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Chung YG, Cho A, Kim H, Kim KJ. Single-channel seizure detection with clinical confirmation of seizure locations using CHB-MIT dataset. Front Neurol 2024; 15:1389731. [PMID: 38836000 PMCID: PMC11148866 DOI: 10.3389/fneur.2024.1389731] [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: 02/22/2024] [Accepted: 05/03/2024] [Indexed: 06/06/2024] Open
Abstract
Introduction Long-term electroencephalography (EEG) monitoring is advised to patients with refractory epilepsy who have a failure of anti-seizure medication and therapy. However, its real-life application is limited mainly due to the use of multiple EEG channels. We proposed a patient-specific deep learning-based single-channel seizure detection approach using the long-term scalp EEG recordings of the Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) dataset, in conjunction with neurologists' confirmation of spatial seizure characteristics of individual patients. Methods We constructed 18-, 4-, and single-channel seizure detectors for 13 patients. Neurologists selected a specific channel among four channels, two close to the behind-the-ear and two at the forehead for each patient, after reviewing the patient's distinctive seizure locations with seizure re-annotation. Results Our multi- and single-channel detectors achieved an average sensitivity of 97.05-100%, false alarm rate of 0.22-0.40/h, and latency of 2.1-3.4 s for identification of seizures in continuous EEG recordings. The results demonstrated that seizure detection performance of our single-channel approach was comparable to that of our multi-channel ones. Discussion We suggest that our single-channel approach in conjunction with clinical designation of the most prominent seizure locations has a high potential for wearable seizure detection on long-term EEG recordings for patients with refractory epilepsy.
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Affiliation(s)
- Yoon Gi Chung
- Department of Pediatrics, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Anna Cho
- Department of Pediatrics, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Gyeonggi-do, Republic of Korea
| | - Hunmin Kim
- Department of Pediatrics, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Gyeonggi-do, Republic of Korea
- Department of Pediatrics, Seoul National University College of Medicine, Seoul, 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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2
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Hwang J, Cho SM, Ritzl EK. Recent applications of quantitative electroencephalography in adult intensive care units: a comprehensive review. J Neurol 2022; 269:6290-6309. [DOI: 10.1007/s00415-022-11337-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 08/10/2022] [Accepted: 08/11/2022] [Indexed: 10/15/2022]
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3
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Minimum Technical Requirements for Performing Ambulatory EEG. J Clin Neurophysiol 2022; 39:435-440. [DOI: 10.1097/wnp.0000000000000950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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4
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Tatum WO, Mani J, Jin K, Halford JJ, Gloss D, Fahoum F, Maillard L, Mothersill I, Beniczky S. Minimum standards for inpatient long-term video-EEG monitoring: A clinical practice guideline of the international league against epilepsy and international federation of clinical neurophysiology. Clin Neurophysiol 2021; 134:111-128. [PMID: 34955428 DOI: 10.1016/j.clinph.2021.07.016] [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] [Indexed: 11/03/2022]
Abstract
The objective of this clinical practice guideline is to provide recommendations on the indications and minimum standards for inpatient long-term video-electroencephalographic monitoring (LTVEM). The Working Group of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology develop guidelines aligned with the Epilepsy Guidelines Task Force. We reviewed published evidence using The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement. We found limited high-level evidence aimed at specific aspects of diagnosis for LTVEM performed to evaluate patients with seizures and nonepileptic events (see Table S1). For classification of evidence, we used the Clinical Practice Guideline Process Manual of the American Academy of Neurology. We formulated recommendations for the indications, technical requirements, and essential practice elements of LTVEM to derive minimum standards used in the evaluation of patients with suspected epilepsy using GRADE (Grading of Recommendations, Assessment, Development, and Evaluation). Further research is needed to obtain evidence about long-term outcome effects of LTVEM and establish its clinical utility.
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Affiliation(s)
- William O Tatum
- Department of Neurology, Mayo Clinic, Jacksonville, FL, USA.
| | - Jayanti Mani
- Department of Neurology, Kokilaben Dhirubai Ambani Hospital, Mumbai, India
| | - Kazutaka Jin
- Department of Epileptology, Tohoku University Graduate School of Medicine, Japan
| | - Jonathan J Halford
- Department of Neurology, Medical University of South Carolina, Charleston, SC, USA.
| | - David Gloss
- Department of Neurology, Charleston Area Medical Center, Charleston, WV, USA
| | - Firas Fahoum
- Department of Neurology, Tel Aviv Sourasky Medical Center and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
| | - Louis Maillard
- Department of Neurology, University of Nancy, UMR7039, University of Lorraine, France.
| | - Ian Mothersill
- Department of Clinical Neurophysiology, Swiss Epilepsy Center, Zurich Switzerland.
| | - Sandor Beniczky
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark; Danish Epilepsy Center, Dianalund, Denmark.
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5
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Tatum WO, Mani J, Jin K, Halford JJ, Gloss D, Fahoum F, Maillard L, Mothersill I, Beniczky S. Minimum standards for inpatient long-term video-electroencephalographic monitoring: A clinical practice guideline of the International League Against Epilepsy and International Federation of Clinical Neurophysiology. Epilepsia 2021; 63:290-315. [PMID: 34897662 DOI: 10.1111/epi.16977] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 06/09/2021] [Accepted: 06/09/2021] [Indexed: 01/02/2023]
Abstract
The objective of this clinical practice guideline is to provide recommendations on the indications and minimum standards for inpatient long-term video-electroencephalographic monitoring (LTVEM). The Working Group of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology develop guidelines aligned with the Epilepsy Guidelines Task Force. We reviewed published evidence using the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) statement. We found limited high-level evidence aimed at specific aspects of diagnosis for LTVEM performed to evaluate patients with seizures and nonepileptic events. For classification of evidence, we used the Clinical Practice Guideline Process Manual of the American Academy of Neurology. We formulated recommendations for the indications, technical requirements, and essential practice elements of LTVEM to derive minimum standards used in the evaluation of patients with suspected epilepsy using GRADE (Grading of Recommendations Assessment, Development, and Evaluation). Further research is needed to obtain evidence about long-term outcome effects of LTVEM and to establish its clinical utility.
