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O'Kula SS, Hill CE. Improving Quality of Care for Status Epilepticus: Putting Protocols into Practice. Curr Neurol Neurosci Rep 2024; 24:373-379. [PMID: 38995482 DOI: 10.1007/s11910-024-01356-9] [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] [Accepted: 06/28/2024] [Indexed: 07/13/2024]
Abstract
PURPOSE OF REVIEW Timely treatment of status epilepticus (SE) improves outcomes, however gaps between recommended and implemented care are common. This review analyzes obstacles and explores interventions to optimize effective, evidence-based treatment of SE. RECENT FINDINGS Seizure action plans, rescue medications, and noninvasive wearables with seizure detection capabilities can facilitate early intervention for prolonged seizures in the home and school. In the field, standardized EMS protocols, EMS education, and screening tools can address variability in SE definitions and treatment, particularly benzodiazepine dosing. In the emergency room and hospital, provider education, SE order sets and alerts, and rapid EEG technologies, can shorten time to first-line therapy, second-line therapy, and EEG initiation. Widespread, sustained improvement in SE care remains challenging. A multipronged approach including emphasis on pre-hospital intervention, treatment protocols adapted to local contexts, and SE databases to systematically collect process and outcome metrics have the potential to transform SE treatment and outcomes.
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Affiliation(s)
- Susanna S O'Kula
- Department of Neurology, SUNY Downstate Health Sciences University, 445 Lenox Road, A7-387, MSC 1275, Brooklyn, NY, 11203, USA.
| | - Chloé E Hill
- Department of Neurology, University of Michigan, 1500 E. Medical Center Drive, Ann Arbor, MI, 48109, USA.
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Gharbi O, Lamrani Y, St-Jean J, Jahani A, Toffa DH, Tran TPY, Robert M, Nguyen DK, Bou Assi E. Detection of focal to bilateral tonic-clonic seizures using a connected shirt. Epilepsia 2024; 65:2280-2294. [PMID: 38780375 DOI: 10.1111/epi.18021] [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: 01/07/2024] [Revised: 05/07/2024] [Accepted: 05/07/2024] [Indexed: 05/25/2024]
Abstract
OBJECTIVE This study was undertaken to develop and evaluate a machine learning-based algorithm for the detection of focal to bilateral tonic-clonic seizures (FBTCS) using a novel multimodal connected shirt. METHODS We prospectively recruited patients with epilepsy admitted to our epilepsy monitoring unit and asked them to wear the connected shirt while under simultaneous video-electroencephalographic monitoring. Electrocardiographic (ECG) and accelerometric (ACC) signals recorded with the connected shirt were used for the development of the seizure detection algorithm. First, we used a sliding window to extract linear and nonlinear features from both ECG and ACC signals. Then, we trained an extreme gradient boosting algorithm (XGBoost) to detect FBTCS according to seizure onset and offset annotated by three board-certified epileptologists. Finally, we applied a postprocessing step to regularize the classification output. A patientwise nested cross-validation was implemented to evaluate the performances in terms of sensitivity, false alarm rate (FAR), time in false warning (TiW), detection latency, and receiver operating characteristic area under the curve (ROC-AUC). RESULTS We recorded 66 FBTCS from 42 patients who wore the connected shirt for a total of 8067 continuous hours. The XGBoost algorithm reached a sensitivity of 84.8% (56/66 seizures), with a median FAR of .55/24 h and a median TiW of 10 s/alarm. ROC-AUC was .90 (95% confidence interval = .88-.91). Median detection latency from the time of progression to the bilateral tonic-clonic phase was 25.5 s. SIGNIFICANCE The novel connected shirt allowed accurate detection of FBTCS with a low false alarm rate in a hospital setting. Prospective studies in a residential setting with a real-time and online seizure detection algorithm are required to validate the performance and usability of this device.
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Affiliation(s)
- Oumayma Gharbi
- Department of Neuroscience, Université de Montréal, Montréal, Quebec, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Quebec, Canada
| | - Yassine Lamrani
- Department of Neuroscience, Université de Montréal, Montréal, Quebec, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Quebec, Canada
| | - Jérôme St-Jean
- Department of Neuroscience, Université de Montréal, Montréal, Quebec, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Quebec, Canada
| | - Amirhossein Jahani
- Department of Neuroscience, Université de Montréal, Montréal, Quebec, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Quebec, Canada
| | - Dènahin Hinnoutondji Toffa
- Department of Neuroscience, Université de Montréal, Montréal, Quebec, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Quebec, Canada
| | - Thi Phuoc Yen Tran
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Quebec, Canada
| | - Manon Robert
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Quebec, Canada
| | - Dang Khoa Nguyen
- Department of Neuroscience, Université de Montréal, Montréal, Quebec, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Quebec, Canada
| | - Elie Bou Assi
- Department of Neuroscience, Université de Montréal, Montréal, Quebec, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Quebec, Canada
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Kerr WT, McFarlane KN, Figueiredo Pucci G. The present and future of seizure detection, prediction, and forecasting with machine learning, including the future impact on clinical trials. Front Neurol 2024; 15:1425490. [PMID: 39055320 PMCID: PMC11269262 DOI: 10.3389/fneur.2024.1425490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 06/03/2024] [Indexed: 07/27/2024] Open
Abstract
Seizures have a profound impact on quality of life and mortality, in part because they can be challenging both to detect and forecast. Seizure detection relies upon accurately differentiating transient neurological symptoms caused by abnormal epileptiform activity from similar symptoms with different causes. Seizure forecasting aims to identify when a person has a high or low likelihood of seizure, which is related to seizure prediction. Machine learning and artificial intelligence are data-driven techniques integrated with neurodiagnostic monitoring technologies that attempt to accomplish both of those tasks. In this narrative review, we describe both the existing software and hardware approaches for seizure detection and forecasting, as well as the concepts for how to evaluate the performance of new technologies for future application in clinical practice. These technologies include long-term monitoring both with and without electroencephalography (EEG) that report very high sensitivity as well as reduced false positive detections. In addition, we describe the implications of seizure detection and forecasting upon the evaluation of novel treatments for seizures within clinical trials. Based on these existing data, long-term seizure detection and forecasting with machine learning and artificial intelligence could fundamentally change the clinical care of people with seizures, but there are multiple validation steps necessary to rigorously demonstrate their benefits and costs, relative to the current standard.
