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Carmo AS, Abreu M, Baptista MF, de Oliveira Carvalho M, Peralta AR, Fred A, Bentes C, da Silva HP. Automated algorithms for seizure forecast: a systematic review and meta-analysis. J Neurol 2024:10.1007/s00415-024-12655-z. [PMID: 39240346 DOI: 10.1007/s00415-024-12655-z] [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: 06/20/2024] [Revised: 08/16/2024] [Accepted: 08/18/2024] [Indexed: 09/07/2024]
Abstract
This study aims to review the proposed methodologies and reported performances of automated algorithms for seizure forecast. A systematic review was conducted on studies reported up to May 10, 2024. Four databases and registers were searched, and studies were included when they proposed an original algorithm for automatic human epileptic seizure forecast that was patient specific, based on intraindividual cyclic distribution of events and/or surrogate measures of the preictal state and provided an evaluation of the performance. Two meta-analyses were performed, one evaluating area under the ROC curve (AUC) and another Brier Skill Score (BSS). Eighteen studies met the eligibility criteria, totaling 43 included algorithms. A total of 419 patients participated in the studies, and 19442 seizures were reported across studies. Of the analyzed algorithms, 23 were eligible for the meta-analysis with AUC and 12 with BSS. The overall mean AUC was 0.71, which was similar between the studies that relied solely on surrogate measures of the preictal state, on cyclic distributions of events, and on a combination of these. BSS was also similar for the three types of input data, with an overall mean BSS of 0.13. This study provides a characterization of the state of the art in seizure forecast algorithms along with their performances, setting a benchmark for future developments. It identified a considerable lack of standardization across study design and evaluation, leading to the proposal of guidelines for the design of seizure forecast solutions.
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Affiliation(s)
- Ana Sofia Carmo
- Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal.
- Instituto de Telecomunicações, Lisboa, Portugal.
| | - Mariana Abreu
- Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
- Instituto de Telecomunicações, Lisboa, Portugal
| | - Maria Fortuna Baptista
- Neurophysiology Monitoring Unit EEG/Sleep Laboratory, Hospital de Santa Maria, Unidade Local de Saúde Santa Maria, Lisboa, Portugal
- Centro de Estudos Egas Moniz. Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal
| | - Miguel de Oliveira Carvalho
- Neurophysiology Monitoring Unit EEG/Sleep Laboratory, Hospital de Santa Maria, Unidade Local de Saúde Santa Maria, Lisboa, Portugal
- Centro de Estudos Egas Moniz. Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal
| | - Ana Rita Peralta
- Neurophysiology Monitoring Unit EEG/Sleep Laboratory, Hospital de Santa Maria, Unidade Local de Saúde Santa Maria, Lisboa, Portugal
- Centro de Estudos Egas Moniz. Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal
| | - Ana Fred
- Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
- Instituto de Telecomunicações, Lisboa, Portugal
| | - Carla Bentes
- Neurophysiology Monitoring Unit EEG/Sleep Laboratory, Hospital de Santa Maria, Unidade Local de Saúde Santa Maria, Lisboa, Portugal
- Centro de Estudos Egas Moniz. Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal
| | - Hugo Plácido da Silva
- Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
- Instituto de Telecomunicações, Lisboa, Portugal
- LUMLIS The Lisbon ELLIS Unit | European Laboratory for Learning and Intelligent Systems, Lisboa, Portugal
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Bari DS, Aldosky HYY, Tronstad C, Martinsen ØG. Disturbances in Electrodermal Activity Recordings Due to Different Noises in the Environment. SENSORS (BASEL, SWITZERLAND) 2024; 24:5434. [PMID: 39205128 PMCID: PMC11359885 DOI: 10.3390/s24165434] [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: 07/04/2024] [Revised: 08/19/2024] [Accepted: 08/21/2024] [Indexed: 09/04/2024]
Abstract
Electrodermal activity (EDA) is a widely used psychophysiological measurement in laboratory-based studies. In recent times, these measurements have seen a transfer from the laboratory to wearable devices due to the simplicity of EDA measurement as well as modern electronics. However, proper conditions for EDA measurement are recommended once wearable devices are used, and the ambient conditions may influence such measurements. It is not completely known how different types of ambient noise impact EDA measurement and how this translates to wearable EDA measurement. Therefore, this study explored the effects of various noise disturbances on the generation of EDA responses using a system for the simultaneous recording of all measures of EDA, i.e., skin conductance responses (SCRs), skin susceptance responses (SSRs), and skin potential responses (SPRs), at the same skin site. The SCRs, SSRs, and SPRs due to five types of noise stimuli at different sound pressure levels (70, 75, 80, 85, and 90 dB) were measured from 40 participants. The obtained results showed that EDA responses were generated at all levels and that the EDA response magnitudes were significantly (p < 0.001) influenced by the increasing noise levels. Different types of environmental noise may elicit EDA responses and influence wearable recordings outside the laboratory, where such noises are more likely than in standardized laboratory tests. Depending on the application, it is recommended to prevent these types of unwanted variation, presenting a challenge for the quality of wearable EDA measurement in real-world conditions. Future developments to shorten the quality gap between standardized laboratory-based and wearable EDA measurements may include adding microphone sensors and algorithms to detect, classify, and process the noise-related EDA.
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Affiliation(s)
- Dindar S. Bari
- Scientific Research Center, University of Zakho, Zakho 42002, Iraq;
- Department of Physics, College of Science, University of Zakho, Zakho 42002, Iraq
| | - Haval Y. Y. Aldosky
- Department of Physics, College of Science, University of Duhok, Duhok 99454, Iraq
| | - Christian Tronstad
- Department of Clinical and Biomedical Engineering, Oslo University Hospital, 0424 Oslo, Norway
| | - Ørjan G. Martinsen
- Department of Clinical and Biomedical Engineering, Oslo University Hospital, 0424 Oslo, Norway
- Department of Physics, University of Oslo, 0371 Oslo, Norway
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3
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Corponi F, Li BM, Anmella G, Valenzuela-Pascual C, Mas A, Pacchiarotti I, Valentí M, Grande I, Benabarre A, Garriga M, Vieta E, Young AH, Lawrie SM, Whalley HC, Hidalgo-Mazzei D, Vergari A. Wearable Data From Subjects Playing Super Mario, Taking University Exams, or Performing Physical Exercise Help Detect Acute Mood Disorder Episodes via Self-Supervised Learning: Prospective, Exploratory, Observational Study. JMIR Mhealth Uhealth 2024; 12:e55094. [PMID: 39018100 PMCID: PMC11292167 DOI: 10.2196/55094] [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: 12/02/2023] [Revised: 04/14/2024] [Accepted: 05/24/2024] [Indexed: 07/18/2024] Open
Abstract
BACKGROUND Personal sensing, leveraging data passively and near-continuously collected with wearables from patients in their ecological environment, is a promising paradigm to monitor mood disorders (MDs), a major determinant of the worldwide disease burden. However, collecting and annotating wearable data is resource intensive. Studies of this kind can thus typically afford to recruit only a few dozen patients. This constitutes one of the major obstacles to applying modern supervised machine learning techniques to MD detection. OBJECTIVE In this paper, we overcame this data bottleneck and advanced the detection of acute MD episodes from wearables' data on the back of recent advances in self-supervised learning (SSL). This approach leverages unlabeled data to learn representations during pretraining, subsequently exploited for a supervised task. METHODS We collected open access data sets recording with the Empatica E4 wristband spanning different, unrelated to MD monitoring, personal sensing tasks-from emotion recognition in Super Mario players to stress detection in undergraduates-and devised a preprocessing pipeline performing on-/off-body detection, sleep/wake detection, segmentation, and (optionally) feature extraction. With 161 E4-recorded subjects, we introduced E4SelfLearning, the largest-to-date open access collection, and its preprocessing pipeline. We developed a novel E4-tailored transformer (E4mer) architecture, serving as the blueprint for both SSL and fully supervised learning; we assessed whether and under which conditions self-supervised pretraining led to an improvement over fully supervised baselines (ie, the fully supervised E4mer and pre-deep learning algorithms) in detecting acute MD episodes from recording segments taken in 64 (n=32, 50%, acute, n=32, 50%, stable) patients. RESULTS SSL significantly outperformed fully supervised pipelines using either our novel E4mer or extreme gradient boosting (XGBoost): n=3353 (81.23%) against n=3110 (75.35%; E4mer) and n=2973 (72.02%; XGBoost) correctly classified recording segments from a total of 4128 segments. SSL performance was strongly associated with the specific surrogate task used for pretraining, as well as with unlabeled data availability. CONCLUSIONS We showed that SSL, a paradigm where a model is pretrained on unlabeled data with no need for human annotations before deployment on the supervised target task of interest, helps overcome the annotation bottleneck; the choice of the pretraining surrogate task and the size of unlabeled data for pretraining are key determinants of SSL success. We introduced E4mer, which can be used for SSL, and shared the E4SelfLearning collection, along with its preprocessing pipeline, which can foster and expedite future research into SSL for personal sensing.
