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Pipatpratarnporn W, Muangthong W, Jirasakuldej S, Limotai C. Wrist-worn smartwatch and predictive models for seizures. Epilepsia 2023; 64:2701-2713. [PMID: 37505115 DOI: 10.1111/epi.17729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 07/24/2023] [Accepted: 07/25/2023] [Indexed: 07/29/2023]
Abstract
OBJECTIVE This study was undertaken to describe extracerebral biosignal characteristics of overall and various seizure types as compared with baseline physical activities using multimodal devices (Empatica E4); develop predictive models for overall and each seizure type; and assess diagnostic performance of each model. METHODS We prospectively recruited patients with focal epilepsy who were admitted to the epilepsy monitoring unit for presurgical evaluation during January to December 2020. All study participants were simultaneously applied gold standard long-term video-electroencephalographic (EEG) monitoring and an index test, E4. Two certified epileptologists independently determined whether captured events were seizures and then indicated ictal semiology and EEG information. Both were blind to multimodal biosignal findings detected by E4. Biosignals during 5-min epochs of both seizure events and baseline were collected and compared. Predictive models for occurrence overall and of each seizure type were developed using a generalized estimating equation. Diagnostic performance of each model was then assessed. RESULTS Thirty patients had events recorded and were recruited for analysis. One hundred eight seizure events and 120 baseline epochs were collected. Heart rate (HR), acceleration (ACC), and electrodermal activity (EDA) but not temperature were significantly elevated during seizures. Cluster analysis showed trends of greatest elevation of HR and ACC in bilateral tonic-clonic seizures (BTCs), as compared with non-BTCs and isolated auras. HR and ACC were independent predictors for overall seizure types, BTCs, and non-BTCs, whereas only HR was a predictor for isolated aura. Diagnostic performance including sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve of the predictive model for overall seizures were 77.78%, 60%, and .696 (95% confidence interval = .628-.764), respectively. SIGNIFICANCE Multimodal extracerebral biosignals (HR, ACC, EDA) detected by a wrist-worn smartwatch can help differentiate between epileptic seizures and normal physical activities. It would be worthwhile to implement our predictive algorithms in commercial seizure detection devices. However, larger studies to externally validate our predictive models are required.
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Affiliation(s)
- Waroth Pipatpratarnporn
- Division of Neurology, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Wichuta Muangthong
- Division of Neurology, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Suda Jirasakuldej
- Chulalongkorn Comprehensive Epilepsy Center of Excellence, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Chusak Limotai
- Division of Neurology, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Chulalongkorn Comprehensive Epilepsy Center of Excellence, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
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Al-Hussaini I, Mitchell CS. SeizFt: Interpretable Machine Learning for Seizure Detection Using Wearables. Bioengineering (Basel) 2023; 10:918. [PMID: 37627803 PMCID: PMC10451805 DOI: 10.3390/bioengineering10080918] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 07/28/2023] [Accepted: 07/31/2023] [Indexed: 08/27/2023] Open
Abstract
This work presents SeizFt-a novel seizure detection framework that utilizes machine learning to automatically detect seizures using wearable SensorDot EEG data. Inspired by interpretable sleep staging, our novel approach employs a unique combination of data augmentation, meaningful feature extraction, and an ensemble of decision trees to improve resilience to variations in EEG and to increase the capacity to generalize to unseen data. Fourier Transform (FT) Surrogates were utilized to increase sample size and improve the class balance between labeled non-seizure and seizure epochs. To enhance model stability and accuracy, SeizFt utilizes an ensemble of decision trees through the CatBoost classifier to classify each second of EEG recording as seizure or non-seizure. The SeizIt1 dataset was used for training, and the SeizIt2 dataset for validation and testing. Model performance for seizure detection was evaluated using two primary metrics: sensitivity using the any-overlap method (OVLP) and False Alarm (FA) rate using epoch-based scoring (EPOCH). Notably, SeizFt placed first among an array of state-of-the-art seizure detection algorithms as part of the Seizure Detection Grand Challenge at the 2023 International Conference on Acoustics, Speech, and Signal Processing (ICASSP). SeizFt outperformed state-of-the-art black-box models in accurate seizure detection and minimized false alarms, obtaining a total score of 40.15, combining OVLP and EPOCH across two tasks and representing an improvement of ~30% from the next best approach. The interpretability of SeizFt is a key advantage, as it fosters trust and accountability among healthcare professionals. The most predictive seizure detection features extracted from SeizFt were: delta wave, interquartile range, standard deviation, total absolute power, theta wave, the ratio of delta to theta, binned entropy, Hjorth complexity, delta + theta, and Higuchi fractal dimension. In conclusion, the successful application of SeizFt to wearable SensorDot data suggests its potential for real-time, continuous monitoring to improve personalized medicine for epilepsy.
