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Aud'hui M, Kachenoura A, Yochum M, Kaminska A, Nabbout R, Wendling F, Kuchenbuch M, Benquet P. Detection of seizure onset in childhood absence epilepsy. Clin Neurophysiol 2024; 163:267-279. [PMID: 38644110 DOI: 10.1016/j.clinph.2024.03.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 03/19/2024] [Accepted: 03/26/2024] [Indexed: 04/23/2024]
Abstract
OBJECTIVE This study aims to detect the seizure onset, in childhood absence epilepsy, as early as possible. Indeed, interfering with absence seizures with sensory simulation has been shown to be possible on the condition that the stimulation occurs soon enough after the seizure onset. METHODS We present four variations (two supervised, two unsupervised) of an algorithm designed to detect the onset of absence seizures from 4 scalp electrodes, and compare their performance with that of a state-of-the-art algorithm. We exploit the characteristic shape of spike-wave discharges to detect the seizure onset. Their performance is assessed on clinical electroencephalograms from 63 patients with confirmed childhood absence epilepsy. RESULTS The proposed approaches succeed in early detection of the seizure onset, contrary to the classical detection algorithm. Indeed, the results clearly show the superiority of the proposed methods for small delays of detection, under 750 ms from the onset. CONCLUSION The performance of the proposed unsupervised methods is equivalent to that of the supervised ones. The use of only four electrodes makes the pipeline suitable to be embedded in a wearable device. SIGNIFICANCE The proposed pipelines perform early detection of absence seizures, which constitutes a prerequisite for a closed-loop system.
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Affiliation(s)
- M Aud'hui
- Univ Rennes, INSERM, LTSI - UMR 1099, Rennes F-35000, France
| | - A Kachenoura
- Univ Rennes, INSERM, LTSI - UMR 1099, Rennes F-35000, France
| | - M Yochum
- Univ Rennes, INSERM, LTSI - UMR 1099, Rennes F-35000, France.
| | - A Kaminska
- Department of Clinical Neurophysiology, Hôpital Necker Enfants Malades, AP-HP, Université de Paris, Paris, France
| | - R Nabbout
- Department of Clinical Neurophysiology, Hôpital Necker Enfants Malades, AP-HP, Université de Paris, Paris, France; Reference Center for Rare Epilepsies, Department of Pediatric Neurology, Member of EPICARE Network, Institute Imagine INSERM 1163, Université de Paris, Paris, France
| | - F Wendling
- Univ Rennes, INSERM, LTSI - UMR 1099, Rennes F-35000, France
| | - M Kuchenbuch
- Pediatric and Genetic Department, CHU, Nancy, France
| | - P Benquet
- Univ Rennes, INSERM, LTSI - UMR 1099, Rennes F-35000, France
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Davidhi F, Costa F, Ramantani G, Sarnthein J. Instantly detecting absence seizures. Clin Neurophysiol 2024; 163:263-264. [PMID: 38760294 DOI: 10.1016/j.clinph.2024.05.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 05/05/2024] [Accepted: 05/07/2024] [Indexed: 05/19/2024]
Affiliation(s)
- Flavia Davidhi
- Klinik für Neurochirurgie, Universitätsspital Zürich, Universität Zürich, Zurich, Switzerland
| | - Filippo Costa
- Klinik für Neurochirurgie, Universitätsspital Zürich, Universität Zürich, Zurich, Switzerland
| | - Georgia Ramantani
- Abteilung für pädiatrische Epileptologie, Universitäts-Kinderspital Zürich, Universität Zürich, Zurich, Switzerland; Zentrum für Neurowissenschaften (ZNZ) Neuroscience Center Zurich, Universität Zürich und ETH Zürich, Zurich, Switzerland
| | - Johannes Sarnthein
- Klinik für Neurochirurgie, Universitätsspital Zürich, Universität Zürich, Zurich, Switzerland; Zentrum für Neurowissenschaften (ZNZ) Neuroscience Center Zurich, Universität Zürich und ETH Zürich, Zurich, Switzerland; Zentrum für klinische Neurowissenschaften (KNZ), Universitätsspital Zürich und Universität Zürich, Zurich, Switzerland.
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Miron G, Halimeh M, Jeppesen J, Loddenkemper T, Meisel C. Autonomic biosignals, seizure detection, and forecasting. Epilepsia 2024. [PMID: 38837428 DOI: 10.1111/epi.18034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 05/17/2024] [Accepted: 05/22/2024] [Indexed: 06/07/2024]
Abstract
Wearable devices have attracted significant attention in epilepsy research in recent years for their potential to enhance patient care through improved seizure monitoring and forecasting. This narrative review presents a detailed overview of the current clinical state of the art while addressing how devices that assess autonomic nervous system (ANS) function reflect seizures and central nervous system (CNS) state changes. This includes a description of the interactions between the CNS and the ANS, including physiological and epilepsy-related changes affecting their dynamics. We first discuss technical aspects of measuring autonomic biosignals and considerations for using ANS sensors in clinical practice. We then review recent seizure detection and seizure forecasting studies, highlighting their performance and capability for seizure detection and forecasting using devices measuring ANS biomarkers. Finally, we address the field's challenges and provide an outlook for future developments.
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Affiliation(s)
- Gadi Miron
- Computational Neurology, Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Mustafa Halimeh
- Computational Neurology, Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Jesper Jeppesen
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Tobias Loddenkemper
- Department of Neurology, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Christian Meisel
- Computational Neurology, Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
- Center for Stroke Research Berlin, Berlin, Germany
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Han K, Liu C, Friedman D. Artificial intelligence/machine learning for epilepsy and seizure diagnosis. Epilepsy Behav 2024; 155:109736. [PMID: 38636146 DOI: 10.1016/j.yebeh.2024.109736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 03/03/2024] [Accepted: 03/10/2024] [Indexed: 04/20/2024]
Abstract
Accurate seizure and epilepsy diagnosis remains a challenging task due to the complexity and variability of manifestations, which can lead to delayed or missed diagnosis. Machine learning (ML) and artificial intelligence (AI) is a rapidly developing field, with growing interest in integrating and applying these tools to aid clinicians facing diagnostic uncertainties. ML algorithms, particularly deep neural networks, are increasingly employed in interpreting electroencephalograms (EEG), neuroimaging, wearable data, and seizure videos. This review discusses the development and testing phases of AI/ML tools, emphasizing the importance of generalizability and interpretability in medical applications, and highlights recent publications that demonstrate the current and potential utility of AI to aid clinicians in diagnosing epilepsy. Current barriers of AI integration in patient care include dataset availability and heterogeneity, which limit studies' quality, interpretability, comparability, and generalizability. ML and AI offer substantial promise in improving the accuracy and efficiency of epilepsy diagnosis. The growing availability of diverse datasets, enhanced processing speed, and ongoing efforts to standardize reporting contribute to the evolving landscape of AI applications in clinical care.
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Affiliation(s)
- Kenneth Han
- Departments of Neurology, NYU Grossman School of Medicine, New York, NY, United States
| | - Chris Liu
- Departments of Neurosurgery, NYU Grossman School of Medicine, New York, NY, United States
| | - Daniel Friedman
- Departments of Neurology, NYU Grossman School of Medicine, New York, NY, United States.