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Affiliation(s)
- Wesley T. Kerr
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States
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Vakilna YS, Li X, Hampson JS, Huang Y, Mosher JC, Dabaghian Y, Luo X, Talavera B, Pati S, Masel T, Hays R, Szabo CA, Zhang GQ, Lhatoo SD. Reliable detection of generalized convulsive seizures using an off-the-shelf digital watch: A multisite phase 2 study. Epilepsia 2024; 65:2054-2068. [PMID: 38738972 PMCID: PMC11251850 DOI: 10.1111/epi.17974] [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: 12/13/2023] [Revised: 03/19/2024] [Accepted: 03/25/2024] [Indexed: 05/14/2024]
Abstract
OBJECTIVE The aim of this study was to develop a machine learning algorithm using an off-the-shelf digital watch, the Samsung watch (SM-R800), and evaluate its effectiveness for the detection of generalized convulsive seizures (GCS) in persons with epilepsy. METHODS This multisite epilepsy monitoring unit (EMU) phase 2 study included 36 adult patients. Each patient wore a Samsung watch that contained accelerometer, gyroscope, and photoplethysmographic sensors. Sixty-eight time and frequency domain features were extracted from the sensor data and were used to train a random forest algorithm. A testing framework was developed that would better reflect the EMU setting, consisting of (1) leave-one-patient-out cross-validation (LOPO CV) on GCS patients, (2) false alarm rate (FAR) testing on nonseizure patients, and (3) "fixed-and-frozen" prospective testing on a prospective patient cohort. Balanced accuracy, precision, sensitivity, and FAR were used to quantify the performance of the algorithm. Seizure onsets and offsets were determined by using video-electroencephalographic (EEG) monitoring. Feature importance was calculated as the mean decrease in Gini impurity during the LOPO CV testing. RESULTS LOPO CV results showed balanced accuracy of .93 (95% confidence interval [CI] = .8-.98), precision of .68 (95% CI = .46-.85), sensitivity of .87 (95% CI = .62-.96), and FAR of .21/24 h (interquartile range [IQR] = 0-.90). Testing the algorithm on patients without seizure resulted in an FAR of .28/24 h (IQR = 0-.61). During the "fixed-and-frozen" prospective testing, two patients had three GCS, which were detected by the algorithm, while generating an FAR of .25/24 h (IQR = 0-.89). Feature importance showed that heart rate-based features outperformed accelerometer/gyroscope-based features. SIGNIFICANCE Commercially available wearable digital watches that reliably detect GCS, with minimum false alarm rates, may overcome usage adoption and other limitations of custom-built devices. Contingent on the outcomes of a prospective phase 3 study, such devices have the potential to provide non-EEG-based seizure surveillance and forecasting in the clinical setting.
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Affiliation(s)
- Yash Shashank Vakilna
- The University of Texas Health Science Center at Houston, Department of Neurology, Houston, TX, USA
- Texas Institute of Restorative Neurotechnologies (TIRN), The University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | - Xiaojin Li
- The University of Texas Health Science Center at Houston, Department of Neurology, Houston, TX, USA
- Texas Institute of Restorative Neurotechnologies (TIRN), The University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | - Jaison S. Hampson
- The University of Texas Health Science Center at Houston, Department of Neurology, Houston, TX, USA
- Texas Institute of Restorative Neurotechnologies (TIRN), The University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | - Yan Huang
- The University of Texas Health Science Center at Houston, Department of Neurology, Houston, TX, USA
- Texas Institute of Restorative Neurotechnologies (TIRN), The University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | - John C. Mosher
- The University of Texas Health Science Center at Houston, Department of Neurology, Houston, TX, USA
- Texas Institute of Restorative Neurotechnologies (TIRN), The University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | - Yuri Dabaghian
- The University of Texas Health Science Center at Houston, Department of Neurology, Houston, TX, USA
- Texas Institute of Restorative Neurotechnologies (TIRN), The University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | - Xi Luo
- The University of Texas Health Science Center at Houston, Department of Biostatistics and Data Science, Houston, Texas, USA
| | - Blanca Talavera
- The University of Texas Health Science Center at Houston, Department of Neurology, Houston, TX, USA
- Texas Institute of Restorative Neurotechnologies (TIRN), The University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | - Sandipan Pati
- The University of Texas Health Science Center at Houston, Department of Neurology, Houston, TX, USA
- Texas Institute of Restorative Neurotechnologies (TIRN), The University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | - Todd Masel
- The University of Texas Medical Branch, Department of Neurology, Galveston, Texas, USA
| | - Ryan Hays
- The University of Texas Southwestern Medical Center, Department of Neurology, Dallas, Texas, USA
| | - Charles Akos Szabo
- The University of Texas Health Science Center at San Antonio, Department of Neurology, Texas, USA
| | - Guo-Qiang Zhang
- The University of Texas Health Science Center at Houston, Department of Neurology, Houston, TX, USA
- Texas Institute of Restorative Neurotechnologies (TIRN), The University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | - Samden D. Lhatoo
- The University of Texas Health Science Center at Houston, Department of Neurology, Houston, TX, USA
- Texas Institute of Restorative Neurotechnologies (TIRN), The University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
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Zhao W, Wang WF, Patnaik LM, Zhang BC, Weng SJ, Xiao SX, Wei DZ, Zhou HF. Residual and bidirectional LSTM for epileptic seizure detection. Front Comput Neurosci 2024; 18:1415967. [PMID: 38952709 PMCID: PMC11215953 DOI: 10.3389/fncom.2024.1415967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Accepted: 05/28/2024] [Indexed: 07/03/2024] Open
Abstract
Electroencephalogram (EEG) plays a pivotal role in the detection and analysis of epileptic seizures, which affects over 70 million people in the world. Nonetheless, the visual interpretation of EEG signals for epilepsy detection is laborious and time-consuming. To tackle this open challenge, we introduce a straightforward yet efficient hybrid deep learning approach, named ResBiLSTM, for detecting epileptic seizures using EEG signals. Firstly, a one-dimensional residual neural network (ResNet) is tailored to adeptly extract the local spatial features of EEG signals. Subsequently, the acquired features are input into a bidirectional long short-term memory (BiLSTM) layer to model temporal dependencies. These output features are further processed through two fully connected layers to achieve the final epileptic seizure detection. The performance of ResBiLSTM is assessed on the epileptic seizure datasets provided by the University of Bonn and Temple University Hospital (TUH). The ResBiLSTM model achieves epileptic seizure detection accuracy rates of 98.88-100% in binary and ternary classifications on the Bonn dataset. Experimental outcomes for seizure recognition across seven epilepsy seizure types on the TUH seizure corpus (TUSZ) dataset indicate that the ResBiLSTM model attains a classification accuracy of 95.03% and a weighted F1 score of 95.03% with 10-fold cross-validation. These findings illustrate that ResBiLSTM outperforms several recent deep learning state-of-the-art approaches.
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Affiliation(s)
- Wei Zhao
- Chengyi College, Jimei University, Xiamen, China
| | - Wen-Feng Wang
- Shanghai Institute of Technology, Shanghai, China
- London Institute of Technology, International Academy of Visual Arts and Engineering, London, United Kingdom
| | | | | | - Su-Jun Weng
- Chengyi College, Jimei University, Xiamen, China
| | | | - De-Zhi Wei
- Chengyi College, Jimei University, Xiamen, China
| | - Hai-Feng Zhou
- Marine Engineering Institute, Jimei University, Xiamen, China
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Miron G, Halimeh M, Jeppesen J, Loddenkemper T, Meisel C. Autonomic biosignals, seizure detection, and forecasting. Epilepsia 2024. [PMID: 38837428 DOI: 10.1111/epi.18034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 05/17/2024] [Accepted: 05/22/2024] [Indexed: 06/07/2024]
Abstract
Wearable devices have attracted significant attention in epilepsy research in recent years for their potential to enhance patient care through improved seizure monitoring and forecasting. This narrative review presents a detailed overview of the current clinical state of the art while addressing how devices that assess autonomic nervous system (ANS) function reflect seizures and central nervous system (CNS) state changes. This includes a description of the interactions between the CNS and the ANS, including physiological and epilepsy-related changes affecting their dynamics. We first discuss technical aspects of measuring autonomic biosignals and considerations for using ANS sensors in clinical practice. We then review recent seizure detection and seizure forecasting studies, highlighting their performance and capability for seizure detection and forecasting using devices measuring ANS biomarkers. Finally, we address the field's challenges and provide an outlook for future developments.