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Affiliation(s)
- Irfan Al-Hussaini
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Cassie S. Mitchell
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
- Machine Learning Center at Georgia Tech, Georgia Institute of Technology, Atlanta, GA 30332, USA
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Whitney R, Sharma S, Ramachandrannair R. Sudden unexpected death in epilepsy in children. Dev Med Child Neurol 2023. [PMID: 36802063 DOI: 10.1111/dmcn.15553] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 01/22/2023] [Accepted: 01/24/2023] [Indexed: 02/20/2023]
Abstract
Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mortality in children and adults living with epilepsy. The incidence of SUDEP is comparable in both children and adults; it is approximately 1.2 per 1000 person years. The pathophysiology of SUDEP is not well understood but may involve mechanisms such as cerebral shutdown, autonomic dysfunction, altered brainstem function, and cardiorespiratory demise. Risk factors for SUDEP include the presence of generalized tonic-clonic seizures, nocturnal seizures, possible genetic predisposition, and non-adherence to antiseizure medications. Pediatric-specific risk factors are not fully elucidated. Despite recommendations from consensus guidelines, many clinicians still do not follow the practice of counseling their patients about SUDEP. SUDEP prevention has been an area of important research focus and includes several strategies, such as obtaining seizure control, optimizing treatment regimens, nocturnal supervision, and seizure detection devices. This review discusses what is currently known about SUDEP risk factors and reviews current and future preventive strategies for SUDEP.
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Affiliation(s)
- Robyn Whitney
- Division of Neurology, Department of Pediatrics, McMaster University, Hamilton, Ontario, Canada
| | - Suvasini Sharma
- Neurology Division, Department of Pediatrics, Lady Harding Medical College and Kalawati Saran Children Hospital, New Delhi, India
| | - Rajesh Ramachandrannair
- Division of Neurology, Department of Pediatrics, McMaster University, Hamilton, Ontario, Canada
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Kok XH, Imtiaz SA, Rodriguez-Villegas E. Assessing the Feasibility of Acoustic Based Seizure Detection. IEEE Trans Biomed Eng 2022; 69:2379-2389. [PMID: 35061585 DOI: 10.1109/tbme.2022.3144634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE Long-term monitoring of epilepsy patients outside of hospital settings is impractical due to the complexity and costs associated with electroencephalogram (EEG) systems. Alternative sensing modalities that can acquire, and automatically interpret signals through easy-to-use wearable devices, are needed to help with at-home management of the disease. In this paper, a novel machine learning algorithm is presented for detecting epileptic seizures using acoustic physiological signals acquired from the neck using a wearable device. METHODS Acoustic signals from an existing database, were processed, to extract their Mel-frequency Cepstral Coefficients (MFCCs) which were used to train RUSBoost classifiers to identify ictal and non-ictal acoustic segments. A postprocessing stage was then applied to the segment classification results to identify seizures episodes. RESULTS Tested on 667 hours of acoustic data acquired from 15 patients with at least one seizure, the algorithm achieved a detection sensitivity of 88.1% (95% CI: 79%-97%) from a total of 36 seizures, out of which 24 had no motor manifestations, with a FPR of 0.83/h, and a median detection latency of -42 s. CONCLUSION The results demonstrated for the first time the ability to identify seizures using acoustic internal body signals acquired on the neck. SIGNIFICANCE The results of this paper validate the feasibility of using internal physiological sounds for seizure detection, which could potentially be of use for the development of novel, wearable, very simple to use, long term monitoring, or seizure detection systems; circumventing the practical limitations of EEG monitoring outside hospital settings, or systems based on sensing modalities that work on convulsive seizures only.