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Gharbi O, Lamrani Y, St-Jean J, Jahani A, Toffa DH, Tran TPY, Robert M, Nguyen DK, Bou Assi E. Detection of focal to bilateral tonic-clonic seizures using a connected shirt. Epilepsia 2024. [PMID: 38780375 DOI: 10.1111/epi.18021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Revised: 05/07/2024] [Accepted: 05/07/2024] [Indexed: 05/25/2024]
Abstract
OBJECTIVE This study was undertaken to develop and evaluate a machine learning-based algorithm for the detection of focal to bilateral tonic-clonic seizures (FBTCS) using a novel multimodal connected shirt. METHODS We prospectively recruited patients with epilepsy admitted to our epilepsy monitoring unit and asked them to wear the connected shirt while under simultaneous video-electroencephalographic monitoring. Electrocardiographic (ECG) and accelerometric (ACC) signals recorded with the connected shirt were used for the development of the seizure detection algorithm. First, we used a sliding window to extract linear and nonlinear features from both ECG and ACC signals. Then, we trained an extreme gradient boosting algorithm (XGBoost) to detect FBTCS according to seizure onset and offset annotated by three board-certified epileptologists. Finally, we applied a postprocessing step to regularize the classification output. A patientwise nested cross-validation was implemented to evaluate the performances in terms of sensitivity, false alarm rate (FAR), time in false warning (TiW), detection latency, and receiver operating characteristic area under the curve (ROC-AUC). RESULTS We recorded 66 FBTCS from 42 patients who wore the connected shirt for a total of 8067 continuous hours. The XGBoost algorithm reached a sensitivity of 84.8% (56/66 seizures), with a median FAR of .55/24 h and a median TiW of 10 s/alarm. ROC-AUC was .90 (95% confidence interval = .88-.91). Median detection latency from the time of progression to the bilateral tonic-clonic phase was 25.5 s. SIGNIFICANCE The novel connected shirt allowed accurate detection of FBTCS with a low false alarm rate in a hospital setting. Prospective studies in a residential setting with a real-time and online seizure detection algorithm are required to validate the performance and usability of this device.
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Affiliation(s)
- Oumayma Gharbi
- Department of Neuroscience, Université de Montréal, Montréal, Quebec, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Quebec, Canada
| | - Yassine Lamrani
- Department of Neuroscience, Université de Montréal, Montréal, Quebec, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Quebec, Canada
| | - Jérôme St-Jean
- Department of Neuroscience, Université de Montréal, Montréal, Quebec, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Quebec, Canada
| | - Amirhossein Jahani
- Department of Neuroscience, Université de Montréal, Montréal, Quebec, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Quebec, Canada
| | - Dènahin Hinnoutondji Toffa
- Department of Neuroscience, Université de Montréal, Montréal, Quebec, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Quebec, Canada
| | - Thi Phuoc Yen Tran
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Quebec, Canada
| | - Manon Robert
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Quebec, Canada
| | - Dang Khoa Nguyen
- Department of Neuroscience, Université de Montréal, Montréal, Quebec, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Quebec, Canada
| | - Elie Bou Assi
- Department of Neuroscience, Université de Montréal, Montréal, Quebec, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Quebec, Canada
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Vakilna YS, Li X, Hampson JS, Huang Y, Mosher JC, Dabaghian Y, Luo X, Talavera B, Pati S, Todd M, Hays R, Szabo CA, Zhang GQ, Lhatoo SD. Reliable detection of generalized convulsive seizures using an off-the-shelf digital watch: A multisite phase 2 study. Epilepsia 2024. [PMID: 38738972 DOI: 10.1111/epi.17974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 03/19/2024] [Accepted: 03/25/2024] [Indexed: 05/14/2024]
Abstract
OBJECTIVE The aim of this study was to develop a machine learning algorithm using an off-the-shelf digital watch, the Samsung watch (SM-R800), and evaluate its effectiveness for the detection of generalized convulsive seizures (GCS) in persons with epilepsy. METHODS This multisite epilepsy monitoring unit (EMU) phase 2 study included 36 adult patients. Each patient wore a Samsung watch that contained accelerometer, gyroscope, and photoplethysmographic sensors. Sixty-eight time and frequency domain features were extracted from the sensor data and were used to train a random forest algorithm. A testing framework was developed that would better reflect the EMU setting, consisting of (1) leave-one-patient-out cross-validation (LOPO CV) on GCS patients, (2) false alarm rate (FAR) testing on nonseizure patients, and (3) "fixed-and-frozen" prospective testing on a prospective patient cohort. Balanced accuracy, precision, sensitivity, and FAR were used to quantify the performance of the algorithm. Seizure onsets and offsets were determined by using video-electroencephalographic (EEG) monitoring. Feature importance was calculated as the mean decrease in Gini impurity during the LOPO CV testing. RESULTS LOPO CV results showed balanced accuracy of .93 (95% confidence interval [CI] = .8-.98), precision of .68 (95% CI = .46-.85), sensitivity of .87 (95% CI = .62-.96), and FAR of .21/24 h (interquartile range [IQR] = 0-.90). Testing the algorithm on patients without seizure resulted in an FAR of .28/24 h (IQR = 0-.61). During the "fixed-and-frozen" prospective testing, two patients had three GCS, which were detected by the algorithm, while generating an FAR of .25/24 h (IQR = 0-.89). Feature importance showed that heart rate-based features outperformed accelerometer/gyroscope-based features. SIGNIFICANCE Commercially available wearable digital watches that reliably detect GCS, with minimum false alarm rates, may overcome usage adoption and other limitations of custom-built devices. Contingent on the outcomes of a prospective phase 3 study, such devices have the potential to provide non-EEG-based seizure surveillance and forecasting in the clinical setting.
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Affiliation(s)
- Yash Shashank Vakilna
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
- Texas Institute of Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Xiaojin Li
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
- Texas Institute of Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Jaison S Hampson
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
- Texas Institute of Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Yan Huang
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
- Texas Institute of Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - John C Mosher
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
- Texas Institute of Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Yuri Dabaghian
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
- Texas Institute of Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Xi Luo
- Department of Biostatistics and Data Science, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Blanca Talavera
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
- Texas Institute of Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Sandipan Pati
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
- Texas Institute of Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Masel Todd
- Department of Neurology, University of Texas Medical Branch, Galveston, Texas, USA
| | - Ryan Hays
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Charles Akos Szabo
- Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
| | - Guo-Qiang Zhang
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
- Texas Institute of Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Samden D Lhatoo
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
- Texas Institute of Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, Texas, USA
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Borges DF, Fernandes J, Soares JI, Casalta-Lopes J, Carvalho D, Beniczky S, Leal A. The sound of silence: Quantification of typical absence seizures by sonifying EEG signals from a custom-built wearable device. Epileptic Disord 2024; 26:188-198. [PMID: 38279944 DOI: 10.1002/epd2.20194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 11/29/2023] [Accepted: 12/22/2023] [Indexed: 01/29/2024]
Abstract
OBJECTIVE To develop and validate a method for long-term (24-h) objective quantification of absence seizures in the EEG of patients with childhood absence epilepsy (CAE) in their real home environment using a wearable device (waEEG), comparing automatic detection methods with auditory recognition after seizure sonification. METHODS The waEEG recording was acquired with two scalp electrodes. Automatic analysis was performed using previously validated software (Persyst® 14) and then fully reviewed by an experienced clinical neurophysiologist. The EEG data were converted into an audio file in waveform format with a 60-fold time compression factor. The sonified EEG was listened to by three inexperienced observers and the number of seizures and the processing time required for each data set were recorded blind to other data. Quantification of seizures from the patient diary was also assessed. RESULTS Eleven waEEG recordings from seven CAE patients with an average age of 8.18 ± 1.60 years were included. No differences in the number of seizures were found between the recordings using automated methods and expert audio assessment, with significant correlations between methods (ρ > .89, p < .001) and between observers (ρ > .96, p < .001). For the entire data set, the audio assessment yielded a sensitivity of .830 and a precision of .841, resulting in an F1 score of .835. SIGNIFICANCE Auditory waEEG seizure detection by lay medical personnel provided similar accuracy to post-processed automatic detection by an experienced clinical neurophysiologist, but in a less time-consuming procedure and without the need for specialized resources. Sonification of long-term EEG recordings in CAE provides a user-friendly and cost-effective clinical workflow for quantifying seizures in clinical practice, minimizing human and technical constraints.
