2021.9630651] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The objective of this study was to develop a computational algorithm capable of locating artifacts and identifying epileptic seizures, which specifically implementing in clinical stereoelectroencephalography (SEEG) recordings. Based on the nonstationary nature and broadband features of SEEG signals, a comprehensive strategy combined with the complex wavelet transform (CWT) and multi-layer thresholding method was implemented for both noise reduction and seizure detection. The artifacts removal pipeline integrated edge artifact removal, discrete spectrum analysis, and peak density evaluation. For automatic seizure detection, integrated power analysis and multi-dynamic thresholding were applied. The F1score was applied to evaluate overall performance of the algorithm. The algorithm was tested using expert-marked, double-blinded, clinical SEEG data from seven patients undergoing presurgical evaluation. This approach achieved the F1 score of 0.86 for noise reduction and 0.88 for seizure detection. This offline-approach method with minimum parameter tuning procedures and no prior information required, proved to be a feasible and solid solution for clinical SEEG data evaluation. Moreover, the algorithm can be improved with additional tuning and implemented with machine learning postprocessing pipelines.
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11
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Vandecasteele K, De Cooman T, Chatzichristos C, Cleeren E, Swinnen L, Macea Ortiz J, Van Huffel S, Dümpelmann M, Schulze-Bonhage A, De Vos M, Van Paesschen W, Hunyadi B. The power of ECG in multimodal patient-specific seizure monitoring: Added value to an EEG-based detector using limited channels. Epilepsia 2021; 62:2333-2343. [PMID: 34240748 PMCID: PMC8518059 DOI: 10.1111/epi.16990] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 06/17/2021] [Accepted: 06/17/2021] [Indexed: 11/27/2022]
Abstract
Objective Wearable seizure detection devices could provide more reliable seizure documentation outside the hospital compared to seizure self‐reporting by patients, which is the current standard. Previously, during the SeizeIT1 project, we studied seizure detection based on behind‐the‐ear electroencephalography (EEG). However, the obtained sensitivities were too low for practical use, because not all seizures are associated with typical ictal EEG patterns. Therefore, in this paper, we aim to develop a multimodal automated seizure detection algorithm integrating behind‐the‐ear EEG and electrocardiography (ECG) for detecting focal seizures. In this framework, we quantified the added value of ECG to behind‐the‐ear EEG. Methods This study analyzed three multicenter databases consisting of 135 patients having focal epilepsy and a total of 896 seizures. A patient‐specific multimodal automated seizure detection algorithm was developed using behind‐the‐ear/temporal EEG and single‐lead ECG. The EEG and ECG data were processed separately using machine learning methods. A late integration approach was applied for fusing those predictions. Results The multimodal algorithm outperformed the EEG‐based algorithm in two of three databases, with an increase of 11% and 8% in sensitivity for the same false alarm rate. Significance ECG can be of added value to an EEG‐based seizure detection algorithm using only behind‐the‐ear/temporal lobe electrodes for patients with focal epilepsy.
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Affiliation(s)
- Kaat Vandecasteele
- Department of Electrical Engineering, STADIUS Center for Dynamic Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - Thomas De Cooman
- Department of Electrical Engineering, STADIUS Center for Dynamic Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - Christos Chatzichristos
- Department of Electrical Engineering, STADIUS Center for Dynamic Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - Evy Cleeren
- Department of Neurology, University Hospitals Leuven, Leuven, Belgium
| | - Lauren Swinnen
- Laboratory for Epilepsy Research, Department of Neurology, University Hospital, KU Leuven, Leuven, Belgium
| | - Jaiver Macea Ortiz
- Laboratory for Epilepsy Research, Department of Neurology, University Hospital, KU Leuven, Leuven, Belgium
| | - Sabine Van Huffel
- Department of Electrical Engineering, STADIUS Center for Dynamic Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - Matthias Dümpelmann
- Faculty of Medicine, Department of Neurosurgery, Epilepsy Center, University of Freiburg, Freiburg, Germany
| | - Andreas Schulze-Bonhage
- Faculty of Medicine, Department of Neurosurgery, Epilepsy Center, University of Freiburg, Freiburg, Germany
| | - Maarten De Vos
- Department of Electrical Engineering, STADIUS Center for Dynamic Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium.,Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Wim Van Paesschen
- Laboratory for Epilepsy Research, Department of Neurology, University Hospital, KU Leuven, Leuven, Belgium
| | - Borbála Hunyadi
- Department of Microelectronics, TU Delft, Delft, Netherlands
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12
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Bruno E, Biondi A, Richardson MP, Consortium OBOTRC. Digital semiology and time-evolution pattern of bio-signals in focal onset motor seizures. Seizure 2021; 87:114-120. [PMID: 33773333 DOI: 10.1016/j.seizure.2021.03.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 03/11/2021] [Accepted: 03/14/2021] [Indexed: 11/25/2022] Open
Abstract