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Bongers J, Gutierrez-Quintana R, Stalin CE. The Prospects of Non-EEG Seizure Detection Devices in Dogs. Front Vet Sci 2022; 9:896030. [PMID: 35677934 PMCID: PMC9168902 DOI: 10.3389/fvets.2022.896030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 04/28/2022] [Indexed: 11/16/2022] Open
Abstract
The unpredictable nature of seizures is challenging for caregivers of epileptic dogs, which calls the need for other management strategies such as seizure detection devices. Seizure detection devices are systems that rely on non-electroencephalographic (non-EEG) ictal changes, designed to detect seizures. The aim for its use in dogs would be to provide owners with a more complete history of their dog's seizures and to help install prompt (and potentially life-saving) intervention. Although seizure detection via wearable intracranial EEG recordings is associated with a higher sensitivity in humans, there is robust evidence for reliable detection of generalized tonic-clonic seizures (GTCS) using non-EEG devices. Promising non-EEG changes described in epileptic humans, include heart rate variability (HRV), accelerometry (ACM), electrodermal activity (EDA), and electromyography (EMG). Their sensitivity and false detection rate to detect seizures vary, however direct comparison of studies is nearly impossible, as there are many differences in study design and standards for testing. A way to improve sensitivity and decrease false-positive alarms is to combine the different parameters thereby profiting from the strengths of each one. Given the challenges of using EEG in veterinary clinical practice, non-EEG ictal changes could be a promising alternative to monitor seizures more objectively. This review summarizes various seizure detection devices described in the human literature, discusses their potential use and limitations in veterinary medicine and describes what is currently known in the veterinary literature.
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Affiliation(s)
| | | | - Catherine Elizabeth Stalin
- Neurology and Neurosurgery Service, The School of Veterinary Medicine, College of Medicine, Veterinary Medicine and Life Sciences, University of Glasgow, Glasgow, United Kingdom
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Onorati F, Regalia G, Caborni C, LaFrance WC, Blum AS, Bidwell J, De Liso P, El Atrache R, Loddenkemper T, Mohammadpour-Touserkani F, Sarkis RA, Friedman D, Jeschke J, Picard R. Prospective Study of a Multimodal Convulsive Seizure Detection Wearable System on Pediatric and Adult Patients in the Epilepsy Monitoring Unit. Front Neurol 2021; 12:724904. [PMID: 34489858 PMCID: PMC8418082 DOI: 10.3389/fneur.2021.724904] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 07/27/2021] [Indexed: 12/03/2022] Open
Abstract
Background: Using machine learning to combine wrist accelerometer (ACM) and electrodermal activity (EDA) has been shown effective to detect primarily and secondarily generalized tonic-clonic seizures, here termed as convulsive seizures (CS). A prospective study was conducted for the FDA clearance of an ACM and EDA-based CS-detection device based on a predefined machine learning algorithm. Here we present its performance on pediatric and adult patients in epilepsy monitoring units (EMUs). Methods: Patients diagnosed with epilepsy participated in a prospective multi-center clinical study. Three board-certified neurologists independently labeled CS from video-EEG. The Detection Algorithm was evaluated in terms of Sensitivity and false alarm rate per 24 h-worn (FAR) on all the data and on only periods of rest. Performance were analyzed also applying the Detection Algorithm offline, with a less sensitive but more specific parameters configuration (“Active mode”). Results: Data from 152 patients (429 days) were used for performance evaluation (85 pediatric aged 6–20 years, and 67 adult aged 21–63 years). Thirty-six patients (18 pediatric) experienced a total of 66 CS (35 pediatric). The Sensitivity (corrected for clustered data) was 0.92, with a 95% confidence interval (CI) of [0.85-1.00] for the pediatric population, not significantly different (p > 0.05) from the adult population's Sensitivity (0.94, CI: [0.89–1.00]). The FAR on the pediatric population was 1.26 (CI: [0.87–1.73]), higher (p < 0.001) than in the adult population (0.57, CI: [0.36–0.81]). Using the Active mode, the FAR decreased by 68% while reducing Sensitivity to 0.95 across the population. During rest periods, the FAR's were 0 for all patients, lower than during activity periods (p < 0.001). Conclusions: Performance complies with FDA's requirements of a lower bound of CI for Sensitivity higher than 0.7 and of a FAR lower than 2, for both age groups. The pediatric FAR was higher than the adult FAR, likely due to higher pediatric activity. The high Sensitivity and precision (having no false alarms) during sleep might help mitigate SUDEP risk by summoning caregiver intervention. The Active mode may be advantageous for some patients, reducing the impact of the FAR on daily life. Future work will examine the performance and usability outside of EMUs.
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Affiliation(s)
| | | | | | - W Curt LaFrance
- Division of Neuropsychiatry and Behavioral Neurology, Rhode Island Hospital, Brown University, Providence, RI, United States
| | - Andrew S Blum
- Department of Neurology, Rhode Island Hospital, Brown University, Providence, RI, United States
| | | | - Paola De Liso
- Department of Neuroscience, Bambino Gesù Children's Hospital, Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy
| | - Rima El Atrache
- Department of Neurology, Boston Children's Hospital, Boston, MA, United States
| | - Tobias Loddenkemper
- Department of Neurology, Boston Children's Hospital, Boston, MA, United States
| | | | - Rani A Sarkis
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, United States
| | - Daniel Friedman
- Department of Neurology, New York University Langone Medical Center, New York, NY, United States
| | - Jay Jeschke
- Department of Neurology, New York University Langone Medical Center, New York, NY, United States
| | - Rosalind Picard
- Empatica, Inc., Boston, MA, United States.,MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA, United States
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Greenlaw C, Elhefnawy Y, Jonas R, Douglass LM. Using an animated video to promote an informed discussion on SUDEP with adolescents. Epilepsy Behav 2021; 122:108182. [PMID: 34256339 DOI: 10.1016/j.yebeh.2021.108182] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 06/11/2021] [Accepted: 06/11/2021] [Indexed: 10/20/2022]
Abstract
PURPOSE The purpose of this study was to create and test an animated video to teach adolescent patients about Sudden Unexpected Death in Epilepsy (SUDEP). METHOD Thirty-nine patients and thirteen parents watched the SUDEP video and completed at least one survey. Patients with epilepsy aged 14+ and their parents were recruited to watch the video during neurology clinic visits. Parents of minors provided verbal permission for their child to view the video. Participants were asked to complete pre- and post-video surveys. Data analysis included Fischer's exact tests for comparative data and percentages for categorical variables. RESULTS After watching the SUDEP video, 100% of parents and patients agreed that the video provided helpful knowledge, and 100% of parents and 96% of patients agreed that patients with epilepsy should know about SUDEP. Half of the parents surveyed, and 20% of patients, felt increased concerns after watching the video. Patients rated their understanding of SUDEP significantly higher after watching the video (p < 0.001). CONCLUSION Participants in this study thought that it was important for patients with epilepsy to know about SUDEP, and all agreed that the animated SUDEP video provided helpful knowledge. While some parents endorsed increased concerns after watching the video, the majority of parents still agreed to allow their child to watch the video. Adolescent education on SUDEP using a family-centered approach may be an important method of encouraging harm-reducing behaviors that can be lifesaving for patients with epilepsy. The standard of practice for SUDEP disclosure should continue to be face-to-face discussion with providers, and we propose this video as a tool to elevate and inform those discussions.