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Affiliation(s)
- William O Tatum
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA
| | - Jayanti Mani
- Department of Neurology, Kokilaben Dhirubai Ambani Hospital, Mumbai, India
| | - Kazutaka Jin
- Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Jonathan J Halford
- Department of Neurology, Medical University of South Carolina, Charleston, South Carolina, USA
| | - David Gloss
- Department of Neurology, Charleston Area Medical Center, Charleston, West Virginia, USA
| | - Firas Fahoum
- Department of Neurology, Tel Aviv Sourasky Medical Center and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Louis Maillard
- Department of Neurology, University of Nancy, UMR7039, University of Lorraine, Nancy, France
| | - Ian Mothersill
- Department of Clinical Neurophysiology, Swiss Epilepsy Center, Zurich,, Switzerland
| | - Sandor Beniczky
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark.,Danish Epilepsy Center, Dianalund, Denmark
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6
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Frankel MA, Lehmkuhle MJ, Spitz MC, Newman BJ, Richards SV, Arain AM. Wearable Reduced-Channel EEG System for Remote Seizure Monitoring. Front Neurol 2021; 12:728484. [PMID: 34733229 PMCID: PMC8558398 DOI: 10.3389/fneur.2021.728484] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 09/20/2021] [Indexed: 11/13/2022] Open
Abstract
Epitel has developed Epilog, a miniature, wireless, wearable electroencephalography (EEG) sensor. Four Epilog sensors are combined as part of Epitel's Remote EEG Monitoring platform (REMI) to create 10 channels of EEG for remote patient monitoring. REMI is designed to provide comprehensive spatial EEG recordings that can be administered by non-specialized medical personnel in any medical center. The purpose of this study was to determine how accurate epileptologists are at remotely reviewing Epilog sensor EEG in the 10-channel “REMI montage,” with and without seizure detection support software. Three board certified epileptologists reviewed the REMI montage from 20 subjects who wore four Epilog sensors for up to 5 days alongside traditional video-EEG in the EMU, 10 of whom experienced a total of 24 focal-onset electrographic seizures and 10 of whom experienced no seizures or epileptiform activity. Epileptologists randomly reviewed the same datasets with and without clinical decision support annotations from an automated seizure detection algorithm tuned to be highly sensitive. Blinded consensus review of unannotated Epilog EEG in the REMI montage detected people who were experiencing electrographic seizure activity with 90% sensitivity and 90% specificity. Consensus detection of individual focal onset seizures resulted in a mean sensitivity of 61%, precision of 80%, and false detection rate (FDR) of 0.002 false positives per hour (FP/h) of data. With algorithm seizure detection annotations, the consensus review mean sensitivity improved to 68% with a slight increase in FDR (0.005 FP/h). As seizure detection software, the automated algorithm detected people who were experiencing electrographic seizure activity with 100% sensitivity and 70% specificity, and detected individual focal onset seizures with a mean sensitivity of 90% and mean false alarm rate of 0.087 FP/h. This is the first study showing epileptologists' ability to blindly review EEG from four Epilog sensors in the REMI montage, and the results demonstrate the clinical potential to accurately identify patients experiencing electrographic seizures. Additionally, the automated algorithm shows promise as clinical decision support software to detect discrete electrographic seizures in individual records as accurately as FDA-cleared predicates.
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Affiliation(s)
| | | | - Mark C Spitz
- Neurology, University of Colorado Anschutz Medical Center, Aurora, CO, United States
| | - Blake J Newman
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, United States
| | - Sindhu V Richards
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, United States
| | - Amir M Arain
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, United States
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7
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Scheuer ML, Wilson SB, Antony A, Ghearing G, Urban A, Bagić AI. Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset. J Clin Neurophysiol 2021; 38:439-447. [PMID: 32472781 PMCID: PMC8404956 DOI: 10.1097/wnp.0000000000000709] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
PURPOSE To compare the seizure detection performance of three expert humans and two computer algorithms in a large set of epilepsy monitoring unit EEG recordings. METHODS One hundred twenty prolonged EEGs, 100 containing clinically reported EEG-evident seizures, were evaluated. Seizures were marked by the experts and algorithms. Pairwise sensitivity and false-positive rates were calculated for each human-human and algorithm-human pair. Differences in human pairwise performance were calculated and compared with the range of algorithm versus human performance differences as a type of statistical modified Turing test. RESULTS A total of 411 individual seizure events were marked by the experts in 2,805 hours of EEG. Mean, pairwise human sensitivities and false-positive rates were 84.9%, 73.7%, and 72.5%, and 1.0, 0.4, and 1.0/day, respectively. Only the Persyst 14 algorithm was comparable with humans-78.2% and 1.0/day. Evaluation of pairwise differences in sensitivity and false-positive rate demonstrated that Persyst 14 met statistical noninferiority criteria compared with the expert humans. CONCLUSIONS Evaluating typical prolonged EEG recordings, human experts had a modest level of agreement in seizure marking and low false-positive rates. The Persyst 14 algorithm was statistically noninferior to the humans. For the first time, a seizure detection algorithm and human experts performed similarly.
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Affiliation(s)
- Mark L. Scheuer
- Persyst Development Corporation, Solana Beach, California, U.S.A
| | - Scott B. Wilson
- Persyst Development Corporation, Solana Beach, California, U.S.A
| | - Arun Antony
- University of Pittsburgh Comprehensive Epilepsy Center (UPCEC), University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, U.S.A.; and
| | - Gena Ghearing
- Department of Neurology, University of Iowa, Iowa City, Iowa, U.S.A
| | - Alexandra Urban
- University of Pittsburgh Comprehensive Epilepsy Center (UPCEC), University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, U.S.A.; and
| | - Anto I. Bagić
- University of Pittsburgh Comprehensive Epilepsy Center (UPCEC), University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, U.S.A.; and
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8
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A Full-Stack Application for Detecting Seizures and Reducing Data During Continuous Electroencephalogram Monitoring. Crit Care Explor 2021; 3:e0476. [PMID: 34278312 PMCID: PMC8280012 DOI: 10.1097/cce.0000000000000476] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Supplemental Digital Content is available in the text. BACKGROUND: Continuous electroencephalogram monitoring is associated with lower mortality in critically ill patients; however, it is underused due to the resource-intensive nature of manually interpreting prolonged streams of continuous electroencephalogram data. Here, we present a novel real-time, machine learning–based alerting and monitoring system for epilepsy and seizures that dramatically reduces the amount of manual electroencephalogram review. METHODS: We developed a custom data reduction algorithm using a random forest and deployed it within an online cloud-based platform, which streams data and communicates interactively with caregivers via a web interface to display algorithm results. We developed real-time, machine learning–based alerting and monitoring system for epilepsy and seizures on continuous electroencephalogram recordings from 77 patients undergoing routine scalp ICU electroencephalogram monitoring and tested it on an additional 20 patients. RESULTS We achieved a mean seizure sensitivity of 84% in cross-validation and 85% in testing, as well as a mean specificity of 83% in cross-validation and 86% in testing, corresponding to a high level of data reduction. This study validates a platform for machine learning–assisted continuous electroencephalogram analysis and represents a meaningful step toward improving utility and decreasing cost of continuous electroencephalogram monitoring. We also make our high-quality annotated dataset of 97 ICU continuous electroencephalogram recordings public for others to validate and improve upon our methods.
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9
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Bahr A, Schneider M, Francis MA, Lehmann HM, Barg I, Buschhoff AS, Wulff P, Strunskus T, Faupel F. Epileptic Seizure Detection on an Ultra-Low-Power Embedded RISC-V Processor Using a Convolutional Neural Network. BIOSENSORS 2021; 11:bios11070203. [PMID: 34201480 PMCID: PMC8301882 DOI: 10.3390/bios11070203] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 06/13/2021] [Accepted: 06/18/2021] [Indexed: 05/13/2023]
Abstract
The treatment of refractory epilepsy via closed-loop implantable devices that act on seizures either by drug release or electrostimulation is a highly attractive option. For such implantable medical devices, efficient and low energy consumption, small size, and efficient processing architectures are essential. To meet these requirements, epileptic seizure detection by analysis and classification of brain signals with a convolutional neural network (CNN) is an attractive approach. This work presents a CNN for epileptic seizure detection capable of running on an ultra-low-power microprocessor. The CNN is implemented and optimized in MATLAB. In addition, the CNN is also implemented on a GAP8 microprocessor with RISC-V architecture. The training, optimization, and evaluation of the proposed CNN are based on the CHB-MIT dataset. The CNN reaches a median sensitivity of 90% and a very high specificity over 99% corresponding to a median false positive rate of 6.8 s per hour. After implementation of the CNN on the microcontroller, a sensitivity of 85% is reached. The classification of 1 s of EEG data takes t=35 ms and consumes an average power of P≈140 μW. The proposed detector outperforms related approaches in terms of power consumption by a factor of 6. The universal applicability of the proposed CNN based detector is verified with recording of epileptic rats. This results enable the design of future medical devices for epilepsy treatment.