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Affiliation(s)
- Filippo Corponi
- School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
| | - Bryan M Li
- School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
- The Alan Turing Institute, London, United Kingdom
| | - Gerard Anmella
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona, Barcelona, Spain
| | - Clàudia Valenzuela-Pascual
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona, Barcelona, Spain
| | - Ariadna Mas
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona, Barcelona, Spain
| | - Isabella Pacchiarotti
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona, Barcelona, Spain
| | - Marc Valentí
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona, Barcelona, Spain
| | - Iria Grande
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona, Barcelona, Spain
| | - Antoni Benabarre
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona, Barcelona, Spain
| | - Marina Garriga
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona, Barcelona, Spain
| | - Eduard Vieta
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona, Barcelona, Spain
| | - Allan H Young
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Stephen M Lawrie
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Heather C Whalley
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Generation Scotland, Institute for Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
| | - Diego Hidalgo-Mazzei
- Bipolar and Depressive Disorders Unit, Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona, Barcelona, Spain
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Antonio Vergari
- School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
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Valenzuela-Pascual C, Mas A, Borràs R, Anmella G, Sanabra M, González-Campos M, Valentí M, Pacchiarotti I, Benabarre A, Grande I, De Prisco M, Oliva V, Bastidas A, Agasi I, Young AH, Garriga M, Murru A, Corponi F, Li BM, de Looff P, Vieta E, Hidalgo-Mazzei D. Sleep-wake variations of electrodermal activity in bipolar disorder. Acta Psychiatr Scand 2024. [PMID: 38890010 DOI: 10.1111/acps.13718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 05/14/2024] [Accepted: 06/05/2024] [Indexed: 06/20/2024]
Abstract
BACKGROUND Affective states influence the sympathetic nervous system, inducing variations in electrodermal activity (EDA), however, EDA association with bipolar disorder (BD) remains uncertain in real-world settings due to confounders like physical activity and temperature. We analysed EDA separately during sleep and wakefulness due to varying confounders and potential differences in mood state discrimination capacities. METHODS We monitored EDA from 102 participants with BD including 35 manic, 29 depressive, 38 euthymic patients, and 38 healthy controls (HC), for 48 h. Fifteen EDA features were inferred by mixed-effect models for repeated measures considering sleep state, group and covariates. RESULTS Thirteen EDA feature models were significantly influenced by sleep state, notably including phasic peaks (p < 0.001). During wakefulness, phasic peaks showed different values for mania (M [SD] = 6.49 [5.74, 7.23]), euthymia (5.89 [4.83, 6.94]), HC (3.04 [1.65, 4.42]), and depression (3.00 [2.07, 3.92]). Four phasic features during wakefulness better discriminated between HC and mania or euthymia, and between depression and euthymia or mania, compared to sleep. Mixed symptoms, average skin temperature, and anticholinergic medication affected the models, while sex and age did not. CONCLUSION EDA measured from awake recordings better distinguished between BD states than sleep recordings, when controlled by confounders.
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Affiliation(s)
- Clàudia Valenzuela-Pascual
- Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Catalonia, Barcelona, Spain
- Bipolar and Depressive Disorders Unit, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Catalonia, Barcelona, Spain
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), Catalonia, Barcelona, Spain
| | - Ariadna Mas
- Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Catalonia, Barcelona, Spain
- Bipolar and Depressive Disorders Unit, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Catalonia, Barcelona, Spain
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), Catalonia, Barcelona, Spain
| | - Roger Borràs
- Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Catalonia, Barcelona, Spain
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
| | - Gerard Anmella
- Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Catalonia, Barcelona, Spain
- Bipolar and Depressive Disorders Unit, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Catalonia, Barcelona, Spain
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), Catalonia, Barcelona, Spain
| | - Miriam Sanabra
- Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Catalonia, Barcelona, Spain
- Bipolar and Depressive Disorders Unit, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Catalonia, Barcelona, Spain
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), Catalonia, Barcelona, Spain
| | - Meritxell González-Campos
- Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Catalonia, Barcelona, Spain
- Bipolar and Depressive Disorders Unit, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Catalonia, Barcelona, Spain
| | - Marc Valentí
- Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Catalonia, Barcelona, Spain
- Bipolar and Depressive Disorders Unit, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Catalonia, Barcelona, Spain
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), Catalonia, Barcelona, Spain
| | - Isabella Pacchiarotti
- Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Catalonia, Barcelona, Spain
- Bipolar and Depressive Disorders Unit, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Catalonia, Barcelona, Spain
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), Catalonia, Barcelona, Spain
| | - Antoni Benabarre
- Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Catalonia, Barcelona, Spain
- Bipolar and Depressive Disorders Unit, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Catalonia, Barcelona, Spain
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), Catalonia, Barcelona, Spain
| | - Iria Grande
- Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Catalonia, Barcelona, Spain
- Bipolar and Depressive Disorders Unit, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Catalonia, Barcelona, Spain
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), Catalonia, Barcelona, Spain
| | - Michele De Prisco
- Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Catalonia, Barcelona, Spain
- Bipolar and Depressive Disorders Unit, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Catalonia, Barcelona, Spain
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), Catalonia, Barcelona, Spain
| | - Vincenzo Oliva
- Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Catalonia, Barcelona, Spain
- Bipolar and Depressive Disorders Unit, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Catalonia, Barcelona, Spain
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), Catalonia, Barcelona, Spain
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Anna Bastidas
- Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Catalonia, Barcelona, Spain
| | - Isabel Agasi
- Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Catalonia, Barcelona, Spain
| | - Allan H Young
- Centre for Affective Disorders (CfAD), Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London, UK
| | - Marina Garriga
- Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Catalonia, Barcelona, Spain
- Bipolar and Depressive Disorders Unit, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Catalonia, Barcelona, Spain
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), Catalonia, Barcelona, Spain
| | - Andrea Murru
- Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Catalonia, Barcelona, Spain
- Bipolar and Depressive Disorders Unit, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Catalonia, Barcelona, Spain
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), Catalonia, Barcelona, Spain
| | - Filippo Corponi
- School of Informatics, University of Edinburgh, Edinburgh, UK
| | - Bryan M Li
- School of Informatics, University of Edinburgh, Edinburgh, UK
| | - Peter de Looff
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
- Fivoor, Science and Treatment Innovation, Expert centre "De Borg", Den Dolder, The Netherlands
| | - Eduard Vieta
- Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Catalonia, Barcelona, Spain
- Bipolar and Depressive Disorders Unit, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Catalonia, Barcelona, Spain
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), Catalonia, Barcelona, Spain