PURPOSE Focal seizures constitute the most common seizure type and are associated with poor control. One of the major difficulties in detecting focal onset with wearable devices seizures is related to their phenomenological complexity. We aimed at capturing focal onset seizures with motor manifestations with a multimodal wearable device to identify the digital semiology and the evolution pattern of ictal manifestations. METHODS Participants were asked to wear a multimodal wearable device (IMEC) aimed at seizure detection while admitted to an epilepsy monitoring unit. Seizures were labelled by a neurologist and start and offset time were noted. The signals captured by the device during the seizure window were plotted and a visual inspection was performed for focal motor seizures with impaired awareness and for focal motor aware seizures. RESULTS Fifty-three seizures from twelve patients with focal seizures with motor manifestations recorded with the device were visually inspected. Overall, a common pattern presented across focal motor seizures with impaired awareness and it was characterized by early cardiac manifestations followed by motor phenomena and final EDA response. Motor seizures with retained awareness appeared to be characterized by brief motor events not associated with major autonomic manifestations Conclusion: an overall common digital phenotype and time-evolution pattern was demonstrated for focal motor seizures with impaired awareness. The identification of the evolution pattern could more precisely inform the development of highly preforming algorithms opening the possibility to a more precise, and potentially customizable way to optimize focal seizure detection.
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Affiliation(s)
- Elisa Bruno
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.
| | - Andrea Biondi
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Mark P Richardson
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
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Koren J, Hafner S, Feigl M, Baumgartner C. Systematic analysis and comparison of commercial seizure-detection software. Epilepsia 2021; 62:426-438. [PMID: 33464580 DOI: 10.1111/epi.16812] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 12/19/2020] [Accepted: 12/22/2020] [Indexed: 12/19/2022]
Abstract
OBJECTIVE To determine if three different commercially available seizure-detection software packages (Besa 2.0, Encevis 1.7, and Persyst 13) accurately detect seizures with high sensitivity, high specificity, and short detection delay in epilepsy patients undergoing long-term video-electroencephalography (EEG) monitoring (VEM). METHODS Comparison of sensitivity (detection rate), specificity (false alarm rate), and detection delay of three commercially available seizure-detection software packages in 81 randomly selected patients with epilepsy undergoing long-term VEM. RESULTS Detection rates on a per-patient basis were not significantly different between Besa (mean 67.6%, range 0-100%), Encevis (77.8%, 0-100%) and Persyst (81%, 0-100%; P = .059). False alarm rate (per hour) was significantly different between Besa (mean 0.7/h, range 0.01-6.2/h), Encevis (0.2/h, 0.01-0.5/h), and Persyst (0.9/h, 0.04-6.5/h; P < .001). Detection delay was significantly different between Besa (mean 30 s, range 0-431 s), Encevis (25 s, 2-163 s), and Persyst (20 s, 0-167 s; P = .007). Kappa statistics showed moderate to substantial agreement between the reference standard and each seizure-detection software (Besa: 0.47, 95% confidence interval [CI] 0.36-0.59; Encevis: 0.59, 95% CI 0.47-0.7; Persyst: 0.63, 95% CI 0.51-0.74). SIGNIFICANCE Three commercially available seizure-detection software packages showed similar, reasonable sensitivities on the same data set, but differed in false alarm rates and detection delay. Persyst 13 showed the highest detection rate and false alarm rate with the shortest detection delay, whereas Encevis 1.7 had a slightly lower sensitivity, the lowest false alarm rate, and longer detection delay.
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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
| | | | - Moritz Feigl
- Department of Medicine I, Institute of Cancer Research, Medical University of Vienna, Austria.,Institute for Hydrology and Water Management, University of Natural Resources and Life Sciences, 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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14
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Comparison of Frontal-Temporal Channels in Epilepsy Seizure Prediction Based on EEMD-ReliefF and DNN. COMPUTERS 2020. [DOI: 10.3390/computers9040078] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Epilepsy patients who do not have their seizures controlled with medication or surgery live in constant fear. The psychological burden of uncertainty surrounding the occurrence of random seizures is one of the most stressful and debilitating aspects of the disease. Despite the research progress in this field, there is a need for a non-invasive prediction system that helps disrupt the seizure epileptiform. Electroencephalogram (EEG) signals are non-stationary, nonlinear and vary with each patient and every recording. Full use of the non-invasive electrode channels is impractical for real-time use. We propose two frontal-temporal electrode channels based on ensemble empirical mode decomposition (EEMD) and Relief methods to address these challenges. The EEMD decomposes the segmented data frame in the ictal state into its intrinsic mode functions, and then we apply Relief to select the most relevant oscillatory components. A deep neural network (DNN) model learns these features to perform seizure prediction and early detection of patient-specific EEG recordings. The model yields an average sensitivity and specificity of 86.7% and 89.5%, respectively. The two-channel model shows the ability to capture patterns from brain locations for non-fontal-temporal seizures.