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Affiliation(s)
- Andreas Bahr
- Sensor System Electronics, Institute of Electrical Engineering and Information Technology, Kiel University, 24143 Kiel, Germany; (M.S.); (M.A.F.)
- Correspondence:
| | - Matthias Schneider
- Sensor System Electronics, Institute of Electrical Engineering and Information Technology, Kiel University, 24143 Kiel, Germany; (M.S.); (M.A.F.)
| | - Maria Avitha Francis
- Sensor System Electronics, Institute of Electrical Engineering and Information Technology, Kiel University, 24143 Kiel, Germany; (M.S.); (M.A.F.)
| | - Hendrik M. Lehmann
- CMOS Design, Technical University Braunschweig, 38106 Braunschweig, Germany;
| | - Igor Barg
- Multicomponent Materials, Institute for Material Science, Kiel University, 24143 Kiel, Germany; (I.B.); (T.S.); (F.F.)
| | | | - Peer Wulff
- Institute of Physiology, Kiel University, 24118 Kiel, Germany; (A.-S.B.); (P.W.)
| | - Thomas Strunskus
- Multicomponent Materials, Institute for Material Science, Kiel University, 24143 Kiel, Germany; (I.B.); (T.S.); (F.F.)
| | - Franz Faupel
- Multicomponent Materials, Institute for Material Science, Kiel University, 24143 Kiel, Germany; (I.B.); (T.S.); (F.F.)
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10
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Frankel MA, Lehmkuhle MJ, Watson M, Fetrow K, Frey L, Drees C, Spitz MC. Electrographic seizure monitoring with a novel, wireless, single-channel EEG sensor. Clin Neurophysiol Pract 2021; 6:172-178. [PMID: 34189361 PMCID: PMC8220094 DOI: 10.1016/j.cnp.2021.04.003] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 03/21/2021] [Accepted: 04/26/2021] [Indexed: 11/18/2022] Open
Abstract
Objective Recording seizures using personal seizure diaries can be challenging during everyday life and many seizures are missed or mis-reported. People living with epilepsy could benefit by having a more accurate and objective wearable EEG system for counting seizures that can be used outside of the hospital. The objective of this study was to (1) determine which seizure types can be electrographically recorded from the scalp below the hairline, (2) determine epileptologists' ability to identify electrographic seizures from single-channels extracted from full-montage wired-EEG, and (3) determine epileptologists' ability to identify electrographic seizures from Epilog, a wireless single-channel EEG sensor. Methods Epilog sensors were worn concurrently during epilepsy monitoring unit (EMU) monitoring. During standard-of-care review, epileptologists were asked if the electrographic portion of the seizure was visible on single channels of wired electrodes at locations proximal to Epilog sensors, and if focal-onset, which electrode was closest to the focus. From these locations, single channels of EEG extracted from wired full-montage EEG and the proximal Epilog sensor were presented to 3 blinded epileptologists along with markers for when known seizures occurred (taken from the standard-of-care review). Control segments at inter-ictal times were included as control. The epileptologists were asked whether a seizure event was visible in the single channel EEG record at or near the marker. Results A total of 75 seizures were recorded from 22 of 40 adults that wore Epilog during their visit to the EMU. Epileptologists were able to visualize known seizure activity on at least one of the wired electrodes proximal to Epilog sensors for all seizure events. Epileptologists accurately identified seizures in 71% of Epilog recordings and 84% of single-channel wired recordings and were 92% accurate identifying seizures with Epilog when those seizures ended in a clinical convulsion compared to those that did not (>55%). Conclusions Epileptologists are able to visualize seizure activity on single-channels of EEG at locations where Epilog sensors are easily placed on the scalp below hairline. Manual review of seizure annotations can be done quickly and accurately (>70% TP and >98% PPV) on single-channel EEG data. Reviewing single-channel EEG is more accurate than what has been reported in the literature on self-reporting seizures in seizure diaries, the current standard of care for seizure counting outside of the EMU. Significance Wearable EEG will be important for seizure monitoring outside of the hospital. Epileptologists can accurately identify seizures in single-channel EEG, better than patient self-reporting in diaries based on the literature. Automated or semi-automated seizure detection on single channels of EEG could be used in the future to objectively count seizures to complement the standard of care outside of the EMU without the overt burden upon epileptologist review.
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Affiliation(s)
| | - Mark J. Lehmkuhle
- Department of Neurology, University of Colorado Anschutz Medical Center, Aurora, CO, USA
- Corresponding author at: Epitel, Inc., 124 South 400 East, Suite 450, Salt Lake City, UT 84111, USA.
| | - Meagan Watson
- Department of Neurology, University of Colorado Anschutz Medical Center, Aurora, CO, USA
| | - Kirsten Fetrow
- Department of Neurology, University of Colorado Anschutz Medical Center, Aurora, CO, USA
| | - Lauren Frey
- Department of Neurology, University of Colorado Anschutz Medical Center, Aurora, CO, USA
| | - Cornelia Drees
- Department of Neurology, University of Colorado Anschutz Medical Center, Aurora, CO, USA
| | - Mark C. Spitz
- Department of Neurology, University of Colorado Anschutz Medical Center, Aurora, CO, USA
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11
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Martini ML, Valliani AA, Sun C, Costa AB, Zhao S, Panov F, Ghatan S, Rajan K, Oermann EK. Deep anomaly detection of seizures with paired stereoelectroencephalography and video recordings. Sci Rep 2021; 11:7482. [PMID: 33820942 PMCID: PMC8021582 DOI: 10.1038/s41598-021-86891-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Accepted: 03/16/2021] [Indexed: 01/30/2023] Open
Abstract
Real-time seizure detection is a resource intensive process as it requires continuous monitoring of patients on stereoelectroencephalography. This study improves real-time seizure detection in drug resistant epilepsy (DRE) patients by developing patient-specific deep learning models that utilize a novel self-supervised dynamic thresholding approach. Deep neural networks were constructed on over 2000 h of high-resolution, multichannel SEEG and video recordings from 14 DRE patients. Consensus labels from a panel of epileptologists were used to evaluate model efficacy. Self-supervised dynamic thresholding exhibited improvements in positive predictive value (PPV; difference: 39.0%; 95% CI 4.5–73.5%; Wilcoxon–Mann–Whitney test; N = 14; p = 0.03) with similar sensitivity (difference: 14.3%; 95% CI − 21.7 to 50.3%; Wilcoxon–Mann–Whitney test; N = 14; p = 0.42) compared to static thresholds. In some models, training on as little as 10 min of SEEG data yielded robust detection. Cross-testing experiments reduced PPV (difference: 56.5%; 95% CI 25.8–87.3%; Wilcoxon–Mann–Whitney test; N = 14; p = 0.002), while multimodal detection significantly improved sensitivity (difference: 25.0%; 95% CI 0.2–49.9%; Wilcoxon–Mann–Whitney test; N = 14; p < 0.05). Self-supervised dynamic thresholding improved the efficacy of real-time seizure predictions. Multimodal models demonstrated potential to improve detection. These findings are promising for future deployment in epilepsy monitoring units to enable real-time seizure detection without annotated data and only minimal training time in individual patients.