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Affiliation(s)
- Gadi Miron
- Computational Neurology, Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Mustafa Halimeh
- Computational Neurology, Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Jesper Jeppesen
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Tobias Loddenkemper
- Department of Neurology, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Christian Meisel
- Computational Neurology, Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
- Center for Stroke Research Berlin, Berlin, Germany
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Han K, Liu C, Friedman D. Artificial intelligence/machine learning for epilepsy and seizure diagnosis. Epilepsy Behav 2024; 155:109736. [PMID: 38636146 DOI: 10.1016/j.yebeh.2024.109736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 03/03/2024] [Accepted: 03/10/2024] [Indexed: 04/20/2024]
Abstract
Accurate seizure and epilepsy diagnosis remains a challenging task due to the complexity and variability of manifestations, which can lead to delayed or missed diagnosis. Machine learning (ML) and artificial intelligence (AI) is a rapidly developing field, with growing interest in integrating and applying these tools to aid clinicians facing diagnostic uncertainties. ML algorithms, particularly deep neural networks, are increasingly employed in interpreting electroencephalograms (EEG), neuroimaging, wearable data, and seizure videos. This review discusses the development and testing phases of AI/ML tools, emphasizing the importance of generalizability and interpretability in medical applications, and highlights recent publications that demonstrate the current and potential utility of AI to aid clinicians in diagnosing epilepsy. Current barriers of AI integration in patient care include dataset availability and heterogeneity, which limit studies' quality, interpretability, comparability, and generalizability. ML and AI offer substantial promise in improving the accuracy and efficiency of epilepsy diagnosis. The growing availability of diverse datasets, enhanced processing speed, and ongoing efforts to standardize reporting contribute to the evolving landscape of AI applications in clinical care.
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Affiliation(s)
- Kenneth Han
- Departments of Neurology, NYU Grossman School of Medicine, New York, NY, United States
| | - Chris Liu
- Departments of Neurosurgery, NYU Grossman School of Medicine, New York, NY, United States
| | - Daniel Friedman
- Departments of Neurology, NYU Grossman School of Medicine, New York, NY, United States.
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Voigtlaender S, Pawelczyk J, Geiger M, Vaios EJ, Karschnia P, Cudkowicz M, Dietrich J, Haraldsen IRJH, Feigin V, Owolabi M, White TL, Świeboda P, Farahany N, Natarajan V, Winter SF. Artificial intelligence in neurology: opportunities, challenges, and policy implications. J Neurol 2024; 271:2258-2273. [PMID: 38367046 DOI: 10.1007/s00415-024-12220-8] [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: 12/20/2023] [Revised: 01/20/2024] [Accepted: 01/22/2024] [Indexed: 02/19/2024]
Abstract
Neurological conditions are the leading cause of disability and mortality combined, demanding innovative, scalable, and sustainable solutions. Brain health has become a global priority with adoption of the World Health Organization's Intersectoral Global Action Plan in 2022. Simultaneously, rapid advancements in artificial intelligence (AI) are revolutionizing neurological research and practice. This scoping review of 66 original articles explores the value of AI in neurology and brain health, systematizing the landscape for emergent clinical opportunities and future trends across the care trajectory: prevention, risk stratification, early detection, diagnosis, management, and rehabilitation. AI's potential to advance personalized precision neurology and global brain health directives hinges on resolving core challenges across four pillars-models, data, feasibility/equity, and regulation/innovation-through concerted pursuit of targeted recommendations. Paramount actions include swift, ethical, equity-focused integration of novel technologies into clinical workflows, mitigating data-related issues, counteracting digital inequity gaps, and establishing robust governance frameworks balancing safety and innovation.
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Affiliation(s)
- Sebastian Voigtlaender
- Systems Neuroscience Division, Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany
- Virtual Diagnostics Team, QuantCo Inc., Cambridge, MA, USA
| | - Johannes Pawelczyk
- Faculty of Medicine, Ruprecht-Karls-University, Heidelberg, Germany
- Graduate Center of Medicine and Health, Technical University Munich, Munich, Germany
| | - Mario Geiger
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- NVIDIA, Zurich, Switzerland
| | - Eugene J Vaios
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Philipp Karschnia
- Department of Neurosurgery, Ludwig-Maximilians-University and University Hospital Munich, Munich, Germany
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Merit Cudkowicz
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jorg Dietrich
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Ira R J Hebold Haraldsen
- Department of Neurology, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
| | - Valery Feigin
- National Institute for Stroke and Applied Neurosciences, Auckland University of Technology, Auckland, New Zealand
| | - Mayowa Owolabi
- Center for Genomics and Precision Medicine, College of Medicine, University of Ibadan, Ibadan, Nigeria
- Neurology Unit, Department of Medicine, University of Ibadan, Ibadan, Nigeria
- Blossom Specialist Medical Center, Ibadan, Nigeria
- Lebanese American University of Beirut, Beirut, Lebanon
| | - Tara L White
- Department of Behavioral and Social Sciences, Brown University, Providence, RI, USA
| | | | | | | | - Sebastian F Winter
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
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Zeydabadinezhad M, Jowers J, Buhl D, Cabaniss B, Mahmoudi B. A personalized earbud for non-invasive long-term EEG monitoring. J Neural Eng 2024; 21:026026. [PMID: 38479008 DOI: 10.1088/1741-2552/ad33af] [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: 09/21/2023] [Accepted: 03/13/2024] [Indexed: 04/05/2024]
Abstract
Objective. The primary objective of this study was to evaluate the reliability, comfort, and performance of a custom-fit, non-invasive long-term electrophysiologic headphone, known as Aware Hearable, for the ambulatory recording of brain activities. These recordings play a crucial role in diagnosing neurological disorders such as epilepsy and in studying neural dynamics during daily activities.Approach.The study uses commercial manufacturing processes common to the hearing aid industry, such as 3D scanning, computer-aided design modeling, and 3D printing. These processes enable the creation of the Aware Hearable with a personalized, custom-fit, thereby ensuring complete and consistent contact with the inner surfaces of the ear for high-quality data recordings. Additionally, the study employs a machine learning data analysis approach to validate the recordings produced by Aware Hearable, by comparing them to the gold standard intracranial electroencephalography recordings in epilepsy patients.Main results.The results indicate the potential of Aware Hearable to expedite the diagnosis of epilepsy by enabling extended periods of ambulatory recording.Significance.This offers significant reductions in burden to patients and their families. Furthermore, the device's utility may extend to a broader spectrum, making it suitable for other applications involving neurophysiological recordings in real-world settings.