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Affiliation(s)
- Celia Greenlaw
- Departments of Pediatrics and Neurology, Division of Pediatric Neurology, Boston University School of Medicine, Boston Medical Center, Boston, MA, USA.
| | - Yasmine Elhefnawy
- Departments of Pediatrics and Neurology, Division of Pediatric Neurology, Boston University School of Medicine, Boston Medical Center, Boston, MA, USA
| | - Rinat Jonas
- Departments of Pediatrics and Neurology, Division of Pediatric Neurology, Boston University School of Medicine, Boston Medical Center, Boston, MA, USA
| | - Laurie M Douglass
- Departments of Pediatrics and Neurology, Division of Pediatric Neurology, Boston University School of Medicine, Boston Medical Center, Boston, MA, USA
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Nair P, Aghoram R, Khilari M. Applications of artificial intelligence in epilepsy. INTERNATIONAL JOURNAL OF ADVANCED MEDICAL AND HEALTH RESEARCH 2021. [DOI: 10.4103/ijamr.ijamr_94_21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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El Atrache R, Tamilia E, Mohammadpour Touserkani F, Hammond S, Papadelis C, Kapur K, Jackson M, Bucciarelli B, Tsuboyama M, Sarkis RA, Loddenkemper T. Photoplethysmography: A measure for the function of the autonomic nervous system in focal impaired awareness seizures. Epilepsia 2020; 61:1617-1626. [DOI: 10.1111/epi.16621] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 06/29/2020] [Accepted: 06/29/2020] [Indexed: 11/26/2022]
Affiliation(s)
- Rima El Atrache
- Division of Epilepsy and Clinical Neurophysiology Department of Neurology Boston Children's HospitalHarvard Medical School Boston Massachusetts USA
| | - Eleonora Tamilia
- Children's Brain Dynamics Division of Newborn Medicine Department of Medicine Boston Children's HospitalHarvard Medical School Boston Massachusetts USA
- Fetal‐Neonatal Neuroimaging and Developmental Science Center Boston Children's Hospital Boston Massachusetts USA
| | - Fatemeh Mohammadpour Touserkani
- Division of Epilepsy and Clinical Neurophysiology Department of Neurology Boston Children's HospitalHarvard Medical School Boston Massachusetts USA
- Department of Neurology SUNY Downstate Medical Center Brooklyn New York USA
| | - Sarah Hammond
- Division of Epilepsy and Clinical Neurophysiology Department of Neurology Boston Children's HospitalHarvard Medical School Boston Massachusetts USA
| | - Christos Papadelis
- Children's Brain Dynamics Division of Newborn Medicine Department of Medicine Boston Children's HospitalHarvard Medical School Boston Massachusetts USA
- Jane and John Justin Neurosciences Center Cook Children's Health Care System Fort Worth Texas USA
- Department of Bioengineering University of Texas at Arlington Arlington Texas USA
| | - Kush Kapur
- Division of Epilepsy and Clinical Neurophysiology Department of Neurology Boston Children's HospitalHarvard Medical School Boston Massachusetts USA
| | - Michele Jackson
- Division of Epilepsy and Clinical Neurophysiology Department of Neurology Boston Children's HospitalHarvard Medical School Boston Massachusetts USA
| | - Bethany Bucciarelli
- Division of Epilepsy and Clinical Neurophysiology Department of Neurology Boston Children's HospitalHarvard Medical School Boston Massachusetts USA
| | - Melissa Tsuboyama
- Division of Epilepsy and Clinical Neurophysiology Department of Neurology Boston Children's HospitalHarvard Medical School Boston Massachusetts USA
| | - Rani A. Sarkis
- Department of Neurology Brigham and Women's HospitalHarvard Medical School Boston Massachusetts USA