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Affiliation(s)
- Daniel Filipe Borges
- Department of Neurophysiology, School of Health (ESS), Polytechnic University of Porto, Porto, Portugal
- Center for Translational Health and Medical Biotechnology Research (TBIO), School of Health, Polytechnic University of Porto, Porto, Portugal
- Faculty of Medicine, University of Porto, Porto, Portugal
| | - João Fernandes
- Department of Clinical Physiology, Medical Imaging and Radiotherapy, Polytechnic University of Coimbra, Coimbra Health School, Coimbra, Portugal
- Refractory Epilepsy Reference Center, Centro Hospitalar de Lisboa Ocidental, Lisboa, Portugal
| | - Joana Isabel Soares
- Department of General Sciences, Polytechnic University of Coimbra, Coimbra Health School, Coimbra, Portugal
- Department of Biomedicine, Faculty of Medicine, University of Porto, Porto, Portugal
- Neuronal Networks Group, Institute for Research and Innovation in Health Sciences (i3S), University of Porto, Porto, Portugal
| | - João Casalta-Lopes
- Department of General Sciences, Polytechnic University of Coimbra, Coimbra Health School, Coimbra, Portugal
- Department of Radiotherapy, Centro Hospitalar Universitário de São João, Porto, Portugal
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
| | - Daniel Carvalho
- Department of Pediatric Neurology, Centro Hospitalar Universitário de Lisboa Central, Lisbon, Portugal
| | - Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund, Denmark
- Department of Clinical Medicine and Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
| | - Alberto Leal
- Unidade Autónoma de Neurofisiologia, Hospital Júlio de Matos, Lisbon, Portugal
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Donner E, Devinsky O, Friedman D. Wearable Digital Health Technology for Epilepsy. N Engl J Med 2024; 390:736-745. [PMID: 38381676 DOI: 10.1056/nejmra2301913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Affiliation(s)
- Elizabeth Donner
- From the Division of Neurology, Hospital for Sick Children, and the Department of Paediatrics, University of Toronto - both in Toronto (E.D.); and the Epilepsy Center, Department of Neurology, New York University Grossman School of Medicine, New York (O.D., D.F.)
| | - Orrin Devinsky
- From the Division of Neurology, Hospital for Sick Children, and the Department of Paediatrics, University of Toronto - both in Toronto (E.D.); and the Epilepsy Center, Department of Neurology, New York University Grossman School of Medicine, New York (O.D., D.F.)
| | - Daniel Friedman
- From the Division of Neurology, Hospital for Sick Children, and the Department of Paediatrics, University of Toronto - both in Toronto (E.D.); and the Epilepsy Center, Department of Neurology, New York University Grossman School of Medicine, New York (O.D., D.F.)
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Lehnen J, Venkatesh P, Yao Z, Aziz A, Nguyen PVP, Harvey J, Alick-Lindstrom S, Doyle A, Podkorytova I, Perven G, Hays R, Zepeda R, Das RR, Ding K. Real-Time Seizure Detection Using Behind-the-Ear Wearable System. J Clin Neurophysiol 2024:00004691-990000000-00128. [PMID: 38376923 DOI: 10.1097/wnp.0000000000001076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024] Open
Abstract
INTRODUCTION This study examines the usability and comfort of a behind-the-ear seizure detection device called brain seizure detection (BrainSD) that captures ictal electroencephalogram (EEG) data using four scalp electrodes. METHODS This is a feasibility study. Thirty-two patients admitted to a level 4 Epilepsy Monitoring Unit were enrolled. The subjects wore BrainSD and the standard 21-channel video-EEG simultaneously. Epileptologists analyzed the EEG signals collected by BrainSD and validated it using video-EEG data to confirm its accuracy. A poststudy survey was completed by each participant to evaluate the comfort and usability of the device. In addition, a focus group of UT Southwestern epileptologists was held to discuss the features they would like to see in a home EEG-based seizure detection device such as BrainSD. RESULTS In total, BrainSD captured 11 of the 14 seizures that occurred while the device was being worn. All 11 seizures captured on BrainSD had focal onset, with three becoming bilateral tonic-clonic and one seizure being of subclinical status. The device was worn for an average of 41 hours. The poststudy survey showed that most users found the device comfortable, easy-to-use, and stated they would be interested in using BrainSD. Epileptologists in the focus group expressed a similar interest in BrainSD. CONCLUSIONS Brain seizure detection is able to detect EEG signals using four behind-the-ear electrodes. Its comfort, ease-of-use, and ability to detect numerous types of seizures make BrainSD an acceptable at-home EEG detection device from both the patient and provider perspective.
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Affiliation(s)
- Jamie Lehnen
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Pooja Venkatesh
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Zhuoran Yao
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Abdul Aziz
- Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX; and
| | - Phuc V P Nguyen
- Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX; and
- College of Information and Computer Science, University of Massachusets Amherst, Amherst, MA
| | - Jay Harvey
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Sasha Alick-Lindstrom
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Alex Doyle
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Irina Podkorytova
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Ghazala Perven
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Ryan Hays
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Rodrigo Zepeda
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Rohit R Das
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Kan Ding
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
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Swinnen L, Chatzichristos C, Bhagubai M, Broux V, Zabler N, Dümpelmann M, Schulze-Bonhage A, De Vos M, Van Paesschen W. Home recording of 3-Hz spike-wave discharges in adults with absence epilepsy using the wearable Sensor Dot. Epilepsia 2024; 65:378-388. [PMID: 38036450 DOI: 10.1111/epi.17839] [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: 07/10/2023] [Revised: 11/17/2023] [Accepted: 11/28/2023] [Indexed: 12/02/2023]
Abstract
OBJECTIVE Home monitoring of 3-Hz spike-wave discharges (SWDs) in patients with refractory absence epilepsy could improve clinical care by replacing the inaccurate seizure diary with objective counts. We investigated the use and performance of the Sensor Dot (Byteflies) wearable in persons with absence epilepsy in their home environment. METHODS Thirteen participants (median age = 22 years, 11 female) were enrolled at the university hospitals of Leuven and Freiburg. At home, participants had to attach the Sensor Dot and behind-the-ear electrodes to record two-channel electroencephalogram (EEG), accelerometry, and gyroscope data. Ground truth annotations were created during a visual review of the full Sensor Dot recording. Generalized SWDs were annotated if they were 3 Hz and at least 3 s on EEG. Potential 3-Hz SWDs were flagged by an automated seizure detection algorithm, (1) using only EEG and (2) with an additional postprocessing step using accelerometer and gyroscope to discard motion artifacts. Afterward, two readers (W.V.P. and L.S.) reviewed algorithm-labeled segments and annotated true positive detections. Sensitivity, precision, and F1 score were calculated. Patients had to keep a seizure diary and complete questionnaires about their experiences. RESULTS Total recording time was 394 h 42 min. Overall, 234 SWDs were captured in 11 of 13 participants. Review of the unimodal algorithm-labeled recordings resulted in a mean sensitivity of .84, precision of .93, and F1 score of .89. Visual review of the multimodal algorithm-labeled segments resulted in a similar F1 score and shorter review time due to fewer false positive labels. Participants reported that the device was comfortable and that they would be willing to wear it on demand of their neurologist, for a maximum of 1 week or with intermediate breaks. SIGNIFICANCE The Sensor Dot improved seizure documentation at home, relative to patient self-reporting. Additional benefits were the short review time and the patients' device acceptance due to user-friendliness and comfortability.