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Affiliation(s)
- Michael L Martini
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Aly A Valliani
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Claire Sun
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.,Department of Neurosciences, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave Levy Place, New York, NY, 10029, USA
| | - Anthony B Costa
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Shan Zhao
- Department of Anesthesiology, Icahn School of Medicine At Mount Sinai, New York, NY, 10029, USA
| | - Fedor Panov
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Saadi Ghatan
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Kanaka Rajan
- Department of Neurosciences, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave Levy Place, New York, NY, 10029, USA.
| | - Eric Karl Oermann
- Department of Neurosurgery, New York University Langone Medical Center, New York University, Skirball, Suite 8S, 530 First Avenue, New York, NY, 10016, USA. .,Department of Radiology, New York University Langone Medical Center, New York, NY, 10016, USA. .,NYU Center for Data Science, New York University, New York, NY, 10011, USA.
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Ruiz Marín M, Villegas Martínez I, Rodríguez Bermúdez G, Porfiri M. Integrating old and new complexity measures toward automated seizure detection from long-term video EEG recordings. iScience 2021; 24:101997. [PMID: 33490905 PMCID: PMC7811137 DOI: 10.1016/j.isci.2020.101997] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 10/23/2020] [Accepted: 12/23/2020] [Indexed: 11/23/2022] Open
Abstract
Automated seizure detection in long-term video-EEG recordings is far from being integrated into common clinical practice. Here, we leverage classical and state-of-the-art complexity measures to robustly and automatically detect seizures from scalp recordings. Brain activity is scored through eight features, encompassing traditional time domain and novel measures of recurrence. A binary classification algorithm tailored to treat unbalanced dataset is used to determine whether a time window is ictal or non-ictal from its features. The application of the algorithm on a cohort of ten adult patients with focal refractory epilepsy indicates sensitivity, specificity, and accuracy of 90%, along with a true alarm rate of 95% and less than four false alarms per day. The proposed approach emphasizes ictal patterns against noisy background without the need of data preprocessing. Finally, we benchmark our approach against previous studies on two publicly available datasets, demonstrating the good performance of our algorithm. Complexity measures are formulated to enhance classical time-domain statistics of EEG The detection algorithm does not need ad-hoc data preprocessing to address artifacts Focal seizures are detected 95% of the time with less than four false alarms per day The approach offers a visual representation of a seizure as a time-evolving network
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Affiliation(s)
- Manuel Ruiz Marín
- Department of Quantitative Methods, Law and Modern Languages, Technical University of Cartagena (UPCT), Cartagena, Murcia 30201, Spain
- Bio-Health Institute (IMIB-Arrixaca), Health Science Campus, Murcia, CP 30120, Spain
- Corresponding author
| | - Irene Villegas Martínez
- Department of Projects and Innovation, Health Service of Murcia (SMS), Murcia, Spain
- Bio-Health Institute (IMIB-Arrixaca), Health Science Campus, Murcia, CP 30120, Spain
- Corresponding author
| | | | - Maurizio Porfiri
- Department of Quantitative Methods, Law and Modern Languages, Technical University of Cartagena (UPCT), Cartagena, Murcia 30201, Spain
- Department of Mechanical and Aerospace Engineering, and Department of Biomedical Engineering New York University Tandon School of Engineering (NYU), Brooklyn, NY, USA
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13
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Rubinos C, Alkhachroum A, Der-Nigoghossian C, Claassen J. Electroencephalogram Monitoring in Critical Care. Semin Neurol 2020; 40:675-680. [PMID: 33176375 DOI: 10.1055/s-0040-1719073] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Seizures are common in critically ill patients. Electroencephalogram (EEG) is a tool that enables clinicians to provide continuous brain monitoring and to guide treatment decisions-brain telemetry. EEG monitoring has particular utility in the intensive care unit as most seizures in this setting are nonconvulsive. Despite the increased use of EEG monitoring in the critical care unit, it remains underutilized. In this review, we summarize the utility of EEG and different EEG modalities to monitor patients in the critical care setting.
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Affiliation(s)
- Clio Rubinos
- Division of Critical Care Neurology, University of North Carolina School of Medicine, Chapel Hill, North Carolina
| | - Ayham Alkhachroum
- Department of Neurology, Miller School of Medicine, Jackson Memorial Health System, University of Miami, Miami, Florida
| | - Caroline Der-Nigoghossian
- Neurosciences Intensive Care Unit, Department of Pharmacy, New York-Presbyterian Hospital/Columbia University Irving Medical Center, New York, New York
| | - Jan Claassen
- Department of Neurology, Columbia University, New York
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14
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Kelly KM. Man and the machine rise to the spike‐wave. Commentary on “An automated, machine learning‐based detection algorithm for spike‐wave discharges (SWDs) in a mouse model of absence epilepsy.”. Epilepsia Open 2020; 5:338-339. [PMID: 32913940 PMCID: PMC7469778 DOI: 10.1002/epi4.12428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 07/29/2020] [Indexed: 12/02/2022] Open
Affiliation(s)
- Kevin M. Kelly
- Neuroscience Institute Allegheny Health Network Pittsburgh PA USA
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15
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16
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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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17
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Swami P, Bhatia M, Tripathi M, Chandra PS, Panigrahi BK, Gandhi TK. Selection of optimum frequency bands for detection of epileptiform patterns. Healthc Technol Lett 2019; 6:126-131. [PMID: 31839968 PMCID: PMC6849498 DOI: 10.1049/htl.2018.5051] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Revised: 04/15/2019] [Accepted: 04/25/2019] [Indexed: 01/03/2023] Open
Abstract
The significant research effort in the domain of epilepsy has been directed toward the development of an automated seizure detection system. In their usage of the electrophysiological recordings, most of the proposals thus far have followed the conventional practise of employing all frequency bands following signal decomposition as input features for a classifier. Although seemingly powerful, this approach may prove counterproductive since some frequency bins may not carry relevant information about seizure episodes and may, instead, add noise to the classification process thus degrading performance. A key thesis of the work described here is that the selection of frequency subsets may enhance seizure classification rates. Additionally, the authors explore whether a conservative selection of frequency bins can reduce the amount of training data needed for achieving good classification performance. They have found compelling evidence that using spectral components with <25 Hz frequency in scalp electroencephalograms can yield state-of-the-art classification accuracy while reducing training data requirements to just a tenth of those employed by current approaches.