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Affiliation(s)
- Mahmoud Zeydabadinezhad
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States of America
| | - Jon Jowers
- United Sciences, LLC, Atlanta, GA, United States of America
| | - Derek Buhl
- Takeda Pharmaceuticals Company Limited, Cambridge, MA, United States of America
| | - Brian Cabaniss
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, United States of America
| | - Babak Mahmoudi
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States of America
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA, United States of America
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Rai P, Knight A, Hiillos M, Kertész C, Morales E, Terney D, Larsen SA, Østerkjerhuus T, Peltola J, Beniczky S. Automated analysis and detection of epileptic seizures in video recordings using artificial intelligence. Front Neuroinform 2024; 18:1324981. [PMID: 38558825 PMCID: PMC10978750 DOI: 10.3389/fninf.2024.1324981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 02/27/2024] [Indexed: 04/04/2024] Open
Abstract
Introduction Automated seizure detection promises to aid in the prevention of SUDEP and improve the quality of care by assisting in epilepsy diagnosis and treatment adjustment. Methods In this phase 2 exploratory study, the performance of a contactless, marker-free, video-based motor seizure detection system is assessed, considering video recordings of patients (age 0-80 years), in terms of sensitivity, specificity, and Receiver Operating Characteristic (ROC) curves, with respect to video-electroencephalographic monitoring (VEM) as the medical gold standard. Detection performances of five categories of motor epileptic seizures (tonic-clonic, hyperkinetic, tonic, unclassified motor, automatisms) and psychogenic non-epileptic seizures (PNES) with a motor behavioral component lasting for >10 s were assessed independently at different detection thresholds (rather than as a categorical classification problem). A total of 230 patients were recruited in the study, of which 334 in-scope (>10 s) motor seizures (out of 1,114 total seizures) were identified by VEM reported from 81 patients. We analyzed both daytime and nocturnal recordings. The control threshold was evaluated at a range of values to compare the sensitivity (n = 81 subjects with seizures) and false detection rate (FDR) (n = all 230 subjects). Results At optimal thresholds, the performance of seizure groups in terms of sensitivity (CI) and FDR/h (CI): tonic-clonic- 95.2% (82.4, 100%); 0.09 (0.077, 0.103), hyperkinetic- 92.9% (68.5, 98.7%); 0.64 (0.59, 0.69), tonic- 78.3% (64.4, 87.7%); 5.87 (5.51, 6.23), automatism- 86.7% (73.5, 97.7%); 3.34 (3.12, 3.58), unclassified motor seizures- 78% (65.4, 90.4%); 4.81 (4.50, 5.14), and PNES- 97.7% (97.7, 100%); 1.73 (1.61, 1.86). A generic threshold recommended for all motor seizures under study asserted 88% sensitivity and 6.48 FDR/h. Discussion These results indicate an achievable performance for major motor seizure detection that is clinically applicable for use as a seizure screening solution in diagnostic workflows.
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Affiliation(s)
| | - Andrew Knight
- Neuro Event Labs, Tampere, Finland
- Department of Medicine and Health Technology, Tampere University, Tampere, Finland
| | | | | | | | - Daniella Terney
- Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund, Denmark
| | - Sidsel Armand Larsen
- Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund, Denmark
| | - Tim Østerkjerhuus
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
| | - Jukka Peltola
- Department of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Neurology, Tampere University Hospital, Tampere, Finland
| | - Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund, Denmark
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
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Ahuja A, Agrawal S, Acharya S, Batra N, Daiya V. Advancements in Wearable Digital Health Technology: A Review of Epilepsy Management. Cureus 2024; 16:e57037. [PMID: 38681418 PMCID: PMC11047798 DOI: 10.7759/cureus.57037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Accepted: 03/27/2024] [Indexed: 05/01/2024] Open
Abstract
This review explores recent advancements in wearable digital health technology specifically designed to manage epilepsy. Epilepsy presents unique challenges in monitoring and management due to the unpredictable nature of seizures. Wearable devices offer continuous monitoring and real-time data collection, providing insights into seizure patterns and trends. Wearable technology is important in epilepsy management because it enables early detection, prediction, and personalized intervention, empowering patients and healthcare providers. Key findings highlight the potential of wearable devices to improve seizure detection accuracy, enhance patient empowerment through real-time monitoring, and facilitate data-driven decision-making in clinical practice. However, further research is needed to validate the accuracy and reliability of these devices across diverse patient populations and clinical settings. Collaborative efforts between researchers, clinicians, technology developers, and patients are essential to drive innovation and advancements in wearable digital health technology for epilepsy management, ultimately improving outcomes and quality of life for individuals with this neurological condition.
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Affiliation(s)
- Abhinav Ahuja
- Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
| | - Sachin Agrawal
- Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
| | - Sourya Acharya
- Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
| | - Nitesh Batra
- Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
| | - Varun Daiya
- Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
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12
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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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13
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Lehnen J, Venkatesh P, Yao Z, Aziz A, Nguyen PVP, Harvey J, Alick-Lindstrom S, Doyle A, Podkorytova I, Perven G, Hays R, Zepeda R, Das RR, Ding K. Real-Time Seizure Detection Using Behind-the-Ear Wearable System. J Clin Neurophysiol 2024:00004691-990000000-00128. [PMID: 38376923 DOI: 10.1097/wnp.0000000000001076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024] Open
Abstract
INTRODUCTION This study examines the usability and comfort of a behind-the-ear seizure detection device called brain seizure detection (BrainSD) that captures ictal electroencephalogram (EEG) data using four scalp electrodes. METHODS This is a feasibility study. Thirty-two patients admitted to a level 4 Epilepsy Monitoring Unit were enrolled. The subjects wore BrainSD and the standard 21-channel video-EEG simultaneously. Epileptologists analyzed the EEG signals collected by BrainSD and validated it using video-EEG data to confirm its accuracy. A poststudy survey was completed by each participant to evaluate the comfort and usability of the device. In addition, a focus group of UT Southwestern epileptologists was held to discuss the features they would like to see in a home EEG-based seizure detection device such as BrainSD. RESULTS In total, BrainSD captured 11 of the 14 seizures that occurred while the device was being worn. All 11 seizures captured on BrainSD had focal onset, with three becoming bilateral tonic-clonic and one seizure being of subclinical status. The device was worn for an average of 41 hours. The poststudy survey showed that most users found the device comfortable, easy-to-use, and stated they would be interested in using BrainSD. Epileptologists in the focus group expressed a similar interest in BrainSD. CONCLUSIONS Brain seizure detection is able to detect EEG signals using four behind-the-ear electrodes. Its comfort, ease-of-use, and ability to detect numerous types of seizures make BrainSD an acceptable at-home EEG detection device from both the patient and provider perspective.
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Affiliation(s)
- Jamie Lehnen
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Pooja Venkatesh
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Zhuoran Yao
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Abdul Aziz
- Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX; and
| | - Phuc V P Nguyen
- Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX; and
- College of Information and Computer Science, University of Massachusets Amherst, Amherst, MA
| | - Jay Harvey
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Sasha Alick-Lindstrom
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Alex Doyle
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Irina Podkorytova
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Ghazala Perven
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Ryan Hays
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Rodrigo Zepeda
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Rohit R Das
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Kan Ding
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
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14
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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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15
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Seth EA, Watterson J, Xie J, Arulsamy A, Md Yusof HH, Ngadimon IW, Khoo CS, Kadirvelu A, Shaikh MF. Feasibility of cardiac-based seizure detection and prediction: A systematic review of non-invasive wearable sensor-based studies. Epilepsia Open 2024; 9:41-59. [PMID: 37881157 PMCID: PMC10839362 DOI: 10.1002/epi4.12854] [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: 05/17/2023] [Accepted: 10/21/2023] [Indexed: 10/27/2023] Open
Abstract
A reliable seizure detection or prediction device can potentially reduce the morbidity and mortality associated with epileptic seizures. Previous findings indicating alterations in cardiac activity during seizures suggest the usefulness of cardiac parameters for seizure detection or prediction. This study aims to examine available studies on seizure detection and prediction based on cardiac parameters using non-invasive wearable devices. The Embase, PubMed, and Scopus databases were used to systematically search according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Human studies that evaluated seizure detection or prediction based on cardiac parameters collected using wearable devices were included. The QUADAS-2 tool and proposed standards for validation for seizure detection devices were used for quality assessment. Twenty-four articles were identified and included in the analysis. Twenty studies evaluated seizure detection algorithms, and four studies focused on seizure prediction. Most studies used either a wrist-worn or chest-worn device for data acquisition. Among the seizure detection studies, cardiac parameters utilized for the algorithms mainly included heart rate (HR) (n = 11) or a combination of HR and heart rate variability (HRV) (n = 6). HR-based seizure detection studies collectively reported a sensitivity range of 56%-100% and a false alarm rate (FAR) of 0.02-8/h, with most studies performing retrospective validation of the algorithms. Three of the seizure prediction studies retrospectively validated multimodal algorithms, combining cardiac features with other physiological signals. Only one study prospectively validated their seizure prediction algorithm using HRV extracted from ECG data collected from a custom wearable device. These studies have demonstrated the feasibility of using cardiac parameters for seizure detection and prediction with wearable devices, with varying algorithmic performance. Many studies are in the proof-of-principle stage, and evidence for real-time detection or prediction is currently limited. Future studies should prioritize further refinement of the algorithm performance with prospective validation using large-scale longitudinal data. PLAIN LANGUAGE SUMMARY: This systematic review highlights the potential use of wearable devices, like wristbands, for detecting and predicting seizures via the measurement of heart activity. By reviewing 24 articles, it was found that most studies focused on using heart rate and changes in heart rate for seizure detection. There was a lack of studies looking at seizure prediction. The results were promising but most studies were not conducted in real-time. Therefore, more real-time studies are needed to verify the usage of heart activity-related wearable devices to detect seizures and even predict them, which will be beneficial to people with epilepsy.