| | - Diego Hidalgo-Mazzei
- Department of Psychiatry and Psychology, Hospital Clínic de Barcelona, Catalonia, Barcelona, Spain
- Bipolar and Depressive Disorders Unit, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Catalonia, Barcelona, Spain
- Biomedical Research Networking Centre Consortium on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Medicine, School of Medicine and Health Sciences, Institute of Neurosciences (UBNeuro), University of Barcelona (UB), Catalonia, Barcelona, Spain
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5
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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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6
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van den Bongard F, Gowik JK, Coenen J, Jakobsmeyer R, Reinsberger C. Exercise-induced central and peripheral sympathetic activity in a community-based group of epilepsy patients differ from healthy controls. Exp Brain Res 2024; 242:1301-1310. [PMID: 38551692 PMCID: PMC11108887 DOI: 10.1007/s00221-024-06792-0] [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: 09/28/2023] [Accepted: 01/22/2024] [Indexed: 05/23/2024]
Abstract
Ictal and interictal activity within the autonomic nervous system is characterized by a sympathetic overshoot in people with epilepsy. This autonomic dysfunction is assumed to be driven by alterations in the central autonomic network. In this study, exercise-induced changes of the interrelation of central and peripheral autonomic activity in patients with epilepsy was assessed. 21 patients with epilepsy (16 seizure-free), and 21 healthy matched controls performed an exhaustive bicycle ergometer test. Immediately before and after the exercise test, resting state electroencephalography measurements (Brain Products GmbH, 128-channel actiCHamp) of 5 min were carried out to investigate functional connectivity assessed by phase locking value in source space for whole brain, central autonomic network and visual network. Additionally, 1-lead ECG (Brain products GmbH) was performed to analyze parasympathetic (root mean square of successive differences (RMSSD) of the heart rate variability) and sympathetic activity (electrodermal activity (meanEDA)). MeanEDA increased (p < 0.001) and RMSSD decreased (p < 0.001) from pre to post-exercise in both groups. Correlation coefficients of meanEDA and central autonomic network functional connectivity differed significantly between the groups (p = 0.004) after exercise. Both patients with epilepsy and normal control subjects revealed the expected physiological peripheral autonomic responses to acute exhaustive exercise, but alterations of the correlation between central autonomic and peripheral sympathetic activity may indicate a different sympathetic reactivity after exercise in patients with epilepsy. The clinical relevance of this finding and its modulators (seizures, anti-seizure medication, etc.) still needs to be elucidated.
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Affiliation(s)
| | - Julia Kristin Gowik
- Institute of Sports Medicine, Paderborn University, Warburger Straße 100, 33098, Paderborn, Germany
| | - Jessica Coenen
- Institute of Sports Medicine, Paderborn University, Warburger Straße 100, 33098, Paderborn, Germany
| | - Rasmus Jakobsmeyer
- Institute of Sports Medicine, Paderborn University, Warburger Straße 100, 33098, Paderborn, Germany
| | - Claus Reinsberger
- Institute of Sports Medicine, Paderborn University, Warburger Straße 100, 33098, Paderborn, Germany.
- Division of Sports Neurology & Neurosciences, Department of Neurology, Mass General Brigham, Harvard Medical School, Boston, MA, USA.
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7
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Vieluf S, Cantley S, Krishnan V, Loddenkemper T. Ultradian rhythms in accelerometric and autonomic data vary based on seizure occurrence in paediatric epilepsy patients. Brain Commun 2024; 6:fcae034. [PMID: 38454964 PMCID: PMC10919479 DOI: 10.1093/braincomms/fcae034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 07/18/2023] [Accepted: 02/09/2024] [Indexed: 03/09/2024] Open
Abstract
Ultradian rhythms are physiological oscillations that resonate with period lengths shorter than 24 hours. This study examined the expression of ultradian rhythms in patients with epilepsy, a disease defined by an enduring seizure risk that may vary cyclically. Using a wearable device, we recorded heart rate, body temperature, electrodermal activity and limb accelerometry in patients admitted to the paediatric epilepsy monitoring unit. In our case-control design, we included recordings from 29 patients with tonic-clonic seizures and 29 non-seizing controls. We spectrally decomposed each signal to identify cycle lengths of interest and compared average spectral power- and period-related markers between groups. Additionally, we related seizure occurrence to the phase of ultradian rhythm in patients with recorded seizures. We observed prominent 2- and 4-hour-long ultradian rhythms of accelerometry, as well as 4-hour-long oscillations in heart rate. Patients with seizures displayed a higher peak power in the 2-hour accelerometry rhythm (U = 287, P = 0.038) and a period-lengthened 4-hour heart rate rhythm (U = 291.5, P = 0.037). Those that seized also displayed greater mean rhythmic electrodermal activity (U = 261; P = 0.013). Most seizures occurred during the falling-to-trough quarter phase of accelerometric rhythms (13 out of 27, χ2 = 8.41, P = 0.038). Fluctuations in seizure risk or the occurrence of seizures may interrelate with ultradian rhythms of movement and autonomic function. Longitudinal assessments of ultradian patterns in larger patient samples may enable us to understand how such rhythms may improve the temporal precision of seizure forecasting models.
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Affiliation(s)
- Solveig Vieluf
- Division of Epilepsy and Clinical Neurophysiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Department of Medicine I, LMU University Hospital, LMU Munich, 81377 Munich, Germany
| | - Sarah Cantley
- Division of Epilepsy and Clinical Neurophysiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Vaishnav Krishnan
- Departments of Neurology, Neuroscience and Psychiatry & Behavioral Sciences, Baylor College of Medicine, Houston, TX 77030, USA
| | - Tobias Loddenkemper
- Division of Epilepsy and Clinical Neurophysiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
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8
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Vieluf S, Cantley S, Jackson M, Zhang B, Bosl WJ, Loddenkemper T. Development of a Multivariable Seizure Likelihood Assessment Based on Clinical Information and Short Autonomic Activity Recordings for Children With Epilepsy. Pediatr Neurol 2023; 148:118-127. [PMID: 37703656 DOI: 10.1016/j.pediatrneurol.2023.07.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 04/10/2023] [Accepted: 07/24/2023] [Indexed: 09/15/2023]
Abstract
BACKGROUND Predicting seizure likelihood for the following day would enable clinicians to extend or potentially schedule video-electroencephalography (EEG) monitoring when seizure risk is high. Combining standardized clinical data with short-term recordings of wearables to predict seizure likelihood could have high practical relevance as wearable data is easy and fast to collect. As a first step toward seizure forecasting, we classified patients based on whether they had seizures or not during the following recording. METHODS Pediatric patients admitted to the epilepsy monitoring unit wore a wearable that recorded the heart rate (HR), heart rate variability (HRV), electrodermal activity (EDA), and peripheral body temperature. We utilized short recordings from 9:00 to 9:15 pm and compared mean values between patients with and without impending seizures. In addition, we collected clinical data: age, sex, age at first seizure, generalized slowing, focal slowing, and spikes on EEG, magnetic resonance imaging findings, and antiseizure medication reduction. We used conventional machine learning techniques with cross-validation to classify patients with and without impending seizures. RESULTS We included 139 patients: 78 had no seizures and 61 had at least one seizure after 9 pm during the concurrent video-EEG and E4 recordings. HR (P < 0.01) and EDA (P < 0.01) were lower and HRV (P = 0.02) was higher for patients with than for patients without impending seizures. The average accuracy of group classification was 66%, and the mean area under the receiver operating characteristics was 0.72. CONCLUSIONS Short-term wearable recordings in combination with clinical data have great potential as an easy-to-use seizure likelihood assessment tool.