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Affiliation(s)
- Piyush Swami
- Centre for Biomedical Engineering, Indian Institute of Technology - Delhi, New Delhi 110 016, India.,Department of Electrical Engineering, Indian Institute of Technology - Delhi, New Delhi 110 016, India
| | - Manvir Bhatia
- Department of Neurosciences, Fortis Escorts Hospital, New Delhi 110 025, India.,Neurology and Sleep Centre, New Delhi 110 016, India
| | - Manjari Tripathi
- Department of Neurology, All India Institute of Medical Sciences, New Delhi 110 029, India
| | - Poodipedi Sarat Chandra
- Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi 110 029, India
| | - Bijaya K Panigrahi
- Department of Electrical Engineering, Indian Institute of Technology - Delhi, New Delhi 110 016, India
| | - Tapan K Gandhi
- Department of Electrical Engineering, Indian Institute of Technology - Delhi, New Delhi 110 016, India
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18
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Baldassano SN, Hill CE, Shankar A, Bernabei J, Khankhanian P, Litt B. Big data in status epilepticus. Epilepsy Behav 2019; 101:106457. [PMID: 31444029 PMCID: PMC6944751 DOI: 10.1016/j.yebeh.2019.106457] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2019] [Accepted: 07/26/2019] [Indexed: 12/23/2022]
Abstract
Status epilepticus care and treatment are already being touched by the revolution in data science. New approaches designed to leverage the tremendous potential of "big data" in the clinical sphere are enabling researchers and clinicians to extract information from sources such as administrative claims data, the electronic medical health record, and continuous physiologic monitoring data streams. Algorithmic methods of data extraction also offer potential to fuse multimodal data (including text-based documentation, imaging data, and time-series data) to improve patient assessment and stratification beyond the manual capabilities of individual physicians. Still, the potential of data science to impact the diagnosis, treatment, and minute-to-minute care of patients with status epilepticus is only starting to be appreciated. In this brief review, we discuss how data science is impacting the field and draw examples from the following three main areas: (1) analysis of insurance claims from large administrative datasets to evaluate the impact of continuous electroencephalogram (EEG) monitoring on clinical outcomes; (2) natural language processing of the electronic health record to find, classify, and stratify patients for prognostication and treatment; and (3) real-time systems for data analysis, data reduction, and multimodal data fusion to guide therapy in real time. While early, it is our hope that these examples will stimulate investigators to leverage data science, computer science, and engineering methods to improve the care and outcome of patients with status epilepticus and other neurological disorders. This article is part of the Special Issue "Proceedings of the 7th London-Innsbruck Colloquium on Status Epilepticus and Acute Seizures".
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Affiliation(s)
- Steven N. Baldassano
- Department of Bioengineering, University of Pennsylvania, 210 South 33rd Street, Philadelphia, PA 19104, United States,Center for Neuroengineering and Therapeutics, University of Pennsylvania, 240 South 33rd Street, Philadelphia, PA 19104, United States
| | - Chloé E. Hill
- Department of Neurology, University of Michigan, 1500 East Medical Center Drive, Ann Arbor, MI 48109, United States
| | - Arjun Shankar
- Department of Bioengineering, University of Pennsylvania, 210 South 33rd Street, Philadelphia, PA 19104, United States,Center for Neuroengineering and Therapeutics, University of Pennsylvania, 240 South 33rd Street, Philadelphia, PA 19104, United States
| | - John Bernabei
- Department of Bioengineering, University of Pennsylvania, 210 South 33rd Street, Philadelphia, PA 19104, United States,Center for Neuroengineering and Therapeutics, University of Pennsylvania, 240 South 33rd Street, Philadelphia, PA 19104, United States
| | - Pouya Khankhanian
- Department of Neurology, University of Michigan, 1500 East Medical Center Drive, Ann Arbor, MI 48109, United States,Department of Neurology, Penn Epilepsy Center, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
| | - Brian Litt
- Department of Bioengineering, University of Pennsylvania, 210 South 33rd Street, Philadelphia, PA 19104, United States,Center for Neuroengineering and Therapeutics, University of Pennsylvania, 240 South 33rd Street, Philadelphia, PA 19104, United States,Department of Neurology, Penn Epilepsy Center, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
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19
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Automated detection of epileptic seizures using successive decomposition index and support vector machine classifier in long-term EEG. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04389-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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20
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Jacobs D, Hilton T, del Campo M, Carlen PL, Bardakjian BL. Classification of Pre-Clinical Seizure States Using Scalp EEG Cross-Frequency Coupling Features. IEEE Trans Biomed Eng 2018; 65:2440-2449. [DOI: 10.1109/tbme.2018.2797919] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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21
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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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22
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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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23
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Tsiouris ΚΜ, Markoula S, Konitsiotis S, Koutsouris DD, Fotiadis DI. A robust unsupervised epileptic seizure detection methodology to accelerate large EEG database evaluation. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2017.09.029] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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24
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Spectral Analysis of Acceleration Data for Detection of Generalized Tonic-Clonic Seizures. SENSORS 2017; 17:s17030481. [PMID: 28264522 PMCID: PMC5375767 DOI: 10.3390/s17030481] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2016] [Revised: 02/06/2017] [Accepted: 02/22/2017] [Indexed: 11/16/2022]
Abstract
Generalized tonic-clonic seizures (GTCSs) can be underestimated and can also increase mortality rates. The monitoring devices used to detect GTCS events in daily life are very helpful for early intervention and precise estimation of seizure events. Several studies have introduced methods for GTCS detection using an accelerometer (ACM), electromyography, or electroencephalography. However, these studies need to be improved with respect to accuracy and user convenience. This study proposes the use of an ACM banded to the wrist and spectral analysis of ACM data to detect GTCS in daily life. The spectral weight function dependent on GTCS was used to compute a GTCS-correlated score that can effectively discriminate between GTCS and normal movement. Compared to the performance of the previous temporal method, which used a standard deviation method, the spectral analysis method resulted in better sensitivity and fewer false positive alerts. Finally, the spectral analysis method can be implemented in a GTCS monitoring device using an ACM and can provide early alerts to caregivers to prevent risks associated with GTCS.
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25
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Seizure detection and neuromodulation: A summary of data presented at the XIII conference on new antiepileptic drug and devices (EILAT XIII). Epilepsy Res 2017; 130:27-36. [DOI: 10.1016/j.eplepsyres.2017.01.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Accepted: 01/08/2017] [Indexed: 01/22/2023]
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26
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Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier. Cogn Neurodyn 2016; 11:51-66. [PMID: 28174612 DOI: 10.1007/s11571-016-9408-y] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2016] [Revised: 08/30/2016] [Accepted: 09/06/2016] [Indexed: 10/21/2022] Open
Abstract
Electroencephalogram shortly termed as EEG is considered as the fundamental segment for the assessment of the neural activities in the brain. In cognitive neuroscience domain, EEG-based assessment method is found to be superior due to its non-invasive ability to detect deep brain structure while exhibiting superior spatial resolutions. Especially for studying the neurodynamic behavior of epileptic seizures, EEG recordings reflect the neuronal activity of the brain and thus provide required clinical diagnostic information for the neurologist. This specific proposed study makes use of wavelet packet based log and norm entropies with a recurrent Elman neural network (REN) for the automated detection of epileptic seizures. Three conditions, normal, pre-ictal and epileptic EEG recordings were considered for the proposed study. An adaptive Weiner filter was initially applied to remove the power line noise of 50 Hz from raw EEG recordings. Raw EEGs were segmented into 1 s patterns to ensure stationarity of the signal. Then wavelet packet using Haar wavelet with a five level decomposition was introduced and two entropies, log and norm were estimated and were applied to REN classifier to perform binary classification. The non-linear Wilcoxon statistical test was applied to observe the variation in the features under these conditions. The effect of log energy entropy (without wavelets) was also studied. It was found from the simulation results that the wavelet packet log entropy with REN classifier yielded a classification accuracy of 99.70 % for normal-pre-ictal, 99.70 % for normal-epileptic and 99.85 % for pre-ictal-epileptic.