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15
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Nasseri M, Nurse E, Glasstetter M, Böttcher S, Gregg NM, Laks Nandakumar A, Joseph B, Pal Attia T, Viana PF, Bruno E, Biondi A, Cook M, Worrell GA, Schulze-Bonhage A, Dümpelmann M, Freestone DR, Richardson MP, Brinkmann BH. Signal quality and patient experience with wearable devices for epilepsy management. Epilepsia 2020; 61 Suppl 1:S25-S35. [PMID: 32497269 DOI: 10.1111/epi.16527] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 04/14/2020] [Accepted: 04/14/2020] [Indexed: 01/24/2023]
Abstract
Noninvasive wearable devices have great potential to aid the management of epilepsy, but these devices must have robust signal quality, and patients must be willing to wear them for long periods of time. Automated machine learning classification of wearable biosensor signals requires quantitative measures of signal quality to automatically reject poor-quality or corrupt data segments. In this study, commercially available wearable sensors were placed on patients with epilepsy undergoing in-hospital or in-home electroencephalographic (EEG) monitoring, and healthy volunteers. Empatica E4 and Biovotion Everion were used to record accelerometry (ACC), photoplethysmography (PPG), and electrodermal activity (EDA). Byteflies Sensor Dots were used to record ACC and PPG, the Activinsights GENEActiv watch to record ACC, and Epitel Epilog to record EEG data. PPG and EDA signals were recorded for multiple days, then epochs of high-quality, marginal-quality, or poor-quality data were visually identified by reviewers, and reviewer annotations were compared to automated signal quality measures. For ACC, the ratio of spectral power from 0.8 to 5 Hz to broadband power was used to separate good-quality signals from noise. For EDA, the rate of amplitude change and prevalence of sharp peaks significantly differentiated between good-quality data and noise. Spectral entropy was used to assess PPG and showed significant differences between good-, marginal-, and poor-quality signals. EEG data were evaluated using methods to identify a spectral noise cutoff frequency. Patients were asked to rate the usability and comfort of each device in several categories. Patients showed a significant preference for the wrist-worn devices, and the Empatica E4 device was preferred most often. Current wearable devices can provide high-quality data and are acceptable for routine use, but continued development is needed to improve data quality, consistency, and management, as well as acceptability to patients.
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Affiliation(s)
- Mona Nasseri
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Ewan Nurse
- Seer Medical, Melbourne, Victoria, Australia.,Department of Medicine, St Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - Martin Glasstetter
- Department of Neurosurgery, Epilepsy Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Sebastian Böttcher
- Department of Neurosurgery, Epilepsy Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Nicholas M Gregg
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Boney Joseph
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Tal Pal Attia
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Pedro F Viana
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK.,Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | - Elisa Bruno
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Andrea Biondi
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Mark Cook
- Department of Medicine, St Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - Gregory A Worrell
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Andreas Schulze-Bonhage
- Department of Neurosurgery, Epilepsy Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Matthias Dümpelmann
- Department of Neurosurgery, Epilepsy Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | | | - Mark P Richardson
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Benjamin H Brinkmann
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
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16
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Ryvlin P, Cammoun L, Hubbard I, Ravey F, Beniczky S, Atienza D. Noninvasive detection of focal seizures in ambulatory patients. Epilepsia 2020; 61 Suppl 1:S47-S54. [PMID: 32484920 PMCID: PMC7754288 DOI: 10.1111/epi.16538] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 04/20/2020] [Accepted: 04/26/2020] [Indexed: 02/02/2023]
Abstract
Reliably detecting focal seizures without secondary generalization during daily life activities, chronically, using convenient portable or wearable devices, would offer patients with active epilepsy a number of potential benefits, such as providing more reliable seizure count to optimize treatment and seizure forecasting, and triggering alarms to promote safeguarding interventions. However, no generic solution is currently available to reach these objectives. A number of biosignals are sensitive to specific forms of focal seizures, in particular heart rate and its variability for seizures affecting the neurovegetative system, and accelerometry for those responsible for prominent motor activity. However, most studies demonstrate high rates of false detection or poor sensitivity, with only a minority of patients benefiting from acceptable levels of accuracy. To tackle this challenging issue, several lines of technological progress are envisioned, including multimodal biosensing with cross‐modal analytics, a combination of embedded and distributed self‐aware machine learning, and ultra–low‐power design to enable appropriate autonomy of such sophisticated portable solutions.