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Affiliation(s)
- Lauren Swinnen
- Laboratory for Epilepsy Research, KU Leuven, Leuven, Belgium
| | - Christos Chatzichristos
- Department of Electrical Engineering, Stadius Center for Dynamical Systems, Signal Processing, and Data Analytics, KU Leuven, Leuven, Belgium
| | - Miguel Bhagubai
- Department of Electrical Engineering, Stadius Center for Dynamical Systems, Signal Processing, and Data Analytics, KU Leuven, Leuven, Belgium
| | - Victoria Broux
- Department of Neurology, University Hospitals Leuven, Leuven, Belgium
| | - Nicolas Zabler
- Epilepsy Center, Department of Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Matthias Dümpelmann
- Epilepsy Center, Department of Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Department of Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Maarten De Vos
- Department of Electrical Engineering, Stadius Center for Dynamical 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, KU Leuven, Leuven, Belgium
- Department of Neurology, University Hospitals Leuven, Leuven, Belgium
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11
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Delazer L, Bao H, Lauseker M, Stauner L, Nübling G, Conrad J, Noachtar S, Havla J, Kaufmann E. Association between retinal thickness and disease characteristics in adult epilepsy: A cross-sectional OCT evaluation. Epilepsia Open 2024; 9:236-249. [PMID: 37920967 PMCID: PMC10839337 DOI: 10.1002/epi4.12859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 10/29/2023] [Indexed: 11/04/2023] Open
Abstract
OBJECTIVE Thinning of the peripapillary retinal nerve fiber layer (p-RNFL), as measured by optical coherence tomography (OCT), was recently introduced as a promising marker for cerebral neuronal loss in people with epilepsy (PwE). However, its clinical implication remains to be elucidated. We thus aimed to (1) systematically characterize the extent of the retinal neuroaxonal loss in a broad spectrum of unselected PwE and (2) to evaluate the main clinical determinants. METHODS In this prospective study, a spectral-domain OCT evaluation was performed on 98 well-characterized PwE and 85 healthy controls (HCs) (18-55 years of age). All inner retinal layers and the total macula volume were assessed. Group comparisons and linear regression analyses with stepwise backward selection were performed to identify relevant clinical and demographic modulators of the retinal neuroaxonal integrity. RESULTS PwE (age: 33.7 ± 10.6 years; 58.2% female) revealed a significant neuroaxonal loss across all assessed retinal layers (global pRNFL, P = 0.001, Δ = 4.24 μm; macular RNFL, P < 0.001, Δ = 0.05 mm3 ; ganglion cell inner plexiform layer, P < 0.001, Δ = 0.11 mm3 ; inner nuclear layer, INL, P = 0.03, Δ = 0.02 mm3 ) as well as significantly reduced total macula volumes (TMV, P < 0.001, Δ = 0.18 mm3 ) compared to HCs (age: 31.2 ± 9.0 years; 57.6% female). The extent of retinal neuroaxonal loss was associated with the occurrence and frequency of tonic-clonic seizures and the number of antiseizure medications, and was most pronounced in male patients. SIGNIFICANCE PwE presented an extensive retinal neuroaxonal loss, affecting not only the peripapillary but also macular structures. The noninvasive and economic measurement via OCT bears the potential to establish as a practical tool to inform patient management, as the extent of the retinal neuroaxonal loss reflects aspects of disease severity and sex-specific vulnerability. PLAIN LANGUAGE SUMMARY The retina is an extension of the brain and closely connected to it. Thus, cerebral alterations like atrophy reflect also on the retinal level. This is advantageous, as the retina is easily accessible and measureable with help of the optical coherence tomography. Here we report that adults with epilepsy have a significantly thinner retina than healthy persons. Especially people with many big seizures and a lot of medications have a thinner retina. We propose that measurement of the retina can be useful as a marker of disease severity and to inform patient management.
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Affiliation(s)
- Luisa Delazer
- Epilepsy Center, Department of NeurologyLMU University Hospital, LMU MunichMunichGermany
| | - Han Bao
- Institute for Medical Information Processing, Biometry, and EpidemiologyLudwig Maximilians UniversityMunichGermany
- Institute for StatisticsMunichGermany
| | - Michael Lauseker
- Institute for Medical Information Processing, Biometry, and EpidemiologyLudwig Maximilians UniversityMunichGermany
| | - Livia Stauner
- Epilepsy Center, Department of NeurologyLMU University Hospital, LMU MunichMunichGermany
| | - Georg Nübling
- Department of NeurologyLMU University Hospital, LMU MunichMunichGermany
- German Center for Neurodegenerative DiseasesMunichGermany
| | - Julian Conrad
- Department of NeurologyLMU University Hospital, LMU MunichMunichGermany
- Division for Neurodegenerative DiseasesUniversitätsmedizin Mannheim, University of HeidelbergHeidelbergGermany
| | - Soheyl Noachtar
- Epilepsy Center, Department of NeurologyLMU University Hospital, LMU MunichMunichGermany
- Department of NeurologyLMU University Hospital, LMU MunichMunichGermany
| | - Joachim Havla
- Institute of Clinical NeuroimmunologyLMU HospitalLMU Hospital, Ludwig Maximilians UniversityMunichGermany
| | - Elisabeth Kaufmann
- Epilepsy Center, Department of NeurologyLMU University Hospital, LMU MunichMunichGermany
- Department of NeurologyLMU University Hospital, LMU MunichMunichGermany
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12
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Avila EK, Tobochnik S, Inati SK, Koekkoek JAF, McKhann GM, Riviello JJ, Rudà R, Schiff D, Tatum WO, Templer JW, Weller M, Wen PY. Brain tumor-related epilepsy management: A Society for Neuro-oncology (SNO) consensus review on current management. Neuro Oncol 2024; 26:7-24. [PMID: 37699031 PMCID: PMC10768995 DOI: 10.1093/neuonc/noad154] [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] [Indexed: 09/14/2023] Open
Abstract
Tumor-related epilepsy (TRE) is a frequent and major consequence of brain tumors. Management of TRE is required throughout the course of disease and a deep understanding of diagnosis and treatment is key to improving quality of life. Gross total resection is favored from both an oncologic and epilepsy perspective. Shared mechanisms of tumor growth and epilepsy exist, and emerging data will provide better targeted therapy options. Initial treatment with antiseizure medications (ASM) in conjunction with surgery and/or chemoradiotherapy is typical. The first choice of ASM is critical to optimize seizure control and tolerability considering the effects of the tumor itself. These agents carry a potential for drug-drug interactions and therefore knowledge of mechanisms of action and interactions is needed. A review of adverse effects is necessary to guide ASM adjustments and decision-making. This review highlights the essential aspects of diagnosis and treatment of TRE with ASMs, surgery, chemotherapy, and radiotherapy while indicating areas of uncertainty. Future studies should consider the use of a standardized method of seizure tracking and incorporating seizure outcomes as a primary endpoint of tumor treatment trials.
