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Böttcher S, Zabler N, Jackson M, Bruno E, Biondi A, Epitashvili N, Vieluf S, Dümpelmann M, Richardson MP, Brinkmann BH, Loddenkemper T, Schulze-Bonhage A. Effects of epileptic seizures on the quality of biosignals recorded from wearables. Epilepsia 2024. [PMID: 39373185 DOI: 10.1111/epi.18138] [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: 06/04/2024] [Revised: 09/19/2024] [Accepted: 09/20/2024] [Indexed: 10/08/2024]
Abstract
OBJECTIVE Wearable nonelectroencephalographic biosignal recordings captured from the wrist offer enormous potential for seizure monitoring. However, signal quality remains a challenging factor affecting data reliability. Models trained for seizure detection depend on the quality of recordings in peri-ictal periods in performing a feature-based separation of ictal periods from interictal periods. Thus, this study aims to investigate the effect of epileptic seizures on signal quality, ensuring accurate and reliable monitoring. METHODS This study assesses the signal quality of wearable data during peri-ictal phases of generalized tonic-clonic and focal to bilateral tonic-clonic seizures (TCS), focal motor seizures (FMS), and focal nonmotor seizures (FNMS). We evaluated accelerometer (ACC) activity and the signal quality of electrodermal activity (EDA) and blood volume pulse (BVP) data. Additionally, we analyzed the influence of peri-ictal movements as assessed by ACC (ACC activity) on signal quality and examined intraictal subphases of focal to bilateral TCS. RESULTS We analyzed 386 seizures from 111 individuals in three international epilepsy monitoring units. BVP signal quality and ACC activity levels differed between all seizure types. We found the largest decrease in BVP signal quality and increase in ACC activity when comparing the ictal phase to the pre- and postictal phases for TCS. Additionally, ACC activity was strongly negatively correlated with BVP signal quality for TCS and FMS, and weakly for FNMS. Intraictal analysis revealed that tonic and clonic subphases have the lowest BVP signal quality and the highest ACC activity. SIGNIFICANCE Motor elements of seizures significantly impair BVP signal quality, but do not have significant effect on EDA signal quality, as assessed by wrist-worn wearables. The results underscore the importance of signal quality assessment methods and careful selection of robust modalities to ensure reliable seizure detection. Future research is needed to explain whether seizure detection models' decisions are based on signal responses induced by physiological processes as opposed to artifacts.
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Affiliation(s)
- Sebastian Böttcher
- Epilepsy Center, University Medical Center-University of Freiburg, Freiburg, Germany
- Department of Microsystems Engineering (IMTEK), University of Freiburg, Freiburg, Germany
| | - Nicolas Zabler
- Epilepsy Center, University Medical Center-University of Freiburg, Freiburg, Germany
- Department of Microsystems Engineering (IMTEK), University of Freiburg, Freiburg, Germany
| | - Michele Jackson
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Elisa Bruno
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Andrea Biondi
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Nino Epitashvili
- Epilepsy Center, University Medical Center-University of Freiburg, Freiburg, Germany
| | - Solveig Vieluf
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Medicine I, LMU University Hospital, LMU Munich, Munich, Germany
- German Center for Cardiovascular Research, Partner Site Munich Heart Alliance, Munich, Germany
| | - Matthias Dümpelmann
- Epilepsy Center, University Medical Center-University of Freiburg, Freiburg, Germany
- Department of Microsystems Engineering (IMTEK), University of Freiburg, Freiburg, Germany
| | - Mark P Richardson
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Benjamin H Brinkmann
- Department of Neurology and Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
| | - Tobias Loddenkemper
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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Adhyapak N, Cardenas GE, Abboud MA, Krishnan V. Rest-Activity Rhythm Phenotypes in Adults with Epilepsy and Intellectual Disability. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.09.09.24313145. [PMID: 39314931 PMCID: PMC11419227 DOI: 10.1101/2024.09.09.24313145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/25/2024]
Abstract
Objective Sleep and rest-activity rhythms (RARs) are perturbed in many forms of neuropsychiatric illness. In this study, we applied wrist actigraphy to describe the extent of RAR perturbations in adults with epilepsy and intellectual disability ("E+ID"), using a cross-sectional case-control design. We examined whether RAR phenotypes correlated with epilepsy severity, deficits in adaptive function and/or comorbid psychopathology. Methods Primary caregivers of E+ID adults provided informed consent during routine ambulatory clinic visits and were asked to complete standardized surveys of overall epilepsy severity (GASE, Global Assessment of Severity of Epilepsy), adaptive function (ABAS-3, Adaptive Behavior Assessment System-3) and psychopathology (ABCL, Adult Behavior Checklist). Caregivers were also asked to ensure that subjects wore an Actiwatch-2 device continuously on their nondominant wrist for at least ten days. From recorded actograms, we calculated RAR amplitude, acrophase, robustness, intradaily variability (IV), interdaily stability (IS) and estimates of sleep quantity and timing. We compared these RAR metrics against those from (i) a previously published cohort of adults with epilepsy without ID (E-ID), and (ii) a cohort of age- and sex-matched intellectually able subjects measured within the Study of Latinos (SOL) Ancillary actigraphy study (SOL). Within E+ID subjects, we applied k-means analysis to divide subjects into three actigraphically distinct clusters. Results 46 E+ID subjects (median age 26 [20-68], 47% female) provided a median recording duration of 11 days [range 6-27]. Surveys reflected low to extremely low levels of adaptive function (ABAS3 General Adaptive Composite score: median 50 [49-75]), and low/subclinical levels of psychopathology (ABCL total score: median 54.5 [25-67]). Compared with E-ID (n=57) and SOL (n=156) cohorts, E+ID subjects displayed significantly lower RAR amplitude, robustness and IS, with significantly higher IV and total daily sleep. K-means clustering of E+ID subjects recognized an intermediate cluster "B", with RAR values indistinguishable to E-ID. Cluster "A" subjects displayed pronounced hypoactivity and hypersomnia with high rates of rhythm fragmentation, while cluster "C" subjects featured hyper-robust and high amplitude RARs. All three clusters were similar in age, body mass index, antiseizure medication (ASM) polytherapy, ABAS3 and ABCL scores. We qualitatively describe RAR examples from all three clusters. Interpretation We show that adults with epilepsy and intellectual disability display a wide spectrum of RAR phenotypes that do not neatly correlate with measures of adaptive function or epilepsy severity. Prospective studies are necessary to determine whether continuous actigraphic monitoring can sensitively capture changes in chronobiological health that may arise with disease progression, iatrogenesis (e.g., ASM toxicity) or acute health deteriorations (e.g., seizure exacerbation, pneumonia). Similar long-term data is necessary to recognize whether behavioral interventions targeted to 'normalize' RARs may promote improvements in adaptive function and therapy engagement.