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Affiliation(s)
- Philippe Ryvlin
- Department of Clinical Neurosciences, Vaud University Hospital, Lausanne, Switzerland
| | - Leila Cammoun
- Department of Clinical Neurosciences, Vaud University Hospital, Lausanne, Switzerland
| | - Ilona Hubbard
- Department of Clinical Neurosciences, Vaud University Hospital, Lausanne, Switzerland
| | - France Ravey
- Department of Clinical Neurosciences, Vaud University Hospital, Lausanne, Switzerland
| | - Sandor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund, Denmark.,Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
| | - David Atienza
- Department of Clinical Neurosciences, Vaud University Hospital, Lausanne, Switzerland.,Embedded Systems Laboratory, Swiss Federal Institute of Technology Lausanne, Lausanne, Switzerland
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17
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Raghu S, Sriraam N, Gommer ED, Hilkman DMW, Temel Y, Rao SV, Hegde AS, Kubben PL. Cross-database evaluation of EEG based epileptic seizures detection driven by adaptive median feature baseline correction. Clin Neurophysiol 2020; 131:1567-1578. [PMID: 32417698 DOI: 10.1016/j.clinph.2020.03.033] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 03/04/2020] [Accepted: 03/12/2020] [Indexed: 01/15/2023]
Abstract
OBJECTIVE In long-term electroencephalogram (EEG) signals, automated classification of epileptic seizures is desirable in diagnosing epilepsy patients, as it otherwise depends on visual inspection. To the best of the author's knowledge, existing studies have validated their algorithms using cross-validation on the same database and less number of attempts have been made to extend their work on other databases to test the generalization capability of the developed algorithms. In this study, we present the algorithm for cross-database evaluation for classification of epileptic seizures using five EEG databases collected from different centers. The cross-database framework helps when sufficient epileptic seizures EEG data are not available to build automated seizure detection model. METHODS Two features, namely successive decomposition index and matrix determinant were extracted at a segmentation length of 4 s (50% overlap). Then, adaptive median feature baseline correction (AM-FBC) was applied to overcome the inter-patient and inter-database variation in the feature distribution. The classification was performed using a support vector machine classifier with leave-one-database-out cross-validation. Different classification scenarios were considered using AM-FBC, smoothing of the train and test data, and post-processing of the classifier output. RESULTS Simulation results revealed the highest area under the curve-sensitivity-specificity-false detections (per hour) of 1-1-1-0.15, 0.89-0.99-0.82-2.5, 0.99-0.73-1-1, 0.95-0.97-0.85-1.7, 0.99-0.99-0.92-1.1 using the Ramaiah Medical College and Hospitals, Children's Hospital Boston-Massachusetts Institute of Technology, Temple University Hospital, Maastricht University Medical Centre, and University of Bonn databases respectively. CONCLUSIONS We observe that the AM-FBC plays a significant role in improving seizure detection results by overcoming inter-database variation of feature distribution. SIGNIFICANCE To the best of the author's knowledge, this is the first study reporting on the cross-database evaluation of classification of epileptic seizures and proven to be better generalization capability when evaluated using five databases and can contribute to accurate and robust detection of epileptic seizures in real-time.
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Affiliation(s)
- S Raghu
- Department of Neurosurgery, School for Mental Health and Neuroscience of the Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands; Center for Medical Electronics and Computing, MS Ramaiah Institute of Technology, Bengaluru, India.
| | - Natarajan Sriraam
- Center for Medical Electronics and Computing, MS Ramaiah Institute of Technology, Bengaluru, India.