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27
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Boon P, Vonck K, van Rijckevorsel K, Tahry RE, Elger CE, Mullatti N, Schulze-Bonhage A, Wagner L, Diehl B, Hamer H, Reuber M, Kostov H, Legros B, Noachtar S, Weber YG, Coenen VA, Rooijakkers H, Schijns OE, Selway R, Van Roost D, Eggleston KS, Van Grunderbeek W, Jayewardene AK, McGuire RM. A prospective, multicenter study of cardiac-based seizure detection to activate vagus nerve stimulation. Seizure 2015; 32:52-61. [DOI: 10.1016/j.seizure.2015.08.011] [Citation(s) in RCA: 101] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2015] [Revised: 08/09/2015] [Accepted: 08/13/2015] [Indexed: 10/23/2022] Open
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28
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Acharya UR, Fujita H, Sudarshan VK, Bhat S, Koh JE. Application of entropies for automated diagnosis of epilepsy using EEG signals: A review. Knowl Based Syst 2015. [DOI: 10.1016/j.knosys.2015.08.004] [Citation(s) in RCA: 192] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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29
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Vahabi Z, Amirfattahi R, Shayegh F, Ghassemi F. Online Epileptic Seizure Prediction Using Wavelet-Based Bi-Phase Correlation of Electrical Signals Tomography. Int J Neural Syst 2015; 25:1550028. [DOI: 10.1142/s0129065715500288] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Considerable efforts have been made in order to predict seizures. Among these methods, the ones that quantify synchronization between brain areas, are the most important methods. However, to date, a practically acceptable result has not been reported. In this paper, we use a synchronization measurement method that is derived according to the ability of bi-spectrum in determining the nonlinear properties of a system. In this method, first, temporal variation of the bi-spectrum of different channels of electro cardiography (ECoG) signals are obtained via an extended wavelet-based time-frequency analysis method; then, to compare different channels, the bi-phase correlation measure is introduced. Since, in this way, the temporal variation of the amount of nonlinear coupling between brain regions, which have not been considered yet, are taken into account, results are more reliable than the conventional phase-synchronization measures. It is shown that, for 21 patients of FSPEEG database, bi-phase correlation can discriminate the pre-ictal and ictal states, with very low false positive rates (FPRs) (average: 0.078/h) and high sensitivity (100%). However, the proposed seizure predictor still cannot significantly overcome the random predictor for all patients.
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Affiliation(s)
- Zahra Vahabi
- Digital Signal Processing Research Lab, Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran
| | - Rasoul Amirfattahi
- Digital Signal Processing Research Lab, Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran
| | - Farzaneh Shayegh
- Department of Electrical Engineering, Payame Noor University (PNU), Isfahan, Iran
- Medical Image and Signal Processing Research Center, Medical University of Isfahan, Isfahan, Iran
| | - Fahimeh Ghassemi
- Department of Advanced Medical Technologies, Medical University of Isfahan, Isfahan, Iran
- Medical Image and Signal Processing Research Center, Medical University of Isfahan, Isfahan, Iran
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30
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Sierra-Marcos A, Scheuer ML, Rossetti AO. Seizure detection with automated EEG analysis: A validation study focusing on periodic patterns. Clin Neurophysiol 2015; 126:456-62. [DOI: 10.1016/j.clinph.2014.06.025] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2014] [Revised: 05/23/2014] [Accepted: 06/15/2014] [Indexed: 11/25/2022]
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31
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Automatic epileptic seizure detection using scalp EEG and advanced artificial intelligence techniques. BIOMED RESEARCH INTERNATIONAL 2015; 2015:986736. [PMID: 25710040 PMCID: PMC4325968 DOI: 10.1155/2015/986736] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2014] [Revised: 12/09/2014] [Accepted: 12/23/2014] [Indexed: 11/17/2022]
Abstract
The epilepsies are a heterogeneous group of neurological disorders and syndromes characterised by recurrent, involuntary, paroxysmal seizure activity, which is often associated with a clinicoelectrical correlate on the electroencephalogram. The diagnosis of epilepsy is usually made by a neurologist but can be difficult to be made in the early stages. Supporting paraclinical evidence obtained from magnetic resonance imaging and electroencephalography may enable clinicians to make a diagnosis of epilepsy and investigate treatment earlier. However, electroencephalogram capture and interpretation are time consuming and can be expensive due to the need for trained specialists to perform the interpretation. Automated detection of correlates of seizure activity may be a solution. In this paper, we present a supervised machine learning approach that classifies seizure and nonseizure records using an open dataset containing 342 records. Our results show an improvement on existing studies by as much as 10% in most cases with a sensitivity of 93%, specificity of 94%, and area under the curve of 98% with a 6% global error using a k-class nearest neighbour classifier. We propose that such an approach could have clinical applications in the investigation of patients with suspected seizure disorders.
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32
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Zheng YX, Zhu JM, Qi Y, Zheng XX, Zhang JM. An Automatic Patient-Specific Seizure Onset Detection Method Using Intracranial Electroencephalography. Neuromodulation 2014; 18:79-84; discussion 84. [DOI: 10.1111/ner.12214] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2013] [Revised: 04/13/2014] [Accepted: 05/13/2014] [Indexed: 11/29/2022]
Affiliation(s)
- Yu-xin Zheng
- Department of Neurosurgery, The Second Affiliated Hospital, College of Medicine; Zhejiang University; Hangzhou China
| | - Jun-ming Zhu
- Department of Neurosurgery, The Second Affiliated Hospital, College of Medicine; Zhejiang University; Hangzhou China
| | - Yu Qi
- Qiushi Academy for Advanced Studies; Zhejiang University; Hangzhou China
| | - Xiao-xiang Zheng
- Qiushi Academy for Advanced Studies; Zhejiang University; Hangzhou China
| | - Jian-min Zhang
- Department of Neurosurgery, The Second Affiliated Hospital, College of Medicine; Zhejiang University; Hangzhou China
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33
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Imtiaz SA, Logesparan L, Rodriguez-Villegas E. Performance-power consumption tradeoff in wearable epilepsy monitoring systems. IEEE J Biomed Health Inform 2014; 19:1019-1028. [PMID: 25069131 DOI: 10.1109/jbhi.2014.2342501] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Automated seizure detection methods can be used to reduce time and costs associated with analyzing large volumes of ambulatory EEG recordings. These methods however have to rely on very complex, power hungry algorithms, implemented on the system backend, in order to achieve acceptable levels of accuracy. In size, and therefore power-constrained EEG systems, an alternative approach to the problem of data reduction is online data selection, in which simpler algorithms select potential epileptiform activity for discontinuous recording but accurate analysis is still left to a medical practitioner. Such a diagnostic decision support system would still provide doctors with information relevant for diagnosis while reducing the time taken to analyze the EEG. For wearable systems with limited power budgets, data selection algorithm must be of sufficiently low complexity in order to reduce the amount of data transmitted and the overall power consumption. In this paper, we present a low-power hardware implementation of an online epileptic seizure data selection algorithm with encryption and data transmission and demonstrate the tradeoffs between its accuracy and the overall system power consumption. We demonstrate that overall power savings by data selection can be achieved by transmitting less than 40% of the data. We also show a 29% power reduction when selecting and transmitting 94% of all seizure events and only 10% of background EEG.