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Affiliation(s)
- Nandani Adhyapak
- Departments of Neurology, Neuroscience and Psychiatry & Behavioral Sciences, Baylor College of Medicine, Houston, TX USA
| | - Grace E Cardenas
- Departments of Neurology, Neuroscience and Psychiatry & Behavioral Sciences, Baylor College of Medicine, Houston, TX USA
| | - Mark A Abboud
- Departments of Neurology, Neuroscience and Psychiatry & Behavioral Sciences, Baylor College of Medicine, Houston, TX USA
| | - Vaishnav Krishnan
- Departments of Neurology, Neuroscience and Psychiatry & Behavioral Sciences, Baylor College of Medicine, Houston, TX USA
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Armstrong B, Weaver RG, McAninch J, Smith MT, Parker H, Lane AD, Wang Y, Pate R, Rahman M, Matolak D, Chandrashekhar MVS. Development and Calibration of a PATCH Device for Monitoring Children's Heart Rate and Acceleration. Med Sci Sports Exerc 2024; 56:1196-1207. [PMID: 38377012 PMCID: PMC11096080 DOI: 10.1249/mss.0000000000003404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
INTRODUCTION Current wearables that collect heart rate and acceleration were not designed for children and/or do not allow access to raw signals, making them fundamentally unverifiable. This study describes the creation and calibration of an open-source multichannel platform (PATCH) designed to measure heart rate and acceleration in children ages 3-8 yr. METHODS Children (N = 63; mean age, 6.3 yr) participated in a 45-min protocol ranging in intensities from sedentary to vigorous activity. Actiheart-5 was used as a comparison measure. We calculated mean bias, mean absolute error (MAE) mean absolute percent error (MA%E), Pearson correlations, and Lin's concordance correlation coefficient (CCC). RESULTS Mean bias between PATCH and Actiheart heart rate was 2.26 bpm, MAE was 6.67 bpm, and M%E was 5.99%. The correlation between PATCH and Actiheart heart rate was 0.89, and CCC was 0.88. For acceleration, mean bias was 1.16 mg and MAE was 12.24 mg. The correlation between PATCH and Actiheart was 0.96, and CCC was 0.95. CONCLUSIONS The PATCH demonstrated clinically acceptable accuracies to measure heart rate and acceleration compared with a research-grade device.
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Affiliation(s)
- Bridget Armstrong
- Department of Exercise Science, University of South Carolina, Columbia, SC
| | - R. Glenn Weaver
- Department of Exercise Science, University of South Carolina, Columbia, SC
| | - Jonas McAninch
- Department of Electrical Engineering, University of South Carolina, Columbia, SC
| | - Michal T. Smith
- Department of Exercise Science, University of South Carolina, Columbia, SC
| | - Hannah Parker
- Department of Exercise Science, University of South Carolina, Columbia, SC
| | - Abbi D. Lane
- Department of Exercise Science, University of South Carolina, Columbia, SC
| | - Yuan Wang
- Epidemiology and Biostatistics at the University of South Carlina, Columbia, SC
| | - Russ Pate
- Department of Exercise Science, University of South Carolina, Columbia, SC
| | - Mafruda Rahman
- Department of Electrical Engineering, University of South Carolina, Columbia, SC
| | - David Matolak
- Department of Electrical Engineering, University of South Carolina, Columbia, SC
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Borges DF, Fernandes J, Soares JI, Casalta-Lopes J, Carvalho D, Beniczky S, Leal A. The sound of silence: Quantification of typical absence seizures by sonifying EEG signals from a custom-built wearable device. Epileptic Disord 2024; 26:188-198. [PMID: 38279944 DOI: 10.1002/epd2.20194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 11/29/2023] [Accepted: 12/22/2023] [Indexed: 01/29/2024]
Abstract
OBJECTIVE To develop and validate a method for long-term (24-h) objective quantification of absence seizures in the EEG of patients with childhood absence epilepsy (CAE) in their real home environment using a wearable device (waEEG), comparing automatic detection methods with auditory recognition after seizure sonification. METHODS The waEEG recording was acquired with two scalp electrodes. Automatic analysis was performed using previously validated software (Persyst® 14) and then fully reviewed by an experienced clinical neurophysiologist. The EEG data were converted into an audio file in waveform format with a 60-fold time compression factor. The sonified EEG was listened to by three inexperienced observers and the number of seizures and the processing time required for each data set were recorded blind to other data. Quantification of seizures from the patient diary was also assessed. RESULTS Eleven waEEG recordings from seven CAE patients with an average age of 8.18 ± 1.60 years were included. No differences in the number of seizures were found between the recordings using automated methods and expert audio assessment, with significant correlations between methods (ρ > .89, p < .001) and between observers (ρ > .96, p < .001). For the entire data set, the audio assessment yielded a sensitivity of .830 and a precision of .841, resulting in an F1 score of .835. SIGNIFICANCE Auditory waEEG seizure detection by lay medical personnel provided similar accuracy to post-processed automatic detection by an experienced clinical neurophysiologist, but in a less time-consuming procedure and without the need for specialized resources. Sonification of long-term EEG recordings in CAE provides a user-friendly and cost-effective clinical workflow for quantifying seizures in clinical practice, minimizing human and technical constraints.