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Affiliation(s)
- Solveig Vieluf
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts; Institute of Sports Medicine, Paderborn University, Paderborn, Germany.
| | - Sarah Cantley
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Michele Jackson
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Bo Zhang
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts
| | - William J Bosl
- Computational Health Informatics Program, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts; Health Informatics Program, University of San Francisco, San Francisco, California
| | - Tobias Loddenkemper
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts
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9
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Meritam Larsen P, Beniczky S. Non-electroencephalogram-based seizure detection devices: State of the art and future perspectives. Epilepsy Behav 2023; 148:109486. [PMID: 37857030 DOI: 10.1016/j.yebeh.2023.109486] [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: 08/22/2023] [Revised: 10/06/2023] [Accepted: 10/07/2023] [Indexed: 10/21/2023]
Abstract
INTRODUCTION AND PURPOSE The continuously expanding research and development of wearable devices for automated seizure detection in epilepsy uses mostly non-invasive technology. Real-time alarms, triggered by seizure detection devices, are needed for safety and prevention to decrease seizure-related morbidity and mortality, as well as objective quantification of seizure frequency and severity. Our review strives to provide a state-of-the-art on automated seizure detection using non-invasive wearable devices in an ambulatory (home) environment and to highlight the prospects for future research. METHODS A joint working group of the International League Against Epilepsy (ILAE) and the International Federation of Clinical Neurophysiology (IFCN) recently published a clinical practice guideline on automated seizure detection using wearable devices. We updated the systematic literature search for the period since the last search by the joint working group. We selected studies qualifying minimally as phase-2 clinical validation trials, in accordance with standards for testing and validation of seizure detection devices. RESULTS High-level evidence (phases 3 and 4) is available only for the detection of tonic-clonic seizures and major motor seizures when using wearable devices based on accelerometry, surface electromyography (EMG), or a multimodal device combining accelerometry and heart rate. The reported sensitivity of these devices is 79.4-96%, with a false alarm rate of 0.20-1.92 per 24 hours (0-0.03 per night). A single phase-3 study validated the detection of absence seizures using a single-channel wearable EEG device. Two phase-4 studies showed overall user satisfaction with wearable seizure detection devices, which helped decrease injuries related to tonic-clonic seizures. Overall satisfaction, perceived sensitivity, and improvement in quality-of-life were significantly higher for validated devices. CONCLUSIONS Among the vast number of studies published on seizure detection devices, most are strongly affected by potential bias, providing a too-optimistic perspective. By applying the standards for clinical validation studies, potential bias can be reduced, and the quality of a continuously growing number of studies in this field can be assessed and compared. The ILAE-IFCN clinical practice guideline on automated seizure detection using wearable devices recommends using clinically validated wearable devices for automated detection of tonic-clonic seizures when significant safety concerns exist. The studies published after the guideline was issued only provide incremental knowledge and would not change the current recommendations.
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Affiliation(s)
- Pirgit Meritam Larsen
- Department of Clinical Neurophysiology, Danish Epilepsy Centre, Visbys Allé 5, 4293 Dianalund, Denmark.
| | - Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Centre, Visbys Allé 5, 4293 Dianalund, Denmark; Department of Clinical Neurophysiology, Aarhus University Hospital, and Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 165, 8200 Aarhus, Denmark.
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10
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Ferreira AF, da Silva HP, Alves H, Marques N, Fred A. Feasibility of Electrodermal Activity and Photoplethysmography Data Acquisition at the Foot Using a Sock Form Factor. SENSORS (BASEL, SWITZERLAND) 2023; 23:620. [PMID: 36679418 PMCID: PMC9865091 DOI: 10.3390/s23020620] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 12/22/2022] [Accepted: 12/24/2022] [Indexed: 06/17/2023]
Abstract
Wearable devices have been shown to play an important role in disease prevention and health management, through the multimodal acquisition of peripheral biosignals. However, many of these wearables are exposed, limiting their long-term acceptability by some user groups. To overcome this, a wearable smart sock integrating a PPG sensor and an EDA sensor with textile electrodes was developed. Using the smart sock, EDA and PPG measurements at the foot/ankle were performed in test populations of 19 and 15 subjects, respectively. Both measurements were validated by simultaneously recording the same signals with a standard device at the hand. For the EDA measurements, Pearson correlations of up to 0.95 were obtained for the SCL component, and a mean consensus of 69% for peaks detected in the two locations was obtained. As for the PPG measurements, after fine-tuning the automatic detection of systolic peaks, the index finger and ankle, accuracies of 99.46% and 87.85% were obtained, respectively. Moreover, an HR estimation error of 17.40±14.80 Beats-Per-Minute (BPM) was obtained. Overall, the results support the feasibility of this wearable form factor for unobtrusive EDA and PPG monitoring.
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Affiliation(s)
- Afonso Fortes Ferreira
- Instituto Superior Técnico (IST), Av. Rovisco Pais n. 1, 1049-001 Lisboa, Portugal
- Instituto de Telecomunicações (IT), Av. Rovisco Pais n. 1, Torre Norte—Piso 10, 1049-001 Lisboa, Portugal
| | - Hugo Plácido da Silva
- Instituto Superior Técnico (IST), Av. Rovisco Pais n. 1, 1049-001 Lisboa, Portugal
- Instituto de Telecomunicações (IT), Av. Rovisco Pais n. 1, Torre Norte—Piso 10, 1049-001 Lisboa, Portugal
| | - Helena Alves
- Instituto Superior Técnico (IST), Av. Rovisco Pais n. 1, 1049-001 Lisboa, Portugal
- Instituto de Engenharia de Sistemas e Computadores-Microsistemas e Nanotecnologias (INESC-MN), Rua Alves Redol 9, 1000-019 Lisboa, Portugal
| | - Nuno Marques
- Meia Mania Lda, Zona Industrial dos Matinhos Pav. 4/5, 3200-100 Lousã, Portugal
| | - Ana Fred
- Instituto Superior Técnico (IST), Av. Rovisco Pais n. 1, 1049-001 Lisboa, Portugal
- Instituto de Telecomunicações (IT), Av. Rovisco Pais n. 1, Torre Norte—Piso 10, 1049-001 Lisboa, Portugal
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11
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Böttcher S, Vieluf S, Bruno E, Joseph B, Epitashvili N, Biondi A, Zabler N, Glasstetter M, Dümpelmann M, Van Laerhoven K, Nasseri M, Brinkman BH, Richardson MP, Schulze-Bonhage A, Loddenkemper T. Data quality evaluation in wearable monitoring. Sci Rep 2022; 12:21412. [PMID: 36496546 PMCID: PMC9741649 DOI: 10.1038/s41598-022-25949-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 11/25/2022] [Indexed: 12/13/2022] Open
Abstract
Wearable recordings of neurophysiological signals captured from the wrist offer enormous potential for seizure monitoring. Yet, data quality remains one of the most challenging factors that impact data reliability. We suggest a combined data quality assessment tool for the evaluation of multimodal wearable data. We analyzed data from patients with epilepsy from four epilepsy centers. Patients wore wristbands recording accelerometry, electrodermal activity, blood volume pulse, and skin temperature. We calculated data completeness and assessed the time the device was worn (on-body), and modality-specific signal quality scores. We included 37,166 h from 632 patients in the inpatient and 90,776 h from 39 patients in the outpatient setting. All modalities were affected by artifacts. Data loss was higher when using data streaming (up to 49% among inpatient cohorts, averaged across respective recordings) as compared to onboard device recording and storage (up to 9%). On-body scores, estimating the percentage of time a device was worn on the body, were consistently high across cohorts (more than 80%). Signal quality of some modalities, based on established indices, was higher at night than during the day. A uniformly reported data quality and multimodal signal quality index is feasible, makes study results more comparable, and contributes to the development of devices and evaluation routines necessary for seizure monitoring.