| | - Tobias Loddenkemper
- Division of Epilepsy and Clinical Neurophysiology Department of Neurology Boston Children's HospitalHarvard Medical School Boston Massachusetts USA
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Jacobs D, Liu YH, Hilton T, Del Campo M, Carlen PL, Bardakjian BL. Classification of Scalp EEG States Prior to Clinical Seizure Onset. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE-JTEHM 2019; 7:2000203. [PMID: 31497409 PMCID: PMC6726463 DOI: 10.1109/jtehm.2019.2926257] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/24/2018] [Revised: 05/13/2019] [Accepted: 06/12/2019] [Indexed: 11/07/2022]
Abstract
OBJECTIVE To investigate the feasibility of improving the performance of an EEG-based multistate classifier (MSC) previously proposed by our group. RESULTS Using the random forest (RF) classifiers on the previously reported dataset of patients, but with three improvements to classification logic, the specificity of our alarm algorithm improves from 82.4% to 92.0%, and sensitivity from 87.9% to 95.2%. DISCUSSION The MSC could be a useful approach for seizure-monitoring both in the clinic and at home. METHODS Three improvements to the MSC are described. Firstly, an additional check using RF outputs is made prior to alarm to confirm increasing probability of a seizure onset state. Secondly, a post-alarm detection horizon that accounts for the seizure state duration is implemented. Thirdly, the alarm decision window is kept constant.
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Affiliation(s)
- Daniel Jacobs
- 1Institute of Biomaterials and Biomedical Engineering, University of TorontoTorontoONM5S 3G9Canada
| | - Yuhan H Liu
- 2Department of Electrical and Computer EngineeringUniversity of TorontoTorontoONM5S 3G9Canada
| | - Trevor Hilton
- 1Institute of Biomaterials and Biomedical Engineering, University of TorontoTorontoONM5S 3G9Canada
| | - Martin Del Campo
- 3Department of Medicine (Neurology)Toronto Western HospitalTorontoONM5T 2S8Canada
| | - Peter L Carlen
- 1Institute of Biomaterials and Biomedical Engineering, University of TorontoTorontoONM5S 3G9Canada.,3Department of Medicine (Neurology)Toronto Western HospitalTorontoONM5T 2S8Canada.,4Krembil Research Institute, University Health NetworkTorontoONM5T 2S8Canada
| | - Berj L Bardakjian
- 1Institute of Biomaterials and Biomedical Engineering, University of TorontoTorontoONM5S 3G9Canada.,2Department of Electrical and Computer EngineeringUniversity of TorontoTorontoONM5S 3G9Canada
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Elmali AD, Bebek N, Baykan B. Let's talk SUDEP. ACTA ACUST UNITED AC 2019; 56:292-301. [PMID: 31903040 DOI: 10.29399/npa.23663] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Accepted: 05/21/2019] [Indexed: 01/17/2023]
Abstract
Sudden unexplained death in epilepsy (SUDEP) is a devastating complication of epilepsy which was under-recognized in the recent past despite its clear importance. In this review, we examine the definition of SUDEP, revise current pathophysiological theories, discuss risk factors and preventative measures, disclose tools for appraising the SUDEP risk, and last but not least dwell upon announcing and explaining the SUDEP risk to the patients and their caretakers. We aim to aid the clinicians in their responsibility of knowing SUDEP, explaining the SUDEP risk to their patients in a reasonable and sensible way and whenever possible, preventing SUDEP. Future studies are definitely needed to increase scientific knowledge and awareness related to this prioritized topic with malign consequences.