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Affiliation(s)
- Eryse Amira Seth
- Neuropharmacology Research Laboratory, Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
- Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
| | - Jessica Watterson
- Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
- Department of Human‐Centred ComputingMonash UniversityMelbourneVictoriaAustralia
| | - Jue Xie
- Department of Human‐Centred ComputingMonash UniversityMelbourneVictoriaAustralia
| | - Alina Arulsamy
- Neuropharmacology Research Laboratory, Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
- Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
| | - Hadri Hadi Md Yusof
- Neuropharmacology Research Laboratory, Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
- Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
| | - Irma Wati Ngadimon
- Neuropharmacology Research Laboratory, Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
- Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
| | - Ching Soong Khoo
- Neurology Unit, Department of MedicineUniversiti Kebangsaan Malaysia Medical CentreKuala LumpurMalaysia
| | - Amudha Kadirvelu
- Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
| | - Mohd Farooq Shaikh
- Neuropharmacology Research Laboratory, Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
- Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
- School of Dentistry and Medical SciencesCharles Sturt UniversityOrangeNew South WalesAustralia
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16
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Castillo Rodriguez MDLA, Brandt A, Schulze-Bonhage A. Differentiation of subclinical and clinical electrographic events in long-term electroencephalographic recordings. Epilepsia 2023; 64 Suppl 4:S47-S58. [PMID: 36008142 DOI: 10.1111/epi.17401] [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: 02/18/2022] [Revised: 08/22/2022] [Accepted: 08/22/2022] [Indexed: 11/28/2022]
Abstract
OBJECTIVE With the advent of ultra-long-term recordings for monitoring of epilepsies, the interpretation of results of isolated electroencephalographic (EEG) recordings covering only selected brain regions attracts considerable interest. In this context, the question arises of whether detected ictal EEG patterns correspond to clinically manifest seizures or rather to purely electrographic events, that is, subclinical events. METHODS EEG patterns from 268 clinical seizures and 252 subclinical electrographic events from 50 patients undergoing video-EEG monitoring were analyzed. Features extracted included predominant frequency band, duration, association with rhythmic muscle artifacts, spatial extent, and propagation patterns. Classification using logistic regression was performed based on data from the whole dataset of 10-20 system EEG recordings and from a subset of two temporal electrode contacts. RESULTS Correct separation of clinically manifest and purely electrographic events based on 10-20 system EEG recordings was possible in up to 83.8% of events, depending on the combination of features included. Correct classification based on two-channel recordings was only slightly inferior, achieving 78.6% accuracy; 74.4% and 74.8%, respectively, of events could be correctly classified when using duration alone with either electrode set, although classification accuracies were lower for some subgroups of seizures, particularly focal aware seizures and epileptic arousals. SIGNIFICANCE A correct classification of subclinical versus clinical EEG events was possible in 74%-83% of events based on full EEG recordings, and in 74%-78% when considering only a subset of two electrodes, matching the channel number available from new implantable diagnostic devices. This is a promising outcome, suggesting that ultra-long-term low-channel EEG recordings may provide sufficient information for objective seizure diaries. Intraindividual optimization using high numbers of ictal events may further improve separation, provided that supervised learning with external validation is feasible.
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Affiliation(s)
| | - Armin Brandt
- Epilepsy Center, University Medical Center Freiburg, Freiburg, Germany
| | - Andreas Schulze-Bonhage
- Epilepsy Center, University Medical Center Freiburg, Freiburg, Germany
- Spemann Graduate School of Biology and Medicine, Freiburg, Germany
- European Reference Network EpiCare, Freiburg, Germany
- NeuroModulBasic, Freiburg, Germany
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17
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Baud MO, Proix T, Gregg NM, Brinkmann BH, Nurse ES, Cook MJ, Karoly PJ. Seizure forecasting: Bifurcations in the long and winding road. Epilepsia 2023; 64 Suppl 4:S78-S98. [PMID: 35604546 PMCID: PMC9681938 DOI: 10.1111/epi.17311] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/20/2022] [Accepted: 05/20/2022] [Indexed: 11/28/2022]
Abstract
To date, the unpredictability of seizures remains a source of suffering for people with epilepsy, motivating decades of research into methods to forecast seizures. Originally, only few scientists and neurologists ventured into this niche endeavor, which, given the difficulty of the task, soon turned into a long and winding road. Over the past decade, however, our narrow field has seen a major acceleration, with trials of chronic electroencephalographic devices and the subsequent discovery of cyclical patterns in the occurrence of seizures. Now, a burgeoning science of seizure timing is emerging, which in turn informs best forecasting strategies for upcoming clinical trials. Although the finish line might be in view, many challenges remain to make seizure forecasting a reality. This review covers the most recent scientific, technical, and medical developments, discusses methodology in detail, and sets a number of goals for future studies.
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Affiliation(s)
- Maxime O Baud
- Sleep-Wake-Epilepsy Center, Center for Experimental Neurology, NeuroTec, Department of Neurology, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland
- Wyss Center for Bio- and Neuro-Engineering, Geneva, Switzerland
| | - Timothée Proix
- Department of Basic Neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Nicholas M Gregg
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Benjamin H Brinkmann
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Ewan S Nurse
- Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Mark J Cook
- Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Philippa J Karoly
- Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
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18
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Kerr WT, McFarlane KN. Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist. Curr Neurol Neurosci Rep 2023; 23:869-879. [PMID: 38060133 DOI: 10.1007/s11910-023-01318-7] [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] [Accepted: 10/24/2023] [Indexed: 12/08/2023]
Abstract
PURPOSE OF REVIEW Machine Learning (ML) and Artificial Intelligence (AI) are data-driven techniques to translate raw data into applicable and interpretable insights that can assist in clinical decision making. Some of these tools have extremely promising initial results, earning both great excitement and creating hype. This non-technical article reviews recent developments in ML/AI in epilepsy to assist the current practicing epileptologist in understanding both the benefits and limitations of integrating ML/AI tools into their clinical practice. RECENT FINDINGS ML/AI tools have been developed to assist clinicians in almost every clinical decision including (1) predicting future epilepsy in people at risk, (2) detecting and monitoring for seizures, (3) differentiating epilepsy from mimics, (4) using data to improve neuroanatomic localization and lateralization, and (5) tracking and predicting response to medical and surgical treatments. We also discuss practical, ethical, and equity considerations in the development and application of ML/AI tools including chatbots based on Large Language Models (e.g., ChatGPT). ML/AI tools will change how clinical medicine is practiced, but, with rare exceptions, the transferability to other centers, effectiveness, and safety of these approaches have not yet been established rigorously. In the future, ML/AI will not replace epileptologists, but epileptologists with ML/AI will replace epileptologists without ML/AI.