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Affiliation(s)
- Sebastian Böttcher
- grid.7708.80000 0000 9428 7911Department of Neurosurgery, Epilepsy Center, Medical Center – University of Freiburg, Freiburg, Germany ,grid.5836.80000 0001 2242 8751Ubiquitous Computing, Department of Electrical Engineering and Computer Science, University of Siegen, Siegen, Germany
| | - Solveig Vieluf
- grid.38142.3c000000041936754XDivision of Epilepsy and Clinical Neurophysiology, Boston Children’s Hospital, Harvard Medical School, Boston, MS USA
| | - Elisa Bruno
- grid.13097.3c0000 0001 2322 6764Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London, UK
| | - Boney Joseph
- grid.66875.3a0000 0004 0459 167XBioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN USA
| | - Nino Epitashvili
- grid.7708.80000 0000 9428 7911Department of Neurosurgery, Epilepsy Center, Medical Center – University of Freiburg, Freiburg, Germany
| | - Andrea Biondi
- grid.13097.3c0000 0001 2322 6764Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London, UK
| | - Nicolas Zabler
- grid.7708.80000 0000 9428 7911Department of Neurosurgery, Epilepsy Center, Medical Center – University of Freiburg, Freiburg, Germany
| | - Martin Glasstetter
- grid.7708.80000 0000 9428 7911Department of Neurosurgery, Epilepsy Center, Medical Center – University of Freiburg, Freiburg, Germany
| | - Matthias Dümpelmann
- grid.7708.80000 0000 9428 7911Department of Neurosurgery, Epilepsy Center, Medical Center – University of Freiburg, Freiburg, Germany ,grid.5963.9Department of Microsystems Engineering (IMTEK), University of Freiburg, Freiburg, Germany
| | - Kristof Van Laerhoven
- grid.5836.80000 0001 2242 8751Ubiquitous Computing, Department of Electrical Engineering and Computer Science, University of Siegen, Siegen, Germany
| | - Mona Nasseri
- grid.66875.3a0000 0004 0459 167XBioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN USA ,grid.266865.90000 0001 2109 4358School of Engineering, University of North Florida, Jacksonville, FL USA
| | - Benjamin H. Brinkman
- grid.66875.3a0000 0004 0459 167XBioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN USA
| | - Mark P. Richardson
- grid.13097.3c0000 0001 2322 6764Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London, UK
| | - Andreas Schulze-Bonhage
- grid.7708.80000 0000 9428 7911Department of Neurosurgery, Epilepsy Center, Medical Center – University of Freiburg, Freiburg, Germany
| | - Tobias Loddenkemper
- grid.38142.3c000000041936754XDivision of Epilepsy and Clinical Neurophysiology, Boston Children’s Hospital, Harvard Medical School, Boston, MS USA
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12
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Mercier MR, Dubarry AS, Tadel F, Avanzini P, Axmacher N, Cellier D, Vecchio MD, Hamilton LS, Hermes D, Kahana MJ, Knight RT, Llorens A, Megevand P, Melloni L, Miller KJ, Piai V, Puce A, Ramsey NF, Schwiedrzik CM, Smith SE, Stolk A, Swann NC, Vansteensel MJ, Voytek B, Wang L, Lachaux JP, Oostenveld R. Advances in human intracranial electroencephalography research, guidelines and good practices. Neuroimage 2022; 260:119438. [PMID: 35792291 PMCID: PMC10190110 DOI: 10.1016/j.neuroimage.2022.119438] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 05/23/2022] [Accepted: 06/30/2022] [Indexed: 12/11/2022] Open
Abstract
Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.
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Affiliation(s)
- Manuel R Mercier
- INSERM, INS, Institut de Neurosciences des Systèmes, Aix-Marseille University, Marseille, France.
| | | | - François Tadel
- Signal & Image Processing Institute, University of Southern California, Los Angeles, CA United States of America
| | - Pietro Avanzini
- Institute of Neuroscience, National Research Council of Italy, Parma, Italy
| | - Nikolai Axmacher
- Department of Neuropsychology, Faculty of Psychology, Institute of Cognitive Neuroscience, Ruhr University Bochum, Universitätsstraße 150, Bochum 44801, Germany; State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, 19 Xinjiekou Outer St, Beijing 100875, China
| | - Dillan Cellier
- Department of Cognitive Science, University of California, La Jolla, San Diego, United States of America
| | - Maria Del Vecchio
- Institute of Neuroscience, National Research Council of Italy, Parma, Italy
| | - Liberty S Hamilton
- Department of Neurology, Dell Medical School, The University of Texas at Austin, Austin, TX, United States of America; Institute for Neuroscience, The University of Texas at Austin, Austin, TX, United States of America; Department of Speech, Language, and Hearing Sciences, Moody College of Communication, The University of Texas at Austin, Austin, TX, United States of America
| | - Dora Hermes
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States of America
| | - Michael J Kahana
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Robert T Knight
- Department of Psychology and the Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720, United States of America
| | - Anais Llorens
- Helen Wills Neuroscience Institute, University of California, Berkeley, United States of America
| | - Pierre Megevand
- Department of Clinical neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Lucia Melloni
- Department of Neuroscience, Max Planck Institute for Empirical Aesthetics, Grüneburgweg 14, Frankfurt am Main 60322, Germany; Department of Neurology, NYU Grossman School of Medicine, 145 East 32nd Street, Room 828, New York, NY 10016, United States of America
| | - Kai J Miller
- Department of Neurosurgery, Mayo Clinic, Rochester, MN 55905, USA
| | - Vitória Piai
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands; Department of Medical Psychology, Radboudumc, Donders Centre for Medical Neuroscience, Nijmegen, the Netherlands
| | - Aina Puce
- Department of Psychological & Brain Sciences, Programs in Neuroscience, Cognitive Science, Indiana University, Bloomington, IN, United States of America
| | - Nick F Ramsey
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, UMC Utrecht, the Netherlands
| | - Caspar M Schwiedrzik
- Neural Circuits and Cognition Lab, European Neuroscience Institute Göttingen - A Joint Initiative of the University Medical Center Göttingen and the Max Planck Society, Göttingen, Germany; Perception and Plasticity Group, German Primate Center, Leibniz Institute for Primate Research, Göttingen, Germany
| | - Sydney E Smith
- Neurosciences Graduate Program, University of California, La Jolla, San Diego, United States of America
| | - Arjen Stolk
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands; Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States of America
| | - Nicole C Swann
- University of Oregon in the Department of Human Physiology, United States of America
| | - Mariska J Vansteensel
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, UMC Utrecht, the Netherlands
| | - Bradley Voytek
- Department of Cognitive Science, University of California, La Jolla, San Diego, United States of America; Neurosciences Graduate Program, University of California, La Jolla, San Diego, United States of America; Halıcıoğlu Data Science Institute, University of California, La Jolla, San Diego, United States of America; Kavli Institute for Brain and Mind, University of California, La Jolla, San Diego, United States of America
| | - Liang Wang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Jean-Philippe Lachaux
- Lyon Neuroscience Research Center, EDUWELL Team, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, Lyon F-69000, France
| | - Robert Oostenveld
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands; NatMEG, Karolinska Institutet, Stockholm, Sweden
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13