| | - Erik D Gommer
- Department of Clinical Neurophysiology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Danny M W Hilkman
- Department of Clinical Neurophysiology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Yasin Temel
- Department of Neurosurgery, Maastricht University Medical Center, Maastricht, the Netherlands
| | | | | | - Pieter L Kubben
- Department of Neurosurgery, Maastricht University Medical Center, Maastricht, the Netherlands
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18
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Fürbass F, Kural MA, Gritsch G, Hartmann M, Kluge T, Beniczky S. An artificial intelligence-based EEG algorithm for detection of epileptiform EEG discharges: Validation against the diagnostic gold standard. Clin Neurophysiol 2020; 131:1174-1179. [PMID: 32299000 DOI: 10.1016/j.clinph.2020.02.032] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 02/14/2020] [Accepted: 02/29/2020] [Indexed: 12/22/2022]
Abstract
OBJECTIVE To validate an artificial intelligence-based computer algorithm for detection of epileptiform EEG discharges (EDs) and subsequent identification of patients with epilepsy. METHODS We developed an algorithm for automatic detection of EDs, based on a novel deep learning method that requires a low amount of labeled EEG data for training. Detected EDs are automatically grouped into clusters, consisting of the same type of EDs, for rapid visual inspection. We validated the algorithm on an independent dataset of 100 patients with sharp transients in their EEG recordings (54 with epilepsy and 46 with non-epileptic paroxysmal events). The diagnostic gold standard was derived from the video-EEG recordings of the patients' habitual events. RESULTS The algorithm had a sensitivity of 89% for identifying EEGs with EDs recorded from patients with epilepsy, a specificity of 70%, and an overall accuracy of 80%. CONCLUSIONS Automated detection of EDs using an artificial intelligence-based computer algorithm had a high sensitivity. Human (expert) supervision is still necessary for confirming the clusters of detected EDs and for describing clinical correlations. Further studies on different patient populations will be needed to confirm our results. SIGNIFICANCE The automated algorithm we describe here is a useful tool, assisting neurophysiologist in rapid assessment of EEG recordings.
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Affiliation(s)
- Franz Fürbass
- Center for Health & Bioresources, AIT Austrian Institute of Technology GmbH, Vienna, Austria
| | - Mustafa Aykut Kural
- Department of Clinical Neurophysiology, Aarhus University Hospital and Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Gerhard Gritsch
- Center for Health & Bioresources, AIT Austrian Institute of Technology GmbH, 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
| | - Sándor Beniczky
- Department of Clinical Neurophysiology, Aarhus University Hospital and Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund, Denmark.
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19
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Vandecasteele K, De Cooman T, Dan J, Cleeren E, Van Huffel S, Hunyadi B, Van Paesschen W. Visual seizure annotation and automated seizure detection using behind-the-ear electroencephalographic channels. Epilepsia 2020; 61:766-775. [PMID: 32160324 PMCID: PMC7217054 DOI: 10.1111/epi.16470] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 02/16/2020] [Accepted: 02/17/2020] [Indexed: 11/30/2022]
Abstract
Objective Seizure diaries kept by patients are unreliable. Automated electroencephalography (EEG)‐based seizure detection systems are a useful support tool to objectively detect and register seizures during long‐term video‐EEG recording. However, this standard full scalp‐EEG recording setup is of limited use outside the hospital, and a discreet, wearable device is needed for capturing seizures in the home setting. We are developing a wearable device that records EEG with behind‐the‐ear electrodes. In this study, we determined whether the recognition of ictal patterns using only behind‐the‐ear EEG channels is possible. Second, an automated seizure detection algorithm was developed using only those behind‐the‐ear EEG channels. Methods Fifty‐four patients with a total of 182 seizures, mostly temporal lobe epilepsy (TLE), and 5284 hours of data, were recorded with a standard video‐EEG at University Hospital Leuven. In addition, extra behind‐the‐ear EEG channels were recorded. First, a neurologist was asked to annotate behind‐the‐ear EEG segments containing selected seizure and nonseizure fragments. Second, a data‐driven algorithm was developed using only behind‐the‐ear EEG. This algorithm was trained using data from other patients (patient‐independent model) or from the same patient (patient‐specific model). Results The visual recognition study resulted in 65.7% sensitivity and 94.4% specificity. By using those seizure annotations, the automated algorithm obtained 64.1% sensitivity and 2.8 false‐positive detections (FPs)/24 hours with the patient‐independent model. The patient‐specific model achieved 69.1% sensitivity and 0.49 FPs/24 hours. Significance Visual recognition of ictal EEG patterns using only behind‐the‐ear EEG is possible in a significant number of patients with TLE. A patient‐specific seizure detection algorithm using only behind‐the‐ear EEG was able to detect more seizures automatically than what patients typically report, with 0.49 FPs/24 hours. We conclude that a large number of refractory TLE patients can benefit from using this device.