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Affiliation(s)
- Daniel Filipe Borges
- Department of Neurophysiology, School of Health (ESS), Polytechnic University of Porto, Porto, Portugal
- Center for Translational Health and Medical Biotechnology Research (TBIO), School of Health, Polytechnic University of Porto, Porto, Portugal
- Faculty of Medicine, University of Porto, Porto, Portugal
| | - João Fernandes
- Department of Clinical Physiology, Medical Imaging and Radiotherapy, Polytechnic University of Coimbra, Coimbra Health School, Coimbra, Portugal
- Refractory Epilepsy Reference Center, Centro Hospitalar de Lisboa Ocidental, Lisboa, Portugal
| | - Joana Isabel Soares
- Department of General Sciences, Polytechnic University of Coimbra, Coimbra Health School, Coimbra, Portugal
- Department of Biomedicine, Faculty of Medicine, University of Porto, Porto, Portugal
- Neuronal Networks Group, Institute for Research and Innovation in Health Sciences (i3S), University of Porto, Porto, Portugal
| | - João Casalta-Lopes
- Department of General Sciences, Polytechnic University of Coimbra, Coimbra Health School, Coimbra, Portugal
- Department of Radiotherapy, Centro Hospitalar Universitário de São João, Porto, Portugal
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
| | - Daniel Carvalho
- Department of Pediatric Neurology, Centro Hospitalar Universitário de Lisboa Central, Lisbon, Portugal
| | - Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund, Denmark
- Department of Clinical Medicine and Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
| | - Alberto Leal
- Unidade Autónoma de Neurofisiologia, Hospital Júlio de Matos, Lisbon, Portugal
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Terman SW, Kirkpatrick L, Akiyama LF, Baajour W, Atilgan D, Dorotan MKC, Choi HW, French JA. Current state of the epilepsy drug and device pipeline. Epilepsia 2024; 65:833-845. [PMID: 38345387 PMCID: PMC11018510 DOI: 10.1111/epi.17884] [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: 11/10/2023] [Revised: 12/14/2023] [Accepted: 01/05/2024] [Indexed: 02/18/2024]
Abstract
The field of epilepsy has undergone substantial advances as we develop novel drugs and devices. Yet considerable challenges remain in developing broadly effective, well-tolerated treatments, but also precision treatments for rare epilepsies and seizure-monitoring devices. We summarize major recent and ongoing innovations in diagnostic and therapeutic products presented at the seventeenth Epilepsy Therapies & Diagnostics Development (ETDD) conference, which occurred May 31 to June 2, 2023, in Aventura, Florida. Therapeutics under development are targeting genetics, ion channels and other neurotransmitters, and many other potentially first-in-class interventions such as stem cells, glycogen metabolism, cholesterol, the gut microbiome, and novel modalities for delivering electrical neuromodulation.
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Affiliation(s)
- Samuel W Terman
- University of Michigan Department of Neurology, Ann Arbor, MI 48109, USA
| | - Laura Kirkpatrick
- University of Pittsburgh Department of Neurology, Pittsburgh, PA 15213, USA
- University of Pittsburgh Department of Pediatrics, Pittsburgh, PA 15213, USA
| | - Lisa F Akiyama
- University of Washington Department of Neurology, Seattle, WA 98105, USA
| | - Wadih Baajour
- University of Texas Health Science Center at Houston, Department of Neurology, Houston, TX 77030, USA
| | - Deniz Atilgan
- University of Texas Health Science Center at Houston, Department of Neurology, Houston, TX 77030, USA
| | | | - Hyoung Won Choi
- Emory University Department of Pediatrics, Division of Neurology, Atlanta, GA 30322
| | - Jacqueline A French
- NYU Grossman School of Medicine and NYU Langone Health, New York, NY 10016, USA
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Komal K, Cleary F, Wells JSG, Bennett L. A systematic review of the literature reporting on remote monitoring epileptic seizure detection devices. Epilepsy Res 2024; 201:107334. [PMID: 38442551 DOI: 10.1016/j.eplepsyres.2024.107334] [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: 12/13/2023] [Revised: 02/19/2024] [Accepted: 02/26/2024] [Indexed: 03/07/2024]
Abstract
BACKGROUND Early detection and alert notification of an impending seizure for people with epilepsy have the potential to reduce Sudden Unexpected Death in Epilepsy (SUDEP). Current remote monitoring seizure detection devices for people with epilepsy are designed to support real-time monitoring of their vital health parameters linked to seizure alert notification. An understanding of the rapidly growing literature on remote seizure detection devices is essential to address the needs of people with epilepsy and their carers. AIM This review aims to examine the technical characteristics, device performance, user preference, and effectiveness of remote monitoring seizure detection devices. METHODOLOGY A systematic review referenced to PRISMA guidelines was used. RESULTS A total of 1095 papers were identified from the initial search with 30 papers included in the review. Sixteen non-invasive remote monitoring seizure detection devices are currently available. Such seizure detection devices were found to have inbuilt intelligent sensor functionality to monitor electroencephalography, muscle movement, and accelerometer-based motion movement for detecting seizures remotely. Current challenges of these devices for people with epilepsy include skin irritation due to the type of patch electrode used and false alarm notifications, particularly during physical activity. The tight-fitted accelerometer-type devices are reported as uncomfortable from a wearability perspective for long-term monitoring. Also, continuous recording of physiological signals and triggering alert notifications significantly reduce the battery life of the devices. The literature highlights that 3.2 out of 5 people with epilepsy are not using seizure detection devices because of the cost and appearance of the device. CONCLUSION Seizure detection devices can potentially reduce morbidity and mortality for people with epilepsy. Therefore, further collaboration of clinicians, technical experts, and researchers is needed for the future development of these devices. Finally, it is important to always take into consideration the expectations and requirements of people with epilepsy and their carers to facilitate the next generation of remote monitoring seizure detection devices.