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Seizure-related differences in biosignal 24-h modulation patterns. Sci Rep 2022; 12:15070. [PMID: 36064877 PMCID: PMC9445076 DOI: 10.1038/s41598-022-18271-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 08/08/2022] [Indexed: 11/08/2022] Open
Abstract
A seizure likelihood biomarker could improve seizure monitoring and facilitate adjustment of treatments based on seizure risk. Here, we tested differences in patient-specific 24-h-modulation patterns of electrodermal activity (EDA), peripheral body temperature (TEMP), and heart rate (HR) between patients with and without seizures. We enrolled patients who underwent continuous video-EEG monitoring at Boston Children's Hospital to wear a biosensor. We divided patients into two groups: those with no seizures and those with at least one seizure during the recording period. We assessed the 24-h modulation level and amplitude of EDA, TEMP, and HR. We performed machine learning including physiological and clinical variables. Subsequently, we determined classifier performance by cross-validated machine learning. Patients with seizures (n = 49) had lower EDA levels (p = 0.031), EDA amplitudes (p = 0.045), and trended toward lower HR levels (p = 0.060) compared to patients without seizures (n = 68). Averaged cross-validated classification accuracy was 69% (AUC-ROC: 0.75). Our results show the potential to monitor and forecast risk for epileptic seizures based on changes in 24-h patterns in wearable recordings in combination with clinical variables. Such biomarkers might be applicable to inform care, such as treatment or seizure injury risk during specific periods, scheduling diagnostic tests, such as admission to the epilepsy monitoring unit, and potentially other neurological and chronic conditions.
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14
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Electrodermal activity response during seizures: A systematic review and meta-analysis. Epilepsy Behav 2022; 134:108864. [PMID: 35952508 DOI: 10.1016/j.yebeh.2022.108864] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 04/22/2022] [Accepted: 07/27/2022] [Indexed: 11/23/2022]
Abstract
INTRODUCTION Wearable devices for continuous seizure monitoring have drawn increasing attention in the field of epilepsy research. One of the parameters recorded by these devices is electrodermal activity (EDA). The aim of this study was to systematically review the literature to estimate the incidence of electrodermal response during seizures. METHODS We searched all articles recording concurrent EDA and EEG activity during the pre-ictal, ictal, and postictal periods in children and adults with epilepsy. Studies reporting the total number of seizures and number of seizures with an EDA response were included for a random-effects meta-analysis. RESULTS Nineteen studies, including 550 participants and 1115 seizures were reviewed. All studies demonstrated an EDA increase during the ictal and postictal periods, while only three reported pre-ictal EDA responses. The meta-analysis showed a pooled EDA response incidence of 82/100 seizures (95% CI 70-91). Tonic-clonic seizures (both generalized tonic-clonic seizures (GTCS) and focal to bilateral tonic-clonic seizures (FBTCS)) elicited a more pronounced (higher and longer-lasting) EDA response when compared with focal seizures (excluding FBTCS). DISCUSSION Epileptic seizures produce an electrodermal response detectable by wearable devices during the pre-ictal, ictal, and postictal periods. Further research is needed to better understand EDA changes and to analyze factors which may influence the EDA response.
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15
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Hupp NJ, Talavera B, Melius S, Lacuey N, Lhatoo SD. Protocols for multimodal polygraphy for cardiorespiratory monitoring in the epilepsy monitoring unit. Part II - Research acquisition. Epilepsy Res 2022; 185:106987. [PMID: 35843018 DOI: 10.1016/j.eplepsyres.2022.106987] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 06/29/2022] [Accepted: 07/07/2022] [Indexed: 11/30/2022]
Abstract
Multimodal polygraphy including cardiorespiratory monitoring is a valuable tool for epilepsy and sudden unexpected death in epilepsy (SUDEP) research. Broader applications in research into stress, anxiety, mood and other domains exist. Polygraphy techniques used during video electroencephalogram (EEG) recordings provide information on cardiac and respiratory changes in the peri-ictal period. In addition, such monitoring in brain mapping during chronic intracranial EEG evaluations has helped the understanding of pathomechanisms that lead to seizure induced cardiorespiratory dysfunction. Our aim here is to provide protocols and information on devices that may be used in the Epilepsy Monitoring Unit, in addition to proposed standard of care data acquisition. These devices include oronasal thermistors, oronasal pressure transducers, capnography, transcutaneous CO2 sensors, and continuous noninvasive blood pressure monitoring. Standard protocols for cardiorespiratory monitoring simultaneously with video EEG recording, may be useful in the study of cardiorespiratory phenomena in persons with epilepsy.
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Affiliation(s)
- Norma J Hupp
- Texas Institute of Restorative Neurotechnologies (TIRN), University of Texas Health Science Center (UTHealth), Houston, TX, USA
| | - Blanca Talavera
- Texas Institute of Restorative Neurotechnologies (TIRN), University of Texas Health Science Center (UTHealth), Houston, TX, USA.
| | - Stephen Melius
- Memorial Hermann. Texas Medical Center, Houston, TX, USA
| | - Nuria Lacuey
- Texas Institute of Restorative Neurotechnologies (TIRN), University of Texas Health Science Center (UTHealth), Houston, TX, USA
| | - Samden D Lhatoo
- Texas Institute of Restorative Neurotechnologies (TIRN), University of Texas Health Science Center (UTHealth), Houston, TX, USA
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16
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Intra- and Inter-Subject Perspectives on the Detection of Focal Onset Motor Seizures in Epilepsy Patients. SENSORS 2022; 22:s22093318. [PMID: 35591007 PMCID: PMC9105312 DOI: 10.3390/s22093318] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 04/19/2022] [Accepted: 04/22/2022] [Indexed: 01/15/2023]
Abstract
Focal onset epileptic seizures are highly heterogeneous in their clinical manifestations, and a robust seizure detection across patient cohorts has to date not been achieved. Here, we assess and discuss the potential of supervised machine learning models for the detection of focal onset motor seizures by means of a wrist-worn wearable device, both in a personalized context as well as across patients. Wearable data were recorded in-hospital from patients with epilepsy at two epilepsy centers. Accelerometry, electrodermal activity, and blood volume pulse data were processed and features for each of the biosignal modalities were calculated. Following a leave-one-out approach, a gradient tree boosting machine learning model was optimized and tested in an intra-subject and inter-subject evaluation. In total, 20 seizures from 9 patients were included and we report sensitivities of 67% to 100% and false alarm rates of down to 0.85 per 24 h in the individualized assessment. Conversely, for an inter-subject seizure detection methodology tested on an out-of-sample data set, an optimized model could only achieve a sensitivity of 75% at a false alarm rate of 13.4 per 24 h. We demonstrate that robustly detecting focal onset motor seizures with tonic or clonic movements from wearable data may be possible for individuals, depending on specific seizure manifestations.