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Affiliation(s)
- Syed Anas Imtiaz
- Department of Electrical and Electronic Engineering, Imperial College London, London, U.K
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34
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Automatic seizure detection in long-term scalp EEG using an adaptive thresholding technique: A validation study for clinical routine. Clin Neurophysiol 2014; 125:1346-52. [DOI: 10.1016/j.clinph.2013.12.104] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2013] [Revised: 10/29/2013] [Accepted: 12/07/2013] [Indexed: 11/21/2022]
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35
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Chen LL, Zhang J, Zou JZ, Zhao CJ, Wang GS. A framework on wavelet-based nonlinear features and extreme learning machine for epileptic seizure detection. Biomed Signal Process Control 2014. [DOI: 10.1016/j.bspc.2013.11.010] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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36
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Long-cherished dreams for epileptologists and clinical neurophysiologists: automatic seizure detection in long-term scalp EEG. Clin Neurophysiol 2014; 125:1289-90. [PMID: 24439072 DOI: 10.1016/j.clinph.2013.12.105] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2013] [Accepted: 12/30/2013] [Indexed: 11/24/2022]
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37
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Niknazar M, Mousavi SR, Vosoughi Vahdat B, Sayyah M. A New Framework Based on Recurrence Quantification Analysis for Epileptic Seizure Detection. IEEE J Biomed Health Inform 2013; 17:572-8. [DOI: 10.1109/jbhi.2013.2255132] [Citation(s) in RCA: 64] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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38
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Conradsen I, Beniczky S, Wolf P, Kjaer TW, Sams T, Sorensen HBD. Automatic multi-modal intelligent seizure acquisition (MISA) system for detection of motor seizures from electromyographic data and motion data. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2012; 107:97-110. [PMID: 21724291 DOI: 10.1016/j.cmpb.2011.06.005] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2010] [Revised: 05/20/2011] [Accepted: 06/08/2011] [Indexed: 05/31/2023]
Abstract
The objective is to develop a non-invasive automatic method for detection of epileptic seizures with motor manifestations. Ten healthy subjects who simulated seizures and one patient participated in the study. Surface electromyography (sEMG) and motion sensor features were extracted as energy measures of reconstructed sub-bands from the discrete wavelet transformation (DWT) and the wavelet packet transformation (WPT). Based on the extracted features all data segments were classified using a support vector machine (SVM) algorithm as simulated seizure or normal activity. A case study of the seizure from the patient showed that the simulated seizures were visually similar to the epileptic one. The multi-modal intelligent seizure acquisition (MISA) system showed high sensitivity, short detection latency and low false detection rate. The results showed superiority of the multi-modal detection system compared to the uni-modal one. The presented system has a promising potential for seizure detection based on multi-modal data.
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Affiliation(s)
- Isa Conradsen
- Technical University of Denmark, Department of Electrical Engineering, Kgs. Lyngby, Denmark.
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39
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Hartmann MM, Fürbass F, Perko H, Skupch A, Lackmayer K, Baumgartner C, Kluge T. EpiScan: online seizure detection for epilepsy monitoring units. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2011:6096-9. [PMID: 22255730 DOI: 10.1109/iembs.2011.6091506] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
An online seizure detection algorithm for long-term EEG monitoring is presented, which is based on a periodic waveform analysis detecting rhythmic EEG patterns and an adaptation module automatically adjusting the algorithm to patient-specific EEG properties. The algorithm was evaluated using 4.300 hours of unselected EEG recordings from 48 patients with temporal lobe epilepsy. For 66% of the patients the algorithm detected 100% of the seizures. A mean sensitivity of 83% was achieved. An average of 7.2 false alarms within 24 hours for unselected EEG makes the algorithm attractive for epilepsy monitoring units.
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Affiliation(s)
- Manfred M Hartmann
- Austrian Institute of Technology, Donau-City-Strasse 1, 1220 Vienna, Austria.
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40
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Logesparan L, Casson AJ, Rodriguez-Villegas E. Optimal features for online seizure detection. Med Biol Eng Comput 2012; 50:659-69. [PMID: 22476713 DOI: 10.1007/s11517-012-0904-x] [Citation(s) in RCA: 62] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2011] [Accepted: 03/21/2012] [Indexed: 11/26/2022]
Abstract
This study identifies characteristic features in scalp EEG that simultaneously give the best discrimination between epileptic seizures and background EEG in minimally pre-processed scalp data; and have minimal computational complexity to be suitable for online, real-time analysis. The discriminative performance of 65 previously reported features has been evaluated in terms of sensitivity, specificity, area under the sensitivity-specificity curve (AUC), and relative computational complexity, on 47 seizures (split in 2,698 2 s sections) in over 172 h of scalp EEG from 24 adults. The best performing features are line length and relative power in the 12.5-25 Hz band. Relative power has a better seizure detection performance (AUC = 0.83; line length AUC = 0.77), but is calculated after the discrete wavelet transform and is thus more computationally complex. Hence, relative power achieves the best performance for offline detection, whilst line length would be preferable for online low complexity detection. These results, from the largest systematic study of seizure detection features, aid future researchers in selecting an optimal set of features when designing algorithms for both standard offline detection and new online low computational complexity detectors.
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Affiliation(s)
- Lojini Logesparan
- Electrical and Electronic Engineering Department, Imperial College London, London, UK.
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41
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Yadav R, Shah AK, Loeb JA, Swamy MNS, Agarwal R. Morphology-based automatic seizure detector for intracerebral EEG recordings. IEEE Trans Biomed Eng 2012; 59:1871-81. [PMID: 22434792 DOI: 10.1109/tbme.2012.2190601] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In this paper, a new seizure detection system aimed at assisting in a rapid review of prolonged intracerebral EEG recordings is described. It is based on quantifying the sharpness of the waveform, one of the most important electrographic EEG features utilized by experts for an accurate and reliable identification of a seizure. The waveform morphology is characterized by a measure of sharpness as defined by the slope of the half-waves. A train of abnormally sharp waves resulting from subsequent filtering are used to identify seizures. The method was optimized using 145 h of single-channel depth EEG from seven patients, and tested on another 158 h of single-channel depth EEG from another seven patients. Additionally, 725 h of depth EEG from 21 patients was utilized to assess the system performance in a multichannel configuration. Single-channel test data resulted in a sensitivity of 87% and a specificity of 71%. The multichannel test data reported a sensitivity of 81% and a specificity of 58.9%. The new system detected a wide range of seizure patterns that included rhythmic and nonrhythmic seizures of varying length, including those missed by the experts. We also compare the proposed system with a popular commercial system.
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Affiliation(s)
- R Yadav
- Department of Electrical and Computer Engineering, Concordia University, Montreal, QC H3G 1M8, Canada.
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43
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Qi Y, Wang Y, Zheng X, Zhang J, Zhu J, Guo J. Efficient epileptic seizure detection by a combined IMF-VoE feature. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2012:5170-5173. [PMID: 23367093 DOI: 10.1109/embc.2012.6347158] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Automatic seizure detection from the electroen-cephalogram (EEG) plays an important role in an on-demand closed-loop therapeutic system. A new feature, called IMF-VoE, is proposed to predict the occurrence of seizures. The IMF-VoE feature combines three intrinsic mode functions (IMFs) from the empirical mode decomposition of a EEG signal and the variance of the range between the upper and lower envelopes (VoE) of the signal. These multiple cues encode the intrinsic characteristics of seizure states, thus are able to distinguish them from the background. The feature is tested on 80.4 hours of EEG data with 10 seizures of 4 patients. The sensitivity of 100% is obtained with a low false detection rate of 0.16 per hour. Average time delays are 19.4s, 13.2s, and 10.7s at the false detection rates of 0.16 per hour, 0.27 per hour, and 0.41 per hour respectively, when different thresholds are used. The result is competitive among recent studies. In addition, since the IMF-VoE is compact, the detection system is of high computational efficiency and able to run in real time.