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Affiliation(s)
- K Komal
- School of Health Sciences, South East Technological University, Cork Road, Waterford, Ireland; Walton Institute, South East Technological University, Cork Road, Waterford, Ireland.
| | - F Cleary
- Walton Institute, South East Technological University, Cork Road, Waterford, Ireland
| | - J S G Wells
- School of Health Sciences, South East Technological University, Cork Road, Waterford, Ireland
| | - L Bennett
- School of Health Sciences, South East Technological University, Cork Road, Waterford, Ireland
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Donner E, Devinsky O, Friedman D. Wearable Digital Health Technology for Epilepsy. N Engl J Med 2024; 390:736-745. [PMID: 38381676 DOI: 10.1056/nejmra2301913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Affiliation(s)
- Elizabeth Donner
- From the Division of Neurology, Hospital for Sick Children, and the Department of Paediatrics, University of Toronto - both in Toronto (E.D.); and the Epilepsy Center, Department of Neurology, New York University Grossman School of Medicine, New York (O.D., D.F.)
| | - Orrin Devinsky
- From the Division of Neurology, Hospital for Sick Children, and the Department of Paediatrics, University of Toronto - both in Toronto (E.D.); and the Epilepsy Center, Department of Neurology, New York University Grossman School of Medicine, New York (O.D., D.F.)
| | - Daniel Friedman
- From the Division of Neurology, Hospital for Sick Children, and the Department of Paediatrics, University of Toronto - both in Toronto (E.D.); and the Epilepsy Center, Department of Neurology, New York University Grossman School of Medicine, New York (O.D., D.F.)
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Zelano J, Beniczky S, Ryvlin P, Surges R, Tomson T. Report of the ILAE SUDEP Task Force on national recommendations and practices around the world regarding the use of wearable seizure detection devices: A global survey. Epilepsia Open 2023; 8:1271-1278. [PMID: 37567865 PMCID: PMC10690692 DOI: 10.1002/epi4.12801] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 07/29/2023] [Indexed: 08/13/2023] Open
Abstract
Wearable seizure detection devices have the potential to address unmet needs of people with epilepsy. A recently published evidence-based international guideline recommends using such devices for safety indications in patients with tonic-clonic seizures (TCS). Our objective was to map existing guidelines and clinical practices at national level. We conducted a survey of the International League Against Epilepsy (ILAE) chapters regarding national recommendations and practical circumstances for prescribing seizure detection devices, and another survey of physicians in the ILAE constituency anywhere in the world, concerning their views and practices regarding recommendations for and prescription of such devices. Fifty-eight ILAE chapters (response rate 48%) and 157 physicians completed the surveys. More than two-thirds of responding countries do not have standards on wearables for seizure detection, although they indicated availability of such devices. The most often recognized indications were safety and objective seizure quantification. In nearly half of countries, devices are purchased by patients or caregivers, and either lack a uniform reimbursement scheme (41%) or patients pay the full cost for the device (48%). Tonic-clonic seizure frequency, nocturnal seizures, and previous injuries were the main factors that influenced the surveyed physicians to recommend wearable seizure detection devices. Our results document the need to implement international clinical practice guidelines at national level and to consider these when deciding upon reimbursement of seizure detection devices.