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Affiliation(s)
- Wesley T Kerr
- Department of Neurology, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA.
- Department of Biomedical Informatics, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA.
- Department of Neurology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
| | - Katherine N McFarlane
- Department of Neurology, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA
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Yu S, El Atrache R, Tang J, Jackson M, Makarucha A, Cantley S, Sheehan T, Vieluf S, Zhang B, Rogers JL, Mareels I, Harrer S, Loddenkemper T. Artificial intelligence-enhanced epileptic seizure detection by wearables. Epilepsia 2023; 64:3213-3226. [PMID: 37715325 DOI: 10.1111/epi.17774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 09/05/2023] [Accepted: 09/14/2023] [Indexed: 09/17/2023]
Abstract
OBJECTIVE Wrist- or ankle-worn devices are less intrusive than the widely used electroencephalographic (EEG) systems for monitoring epileptic seizures. Using custom-developed deep-learning seizure detection models, we demonstrate the detection of a broad range of seizure types by wearable signals. METHODS Patients admitted to the epilepsy monitoring unit were enrolled and asked to wear wearable sensors on either wrists or ankles. We collected patients' electrodermal activity, accelerometry (ACC), and photoplethysmography, from which blood volume pulse (BVP) is derived. Board-certified epileptologists determined seizure onset, offset, and types using video and EEG recordings per the International League Against Epilepsy 2017 classification. We applied three neural network models-a convolutional neural network (CNN) and a CNN-long short-term memory (LSTM)-based generalized detection model and an autoencoder-based personalized detection model-to the raw time-series sensor data to detect seizures and utilized performance measures, including sensitivity, false positive rate (the number of false alarms divided by the total number of nonseizure segments), number of false alarms per day, and detection delay. We applied a 10-fold patientwise cross-validation scheme to the multisignal biosensor data and evaluated model performance on 28 seizure types. RESULTS We analyzed 166 patients (47.6% female, median age = 10.0 years) and 900 seizures (13 254 h of sensor data) for 28 seizure types. With a CNN-LSTM-based seizure detection model, ACC, BVP, and their fusion performed better than chance; ACC and BVP data fusion reached the best detection performance of 83.9% sensitivity and 35.3% false positive rate. Nineteen of 28 seizure types could be detected by at least one data modality with area under receiver operating characteristic curve > .8 performance. SIGNIFICANCE Results from this in-hospital study contribute to a paradigm shift in epilepsy care that entails noninvasive seizure detection, provides time-sensitive and accurate data on additional clinical seizure types, and proposes a novel combination of an out-of-the-box monitoring algorithm with an individualized person-oriented seizure detection approach.
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Affiliation(s)
- Shuang Yu
- IBM Australia, Melbourne, Victoria, Australia
| | - Rima El Atrache
- Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | | | - Michele Jackson
- Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | | | - Sarah Cantley
- Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Theodore Sheehan
- Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Solveig Vieluf
- Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Bo Zhang
- Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Jeffrey L Rogers
- Digital Health, IBM T. J. Watson Research Center, Yorktown Heights, New York, USA
| | | | - Stefan Harrer
- IBM Australia, Melbourne, Victoria, Australia
- Digital Health Cooperative Research Centre, Melbourne, Victoria, Australia
| | - Tobias Loddenkemper
- Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
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20
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Proost R, Macea J, Lagae L, Van Paesschen W, Jansen K. Wearable detection of tonic seizures in childhood epilepsy: An exploratory cohort study. Epilepsia 2023; 64:3013-3024. [PMID: 37602476 DOI: 10.1111/epi.17756] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 08/18/2023] [Accepted: 08/18/2023] [Indexed: 08/22/2023]
Abstract
OBJECTIVE To investigate the performance of a multimodal wearable device for the offline detection of tonic seizures (TS) in a pediatric childhood epilepsy cohort, with a focus on patients with Lennox-Gastaut syndrome. METHODS Parallel with prolonged video-electroencephalography (EEG), the Plug 'n Patch system, a multimodal wearable device using the Sensor Dot and replaceable electrode adhesives, was used to detect TS. Multiple biosignals were recorded: behind-the-ear EEG, surface electromyography, electrocardiography, and accelerometer/gyroscope. Biosignals were annotated blindly by a neurologist. Seizure characteristics were described, and performance was assessed by sensitivity, positive predictive value (PPV), F1 score, and false alarm rate (FAR) per hour. Performance was compared to seizure diaries kept by the caretaker. RESULTS Ninety-nine TS were detected in 13 patients. Seven patients (54%) had Lennox-Gastaut syndrome and six patients (46%) had other forms of (developmental) epileptic encephalopathies or drug-resistant epilepsy. All but one patient had intellectual disability. Overall sensitivity was 41%, with a PPV of 9%, an F1 score of 14%, and a median FAR per hour of 0.75. Performance increased to an F1 score of 66% for nightly seizures lasting at least 10 s (sensitivity 66%, PPV 66%) and 71% for nightly seizures lasting at least 20 s (sensitivity 62%, PPV 82%). For these seizures there were no false alarms in 10 of 13 patients. Sensitivity of seizure diaries reached a maximum of 52% for prolonged (≥20 s) nightly seizures, even though caretakers slept in the same room. SIGNIFICANCE We showed that it is feasible to use a multimodal wearable device with multiple adhesive sites in children with epilepsy and intellectual disability. For prolonged nightly seizures, offline manual detection of TS outperformed seizure diaries. The recognition of seizure-specific signatures using multiple modalities can help in the development of automated TS detection algorithms.
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Affiliation(s)
- Renee Proost
- Paediatric Neurology Department, University Hospitals Leuven, KU Leuven, Leuven, Belgium
| | - Jaiver Macea
- Neurology Department, University Hospitals Leuven, KU Leuven, Leuven, Belgium
| | - Lieven Lagae
- Paediatric Neurology Department, University Hospitals Leuven, KU Leuven, Leuven, Belgium
| | - Wim Van Paesschen
- Neurology Department, University Hospitals Leuven, KU Leuven, Leuven, Belgium
| | - Katrien Jansen
- Paediatric Neurology Department, University Hospitals Leuven, KU Leuven, Leuven, Belgium
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21
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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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22
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Faria M, Zin STP, Chestnov R, Novak AM, Lev-Ari S, Snyder M. Mental Health for All: The Case for Investing in Digital Mental Health to Improve Global Outcomes, Access, and Innovation in Low-Resource Settings. J Clin Med 2023; 12:6735. [PMID: 37959201 PMCID: PMC10649112 DOI: 10.3390/jcm12216735] [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: 08/11/2023] [Revised: 10/23/2023] [Accepted: 10/23/2023] [Indexed: 11/15/2023] Open
Abstract
Mental health disorders are an increasing global public health concern that contribute to morbidity, mortality, disability, and healthcare costs across the world. Biomedical and psychological research has come a long way in identifying the importance of mental health and its impact on behavioral risk factors, physiological health, and overall quality of life. Despite this, access to psychological and psychiatric services remains widely unavailable and is a challenge for many healthcare systems, particularly those in developing countries. This review article highlights the strengths and opportunities brought forward by digital mental health in narrowing this divide. Further, it points to the economic and societal benefits of effectively managing mental illness, making a case for investing resources into mental healthcare as a larger priority for large non-governmental organizations and individual nations across the globe.