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Affiliation(s)
- Yu Qi
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, China
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44
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A machine-learning algorithm for detecting seizure termination in scalp EEG. Epilepsy Behav 2011; 22 Suppl 1:S36-43. [PMID: 22078516 DOI: 10.1016/j.yebeh.2011.08.040] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2011] [Revised: 08/27/2011] [Accepted: 08/29/2011] [Indexed: 11/24/2022]
Abstract
Efforts to develop algorithms that can robustly detect the cessation of seizure activity within scalp EEGs are now underway. Such algorithms can facilitate novel clinical applications such as the estimation of a seizure's duration; the delivery of therapies designed to mitigate postictal period symptoms; or detection of the presence of status epilepticus. In this article, we present and evaluate a novel, machine learning-based method for detecting the termination of electrographic seizure activity. When tested on 133 seizures from a public database, our method successfully detected the end of 132 seizures within 10.3 ± 5.5 seconds of the time determined by an electroencephalographer to represent the electrographic end of seizure. Furthermore, by pairing our seizure end detector with a previously published seizure onset detector, we could automatically estimate the duration of 85% of test electrographic seizures within a 15-second error margin compared with electroencephalographer determinations. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.
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45
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Sackellares JC, Shiau DS, Halford JJ, LaRoche SM, Kelly KM. Quantitative EEG analysis for automated detection of nonconvulsive seizures in intensive care units. Epilepsy Behav 2011; 22 Suppl 1:S69-73. [PMID: 22078521 PMCID: PMC4342615 DOI: 10.1016/j.yebeh.2011.08.028] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2011] [Accepted: 08/23/2011] [Indexed: 11/30/2022]
Abstract
Because of increased awareness of the high prevalence of nonconvulsive seizures in critically ill patients, use of continuous EEG (cEEG) monitoring is rapidly increasing in ICUs. However, cEEG monitoring is labor intensive, and manual review and interpretation of the EEG are impractical in most ICUs. Effective methods to assist in rapid and accurate detection of nonconvulsive seizures would greatly reduce the cost of cEEG monitoring and enhance the quality of patient care. In this study, we report a preliminary investigation of a novel ICU EEG analysis and seizure detection algorithm. Twenty-four prolonged cEEG recordings were included in this study. Seizure detection sensitivity and specificity were assessed for the new algorithm and for the two commercial seizure detection software systems. The new algorithm performed with a mean sensitivity of 90.4% and a mean false detection rate of 0.066/hour. The two commercial detection products performed with low sensitivities (12.9 and 10.1%) and false detection rates of 1.036/hour and 0.013/hour, respectively. These findings suggest that the novel algorithm has potential to be the basis of clinically useful software that can assist ICU staff in timely identification of nonconvulsive seizures. This study also suggests that currently available seizure detection software does not perform sufficiently in detection of nonconvulsive seizures in critically ill patients. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.
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Affiliation(s)
- J. Chris Sackellares
- Optima Neuroscience, Inc., Alachua, FL, USA
- Correspondence and reprint requests: J. Chris Sackellares, M.D., Optima Neuroscience, Inc., 13420 Progress Blvd., Suit 200, Alachua, FL 32615, USA, Tel: +1 352-371-8281, Fax: +1 386-462-0606,
| | | | - Jonathon J. Halford
- Department of Neuroscience, Division of Neurology, Medical University of South Carolina, Charleston, SC, USA
| | - Suzette M. LaRoche
- Department of Neurology, Emory University School of Medicine, Atlanta, George, USA
| | - Kevin M. Kelly
- Center for Neuroscience Research, Allegheny-Singer Research Institute, Allegheny General Hospital, Pittsburgh, PA, USA
- Department of Neurology; Departments of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, PA, USA
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46
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Kelly KM, Shiau DS, Jukkola PI, Miller ER, Mercadante AL, Quigley MM, Nair SP, Sackellares JC. Effects of age and cortical infarction on EEG dynamic changes associated with spike wave discharges in F344 rats. Exp Neurol 2011; 232:15-21. [PMID: 21820433 DOI: 10.1016/j.expneurol.2011.07.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2011] [Revised: 07/07/2011] [Accepted: 07/18/2011] [Indexed: 12/14/2022]
Abstract
Rodent models of absence seizures are used to investigate the network properties and regulatory mechanisms of the seizure's generalized spike and wave discharge (SWD). As rats age, SWDs occur more frequently, suggesting aging-related changes in the regulation of the corticothalamic mechanisms generating the SWD. We hypothesized that brain resetting mechanisms - how the brain "resets" itself to a more normal functional state following a transient period of abnormal function, e.g., a SWD - are impaired in aged animals and that brain infarction would further affect these resetting mechanisms. The main objective of this study was to determine the effects of aging, infarction, and their potential interaction on the resetting of EEG dynamics assessed by quantitative EEG (qEEG) measures of linear (signal energy measured by amplitude variation; signal frequency measured by mean zero-crossings) and nonlinear (signal complexity measured by the pattern match regularity statistic and the short-term maximum Lyapunov exponent) brain EEG dynamics in 4- and 20-month-old F344 rats with and without brain infarction. The main findings of the study were: 1) dynamic resetting of both linear and nonlinear EEG characteristics occurred following SWDs; 2) animal age significantly affected the degree of dynamic resetting in all four qEEG measures: SWDs in older rats exhibited a lower degree of dynamic resetting; 3) infarction significantly affected the degree of dynamic resetting only in terms of EEG signal complexity: SWDs in infarcted rats exhibited a lower degree of dynamic resetting; and 4) in all four qEEG measures, there was no significant interaction effect between age and infarction on dynamic resetting. We conclude that recovery of the brain to its interictal state following SWDs was better in young adult animals compared with aged animals, and to a lesser degree, in age-matched controls compared with infarction-injured animal groups, suggesting possible effects of brain resetting mechanisms and/or the disruption of the epileptogenic network that triggers SWDs.
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Affiliation(s)
- Kevin M Kelly
- Center for Neuroscience Research, Allegheny-Singer Research Institute, Department of Neurology, Allegheny General Hospital, Pittsburgh, PA, USA.
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47
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Halford JJ, Shiau DS, Kern RT, Stroman CA, Kelly KM, Sackellares JC. Seizure Detection Software Used to Complement the Visual Screening Process for Long-Term EEG Monitoring. AMERICAN JOURNAL OF ELECTRONEURODIAGNOSTIC TECHNOLOGY 2010; 50:133-147. [PMID: 26658426 PMCID: PMC4674077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
It is widely recognized that visual screening of long-term EEG recordings can be time-consuming and labor-intensive due to the large volume of patient data produced daily in most Epilepsy Monitoring Units (EMUs). As a result, seizures, especially those with only electrographic changes, are sometimes overlooked, which for some patients could result in missed information for diagnosis, an unnecessarily prolonged hospital stay, and unavailable EMU beds for others. In this report, we propose that a better solution for identifying seizures in long-term EEG recording is to combine detection results from a reliable (high sensitivity and low false detection rate) automated detection system with EEG technologists' visual screening process. Using commercially available detection software, we present case studies that demonstrate potential benefits of this method that could help improve detection rates and bring greater efficiency to the seizure identification process in long-term EEG monitoring.
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Affiliation(s)
- Jonathan J Halford
- Department of Neurosciences, Medical University of South Carolina, Charleston, South Carolina
| | | | | | | | - Kevin M Kelly
- Center for Neuroscience Research, Allegheny-Singer Research Institute, Allegheny General Hospital, Pittsburgh, Pennsylvania ; Department of Neurology, Philadelphia, Pennsylvania ; Department of Neurobiology and Anatomy, Philadelphia, Pennsylvania ; Drexel University College of Medicine, Philadelphia, Pennsylvania
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