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Affiliation(s)
- Johan Zelano
- Institute of Neuroscience and Physiology, Department of Clinical Neuroscience, Sahlgrenska AcademyUniversity of GothenburgGothenburgSweden
- Department of NeurologySahlgrenska University HospitalGothenburgSweden
- Wallenberg Center of Molecular and Translational MedicineUniversity of GothenburgGothenburgSweden
| | - Sandor Beniczky
- Department of Clinical NeurophysiologyDanish Epilepsy CenterDianalundDenmark
- Department of Clinical NeurophysiologyAarhus University HospitalAarhusDenmark
- Department of Clinical MedicineAarhus UniversityAarhusmDenmark
| | - Philippe Ryvlin
- Department of Clinical NeurosciencesLausanne University Hospital (CHUV)LausanneSwitzerland
| | - Rainer Surges
- Department of EpileptologyUniversity Hospital BonnBonnGermany
| | - Torbjörn Tomson
- Department of Clinical NeuroscienceKarolinska InstitutetStockholmSweden
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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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Laiou P, Biondi A, Bruno E, Viana PF, Winston JS, Rashid Z, Ranjan Y, Conde P, Stewart C, Sun S, Zhang Y, Folarin A, Dobson RJB, Schulze-Bonhage A, Dümpelmann M, Richardson MP. Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence. Biomedicines 2022; 10:2662. [PMID: 36289925 PMCID: PMC9599905 DOI: 10.3390/biomedicines10102662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 10/10/2022] [Accepted: 10/11/2022] [Indexed: 12/02/2022] Open
Abstract
Epilepsy is one of the most common neurological disorders, characterized by the occurrence of repeated seizures. Given that epilepsy is considered a network disorder, tools derived from network neuroscience may confer the valuable ability to quantify the properties of epileptic brain networks. In this study, we use well-established brain network metrics (i.e., mean strength, variance of strength, eigenvector centrality, betweenness centrality) to characterize the temporal evolution of epileptic functional networks over several days prior to seizure occurrence. We infer the networks using long-term electroencephalographic recordings from 12 people with epilepsy. We found that brain network metrics are variable across days and show a circadian periodicity. In addition, we found that in 9 out of 12 patients the distribution of the variance of strength in the day (or even two last days) prior to seizure occurrence is significantly different compared to the corresponding distributions on all previous days. Our results suggest that brain network metrics computed fromelectroencephalographic recordings could potentially be used to characterize brain network changes that occur prior to seizures, and ultimately contribute to seizure warning systems.
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Affiliation(s)
- Petroula Laiou
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Andrea Biondi
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Elisa Bruno
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Pedro F. Viana
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
- Faculty of Medicine, University of Lisbon, 1649-028 Lisbon, Portugal
| | - Joel S. Winston
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Zulqarnain Rashid
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Yatharth Ranjan
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Pauline Conde
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Callum Stewart
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Shaoxiong Sun
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Yuezhou Zhang
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Amos Folarin
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
- Institute of Health Informatics, University College London, London WC1E 6BT, UK
- NIHR Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, King’s College London, London SE5 8AF, UK
- Health Data Research UK London, University College London, London WC1E 6BT, UK
- NIHR Biomedical Research Centre, University College London Hospitals NHS Foundation Trust, London W1T 7DN, UK
| | - Richard J. B. Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
- Institute of Health Informatics, University College London, London WC1E 6BT, UK
- NIHR Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, King’s College London, London SE5 8AF, UK
- Health Data Research UK London, University College London, London WC1E 6BT, UK
- NIHR Biomedical Research Centre, University College London Hospitals NHS Foundation Trust, London W1T 7DN, UK
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
| | - Matthias Dümpelmann
- Epilepsy Center, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
- Department of Microsystems Engineering (IMTEK), University of Freiburg, 79110 Freiburg, Germany
| | - Mark P. Richardson
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
- NIHR Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, King’s College London, London SE5 8AF, UK
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11
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Fong SL, Suppiah PD, Tee SK, Khoo CS, Tan HJ, Hung SKY, Looi I, Lim KS. Seizure remission rates remain low in a resource-limited country, a multicentre comparison study in Malaysia. J Clin Neurosci 2022; 102:60-64. [PMID: 35728396 DOI: 10.1016/j.jocn.2022.05.028] [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: 04/09/2022] [Revised: 05/23/2022] [Accepted: 05/31/2022] [Indexed: 11/26/2022]
Abstract
Seizure remission rates of 60% with antiseizure medications were reported in developed countries, but might be lower in resource-limited countries. The challenges in epilepsy care in resource-limited regions were highlighted 10 years ago, and still remain an ongoing issue. This study aimed to determine the seizure freedom rates in level-2 epilepsy care centres (centres with general neurologists) compared to level-3/4 centres (centres with epileptologists providing epilepsy surgery evaluation) in Malaysia. This is a retrospective study of 1,347 adult epilepsy patients from two level-2 (n = 290) and two level-3/4 epilepsy care centres (n = 1,057). The seizure remission rates were significantly lower in level-2 centres (42.5%) compared to the level 3/4 centres (61.9%, p < 0.05). Level-2 centres had significantly more patients with undetermined seizure types compared to level-3/4 centres (6.6% vs 3.1%, p < 0.05). Level-3/4 centres had significantly more patients with epilepsy of structural and genetic origins, whereas more patients in level-2 centres had unknown aetiology (46.2% vs. 34.0% in level-3/4, p < 0.05). Level-2 centres had a lower neurologist-to-patient ratio (1:97 vs. 1:50 in level-3/4 centres, p < 0.05). Level-2 centres also had fewer patients, who underwent investigations such as EEG (74.1% vs. 89.6%) and brain MRI (54.1% vs. 72.4%, p < 0.05) in comparison with level-3/4 centres. Our study emphasized the existing challenges in epilepsy care in a resource-limited country to achieve the ideal 60% seizure remission rate.
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Affiliation(s)
- Si-Lei Fong
- Division of Neurology, Faculty of Medicine, University Malaya, Kuala Lumpur, Malaysia
| | | | - Sow-Kuan Tee
- Department of Medicine, Tengku Ampuan Rahimah Hospital, Selangor, Malaysia
| | - Ching-Soong Khoo
- Neurology Unit, Department of Medicine, Universiti Kebangsaan Malaysia Medical Centre, Kuala Lumpur, Malaysia
| | - Hui-Jan Tan
- Neurology Unit, Department of Medicine, Universiti Kebangsaan Malaysia Medical Centre, Kuala Lumpur, Malaysia
| | | | - Irene Looi
- Clinical Research Centre, Seberang Jaya Hospital, Penang, Malaysia; Department of Medicine, Seberang Jaya Hospital, Penang, Malaysia
| | - Kheng-Seang Lim
- Division of Neurology, Faculty of Medicine, University Malaya, Kuala Lumpur, Malaysia.