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Affiliation(s)
- Manuel Faria
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA;
- Department of Psychology, Stanford University, Stanford, CA 94305, USA
- Health and Development, United Nations Development Programme, 1219 Geneva, Switzerland; (S.T.P.Z.); (R.C.)
| | - Stella Tan Pei Zin
- Health and Development, United Nations Development Programme, 1219 Geneva, Switzerland; (S.T.P.Z.); (R.C.)
| | - Roman Chestnov
- Health and Development, United Nations Development Programme, 1219 Geneva, Switzerland; (S.T.P.Z.); (R.C.)
| | - Anne Marie Novak
- Department of Health Promotion, Tel Aviv University School of Medicine, Tel Aviv 6997801, Israel;
| | - Shahar Lev-Ari
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA;
- Department of Health Promotion, Tel Aviv University School of Medicine, Tel Aviv 6997801, Israel;
| | - Michael Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA;
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23
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van Westrhenen A, Lazeron RHC, van Dijk JP, Leijten FSS, Thijs RD. Multimodal nocturnal seizure detection in children with epilepsy: A prospective, multicenter, long-term, in-home trial. Epilepsia 2023; 64:2137-2152. [PMID: 37195144 DOI: 10.1111/epi.17654] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 05/14/2023] [Accepted: 05/15/2023] [Indexed: 05/18/2023]
Abstract
OBJECTIVE There is a pressing need for reliable automated seizure detection in epilepsy care. Performance evidence on ambulatory non-electroencephalography-based seizure detection devices is low, and evidence on their effect on caregiver's stress, sleep, and quality of life (QoL) is still lacking. We aimed to determine the performance of NightWatch, a wearable nocturnal seizure detection device, in children with epilepsy in the family home setting and to assess its impact on caregiver burden. METHODS We conducted a phase 4, multicenter, prospective, video-controlled, in-home NightWatch implementation study (NCT03909984). We included children aged 4-16 years, with ≥1 weekly nocturnal major motor seizure, living at home. We compared a 2-month baseline period with a 2-month NightWatch intervention. The primary outcome was the detection performance of NightWatch for major motor seizures (focal to bilateral or generalized tonic-clonic [TC] seizures, focal to bilateral or generalized tonic seizures lasting >30 s, hyperkinetic seizures, and a remainder category of focal to bilateral or generalized clonic seizures and "TC-like" seizures). Secondary outcomes included caregivers' stress (Caregiver Strain Index [CSI]), sleep (Pittsburgh Quality of Sleep Index), and QoL (EuroQol five-dimension five-level scale). RESULTS We included 53 children (55% male, mean age = 9.7 ± 3.6 years, 68% learning disability) and analyzed 2310 nights (28 173 h), including 552 major motor seizures. Nineteen participants did not experience any episode of interest during the trial. The median detection sensitivity per participant was 100% (range = 46%-100%), and the median individual false alarm rate was .04 per hour (range = 0-.53). Caregiver's stress decreased significantly (mean total CSI score = 8.0 vs. 7.1, p = .032), whereas caregiver's sleep and QoL did not change significantly during the trial. SIGNIFICANCE The NightWatch system demonstrated high sensitivity for detecting nocturnal major motor seizures in children in a family home setting and reduced caregiver stress.
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Affiliation(s)
- Anouk van Westrhenen
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede and Zwolle, the Netherlands
- Department of Neurology and Clinical Neurophysiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Richard H C Lazeron
- Academic Center of Epileptology Kempenhaeghe, Heeze, the Netherlands
- Faculty of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Johannes P van Dijk
- Academic Center of Epileptology Kempenhaeghe, Heeze, the Netherlands
- Faculty of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
- Department of Orthodontics, Ulm University, Ulm, Germany
| | - Frans S S Leijten
- Brain Center, Department of Neurology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Roland D Thijs
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede and Zwolle, the Netherlands
- Department of Neurology and Clinical Neurophysiology, Leiden University Medical Center, Leiden, the Netherlands
- UCL Queen Square Institute of Neurology, London, UK
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Halimeh M, Jackson M, Vieluf S, Loddenkemper T, Meisel C. Explainable AI for wearable seizure logging: Impact of data quality, patient age, and antiseizure medication on performance. Seizure 2023; 110:99-108. [PMID: 37336056 DOI: 10.1016/j.seizure.2023.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 05/16/2023] [Accepted: 06/04/2023] [Indexed: 06/21/2023] Open
Abstract
OBJECTIVE Objective seizure count estimates are crucial for ambulatory epilepsy management. Wearables have shown promise for the detection of tonic-clonic seizures but may suffer from false alarms and undetected seizures. Seizure signatures recorded by wearables often occur over prolonged periods, including increased levels of electrodermal activity and heart rate long after seizure EEG onset, however, previous detection methods only partially exploited these signatures. Understanding the utility of these prolonged signatures for seizure count estimation and what factors generally determine seizure logging performance, including the role of data quality vs. algorithm performance, is thus crucial for improving wearables-based epilepsy monitoring and determining which patients benefit most from this technology. METHODS In this retrospective study we examined 76 pediatric epilepsy patients during multiday video-EEG monitoring equipped with a wearable (Empatica E4; records of electrodermal activity, EDA, accelerometry, ACC, heart rate, HR; 1983 h total recording time; 45 tonic-clonic seizures). To log seizures on prolonged data trends, we applied deep learning on continuous overlapping 1-hour segments of multimodal data in a leave-one-subject-out approach. We systematically examined factors influencing logging performance, including patient age, antiseizure medication (ASM) load, seizure type and duration, and data artifacts. To gain insights into algorithm function and feature importance we applied Uniform Manifold Approximation and Projection (UMAP, to represent the separability of learned features) and SHapley Additive exPlanations (SHAP, to represent the most informative data signatures). RESULTS Performance for tonic-clonic seizure logging increased systematically with patient age (AUC 0.61 for patients 〈 11 years, AUC 0.77 for patients between 11-15 years, AUC 0.85 for patients 〉 15 years). Across all ages, AUC was 0.75 corresponding to a sensitivity of 0.52 and a false alarm rate of 0.28/24 h. Seizures under high ASM load or with shorter duration were detected worse (P=.025, P=.033, respectively). UMAP visualized discriminatory power at the individual patient level, SHAP analyses identified clonic motor activity and peri/postictal increases in HR and EDA as most informative. In contrast, in missed seizures, these features were absent indicating that recording quality but not the algorithm caused the low sensitivity in these patients. SIGNIFICANCE Our results demonstrate the utility of prolonged, postictal data segments for seizure logging, contribute to algorithm explainability and point to influencing factors, including high ASM dose and short seizure duration. Collectively, these results may help to identify patients who particularly benefit from such technology.
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Affiliation(s)
- Mustafa Halimeh
- Computational Neurology Lab, Department of Neurology, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Michele Jackson
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States
| | - Solveig Vieluf
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States
| | - Tobias Loddenkemper
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States
| | - Christian Meisel
- Computational Neurology Lab, Department of Neurology, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany; Center for Stroke Research Berlin, Berlin, Germany; Berlin Institute of Health, Berlin, Germany; NeuroCure Cluster of Excellence, Charité - Universitätsmedizin Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany.