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Affiliation(s)
- Edward K Avila
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Steven Tobochnik
- Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
- Department of Neurology, VA Boston Healthcare System, Boston, Massachusetts, USA
| | - Sara K Inati
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA
| | - Johan A F Koekkoek
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Neurology, Haaglanden Medical Center, The Hague, The Netherlands
| | - Guy M McKhann
- Department of Neurosurgery, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, New York, USA
| | - James J Riviello
- Division of Neurology and Developmental Neuroscience, Department of Pediatrics, Baylor College of Medicine, Texas Children’s Hospital, Houston, Texas, USA
| | - Roberta Rudà
- Division of Neuro-Oncology, Department of Neuroscience “Rita Levi Montalcini,” University of Turin, Italy
| | - David Schiff
- Department of Neurology, Division of Neuro-Oncology, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - William O Tatum
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA
| | - Jessica W Templer
- Department of Neurology, Northwestern University, Chicago, Illinois, USA
| | - Michael Weller
- Department of Neurology, Clinical Neuroscience Centre, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Patrick Y Wen
- Center for Neuro-Oncology, Dana-Farber Cancer Center, and Division of Neuro-Oncology, Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
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13
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Sigsgaard GM, Gu Y. Improving the generalization of patient non-specific model for epileptic seizure detection. Biomed Phys Eng Express 2023; 10:015010. [PMID: 37922541 DOI: 10.1088/2057-1976/ad097f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 11/03/2023] [Indexed: 11/07/2023]
Abstract
Epilepsy is the second most common neurological disorder characterized by recurrent and unpredictable seizures. Accurate seizure detection is important for diagnosis and treatment of epilepsy. Many researches achieved good performance on patient-specific seizure detection. However, they were tailored to each specific individual which are less applicable clinically than the patient non-specific detection, which lacked good performance. Despite several decades of research on automatic seizure detection, seizure detection is currently still based on visual inspection of video-EEG (Electroencephalogram) in clinical setting. It is time consuming and prone to human error and subjectivity. This study aims to improve patient non-specific seizure detection to assist neurologist with efficient and objective evaluation of epileptic EEG. The clinical data used was from the open access Siena Scalp EEG Database which consists of 14 patients. First the data were pre-processed to remove artifacts and noises. Second the features from time domain, frequency domain and entropy were extracted from each channel and then concatenated into a feature vector. Finally, a machine learning approach based on random forest was employed for seizure detection with leave-one-patient-out cross-validation scheme. Automatic seizure detection was carried out with the trained model. The study achieved a specificity of 99.38%, sensitivity of 81.43% and 3.61 FP/h (False Positives per hour), which outperformed some other patient non-specific detectors found in literature. The findings from the study shows the possibility of clinical application of automatic seizure detection and indicate that further work should focus on dealing with reducing false positives.
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Affiliation(s)
- Gustav Munk Sigsgaard
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Ying Gu
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
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14
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Brunnhuber F, Slater JD, Goyal S, Amin D, Winston JS. The unforeseen future: Impacts of the COVID-19 pandemic on home video-EEG telemetry. Epilepsia 2023; 64 Suppl 4:S12-S22. [PMID: 36453720 PMCID: PMC9877725 DOI: 10.1111/epi.17473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 11/27/2022] [Accepted: 11/29/2022] [Indexed: 12/03/2022]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic had widespread impact on health care systems globally-particularly services arranged around elective admission and attendance such as epilepsy monitoring units and home video-EEG telemetry (HVET). Here, we review the ongoing impacts of the pandemic on HVET services among several different providers who used different initial models of HVET. We discuss the features of HVET that led to success in providing continued diagnostic services to patients with epilepsy and related disorders and through retrospective audit of our services demonstrate the high diagnostic yield of HVET. We reflect on this unforeseen future and its implications for other diagnostic techniques and approaches.
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Affiliation(s)
- Franz Brunnhuber
- Department of Clinical NeurophysiologyKing's College HospitalLondonUK
| | | | - Sushma Goyal
- Department of Clinical NeurophysiologyKing's College HospitalLondonUK
- Evelina London Children's Hospital, Guy's & St Thomas' NHS Foundation TrustLondonUK
| | - Devyani Amin
- Department of Clinical NeurophysiologyKing's College HospitalLondonUK
| | - Joel S. Winston
- Department of Clinical NeurophysiologyKing's College HospitalLondonUK
- Department of Basic and Clinical Neurosciences, Institute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
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15
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Yu S, El Atrache R, Tang J, Jackson M, Makarucha A, Cantley S, Sheehan T, Vieluf S, Zhang B, Rogers JL, Mareels I, Harrer S, Loddenkemper T. Artificial intelligence-enhanced epileptic seizure detection by wearables. Epilepsia 2023; 64:3213-3226. [PMID: 37715325 DOI: 10.1111/epi.17774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 09/05/2023] [Accepted: 09/14/2023] [Indexed: 09/17/2023]
Abstract
OBJECTIVE Wrist- or ankle-worn devices are less intrusive than the widely used electroencephalographic (EEG) systems for monitoring epileptic seizures. Using custom-developed deep-learning seizure detection models, we demonstrate the detection of a broad range of seizure types by wearable signals. METHODS Patients admitted to the epilepsy monitoring unit were enrolled and asked to wear wearable sensors on either wrists or ankles. We collected patients' electrodermal activity, accelerometry (ACC), and photoplethysmography, from which blood volume pulse (BVP) is derived. Board-certified epileptologists determined seizure onset, offset, and types using video and EEG recordings per the International League Against Epilepsy 2017 classification. We applied three neural network models-a convolutional neural network (CNN) and a CNN-long short-term memory (LSTM)-based generalized detection model and an autoencoder-based personalized detection model-to the raw time-series sensor data to detect seizures and utilized performance measures, including sensitivity, false positive rate (the number of false alarms divided by the total number of nonseizure segments), number of false alarms per day, and detection delay. We applied a 10-fold patientwise cross-validation scheme to the multisignal biosensor data and evaluated model performance on 28 seizure types. RESULTS We analyzed 166 patients (47.6% female, median age = 10.0 years) and 900 seizures (13 254 h of sensor data) for 28 seizure types. With a CNN-LSTM-based seizure detection model, ACC, BVP, and their fusion performed better than chance; ACC and BVP data fusion reached the best detection performance of 83.9% sensitivity and 35.3% false positive rate. Nineteen of 28 seizure types could be detected by at least one data modality with area under receiver operating characteristic curve > .8 performance. SIGNIFICANCE Results from this in-hospital study contribute to a paradigm shift in epilepsy care that entails noninvasive seizure detection, provides time-sensitive and accurate data on additional clinical seizure types, and proposes a novel combination of an out-of-the-box monitoring algorithm with an individualized person-oriented seizure detection approach.
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Affiliation(s)
- Shuang Yu
- IBM Australia, Melbourne, Victoria, Australia
| | - Rima El Atrache
- Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | | | - Michele Jackson
- Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | | | - Sarah Cantley
- Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Theodore Sheehan
- Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Solveig Vieluf
- Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Bo Zhang
- Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Jeffrey L Rogers
- Digital Health, IBM T. J. Watson Research Center, Yorktown Heights, New York, USA
| | | | - Stefan Harrer
- IBM Australia, Melbourne, Victoria, Australia
- Digital Health Cooperative Research Centre, Melbourne, Victoria, Australia
| | - Tobias Loddenkemper
- Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
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16
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Knight A, Gschwind T, Galer P, Worrell GA, Litt B, Soltesz I, Beniczky S. Artificial intelligence in epilepsy phenotyping. Epilepsia 2023:10.1111/epi.17833. [PMID: 37983589 PMCID: PMC11102939 DOI: 10.1111/epi.17833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Accepted: 11/17/2023] [Indexed: 11/22/2023]
Abstract
Artificial intelligence (AI) allows data analysis and integration at an unprecedented granularity and scale. Here we review the technological advances, challenges, and future perspectives of using AI for electro-clinical phenotyping of animal models and patients with epilepsy. In translational research, AI models accurately identify behavioral states in animal models of epilepsy, allowing identification of correlations between neural activity and interictal and ictal behavior. Clinical applications of AI-based automated and semi-automated analysis of audio and video recordings of people with epilepsy, allow significant data reduction and reliable detection and classification of major motor seizures. AI models can accurately identify electrographic biomarkers of epilepsy, such as spikes, high-frequency oscillations, and seizure patterns. Integrating AI analysis of electroencephalographic, clinical, and behavioral data will contribute to optimizing therapy for patients with epilepsy.