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12
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Sivathamboo S, Nhu D, Piccenna L, Yang A, Antonic-Baker A, Vishwanath S, Todaro M, Yap LW, Kuhlmann L, Cheng W, O'Brien TJ, Lannin NA, Kwan P. Preferences and User Experiences of Wearable Devices in Epilepsy: A Systematic Review and Mixed-Methods Synthesis. Neurology 2022; 99:e1380-e1392. [PMID: 35705497 DOI: 10.1212/wnl.0000000000200794] [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: 11/28/2021] [Accepted: 04/12/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES To examine the preferences and user experiences of people with epilepsy and caregivers regarding automated wearable seizure detection devices. METHODS We performed a mixed-methods systematic review. We searched electronic databases for original peer-reviewed publications between January 1, 2000, and May 26, 2021. Key search terms included "epilepsy", "seizure", "wearable", and "non-invasive". We performed a descriptive and a qualitative thematic analysis of the studies included according to the technology acceptance model. Full texts of the discussion sections were further analyzed to identify word frequency and word mapping. RESULTS Twenty-two observational studies were identified. Collectively, they comprised responses from 3299 participants including patients with epilepsy, caregivers and healthcare workers. Sixteen studies examined user preferences, five examined user experiences, and one examined both experiences and preferences. Important preferences for wearables included improving care, cost, accuracy, and design. Patients desired real-time detection with a latency of ≤15 minutes from seizure occurrence, along with high sensitivity (≥90%) and low false-alarm rates. Device related costs were a major factor for device acceptance, where device costs of <$300 USD and a monthly subscription fee of <$20 USD were preferred. Despite being a major driver of wearable-based technologies, sudden unexpected death in epilepsy (SUDEP) was rarely discussed. Among studies evaluating user experiences, there was a greater acceptance towards wristwatches. Thematic coding analysis showed that attitudes towards device use, and perceived usefulness were reported consistently. Word mapping identified 'specificity', 'cost', and 'battery' as key single terms, and 'battery life', 'insurance coverage', 'prediction/detection quality', and the effect of devices on 'daily life' as key bigrams. DISCUSSION User acceptance of wearable technology for seizure detection was strongly influenced by accuracy, design, comfort, and cost. Our findings emphasise the need for standardised and validated tools to comprehensively examine preferences and user experiences of wearable devices in this population, using the themes identified in this study. Greater efforts to incorporate perspectives and user experiences in developing wearables for seizure detection, particularly in community-based settings are needed. PROSPERO REGISTRATION CRD42020193565.
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Affiliation(s)
- Shobi Sivathamboo
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, 3004, Victoria, Australia.,Department of Neurology, Alfred Health, Melbourne, 3004, Victoria, Australia.,Department of Medicine (The Royal Melbourne Hospital), The University of Melbourne, 3000, Victoria, Australia.,Department of Neurology, The Royal Melbourne Hospital, Melbourne, 3000, Victoria, Australi
| | - Duong Nhu
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton, 3800, Victoria, Australia
| | - Loretta Piccenna
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, 3004, Victoria, Australia.,Department of Neurology, Alfred Health, Melbourne, 3004, Victoria, Australia.,Department of Medicine (The Royal Melbourne Hospital), The University of Melbourne, 3000, Victoria, Australia
| | - Anthony Yang
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, 3004, Victoria, Australia.,Department of Neurology, Alfred Health, Melbourne, 3004, Victoria, Australia.,Department of Medicine (The Royal Melbourne Hospital), The University of Melbourne, 3000, Victoria, Australia
| | - Ana Antonic-Baker
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, 3004, Victoria, Australia
| | - Swarna Vishwanath
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, 3004, Victoria, Australia
| | - Marian Todaro
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, 3004, Victoria, Australia.,Department of Neurology, Alfred Health, Melbourne, 3004, Victoria, Australia.,Department of Medicine (The Royal Melbourne Hospital), The University of Melbourne, 3000, Victoria, Australia.,Department of Neurology, The Royal Melbourne Hospital, Melbourne, 3000, Victoria, Australi
| | - Lim Wei Yap
- Department of Chemical and Biological Engineering, Monash University, Clayton, 3800, Victoria, Australi
| | - Levin Kuhlmann
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton, 3800, Victoria, Australia
| | - Wenlong Cheng
- Department of Chemical and Biological Engineering, Monash University, Clayton, 3800, Victoria, Australi
| | - Terence J O'Brien
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, 3004, Victoria, Australia.,Department of Neurology, Alfred Health, Melbourne, 3004, Victoria, Australia.,Department of Medicine (The Royal Melbourne Hospital), The University of Melbourne, 3000, Victoria, Australia.,Department of Neurology, The Royal Melbourne Hospital, Melbourne, 3000, Victoria, Australi
| | - Natasha A Lannin
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, 3004, Victoria, Australia.,Alfred Health (Allied Health Directorate), Melbourne, 3004, Victoria, Australia
| | - Patrick Kwan
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, 3004, Victoria, Australia .,Department of Neurology, Alfred Health, Melbourne, 3004, Victoria, Australia.,Department of Medicine (The Royal Melbourne Hospital), The University of Melbourne, 3000, Victoria, Australia.,Department of Neurology, The Royal Melbourne Hospital, Melbourne, 3000, Victoria, Australi
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13
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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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14
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Bond SJ, Parikh N, Majmudar S, Pin S, Wang C, Willis L, Haga SB. A Systematic Review of the Scope of Study of mHealth Interventions for Wellness and Related Challenges in Pediatric and Young Adult Populations. Adolesc Health Med Ther 2022; 13:23-38. [PMID: 35173502 PMCID: PMC8835977 DOI: 10.2147/ahmt.s342811] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 01/25/2022] [Indexed: 11/23/2022] Open
Abstract
Background Despite the purported advantages and potential efficacy of mHealth interventions to promote wellness in children, adolescents, and young adults, it is not clear what areas have been explored and the challenges reported in the biomedical literature. Methods We conducted a scoping review of publications between 2015 and 2019. Results We identified 54 papers that met our inclusion criteria. Studies were conducted in 21 countries and ranged in size from six to 9851 participants (median: 184). A total of 41% of studies enrolled adolescents only (n = 19). Of the seven types of mHealth interventions identified, apps were the most common intervention (59%; n = 32) evaluated and 44% of the studies evaluated two or more interventions. The most common topic of the studies reviewed was sexual and reproductive health (24%; n = 13). Conclusion Most pediatric mHealth intervention studies are conducted in adolescents in large part, and sexual and reproductive health is the most commonly studied topic. With the easy and widespread accessibility to smartphone technology, the use of mobile apps for wellness interventions will likely continue to expand to other wellness topics.