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Affiliation(s)
- Kaat Vandecasteele
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - Thomas De Cooman
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - Jonathan Dan
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium.,Byteflies, Antwerp, Belgium
| | - Evy Cleeren
- Department of Neurology, UZ Leuven, Leuven, Belgium
| | - Sabine Van Huffel
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - Borbála Hunyadi
- Department of Microelectronics, Delft University of Technology, Delft, the Netherlands
| | - Wim Van Paesschen
- Department of Neurology, UZ Leuven, Leuven, Belgium.,Laboratory for Epilepsy Research, KU Leuven, Leuven, Belgium
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20
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Karabiber Cura O, Kocaaslan Atli S, Türe HS, Akan A. Epileptic seizure classifications using empirical mode decomposition and its derivative. Biomed Eng Online 2020; 19:10. [PMID: 32059668 PMCID: PMC7023773 DOI: 10.1186/s12938-020-0754-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Accepted: 02/06/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Epilepsy is one of the most common neurological disorders associated with disruption of brain activity. In the classification and detection of epileptic seizures, electroencephalography (EEG) measurements, which record the electrical activities of the brain, are frequently used. Empirical mode decomposition (EMD) and its derivative, ensemble EMD (EEMD) are recently developed methods used to decompose non-stationary and nonlinear signals such as EEG into a finite number of oscillations called intrinsic mode functions (IMFs). Our main objective in this study is to present a hybrid IMF selection method combining four different approaches (energy, correlation, power spectral distance, and statistical significance measures), and investigate the effect of selected IMFs extracted by EMD and EEMD on the classification. We have applied the proposed IMF selection approach on the classification of EEG signals recorded from epilepsy patients who are under treatment at our collaborator hospital. Multichannel EEG signals collected from epilepsy patients are decomposed into IMFs, and then IMF selection was performed. Finally, time- and spectral-domain, and nonlinear features are extracted and feature sets are created for the classification. RESULTS The maximum classification accuracies obtained using various combinations of IMFs were 94.56%, 95.63%, 96.8%, and 96.25% for SVM, KNN, naive Bayes, and logistic regression classifiers, respectively, by using EMD analysis; whereas, the EEMD approach has provided maximum classification accuracies of 96.06%, 97%, 97%, and 96.25% for SVM, KNN, naive Bayes, and logistic regression, respectively. Classification performance with the same features obtained using direct EEG signals instead of the decomposed IMFs was worse than the aforementioned 2 approaches for every combination. CONCLUSION Simulation results demonstrate that the proposed IMF selection approach affects the classification results. Also, EEMD provides a robust method for feature extraction from EEG signals in order to classify pre-seizure and seizure segments.
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Affiliation(s)
- Ozlem Karabiber Cura
- Department of Biomedical Engineering, Faculty of Engineering and Architecture, Izmir Katip Celebi University, Cigli, Izmir, Turkey
| | - Sibel Kocaaslan Atli
- Department of Biophysics, Faculty of Medicine, Izmir Katip Celebi University, Cigli, Izmir, Turkey
| | - Hatice Sabiha Türe
- Department of Neurology, Faculty of Medicine, Izmir Katip Celebi University, Cigli, Izmir, Turkey
| | - Aydin Akan
- Department of Electrical and Electronics Engineering, Faculty of Engineering, Izmir University of Economics, Balcova, Izmir, Turkey.
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21
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Beniczky S, Conradsen I, Wolf P. Detection of convulsive seizures using surface electromyography. Epilepsia 2018; 59 Suppl 1:23-29. [PMID: 29873829 DOI: 10.1111/epi.14048] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/22/2017] [Indexed: 02/04/2023]
Abstract
Bilateral (generalized) tonic-clonic seizures (TCS) increase the risk of sudden unexpected death in epilepsy (SUDEP), especially when patients are unattended. In sleep, TCS often remain unnoticed, which can result in suboptimal treatment decisions. There is a need for automated detection of these major epileptic seizures, using wearable devices. Quantitative surface electromyography (EMG) changes are specific for TCS and characterized by a dynamic evolution of low- and high-frequency signal components. Algorithms targeting increase in high-frequency EMG signals constitute biomarkers of TCS; they can be used both for seizure detection and for differentiating TCS from convulsive nonepileptic seizures. Two large-scale, blinded, prospective studies demonstrated the accuracy of wearable EMG devices for detecting TCS with high sensitivity (76%-100%). The rate of false alarms (0.7-2.5/24 h) needs further improvement. This article summarizes the pathophysiology of muscle activation during convulsive seizures and reviews the published evidence on the accuracy of EMG-based seizure detection.