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Affiliation(s)
| | - Tilo Gschwind
- Department of Neurosurgery, Stanford University, Stanford, USA
| | - Peter Galer
- Center for Neuroengineering and Therapeutics; Department of Bioengineering; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, USA
| | | | - Brian Litt
- Center for Neuroengineering and Therapeutics; Department of Bioengineering; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, USA
| | - Ivan Soltesz
- Department of Neurosurgery, Stanford University, Stanford, USA
| | - Sándor Beniczky
- Danish Epilepsy Centre Filadelfia, Dianalund, Aarhus University Hospital and Aarhus University, Aarhus, Denmark
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17
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Proost R, Macea J, Lagae L, Van Paesschen W, Jansen K. Wearable detection of tonic seizures in childhood epilepsy: An exploratory cohort study. Epilepsia 2023; 64:3013-3024. [PMID: 37602476 DOI: 10.1111/epi.17756] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 08/18/2023] [Accepted: 08/18/2023] [Indexed: 08/22/2023]
Abstract
OBJECTIVE To investigate the performance of a multimodal wearable device for the offline detection of tonic seizures (TS) in a pediatric childhood epilepsy cohort, with a focus on patients with Lennox-Gastaut syndrome. METHODS Parallel with prolonged video-electroencephalography (EEG), the Plug 'n Patch system, a multimodal wearable device using the Sensor Dot and replaceable electrode adhesives, was used to detect TS. Multiple biosignals were recorded: behind-the-ear EEG, surface electromyography, electrocardiography, and accelerometer/gyroscope. Biosignals were annotated blindly by a neurologist. Seizure characteristics were described, and performance was assessed by sensitivity, positive predictive value (PPV), F1 score, and false alarm rate (FAR) per hour. Performance was compared to seizure diaries kept by the caretaker. RESULTS Ninety-nine TS were detected in 13 patients. Seven patients (54%) had Lennox-Gastaut syndrome and six patients (46%) had other forms of (developmental) epileptic encephalopathies or drug-resistant epilepsy. All but one patient had intellectual disability. Overall sensitivity was 41%, with a PPV of 9%, an F1 score of 14%, and a median FAR per hour of 0.75. Performance increased to an F1 score of 66% for nightly seizures lasting at least 10 s (sensitivity 66%, PPV 66%) and 71% for nightly seizures lasting at least 20 s (sensitivity 62%, PPV 82%). For these seizures there were no false alarms in 10 of 13 patients. Sensitivity of seizure diaries reached a maximum of 52% for prolonged (≥20 s) nightly seizures, even though caretakers slept in the same room. SIGNIFICANCE We showed that it is feasible to use a multimodal wearable device with multiple adhesive sites in children with epilepsy and intellectual disability. For prolonged nightly seizures, offline manual detection of TS outperformed seizure diaries. The recognition of seizure-specific signatures using multiple modalities can help in the development of automated TS detection algorithms.
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Affiliation(s)
- Renee Proost
- Paediatric Neurology Department, University Hospitals Leuven, KU Leuven, Leuven, Belgium
| | - Jaiver Macea
- Neurology Department, University Hospitals Leuven, KU Leuven, Leuven, Belgium
| | - Lieven Lagae
- Paediatric Neurology Department, University Hospitals Leuven, KU Leuven, Leuven, Belgium
| | - Wim Van Paesschen
- Neurology Department, University Hospitals Leuven, KU Leuven, Leuven, Belgium
| | - Katrien Jansen
- Paediatric Neurology Department, University Hospitals Leuven, KU Leuven, Leuven, Belgium
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18
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Cai Y, Chang K, Nazeha N, Gosavi TD, Shen JY, Hong W, Tan YL, Graves N. The cost-effectiveness of a real-time seizure detection application for people with epilepsy. Epilepsy Behav 2023; 148:109441. [PMID: 37748415 DOI: 10.1016/j.yebeh.2023.109441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 09/04/2023] [Accepted: 09/05/2023] [Indexed: 09/27/2023]
Abstract
OBJECTIVES Automated seizure detection modalities can increase safety among people with epilepsy (PWE) and reduce seizure-related anxiety. We evaluated the potential cost-effectiveness of a seizure detection mobile application for PWE in Singapore. METHODS We used a Markov cohort model to estimate the expected changes to total costs and health outcomes from a decision to adopt the seizure detection application versus the current standard of care from the health provider perspective. The time horizon is ten years and cycle duration is one month. Parameter values were updated from national databases and published literature. As we do not know the application efficacy in reducing seizure-related injuries, a conservative estimate of 1% reduction was used. Probabilistic sensitivity analysis, scenario analyses, and value of information analysis were performed. RESULTS At a willingness-to-pay of $45,000/ quality-adjusted life-years (QALY), the incremental cost-effectiveness ratio was $1,096/QALY, and the incremental net monetary benefit was $13,656. Probabilistic sensitivity analyses reported that the application had a 99.5% chance of being cost-effective. In a scenario analysis in which the reduction in risk of seizure-related injury was 20%, there was a 99.8% chance that the application was cost-effective. Value of information analysis revealed that health utilities was the most important parameter group contributing to model uncertainty. CONCLUSIONS This early-stage modeling study reveals that the seizure detection application is likely to be cost-effective compared to current standard of care. Future prospective trials will be needed to demonstrate the real-world impact of the application. Changes in health-related quality of life should also be measured in future trials.
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Affiliation(s)
- Yiying Cai
- Programme in Health Services and Systems Research, Duke-NUS Medical School, 8 College Rd, Singapore 169857, Singapore
| | - Kevin Chang
- Office for Service Transformation, SingHealth, 10 Hospital Boulevard, SingHealth Tower, Singapore 168582, Singapore
| | - Nuraini Nazeha
- Programme in Health Services and Systems Research, Duke-NUS Medical School, 8 College Rd, Singapore 169857, Singapore
| | - Tushar Divakar Gosavi
- Department of Neurology, National Neuroscience Institute, 11 Jln Tan Tock Seng, Singapore 308433, Singapore
| | - Jia Yi Shen
- Department of Neurology, National Neuroscience Institute, 11 Jln Tan Tock Seng, Singapore 308433, Singapore
| | - Weiwei Hong
- Office for Service Transformation, SingHealth, 10 Hospital Boulevard, SingHealth Tower, Singapore 168582, Singapore
| | - Yee-Leng Tan
- Department of Neurology, National Neuroscience Institute, 11 Jln Tan Tock Seng, Singapore 308433, Singapore
| | - Nicholas Graves
- Programme in Health Services and Systems Research, Duke-NUS Medical School, 8 College Rd, Singapore 169857, Singapore.
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El Youssef N, Marchi A, Bartolomei F, Bonini F, Lambert I. Sleep and epilepsy: A clinical and pathophysiological overview. Rev Neurol (Paris) 2023; 179:687-702. [PMID: 37598088 DOI: 10.1016/j.neurol.2023.07.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 07/28/2023] [Accepted: 07/29/2023] [Indexed: 08/21/2023]
Abstract
The interaction between sleep and epilepsy is complex. A better understanding of the mechanisms linking sleep and epilepsy appears increasingly important as it may improve diagnosis and therapeutic strategies in patients with epilepsy. In this narrative review, we aim to (i) provide an overview of the physiological and pathophysiological processes linking sleep and epilepsy; (ii) present common sleep disorders in patients with epilepsy; (iii) discuss how sleep and sleep disorders should be considered in new therapeutic approaches to epilepsy such as neurostimulation; and (iv) present the overall nocturnal manifestations and differential diagnosis between epileptic seizures and parasomnia.