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Affiliation(s)
| | | | | | | | | | | | - Susanne B Haga
- Duke University, Durham, NC, 27708, USA
- Duke University School of Medicine, Durham, NC, 27708, USA
- Correspondence: Susanne B Haga, Duke University, 101 Science Drive, Box 3382, Durham, NC, 27708, USA, Tel +1 919 684 0325, Fax +1 919 681 8973, Email
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15
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Ali A, Dindoust D, Grant J, Clarke D. Delivering epilepsy care in low-resource settings: the role of technology. Expert Rev Med Devices 2021; 18:13-23. [PMID: 34851222 DOI: 10.1080/17434440.2021.2013198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
INTRODUCTION The implementation of technology in the field of epileptology has traditionally focused on its use for diagnosis and treatment and has, unsurprisingly, been capital-intensive, making it therefore mainly implementable in advanced high-income countries. Because of technological innovations over the past 20 years there has been almost a paradigm shift, particularly in access to and the potential for implementing relevant technology in lesser developed environments. Nearly 80% of people living with epilepsy live in low and middle-income countries. AREAS COVERED The challenge and the purpose of this paper is to discuss how technology can be implemented into lesser-resourced contexts not only cost-effectively but in a cost-saving way while also building capacity and thus sustainability. EXPERT OPINION The rate of technological advancement presents the risk of progressive widening of the technology and care gaps between advanced and lesser developed regions. Implementing technology is both about finding relevant appropriate technologies for the individual contexts of a diverse range of countries but also about repurposing low-tech technologies for application in epilepsy care in these areas. Finally exciting advances such as autonomous driving, digital twinning and robotic surgery will likely transform epilepsy care in several lower-resourced settings in the next 5-10 years.
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Affiliation(s)
- Amza Ali
- Departments of Medicine, Kingston Public Hospital and University of the West Indies, Mona, Jamaica
| | | | - Justin Grant
- Rotman School of Management, University of Toronto, Toronto, Canada
| | - Dave Clarke
- Dell Medical School, University of Texas, Austin, Texas, USA
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16
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Schulze-Bonhage A. Seizure prediction: Time for new, multimodal and ultra-long-term approaches. Clin Neurophysiol 2021; 133:152-153. [PMID: 34802924 DOI: 10.1016/j.clinph.2021.10.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 10/28/2021] [Indexed: 11/25/2022]
Affiliation(s)
- Andreas Schulze-Bonhage
- Epilepsy Center, University Medical Center, Faculty of Medicine, University of Freiburg, Germany; European Reference Network EpiCare, Europe; Bernstein Center Freiburg, University of Freiburg, Germany; NeuroModule Basic, University Medical Center, Faculty of Medicine, University of Freiburg, Germany.
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17
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Böttcher S, Bruno E, Manyakov NV, Epitashvili N, Claes K, Glasstetter M, Thorpe S, Lees S, Dümpelmann M, Van Laerhoven K, Richardson MP, Schulze-Bonhage A. Detecting Tonic-Clonic Seizures in Multimodal Biosignal Data From Wearables: Methodology Design and Validation. JMIR Mhealth Uhealth 2021; 9:e27674. [PMID: 34806993 PMCID: PMC8663471 DOI: 10.2196/27674] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 07/23/2021] [Accepted: 09/03/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Video electroencephalography recordings, routinely used in epilepsy monitoring units, are the gold standard for monitoring epileptic seizures. However, monitoring is also needed in the day-to-day lives of people with epilepsy, where video electroencephalography is not feasible. Wearables could fill this gap by providing patients with an accurate log of their seizures. OBJECTIVE Although there are already systems available that provide promising results for the detection of tonic-clonic seizures (TCSs), research in this area is often limited to detection from 1 biosignal modality or only during the night when the patient is in bed. The aim of this study is to provide evidence that supervised machine learning can detect TCSs from multimodal data in a new data set during daytime and nighttime. METHODS An extensive data set of biosignals from a multimodal watch worn by people with epilepsy was recorded during their stay in the epilepsy monitoring unit at 2 European clinical sites. From a larger data set of 243 enrolled participants, those who had data recorded during TCSs were selected, amounting to 10 participants with 21 TCSs. Accelerometry and electrodermal activity recorded by the wearable device were used for analysis, and seizure manifestation was annotated in detail by clinical experts. Ten accelerometry and 3 electrodermal activity features were calculated for sliding windows of variable size across the data. A gradient tree boosting algorithm was used for seizure detection, and the optimal parameter combination was determined in a leave-one-participant-out cross-validation on a training set of 10 seizures from 8 participants. The model was then evaluated on an out-of-sample test set of 11 seizures from the remaining 2 participants. To assess specificity, we additionally analyzed data from up to 29 participants without TCSs during the model evaluation. RESULTS In the leave-one-participant-out cross-validation, the model optimized for sensitivity could detect all 10 seizures with a false alarm rate of 0.46 per day in 17.3 days of data. In a test set of 11 out-of-sample TCSs, amounting to 8.3 days of data, the model could detect 10 seizures and produced no false positives. Increasing the test set to include data from 28 more participants without additional TCSs resulted in a false alarm rate of 0.19 per day in 78 days of wearable data. CONCLUSIONS We show that a gradient tree boosting machine can robustly detect TCSs from multimodal wearable data in an original data set and that even with very limited training data, supervised machine learning can achieve a high sensitivity and low false-positive rate. This methodology may offer a promising way to approach wearable-based nonconvulsive seizure detection.