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Affiliation(s)
- Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund, Denmark.,Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
| | | | - Peter Wolf
- Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund, Denmark.,Department of Clinical Medicine, Neurological Service, Federal University of Santa Catarina, Florianópolis, SC, Brazil
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22
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Baumgartner C, Koren JP, Rothmayer M. Automatic Computer-Based Detection of Epileptic Seizures. Front Neurol 2018; 9:639. [PMID: 30140254 PMCID: PMC6095028 DOI: 10.3389/fneur.2018.00639] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Accepted: 07/17/2018] [Indexed: 11/28/2022] Open
Abstract
Automatic computer-based seizure detection and warning devices are important for objective seizure documentation, for SUDEP prevention, to avoid seizure related injuries and social embarrassments as a consequence of seizures, and to develop on demand epilepsy therapies. Automatic seizure detection systems can be based on direct analysis of epileptiform discharges on scalp-EEG or intracranial EEG, on the detection of motor manifestations of epileptic seizures using surface electromyography (sEMG), accelerometry (ACM), video detection systems and mattress sensors and finally on the assessment of changes of physiologic parameters accompanying epileptic seizures measured by electrocardiography (ECG), respiratory monitors, pulse oximetry, surface temperature sensors, and electrodermal activity. Here we review automatic seizure detection based on scalp-EEG, ECG, and sEMG. Different seizure types affect preferentially different measurement parameters. While EEG changes accompany all types of seizures, sEMG and ACM are suitable mainly for detection of seizures with major motor manifestations. Therefore, seizure detection can be optimized by multimodal systems combining several measurement parameters. While most systems provide sensitivities over 70%, specificity expressed as false alarm rates still needs to be improved. Patients' acceptance and comfort of a specific device are of critical importance for its long-term application in a meaningful clinical way.
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Affiliation(s)
- Christoph Baumgartner
- Department of Neurology, General Hospital Hietzing with Neurological Center Rosenhügel, Vienna, Austria.,Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology, Vienna, Austria.,Medical Faculty, Sigmund Freud University, Vienna, Austria
| | - Johannes P Koren
- Department of Neurology, General Hospital Hietzing with Neurological Center Rosenhügel, Vienna, Austria.,Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology, Vienna, Austria
| | - Michaela Rothmayer
- Department of Neurology, General Hospital Hietzing with Neurological Center Rosenhügel, Vienna, Austria
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23
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Baumgartner C, Koren JP. Seizure detection using scalp-EEG. Epilepsia 2018; 59 Suppl 1:14-22. [DOI: 10.1111/epi.14052] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/09/2018] [Indexed: 11/27/2022]
Affiliation(s)
- Christoph Baumgartner
- Department for Epileptology and Clinical Neurophysiology; Medical Faculty; Sigmund Freud University; Vienna Austria
- Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology; Vienna Austria
- Department of Neurology; General Hospital Hietzing with Neurological Center Rosenhügel; Vienna Austria
| | - Johannes P. Koren
- Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology; Vienna Austria
- Department of Neurology; General Hospital Hietzing with Neurological Center Rosenhügel; Vienna Austria
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24
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Zhao X, Lhatoo SD. Seizure detection: do current devices work? And when can they be useful? Curr Neurol Neurosci Rep 2018; 18:40. [PMID: 29796939 DOI: 10.1007/s11910-018-0849-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
PURPOSE OF REVIEW The unpredictability and apparent randomness of epileptic seizures is one of the most vexing aspects of epilepsy. Methods or devices capable of detecting seizures may help prevent injury or even death and significantly improve quality of life. Here, we summarize and evaluate currently available, unimodal, or polymodal detection systems for epileptic seizures, mainly in the ambulatory setting. RECENT FINDINGS There are two broad categories of detection devices: EEG-based and non-EEG-based systems. Wireless wearable EEG devices are now available both in research and commercial arenas. Neuro-stimulation devices are currently evolving and initial experiences of these show potential promise. As for non-EEG devices, different detecting systems show different sensitivity according to the different patient and seizure types. Regardless, when used in combination, these modalities may complement each other to increase positive predictive value. Although some devices with high sensitivity are promising, practical widespread use of such detection systems is still some way away. More research and experience are needed to evaluate the most efficient and integrated systems, to allow for better approaches to detection and prediction of seizures. The concept of closed-loop systems and prompt intervention may substantially improve quality of life for patients and carers.
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Affiliation(s)
- Xiuhe Zhao
- Epilepsy Center, University Hospitals Cleveland Medical Center, 11100 Euclid Avenue, Cleveland, OH, 44106, USA.,Neurology Department, Qilu Hospital of Shandong University, 107 Wenhuaxi Road, Jinan, 250012, Shandong Province, China
| | - Samden D Lhatoo
- Epilepsy Center, University Hospitals Cleveland Medical Center, 11100 Euclid Avenue, Cleveland, OH, 44106, USA. .,NIH/NINDS Center for SUDEP Research, Boston, MA, USA.