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17
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Halimeh M, Yang Y, Sheehan T, Vieluf S, Jackson M, Loddenkemper T, Meisel C. Wearable device assessments of antiseizure medication effects on diurnal patterns of electrodermal activity, heart rate, and heart rate variability. Epilepsy Behav 2022; 129:108635. [PMID: 35278938 DOI: 10.1016/j.yebeh.2022.108635] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 02/04/2022] [Accepted: 02/19/2022] [Indexed: 11/03/2022]
Abstract
Patient-generated health data provide a great opportunity for more detailed ambulatory monitoring and more personalized treatments in many diseases. In epilepsy, robust diagnostics applicable to the ambulatory setting are needed as diagnosis and treatment decisions in current clinical practice are primarily reliant on patient self-reports, which are often inaccurate. Recent work using wearable devices has focused on methods to detect and forecast epileptic seizures. Whether wearable device signals may also contain information about the effect of antiseizure medications (ASMs), which may ultimately help to better monitor their efficacy, has not been evaluated yet. Here we systematically investigated the effect of ASMs on different data modalities (electrodermal activity, EDA, heart rate, HR, and heart rate variability, HRV) simultaneously recorded by a wearable device in 48 patients with epilepsy over several days in the epilepsy long-term monitoring unit at a tertiary hospital. All signals exhibited characteristic diurnal variations. HRV, but not HR or EDA-based metrics, were reduced by ASMs. By assessing multiple signals related to the autonomic nervous system simultaneously, our results provide novel insights into the effects of ASMs on the sympathetic and parasympathetic interplay in the setting of epilepsy and indicate the potential of easy-to-wear wearable devices for monitoring ASM action. Future work using longer data may investigate these metrics on multidien cycles and their utility for detecting seizures, assessing seizure risk, or informing treatment interventions.
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Affiliation(s)
- Mustafa Halimeh
- Computational Neurology, Department of Neurology, Charité - Universitätsmedizin Berlin, Germany; Berlin Institute of Health, Germany
| | - Yonghua Yang
- Hospital of Xi'an Jiaotong University, Pediatric Department, Shaanxi, China
| | | | | | | | | | - Christian Meisel
- Computational Neurology, Department of Neurology, Charité - Universitätsmedizin Berlin, Germany; Berlin Institute of Health, Germany.
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18
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Tronstad C, Amini M, Bach DR, Martinsen OG. Current trends and opportunities in the methodology of electrodermal activity measurement. Physiol Meas 2022; 43. [PMID: 35090148 DOI: 10.1088/1361-6579/ac5007] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 01/28/2022] [Indexed: 11/12/2022]
Abstract
Electrodermal activity (EDA) has been measured in the laboratory since the late 1800s. Although the influence of sudomotor nerve activity and the sympathetic nervous system on EDA is well established, the mechanisms underlying EDA signal generation are not completely understood. Owing to simplicity of instrumentation and modern electronics, these measurements have recently seen a transfer from the laboratory to wearable devices, sparking numerous novel applications while bringing along both challenges and new opportunities. In addition to developments in electronics and miniaturization, current trends in material technology and manufacturing have sparked innovations in electrode technologies, and trends in data science such as machine learning and sensor fusion are expanding the ways that measurement data can be processed and utilized. Although challenges remain for the quality of wearable EDA measurement, ongoing research and developments may shorten the quality gap between wearable EDA and standardized recordings in the laboratory. In this topical review, we provide an overview of the basics of EDA measurement, discuss the challenges and opportunities of wearable EDA, and review recent developments in instrumentation, material technology, signal processing, modeling and data science tools that may advance the field of EDA research and applications over the coming years.
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Affiliation(s)
- Christian Tronstad
- Department of Clinical and Biomedical Engineering, Oslo University Hospital, Sognsvannsveien 20, Oslo, 0372, NORWAY
| | - Maryam Amini
- Physics, University of Oslo Faculty of Mathematics and Natural Sciences, Sem Sælands vei 24, Oslo, 0371, NORWAY
| | - Dominik R Bach
- Wellcome Centre for Human Neuroimaging, University College London, 12 Queen Square, London, London, WC1N 3AZ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
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19
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Abstract
This paper presents the findings of a 6-week long, five-participant experiment in a controlled climate chamber. The experiment was designed to understand the effect of time on thermal behaviour, electrodermal activity (EDA) and the adaptive behavior of occupants in response to a thermal non-uniform indoor environment were continuously logged. The results of the 150 h-long longitudinal study suggested a significant difference in tonic EDA levels between “morning” and “afternoon” clusters although the environmental parameters were the same, suggesting a change in the human body’s thermal reception over time. The correlation of the EDA and temperature was greater for the afternoon cluster (r = 0.449, p < 0.001) in relation to the morning cluster (r = 0.332, p < 0.001). These findings showed a strong temporal dependency of the skin conductance level of the EDA to the operative temperature, following the person’s circadian rhythm. Even further, based on the person’s chronotype, the beginning of the “afternoon” cluster was observed to have shifted according to the person’s circadian rhythm. Furthermore, the study is able to show how the body reacts differently under the same PMV values, both within and between subjects; pointing to the lack of temporal parameter in the PMV model.
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20
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Hubbard I, Beniczky S, Ryvlin P. The Challenging Path to Developing a Mobile Health Device for Epilepsy: The Current Landscape and Where We Go From Here. Front Neurol 2021; 12:740743. [PMID: 34659099 PMCID: PMC8517120 DOI: 10.3389/fneur.2021.740743] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 09/03/2021] [Indexed: 11/13/2022] Open
Abstract
Seizure detection, and more recently seizure forecasting, represent important avenues of clinical development in epilepsy, promoted by progress in wearable devices and mobile health (mHealth), which might help optimizing seizure control and prevention of seizure-related mortality and morbidity in persons with epilepsy. Yet, very long-term continuous monitoring of seizure-sensitive biosignals in the ambulatory setting presents a number of challenges. We herein provide an overview of these challenges and current technological landscape of mHealth devices for seizure detection. Specifically, we display, which types of sensor modalities and analytical methods are available, and give insight into current clinical practice guidelines, main outcomes of clinical validation studies, and discuss how to evaluate device performance at point-of-care facilities. We then address pitfalls which may arise in patient compliance and the need to design solutions adapted to user experience.