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Affiliation(s)
- Sebastian Böttcher
- Epilepsy Center, Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany.,Ubiquitous Computing, Department of Electrical Engineering and Computer Science, University of Siegen, Siegen, Germany
| | - Elisa Bruno
- Division of Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Nikolay V Manyakov
- Data Science Analytics & Insights, Janssen Research & Development, Beerse, Belgium
| | - Nino Epitashvili
- Epilepsy Center, Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany
| | | | - Martin Glasstetter
- Epilepsy Center, Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany
| | - Sarah Thorpe
- The RADAR-CNS Patient Advisory Board, King's College London, London, United Kingdom
| | - Simon Lees
- The RADAR-CNS Patient Advisory Board, King's College London, London, United Kingdom
| | - Matthias Dümpelmann
- Epilepsy Center, Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany
| | - Kristof Van Laerhoven
- Ubiquitous Computing, Department of Electrical Engineering and Computer Science, University of Siegen, Siegen, Germany
| | - Mark P Richardson
- Division of Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.,National Institute of Health Research Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany
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- see Acknowledgements, London, United Kingdom
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18
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Glasstetter M, Böttcher S, Zabler N, Epitashvili N, Dümpelmann M, Richardson MP, Schulze-Bonhage A. Identification of Ictal Tachycardia in Focal Motor- and Non-Motor Seizures by Means of a Wearable PPG Sensor. SENSORS (BASEL, SWITZERLAND) 2021; 21:6017. [PMID: 34577222 PMCID: PMC8470979 DOI: 10.3390/s21186017] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 08/31/2021] [Accepted: 09/01/2021] [Indexed: 11/16/2022]
Abstract
Photoplethysmography (PPG) as an additional biosignal for a seizure detector has been underutilized so far, which is possibly due to its susceptibility to motion artifacts. We investigated 62 focal seizures from 28 patients with electrocardiography-based evidence of ictal tachycardia (IT). Seizures were divided into subgroups: those without epileptic movements and those with epileptic movements not affecting and affecting the extremities. PPG-based heart rate (HR) derived from a wrist-worn device was calculated for sections with high signal quality, which were identified using spectral entropy. Overall, IT based on PPG was identified in 37 of 62 (60%) seizures (9/19, 7/8, and 21/35 in the three groups, respectively) and could be found prior to the onset of epileptic movements affecting the extremities in 14/21 seizures. In 30/37 seizures, PPG-based IT was in good temporal agreement (<10 s) with ECG-based IT, with an average delay of 5.0 s relative to EEG onset. In summary, we observed that the identification of IT by means of a wearable PPG sensor is possible not only for non-motor seizures but also in motor seizures, which is due to the early manifestation of IT in a relevant subset of focal seizures. However, both spontaneous and epileptic movements can impair PPG-based seizure detection.
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Affiliation(s)
- Martin Glasstetter
- Epilepsy Center, Department of Neurosurgery, Medical Center—University of Freiburg, 79106 Freiburg im Breisgau, Germany; (S.B.); (N.Z.); (N.E.); (M.D.); (A.S.-B.)
| | - Sebastian Böttcher
- Epilepsy Center, Department of Neurosurgery, Medical Center—University of Freiburg, 79106 Freiburg im Breisgau, Germany; (S.B.); (N.Z.); (N.E.); (M.D.); (A.S.-B.)
| | - Nicolas Zabler
- Epilepsy Center, Department of Neurosurgery, Medical Center—University of Freiburg, 79106 Freiburg im Breisgau, Germany; (S.B.); (N.Z.); (N.E.); (M.D.); (A.S.-B.)
| | - Nino Epitashvili
- Epilepsy Center, Department of Neurosurgery, Medical Center—University of Freiburg, 79106 Freiburg im Breisgau, Germany; (S.B.); (N.Z.); (N.E.); (M.D.); (A.S.-B.)
| | - Matthias Dümpelmann
- Epilepsy Center, Department of Neurosurgery, Medical Center—University of Freiburg, 79106 Freiburg im Breisgau, Germany; (S.B.); (N.Z.); (N.E.); (M.D.); (A.S.-B.)
| | - Mark P. Richardson
- Division of Neuroscience, Institute of Psychiatry, Psychology & Neuroscience King’s College London, London SE5 9RT, UK;
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Department of Neurosurgery, Medical Center—University of Freiburg, 79106 Freiburg im Breisgau, Germany; (S.B.); (N.Z.); (N.E.); (M.D.); (A.S.-B.)
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