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Affiliation(s)
- N El Youssef
- AP-HM, Timone hospital, Sleep Unit, Epileptology and Cerebral Rhythmology, Marseille, France
| | - A Marchi
- AP-HM, Timone hospital, Sleep Unit, Epileptology and Cerebral Rhythmology, Marseille, France
| | - F Bartolomei
- AP-HM, Timone hospital, Sleep Unit, Epileptology and Cerebral Rhythmology, Marseille, France; Aix-Marseille University, Inserm, Inst Neurosci Syst (INS), Marseille, France
| | - F Bonini
- AP-HM, Timone hospital, Sleep Unit, Epileptology and Cerebral Rhythmology, Marseille, France; Aix-Marseille University, Inserm, Inst Neurosci Syst (INS), Marseille, France
| | - I Lambert
- AP-HM, Timone hospital, Sleep Unit, Epileptology and Cerebral Rhythmology, Marseille, France; Aix-Marseille University, Inserm, Inst Neurosci Syst (INS), Marseille, France.
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20
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van Westrhenen A, Lazeron RHC, van Dijk JP, Leijten FSS, Thijs RD. Multimodal nocturnal seizure detection in children with epilepsy: A prospective, multicenter, long-term, in-home trial. Epilepsia 2023; 64:2137-2152. [PMID: 37195144 DOI: 10.1111/epi.17654] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 05/14/2023] [Accepted: 05/15/2023] [Indexed: 05/18/2023]
Abstract
OBJECTIVE There is a pressing need for reliable automated seizure detection in epilepsy care. Performance evidence on ambulatory non-electroencephalography-based seizure detection devices is low, and evidence on their effect on caregiver's stress, sleep, and quality of life (QoL) is still lacking. We aimed to determine the performance of NightWatch, a wearable nocturnal seizure detection device, in children with epilepsy in the family home setting and to assess its impact on caregiver burden. METHODS We conducted a phase 4, multicenter, prospective, video-controlled, in-home NightWatch implementation study (NCT03909984). We included children aged 4-16 years, with ≥1 weekly nocturnal major motor seizure, living at home. We compared a 2-month baseline period with a 2-month NightWatch intervention. The primary outcome was the detection performance of NightWatch for major motor seizures (focal to bilateral or generalized tonic-clonic [TC] seizures, focal to bilateral or generalized tonic seizures lasting >30 s, hyperkinetic seizures, and a remainder category of focal to bilateral or generalized clonic seizures and "TC-like" seizures). Secondary outcomes included caregivers' stress (Caregiver Strain Index [CSI]), sleep (Pittsburgh Quality of Sleep Index), and QoL (EuroQol five-dimension five-level scale). RESULTS We included 53 children (55% male, mean age = 9.7 ± 3.6 years, 68% learning disability) and analyzed 2310 nights (28 173 h), including 552 major motor seizures. Nineteen participants did not experience any episode of interest during the trial. The median detection sensitivity per participant was 100% (range = 46%-100%), and the median individual false alarm rate was .04 per hour (range = 0-.53). Caregiver's stress decreased significantly (mean total CSI score = 8.0 vs. 7.1, p = .032), whereas caregiver's sleep and QoL did not change significantly during the trial. SIGNIFICANCE The NightWatch system demonstrated high sensitivity for detecting nocturnal major motor seizures in children in a family home setting and reduced caregiver stress.
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Affiliation(s)
- Anouk van Westrhenen
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede and Zwolle, the Netherlands
- Department of Neurology and Clinical Neurophysiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Richard H C Lazeron
- Academic Center of Epileptology Kempenhaeghe, Heeze, the Netherlands
- Faculty of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Johannes P van Dijk
- Academic Center of Epileptology Kempenhaeghe, Heeze, the Netherlands
- Faculty of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
- Department of Orthodontics, Ulm University, Ulm, Germany
| | - Frans S S Leijten
- Brain Center, Department of Neurology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Roland D Thijs
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede and Zwolle, the Netherlands
- Department of Neurology and Clinical Neurophysiology, Leiden University Medical Center, Leiden, the Netherlands
- UCL Queen Square Institute of Neurology, London, UK
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21
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Halimeh M, Jackson M, Vieluf S, Loddenkemper T, Meisel C. Explainable AI for wearable seizure logging: Impact of data quality, patient age, and antiseizure medication on performance. Seizure 2023; 110:99-108. [PMID: 37336056 DOI: 10.1016/j.seizure.2023.06.002] [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: 02/27/2023] [Revised: 05/16/2023] [Accepted: 06/04/2023] [Indexed: 06/21/2023] Open
Abstract
OBJECTIVE Objective seizure count estimates are crucial for ambulatory epilepsy management. Wearables have shown promise for the detection of tonic-clonic seizures but may suffer from false alarms and undetected seizures. Seizure signatures recorded by wearables often occur over prolonged periods, including increased levels of electrodermal activity and heart rate long after seizure EEG onset, however, previous detection methods only partially exploited these signatures. Understanding the utility of these prolonged signatures for seizure count estimation and what factors generally determine seizure logging performance, including the role of data quality vs. algorithm performance, is thus crucial for improving wearables-based epilepsy monitoring and determining which patients benefit most from this technology. METHODS In this retrospective study we examined 76 pediatric epilepsy patients during multiday video-EEG monitoring equipped with a wearable (Empatica E4; records of electrodermal activity, EDA, accelerometry, ACC, heart rate, HR; 1983 h total recording time; 45 tonic-clonic seizures). To log seizures on prolonged data trends, we applied deep learning on continuous overlapping 1-hour segments of multimodal data in a leave-one-subject-out approach. We systematically examined factors influencing logging performance, including patient age, antiseizure medication (ASM) load, seizure type and duration, and data artifacts. To gain insights into algorithm function and feature importance we applied Uniform Manifold Approximation and Projection (UMAP, to represent the separability of learned features) and SHapley Additive exPlanations (SHAP, to represent the most informative data signatures). RESULTS Performance for tonic-clonic seizure logging increased systematically with patient age (AUC 0.61 for patients 〈 11 years, AUC 0.77 for patients between 11-15 years, AUC 0.85 for patients 〉 15 years). Across all ages, AUC was 0.75 corresponding to a sensitivity of 0.52 and a false alarm rate of 0.28/24 h. Seizures under high ASM load or with shorter duration were detected worse (P=.025, P=.033, respectively). UMAP visualized discriminatory power at the individual patient level, SHAP analyses identified clonic motor activity and peri/postictal increases in HR and EDA as most informative. In contrast, in missed seizures, these features were absent indicating that recording quality but not the algorithm caused the low sensitivity in these patients. SIGNIFICANCE Our results demonstrate the utility of prolonged, postictal data segments for seizure logging, contribute to algorithm explainability and point to influencing factors, including high ASM dose and short seizure duration. Collectively, these results may help to identify patients who particularly benefit from such technology.
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Affiliation(s)
- Mustafa Halimeh
- Computational Neurology Lab, Department of Neurology, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Michele Jackson
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States
| | - Solveig Vieluf
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States
| | - Tobias Loddenkemper
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States
| | - Christian Meisel
- Computational Neurology Lab, Department of Neurology, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany; Center for Stroke Research Berlin, Berlin, Germany; Berlin Institute of Health, Berlin, Germany; NeuroCure Cluster of Excellence, Charité - Universitätsmedizin Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany.
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22
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Maher C, Yang Y, Truong ND, Wang C, Nikpour A, Kavehei O. Seizure detection with reduced electroencephalogram channels: research trends and outlook. ROYAL SOCIETY OPEN SCIENCE 2023; 10:230022. [PMID: 37153360 PMCID: PMC10154941 DOI: 10.1098/rsos.230022] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Accepted: 04/11/2023] [Indexed: 05/09/2023]
Abstract
Epilepsy is a prevalent condition characterized by recurrent, unpredictable seizures. Monitoring with surface electroencephalography (EEG) is the gold standard for diagnosing epilepsy, but a time-consuming, uncomfortable and sometimes ineffective process for patients. Further, using EEG over a brief monitoring period has variable success, dependent on patient tolerance and seizure frequency. The availability of hospital resources and hardware and software specifications inherently restrict the options for comfortable, long-term data collection, resulting in limited data for training machine-learning models. This mini-review examines the current patient journey, providing an overview of the current state of EEG monitoring with reduced electrodes and automated channel reduction methods. Opportunities for improving data reliability through multi-modal data fusion are suggested. We assert the need for further research in electrode reduction to advance brain monitoring solutions towards portable, reliable devices that simultaneously offer patient comfort, perform ultra-long-term monitoring and expedite the diagnosis process.
