This paper is in the following e-collection/theme issue:

mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1309) Machine Learning (1385) Wearable Devices and Sensors (818) mHealth for Data Collection and Research (914) Mobile Health (mhealth) (2765) New Methods (909) Neurology and Neurosciences (149)

Published on in Vol 9, No 11 (2021): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27674, first published .
Detecting Tonic-Clonic Seizures in Multimodal Biosignal Data From Wearables: Methodology Design and Validation

Detecting Tonic-Clonic Seizures in Multimodal Biosignal Data From Wearables: Methodology Design and Validation

Detecting Tonic-Clonic Seizures in Multimodal Biosignal Data From Wearables: Methodology Design and Validation

Authors of this article:

Sebastian Böttcher1, 2 Author Orcid Image ;   Elisa Bruno3 Author Orcid Image ;   Nikolay V Manyakov4 Author Orcid Image ;   Nino Epitashvili1 Author Orcid Image ;   Kasper Claes5 Author Orcid Image ;   Martin Glasstetter1 Author Orcid Image ;   Sarah Thorpe6 Author Orcid Image ;   Simon Lees6 Author Orcid Image ;   Matthias Dümpelmann1 Author Orcid Image ;   Kristof Van Laerhoven2 Author Orcid Image ;   Mark P Richardson3, 7 Author Orcid Image ;   Andreas Schulze-Bonhage1 Author Orcid Image ;   The RADAR-CNS Consortium8
Article Authors Cited by (16) Tweetations (3) Metrics

Journals

  1. Shayeste H, Asl B. Automatic seizure detection based on Gray Level Co-occurrence Matrix of STFT imaged-EEG. Biomedical Signal Processing and Control 2023;79:104109 View
  2. Böttcher S, Vieluf S, Bruno E, Joseph B, Epitashvili N, Biondi A, Zabler N, Glasstetter M, Dümpelmann M, Van Laerhoven K, Nasseri M, Brinkman B, Richardson M, Schulze-Bonhage A, Loddenkemper T. Data quality evaluation in wearable monitoring. Scientific Reports 2022;12(1) View
  3. Esmaeili B, Vieluf S, Dworetzky B, Reinsberger C. The Potential of Wearable Devices and Mobile Health Applications in the Evaluation and Treatment of Epilepsy. Neurologic Clinics 2022;40(4):729 View
  4. Li W, Wang G, Lei X, Sheng D, Yu T, Wang G. Seizure detection based on wearable devices: A review of device, mechanism, and algorithm. Acta Neurologica Scandinavica 2022;146(6):723 View
  5. Glasstetter M, Böttcher S, Zabler N, Epitashvili N, Dümpelmann M, Richardson M, Schulze-Bonhage A. Identification of Ictal Tachycardia in Focal Motor- and Non-Motor Seizures by Means of a Wearable PPG Sensor. Sensors 2021;21(18):6017 View
  6. Böttcher S, Bruno E, Epitashvili N, Dümpelmann M, Zabler N, Glasstetter M, Ticcinelli V, Thorpe S, Lees S, Van Laerhoven K, Richardson M, Schulze-Bonhage A. Intra- and Inter-Subject Perspectives on the Detection of Focal Onset Motor Seizures in Epilepsy Patients. Sensors 2022;22(9):3318 View
  7. Andrews J, Craven M, Guo B, Weyer J, Lees S, Zormpas S, Thorpe S, Devonshire J, San Antonio-Arce V, Whitehouse W, Julie J, Malins S, Hammers A, Reif A, Ruhe H, Durbano F, Barlati S, Sen A, Frederiksen J, Martinelli A, Callen A, Torras-Borrell J, Berrocal-Izquierdo N, Zabalza A, Morriss R, Hollis C. Clinical Perspectives on Using Remote Measurement Technology in Assessing Epilepsy, Multiple Sclerosis, and Depression: Delphi Study. JMIR Neurotechnology 2023;2:e41439 View
  8. Halimeh M, Jackson M, Vieluf S, Loddenkemper T, Meisel C. Explainable AI for wearable seizure logging: Impact of data quality, patient age, and antiseizure medication on performance. Seizure: European Journal of Epilepsy 2023;110:99 View
  9. Meritam Larsen P, Beniczky S. Non-electroencephalogram-based seizure detection devices: State of the art and future perspectives. Epilepsy & Behavior 2023;148:109486 View
  10. Sun S, Folarin A, Zhang Y, Cummins N, Garcia-Dias R, Stewart C, Ranjan Y, Rashid Z, Conde P, Laiou P, Sankesara H, Matcham F, Leightley D, White K, Oetzmann C, Ivan A, Lamers F, Siddi S, Simblett S, Nica R, Rintala A, Mohr D, Myin-Germeys I, Wykes T, Haro J, Penninx B, Vairavan S, Narayan V, Annas P, Hotopf M, Dobson R. Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis. Journal of Medical Internet Research 2023;25:e45233 View
  11. Seth E, Watterson J, Xie J, Arulsamy A, Md Yusof H, Ngadimon I, Khoo C, Kadirvelu A, Shaikh M. Feasibility of cardiac‐based seizure detection and prediction: A systematic review of non‐invasive wearable sensor‐based studies. Epilepsia Open 2024;9(1):41 View
  12. Vakilna Y, Li X, Hampson J, Huang Y, Mosher J, Dabaghian Y, Luo X, Talavera B, Pati S, Todd M, Hays R, Szabo C, Zhang G, Lhatoo S. Reliable detection of generalized convulsive seizures using an off‐the‐shelf digital watch: A multisite phase 2 study. Epilepsia 2024 View
  13. Gharbi O, Lamrani Y, St‐Jean J, Jahani A, Toffa D, Tran T, Robert M, Nguyen D, Bou Assi E. Detection of focal to bilateral tonic–clonic seizures using a connected shirt. Epilepsia 2024 View
  14. Miron G, Halimeh M, Jeppesen J, Loddenkemper T, Meisel C. Autonomic biosignals, seizure detection, and forecasting. Epilepsia 2024 View
  15. Zhang J, Swinnen L, Chatzichristos C, Broux V, Proost R, Jansen K, Mahler B, Zabler N, Epitashvilli N, Dümpelmann M, Schulze-Bonhage A, Schriewer E, Ermis U, Wolking S, Linke F, Weber Y, Symmonds M, Sen A, Biondi A, Richardson M, I A, Silva A, Sales F, Vértes G, Paesschen W, Vos M. Multimodal wearable EEG, EMG and accelerometry measurements improve the accuracy of tonic-clonic seizure detection. Physiological Measurement 2024;45(6):065005 View