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Affiliation(s)
- Ilona Hubbard
- Department of Clinical Neurosciences, Vaud University Hospital, Lausanne, Switzerland
| | - Sandor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund, Denmark.,Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
| | - Philippe Ryvlin
- Department of Clinical Neurosciences, Vaud University Hospital, Lausanne, Switzerland
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21
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El Atrache R, Tamilia E, Amengual-Gual M, Mohammadpour Touserkani F, Yang Y, Wang X, Ufongene C, Sheehan T, Cantley S, Jackson M, Zhang B, Papadelis C, Sarkis RA, Loddenkemper T. Association between semiologic, autonomic, and electrographic seizure characteristics in children with generalized tonic-clonic seizures. Epilepsy Behav 2021; 122:108228. [PMID: 34388667 DOI: 10.1016/j.yebeh.2021.108228] [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: 07/07/2021] [Accepted: 07/16/2021] [Indexed: 10/20/2022]
Abstract
INTRODUCTION Generalized tonic-clonic seizures (GTCS) are associated with elevated electrodermal activity (EDA) and postictal generalized electroencephalographic suppression (PGES), markers that may indicate sudden unexpected death in epilepsy (SUDEP) risk. This study investigated the association of GTCS semiology, EDA, and PGES in children with epilepsy. METHODS Patients admitted to the Boston Children's Hospital long-term video-EEG monitoring unit wore a sensor that records EDA. We selected patients with at least one GTCS and reviewed video-EEGs for semiology, tonic and clonic phase duration, total clinical seizure duration, electrographic onset, offset, and PGES. We grouped patients into three semiology classes: GTCS 1: bilateral symmetric tonic arm extension, GTCS 2: no specific tonic arm extension or flexion, GTCS 3: unilateral or asymmetrical arm extension, tonic arm flexion or posturing that does not fit into GTCS 1 or 2. We analyzed the correlation between semiology, EDA, and PGES, and measured the area under the curve (AUC) of the ictal EDA (seizure onset to one hour after), subtracting baseline EDA (one-hour seizure-free before seizure onset). Using generalized estimating equation (GEE) and linear regression, we analyzed all seizures and single episodes per patient. RESULTS We included 30 patients (median age 13.8 ± 3.6 years, 46.7% females) and 53 seizures. With GEE, GTCS 1 was associated with longer PGES duration compared to GTCS 2 (Estimate (β) = -26.32 s, 95% Confidence Interval (CI): -36.46 to -16.18, p < 0.001), and the presence of PGES was associated with greater EDA change (β = 429604 μS, 95% CI: 3550.96 to 855657.04, p = 0.048). With single-episode analysis, GTCS 1 had greater EDA change than GTCS 2 ((β = -601339 μS, 95% CI: -1167016.56 to -35661.44, p = 0.047). EDA increased with PGES presence (β = 637500 μS, 95% CI: 183571.84 to 1091428.16, p = 0.01) and duration (β = 16794 μS, 95% CI: 5729.8 to 27858.2, p = 0.006). Patients with GTCS 1 had longer PGES duration compared to GTCS 2 (β = -30.53 s, 95% CI: -44.6 to -16.46, p < 0.001) and GTCS 3 (β = -22.07 s, 95% CI: -38.95 to -5.19, p = 0.016). CONCLUSION In children with epilepsy, PGES correlates with greater ictal EDA. GTCS 1 correlated with longer PGES duration and may indirectly correlate with greater ictal EDA. Our study suggests potential applications in monitoring and preventing SUDEP in these patients.
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Affiliation(s)
- Rima El Atrache
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Eleonora Tamilia
- Laboratory of Children's Brain Dynamics, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, USA
| | - Marta Amengual-Gual
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Fatemeh Mohammadpour Touserkani
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Neurology, SUNY Downstate Medical Center, Brooklyn, NY, USA
| | - Yonghua Yang
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pediatrics, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Xiaofan Wang
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Claire Ufongene
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Theodore Sheehan
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sarah Cantley
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michele Jackson
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Bo Zhang
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Biostatistics and Research Design Center, Institutional Centers for Clinical and Translational Research, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Christos Papadelis
- Laboratory of Children's Brain Dynamics, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Jane and John Justin Neurosciences Center, Cook Children's Health Care System, Fort Worth, TX, USA; Department of Bioengineering, University of Texas at Arlington, Arlington, TX, USA; School of Medicine, Texas Christian University and University of North Texas Health Science Center, Fort Worth, Texas, USA
| | - Rani A Sarkis
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Tobias Loddenkemper
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
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22
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Brinkmann BH, Karoly PJ, Nurse ES, Dumanis SB, Nasseri M, Viana PF, Schulze-Bonhage A, Freestone DR, Worrell G, Richardson MP, Cook MJ. Seizure Diaries and Forecasting With Wearables: Epilepsy Monitoring Outside the Clinic. Front Neurol 2021; 12:690404. [PMID: 34326807 PMCID: PMC8315760 DOI: 10.3389/fneur.2021.690404] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 06/10/2021] [Indexed: 12/14/2022] Open
Abstract
It is a major challenge in clinical epilepsy to diagnose and treat a disease characterized by infrequent seizures based on patient or caregiver reports and limited duration clinical testing. The poor reliability of self-reported seizure diaries for many people with epilepsy is well-established, but these records remain necessary in clinical care and therapeutic studies. A number of wearable devices have emerged, which may be capable of detecting seizures, recording seizure data, and alerting caregivers. Developments in non-invasive wearable sensors to measure accelerometry, photoplethysmography (PPG), electrodermal activity (EDA), electromyography (EMG), and other signals outside of the traditional clinical environment may be able to identify seizure-related changes. Non-invasive scalp electroencephalography (EEG) and minimally invasive subscalp EEG may allow direct measurement of seizure activity. However, significant network and computational infrastructure is needed for continuous, secure transmission of data. The large volume of data acquired by these devices necessitates computer-assisted review and detection to reduce the burden on human reviewers. Furthermore, user acceptability of such devices must be a paramount consideration to ensure adherence with long-term device use. Such devices can identify tonic–clonic seizures, but identification of other seizure semiologies with non-EEG wearables is an ongoing challenge. Identification of electrographic seizures with subscalp EEG systems has recently been demonstrated over long (>6 month) durations, and this shows promise for accurate, objective seizure records. While the ability to detect and forecast seizures from ambulatory intracranial EEG is established, invasive devices may not be acceptable for many individuals with epilepsy. Recent studies show promising results for probabilistic forecasts of seizure risk from long-term wearable devices and electronic diaries of self-reported seizures. There may also be predictive value in individuals' symptoms, mood, and cognitive performance. However, seizure forecasting requires perpetual use of a device for monitoring, increasing the importance of the system's acceptability to users. Furthermore, long-term studies with concurrent EEG confirmation are lacking currently. This review describes the current evidence and challenges in the use of minimally and non-invasive devices for long-term epilepsy monitoring, the essential components in remote monitoring systems, and explores the feasibility to detect and forecast impending seizures via long-term use of these systems.
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Affiliation(s)
| | - Philippa J Karoly
- Department of Medicine, Graeme Clark Institute and St Vincent's Hospital, The University of Melbourne, Fitzroy, VIC, Australia
| | - Ewan S Nurse
- Department of Medicine, Graeme Clark Institute and St Vincent's Hospital, The University of Melbourne, Fitzroy, VIC, Australia.,Seer Medical, Melbourne, VIC, Australia
| | | | - Mona Nasseri
- Department of Neurology, Mayo Foundation, Rochester, MN, United States.,School of Engineering, University of North Florida, Jacksonville, FL, United States
| | - Pedro F Viana
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Faculty of Medicine, University of Lisbon, Lisboa, Portugal
| | - Andreas Schulze-Bonhage
- Faculty of Medicine, Epilepsy Center, Medical Center, University of Freiburg, Freiburg, Germany
| | | | - Greg Worrell
- Department of Neurology, Mayo Foundation, Rochester, MN, United States
| | - Mark P Richardson
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Mark J Cook
- Department of Medicine, Graeme Clark Institute and St Vincent's Hospital, The University of Melbourne, Fitzroy, VIC, Australia
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