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Affiliation(s)
- Christina Maher
- School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia
- Brain and Mind Centre, The University of Sydney, Sydney, New South Wales 2006, Australia
| | - Yikai Yang
- School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia
| | - Nhan Duy Truong
- School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia
| | - Chenyu Wang
- Brain and Mind Centre, The University of Sydney, Sydney, New South Wales 2006, Australia
- Translational Research Collective, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales 2050, Australia
- Sydney Neuroimaging Analysis Centre, Camperdown, New South Wales 2050, Australia
| | - Armin Nikpour
- Brain and Mind Centre, The University of Sydney, Sydney, New South Wales 2006, Australia
- Central Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales 2006, Australia
- Translational Research Collective, Faculty of Medicine and Health, The University of Sydney, Camperdown, New South Wales 2050, Australia
| | - Omid Kavehei
- School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia
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23
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Li Z, Chen L, Xu C, Chen Z, Wang Y. Non-invasive sensory neuromodulation in epilepsy: Updates and future perspectives. Neurobiol Dis 2023; 179:106049. [PMID: 36813206 DOI: 10.1016/j.nbd.2023.106049] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 02/15/2023] [Accepted: 02/16/2023] [Indexed: 02/22/2023] Open
Abstract
Epilepsy, one of the most common neurological disorders, often is not well controlled by current pharmacological and surgical treatments. Sensory neuromodulation, including multi-sensory stimulation, auditory stimulation, olfactory stimulation, is a kind of novel noninvasive mind-body intervention and receives continued attention as complementary safe treatment of epilepsy. In this review, we summarize the recent advances of sensory neuromodulation, including enriched environment therapy, music therapy, olfactory therapy, other mind-body interventions, for the treatment of epilepsy based on the evidence from both clinical and preclinical studies. We also discuss their possible anti-epileptic mechanisms on neural circuit level and propose perspectives on possible research directions for future studies.
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Affiliation(s)
- Zhongxia Li
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China; Zhejiang Rehabilitation Medical Center Department, The Third Affiliated Hospital, Zhejiang Chinese Medical University, Hangzhou, China
| | - Liying Chen
- Department of Pharmacy, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Cenglin Xu
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Zhong Chen
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Yi Wang
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China; Zhejiang Rehabilitation Medical Center Department, The Third Affiliated Hospital, Zhejiang Chinese Medical University, Hangzhou, China.
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24
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Nouboue C, Selfi S, Diab E, Chen S, Périn B, Szurhaj W. Assessment of an under-mattress sensor as a seizure detection tool in an adult epilepsy monitoring unit. Seizure 2023; 105:17-21. [PMID: 36652886 DOI: 10.1016/j.seizure.2023.01.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 01/06/2023] [Accepted: 01/08/2023] [Indexed: 01/15/2023] Open
Abstract
OBJECTIVE Because of SUDEP (Sudden and unexpected death in epilepsy) and other direct consequences of generalized tonic-clonic seizures, the use of efficient seizure detection tool may be helpful for patients, relatives and caregivers. We aimed to evaluate an under-mattress detection tool (EMFIT®) in real-life hospital conditions, in particular its sensitivity and false alarm rate (FAR), as well as its impact on patient care. METHODS We carried out a retrospective study on a cohort of patients with epilepsy admitted between September 2017 and June 2021 to Amiens University Hospital for a video-EEG of at least 24 h, during which at least one epileptic seizure was recorded. All video-EEGs records were analyzed visually in order to assess the sensitivity of the under-mattress tool (triggering of the alarm) and to classify the seizure type (convulsive/non convulsive). We also considered whether nurses intervened during the seizure, and the time of their intervention if applicable. An additional prospective survey was conducted over 272 days to analyze the FAR of the tool. RESULTS A total of 220 seizures were included in the study, from 55 patients, including 23 convulsive seizures from 15 patients and 197 non-convulsive seizures. Sensitivity for convulsive seizure detection was 69.6%. As expected, none of the non-convulsive seizures was detected. The false alarm rate was 0.007/day. Median trigger time was 74 s, decreasing to 5 s for generalized tonic-clonic seizure. The frequency of nurses' intervention during convulsive seizures was significantly greater in case of the alarm triggering (100% vs 57%, p<0.02). SIGNIFICANCE These results suggest that EMFIT® sensor is able to detect convulsive seizures with good sensitivity and low FAR, and allows caregivers to intervene more often in the event of a nocturnal seizure. This would be an interesting complementary tool to better secure the patients with epilepsy during hospitalization or at home.
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Affiliation(s)
- Carole Nouboue
- Clinical Neurophysiology Department, CHU Amiens, France; UR 7516, CHIMERE, University of Picardie Jules Verne, Amiens, France
| | - Sarah Selfi
- Clinical Neurophysiology Department, CHU Amiens, France
| | - Eva Diab
- Clinical Neurophysiology Department, CHU Amiens, France; UR 7516, CHIMERE, University of Picardie Jules Verne, Amiens, France
| | - Simone Chen
- Clinical Neurophysiology Department, CHU Amiens, France
| | | | - William Szurhaj
- Clinical Neurophysiology Department, CHU Amiens, France; UR 7516, CHIMERE, University of Picardie Jules Verne, Amiens, France.
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Prieto-Avalos G, Sánchez-Morales LN, Alor-Hernández G, Sánchez-Cervantes JL. A Review of Commercial and Non-Commercial Wearables Devices for Monitoring Motor Impairments Caused by Neurodegenerative Diseases. BIOSENSORS 2022; 13:72. [PMID: 36671907 PMCID: PMC9856141 DOI: 10.3390/bios13010072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 12/24/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
Neurodegenerative diseases (NDDs) are among the 10 causes of death worldwide. The effects of NDDs, including irreversible motor impairments, have an impact not only on patients themselves but also on their families and social environments. One strategy to mitigate the pain of NDDs is to early identify and remotely monitor related motor impairments using wearable devices. Technological progress has contributed to reducing the hardware complexity of mobile devices while simultaneously improving their efficiency in terms of data collection and processing and energy consumption. However, perhaps the greatest challenges of current mobile devices are to successfully manage the security and privacy of patient medical data and maintain reasonable costs with respect to the traditional patient consultation scheme. In this work, we conclude: (1) Falls are most monitored for Parkinson's disease, while tremors predominate in epilepsy and Alzheimer's disease. These findings will provide guidance for wearable device manufacturers to strengthen areas of opportunity that need to be addressed, and (2) Of the total universe of commercial wearables devices that are available on the market, only a few have FDA approval, which means that there is a large number of devices that do not safeguard the integrity of the users who use them.
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Affiliation(s)
- Guillermo Prieto-Avalos
- Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
| | - Laura Nely Sánchez-Morales
- CONACYT-Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
| | - Giner Alor-Hernández
- Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
| | - José Luis Sánchez-Cervantes
- CONACYT-Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
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Bjurulf B, Reilly C, Hallböök T. Caregiver reported seizure precipitants and measures to prevent seizures in children with Dravet syndrome. Seizure 2022; 103:3-10. [DOI: 10.1016/j.seizure.2022.09.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 08/19/2022] [Accepted: 09/25/2022] [Indexed: 11/26/2022] Open
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