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25
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Elger CE, Hoppe C. Diagnostic challenges in epilepsy: seizure under-reporting and seizure detection. Lancet Neurol 2018; 17:279-288. [DOI: 10.1016/s1474-4422(18)30038-3] [Citation(s) in RCA: 108] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2017] [Revised: 12/05/2017] [Accepted: 12/06/2017] [Indexed: 12/24/2022]
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Comparative sensitivity of quantitative EEG (QEEG) spectrograms for detecting seizure subtypes. Seizure 2018; 55:70-75. [PMID: 29414138 DOI: 10.1016/j.seizure.2018.01.008] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Revised: 01/07/2018] [Accepted: 01/09/2018] [Indexed: 10/18/2022] Open
Abstract
PURPOSE To assess the sensitivity of Persyst version 12 QEEG spectrograms to detect focal, focal with secondarily generalized, and generalized onset seizures. METHODS A cohort of 562 seizures from 58 patients was analyzed. Successive recordings with 2 or more seizures during continuous EEG monitoring for clinical indications in the ICU or EMU between July 2016 and January 2017 were included. Patient ages ranged from 5 to 64 years (mean = 36 years). There were 125 focal seizures, 187 secondarily generalized and 250 generalized seizures from 58 patients analyzed. Seizures were identified and classified independently by two epileptologists. A correlate to the seizure pattern in the raw EEG was sought in the QEEG spectrograms in 4-6 h EEG epochs surrounding the identified seizures. A given spectrogram was interpreted as indicating a seizure, if at the time of a seizure it showed a visually significant departure from the pre-event baseline. Sensitivities for seizure detection using each spectrogram were determined for each seizure subtype. RESULTS Overall sensitivities of the QEEG spectrograms for detecting seizures ranged from 43% to 72%, with highest sensitivity (402/562,72%) by the seizure detection trend. The asymmetry spectrogram had the highest sensitivity for detecting focal seizures (117/125,94%). The FFT spectrogram was most sensitive for detecting secondarily generalized seizures (158/187, 84%). The seizure detection trend was the most sensitive for generalized onset seizures (197/250,79%). CONCLUSIONS Our study suggests that different seizure types have specific patterns in the Persyst QEEG spectrograms. Identifying these patterns in the EEG can significantly increase the sensitivity for seizure identification.
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Fan X, Gaspard N, Legros B, Lucchetti F, Ercek R, Nonclercq A. Dynamics underlying interictal to ictal transition in temporal lobe epilepsy: insights from a neural mass model. Eur J Neurosci 2018; 47:258-268. [PMID: 29282779 DOI: 10.1111/ejn.13812] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Revised: 11/27/2017] [Accepted: 12/18/2017] [Indexed: 12/15/2022]
Abstract
We propose an approach that combines a neural mass model and clinical intracranial electroencephalographic (iEEG) recordings to explore the potential pathophysiological mechanisms (at the neuronal population level) of ictogenesis. Thirty iEEG recordings from 10 temporal lobe epilepsy (TLE) patients around seizure onset were investigated. Physiologically meaningful parameters [average excitatory (Ae ), slow (B), and fast (G) inhibitory synaptic gain] were identified during interictal to ictal transition. Four ratios (Ae /G, Ae /B, Ae /(B + G), and B/G) were derived from these parameters, and their evolution over time was analyzed. The excitation/inhibition ratio increased around seizure onset and decreased before seizure offset, indicating the impairment and re-emergence of excitation/inhibition balance around seizure onset and before seizure offset, respectively. Moreover, the slow inhibition may have an earlier effect on excitation/inhibition imbalance. We confirm the decrease in excitation/inhibition ratio upon seizure termination in human temporal lobe epilepsy, as revealed by optogenetic approaches both in vivo in animal models and in vitro. The increase in excitation/inhibition ratio around seizure occurrence could be an indicator to detect seizures.
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Affiliation(s)
- Xiaoya Fan
- Bio, Electro And Mechanical Systems (BEAMS), Université Libre de Bruxelles (ULB), Avenue F.D. Roosevelt 50 CP165/56, 1050, Brussels, Belgium
| | - Nicolas Gaspard
- Department of Neurology, Hôpital Erasme, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Benjamin Legros
- Department of Neurology, Hôpital Erasme, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Federico Lucchetti
- Bio, Electro And Mechanical Systems (BEAMS), Université Libre de Bruxelles (ULB), Avenue F.D. Roosevelt 50 CP165/56, 1050, Brussels, Belgium.,Laboratoire de Neurophysiologie Sensorielle et Cognitive, Hôpital Brugmann, Brussels, Belgium
| | - Rudy Ercek
- Laboratories of Image, Signal processing and Acoustics (LISA), Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Antoine Nonclercq
- Bio, Electro And Mechanical Systems (BEAMS), Université Libre de Bruxelles (ULB), Avenue F.D. Roosevelt 50 CP165/56, 1050, Brussels, Belgium
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