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Affiliation(s)
- Ayşe Deniz Elmali
- İstanbul University, İstanbul Faculty of Medicine, Department of Neurology, İstanbul, Turkey
| | - Nerses Bebek
- İstanbul University, İstanbul Faculty of Medicine, Department of Neurology, İstanbul, Turkey
| | - Betül Baykan
- İstanbul University, İstanbul Faculty of Medicine, Department of Neurology, İstanbul, Turkey
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12
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Regalia G, Onorati F, Lai M, Caborni C, Picard RW. Multimodal wrist-worn devices for seizure detection and advancing research: Focus on the Empatica wristbands. Epilepsy Res 2019; 153:79-82. [DOI: 10.1016/j.eplepsyres.2019.02.007] [Citation(s) in RCA: 97] [Impact Index Per Article: 19.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2018] [Revised: 02/19/2019] [Accepted: 02/22/2019] [Indexed: 11/30/2022]
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13
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Biosensors for Epilepsy Management: State-of-Art and Future Aspects. SENSORS 2019; 19:s19071525. [PMID: 30925837 PMCID: PMC6480455 DOI: 10.3390/s19071525] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Revised: 03/24/2019] [Accepted: 03/25/2019] [Indexed: 12/16/2022]
Abstract
Epilepsy is a serious neurological disorder which affects every aspect of patients’ life, including added socio-economic burden. Unfortunately, only a few suppressive medicines are available, and a complete cure for the disease has not been found yet. Excluding the effectiveness of available therapies, the timely detection and monitoring of epilepsy are of utmost priority for early remediation and prevention. Inability to detect underlying epileptic signatures at early stage causes serious damage to the central nervous system (CNS) and irreversible detrimental variations in the organ system. Therefore, development of a multi-task solving novel smart biosensing systems is urgently required. The present review highlights advancements in state-of-art biosensing technology investigated for epilepsy diseases diagnostics and progression monitoring or both together. State of art epilepsy biosensors are composed of nano-enabled smart sensing platform integrated with micro/electronics and display. These diagnostics systems provide bio-information needed to understand disease progression and therapy optimization timely. The associated challenges related to the development of an efficient epilepsy biosensor and vision considering future prospects are also discussed in this report. This review will serve as a guide platform to scholars for understanding and planning of future research aiming to develop a smart bio-sensing system to detect and monitor epilepsy for point-of-care (PoC) applications.
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Whitney R, Donner EJ. Risk Factors for Sudden Unexpected Death in Epilepsy (SUDEP) and Their Mitigation. Curr Treat Options Neurol 2019; 21:7. [PMID: 30758730 DOI: 10.1007/s11940-019-0547-4] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
PURPOSE OF REVIEW People with epilepsy have an increased risk of mortality when compared to the general population. Sudden unexpected death in epilepsy (SUDEP) is the most common cause of epilepsy-related death in children and adults. The purpose of this review is to discuss SUDEP, with an emphasis on SUDEP risk factors, their mitigation and prevention. RECENT FINDINGS SUDEP affects approximately 1 in 1000 people with epilepsy each year. Recent studies suggest that the incidence in children is similar to that of adults. The most important risk factor for SUDEP is the presence and frequency of generalized tonic-clonic seizures. The presence of nocturnal supervision may decrease risk along with the use of nocturnal listening devices. Underlying genetic influences, both cardiac and epilepsy-related may further alter risk. Risk mitigation strategies include reducing seizure frequency, optimizing therapy, and the use of nocturnal supervision/seizure detection devices. Risk factors for SUDEP are well established; however, pediatric specific risk factors have not been identified. Current prevention strategies are focused on reduction of risk factors and the possible role of seizure detection devices. More research is needed to better understand the varied underlying pathological mechanisms and develop targeted prevention strategies. Further understanding the genetic factors that influence SUDEP risk may potentially aid in understanding the underlying pathophysiology of SUDEP.
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Affiliation(s)
- Robyn Whitney
- Division of Neurology, Department of Paediatrics, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario, M5G 1X8, Canada
| | - Elizabeth J Donner
- Division of Neurology, Department of Paediatrics, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario, M5G 1X8, Canada.
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