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25
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Vos G, Trinh K, Sarnyai Z, Rahimi Azghadi M. Generalizable machine learning for stress monitoring from wearable devices: A systematic literature review. Int J Med Inform 2023; 173:105026. [PMID: 36893657 DOI: 10.1016/j.ijmedinf.2023.105026] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 02/21/2023] [Accepted: 02/23/2023] [Indexed: 03/06/2023]
Abstract
INTRODUCTION Wearable sensors have shown promise as a non-intrusive method for collecting biomarkers that may correlate with levels of elevated stress. Stressors cause a variety of biological responses, and these physiological reactions can be measured using biomarkers including Heart Rate Variability (HRV), Electrodermal Activity (EDA) and Heart Rate (HR) that represent the stress response from the Hypothalamic-Pituitary-Adrenal (HPA) axis, the Autonomic Nervous System (ANS), and the immune system. While Cortisol response magnitude remains the gold standard indicator for stress assessment [1], recent advances in wearable technologies have resulted in the availability of a number of consumer devices capable of recording HRV, EDA and HR sensor biomarkers, amongst other signals. At the same time, researchers have been applying machine learning techniques to the recorded biomarkers in order to build models that may be able to predict elevated levels of stress. OBJECTIVE The aim of this review is to provide an overview of machine learning techniques utilized in prior research with a specific focus on model generalization when using these public datasets as training data. We also shed light on the challenges and opportunities that machine learning-enabled stress monitoring and detection face. METHODS This study reviewed published works contributing and/or using public datasets designed for detecting stress and their associated machine learning methods. The electronic databases of Google Scholar, Crossref, DOAJ and PubMed were searched for relevant articles and a total of 33 articles were identified and included in the final analysis. The reviewed works were synthesized into three categories of publicly available stress datasets, machine learning techniques applied using those, and future research directions. For the machine learning studies reviewed, we provide an analysis of their approach to results validation and model generalization. The quality assessment of the included studies was conducted in accordance with the IJMEDI checklist [2]. RESULTS A number of public datasets were identified that are labeled for stress detection. These datasets were most commonly produced from sensor biomarker data recorded using the Empatica E4 device, a well-studied, medical-grade wrist-worn wearable that provides sensor biomarkers most notable to correlate with elevated levels of stress. Most of the reviewed datasets contain less than twenty-four hours of data, and the varied experimental conditions and labeling methodologies potentially limit their ability to generalize for unseen data. In addition, we discuss that previous works show shortcomings in areas such as their labeling protocols, lack of statistical power, validity of stress biomarkers, and model generalization ability. CONCLUSION Health tracking and monitoring using wearable devices is growing in popularity, while the generalization of existing machine learning models still requires further study, and research in this area will continue to provide improvements as newer and more substantial datasets become available.
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Affiliation(s)
- Gideon Vos
- College of Science and Engineering, James Cook University, James Cook Dr, Townsville, 4811, QLD, Australia
| | - Kelly Trinh
- College of Science and Engineering, James Cook University, James Cook Dr, Townsville, 4811, QLD, Australia
| | - Zoltan Sarnyai
- College of Public Health, Medical, and Vet Sciences, James Cook University, James Cook Dr, Townsville, 4811, QLD, Australia
| | - Mostafa Rahimi Azghadi
- College of Science and Engineering, James Cook University, James Cook Dr, Townsville, 4811, QLD, Australia.
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26
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Zhu Y, Li J, Kim J, Li S, Zhao Y, Bahari J, Eliahoo P, Li G, Kawakita S, Haghniaz R, Gao X, Falcone N, Ermis M, Kang H, Liu H, Kim H, Tabish T, Yu H, Li B, Akbari M, Emaminejad S, Khademhosseini A. Skin-interfaced electronics: A promising and intelligent paradigm for personalized healthcare. Biomaterials 2023; 296:122075. [PMID: 36931103 PMCID: PMC10085866 DOI: 10.1016/j.biomaterials.2023.122075] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 02/23/2023] [Accepted: 03/02/2023] [Indexed: 03/09/2023]
Abstract
Skin-interfaced electronics (skintronics) have received considerable attention due to their thinness, skin-like mechanical softness, excellent conformability, and multifunctional integration. Current advancements in skintronics have enabled health monitoring and digital medicine. Particularly, skintronics offer a personalized platform for early-stage disease diagnosis and treatment. In this comprehensive review, we discuss (1) the state-of-the-art skintronic devices, (2) material selections and platform considerations of future skintronics toward intelligent healthcare, (3) device fabrication and system integrations of skintronics, (4) an overview of the skintronic platform for personalized healthcare applications, including biosensing as well as wound healing, sleep monitoring, the assessment of SARS-CoV-2, and the augmented reality-/virtual reality-enhanced human-machine interfaces, and (5) current challenges and future opportunities of skintronics and their potentials in clinical translation and commercialization. The field of skintronics will not only minimize physical and physiological mismatches with the skin but also shift the paradigm in intelligent and personalized healthcare and offer unprecedented promise to revolutionize conventional medical practices.
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Affiliation(s)
- Yangzhi Zhu
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States.
| | - Jinghang Li
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States
| | - Jinjoo Kim
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States
| | - Shaopei Li
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States
| | - Yichao Zhao
- Interconnected and Integrated Bioelectronics Lab, Department of Electrical and Computer Engineering, and Materials Science and Engineering, University of California, Los Angeles, CA, 90095, United States
| | - Jamal Bahari
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States
| | - Payam Eliahoo
- Biomedical Engineering Department, University of Southern California, Los Angeles, CA, 90007, United States
| | - Guanghui Li
- The Centre of Nanoscale Science and Technology and Key Laboratory of Functional Polymer Materials, Institute of Polymer Chemistry, College of Chemistry, Nankai University, Tianjin, 300071, China; Renewable Energy Conversion and Storage Center (RECAST), Nankai University, Tianjin, 300071, China
| | - Satoru Kawakita
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States
| | - Reihaneh Haghniaz
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States
| | - Xiaoxiang Gao
- Department of Nanoengineering, University of California, San Diego, La Jolla, CA, 92093, United States
| | - Natashya Falcone
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States
| | - Menekse Ermis
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States
| | - Heemin Kang
- Department of Materials Science and Engineering, Korea University, Seoul, 02841, Republic of Korea
| | - Hao Liu
- Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an, 710049, PR China
| | - HanJun Kim
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States; College of Pharmacy, Korea University, Sejong, 30019, Republic of Korea
| | - Tanveer Tabish
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 7BN, United Kingdom
| | - Haidong Yu
- Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering, Northwestern Polytechnical University, Xi'an, 710072, PR China
| | - Bingbing Li
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States; Department of Manufacturing Systems Engineering and Management, California State University, Northridge, CA, 91330, United States
| | - Mohsen Akbari
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States; Laboratory for Innovation in Microengineering (LiME), Department of Mechanical Engineering, Center for Biomedical Research, University of Victoria, Victoria, BC V8P 2C5, Canada
| | - Sam Emaminejad
- Interconnected and Integrated Bioelectronics Lab, Department of Electrical and Computer Engineering, and Materials Science and Engineering, University of California, Los Angeles, CA, 90095, United States
| | - Ali Khademhosseini
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90064, United States.
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Li Z, Chen L, Xu C, Chen Z, Wang Y. Non-invasive sensory neuromodulation in epilepsy: Updates and future perspectives. Neurobiol Dis 2023; 179:106049. [PMID: 36813206 DOI: 10.1016/j.nbd.2023.106049] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 02/15/2023] [Accepted: 02/16/2023] [Indexed: 02/22/2023] Open
Abstract
Epilepsy, one of the most common neurological disorders, often is not well controlled by current pharmacological and surgical treatments. Sensory neuromodulation, including multi-sensory stimulation, auditory stimulation, olfactory stimulation, is a kind of novel noninvasive mind-body intervention and receives continued attention as complementary safe treatment of epilepsy. In this review, we summarize the recent advances of sensory neuromodulation, including enriched environment therapy, music therapy, olfactory therapy, other mind-body interventions, for the treatment of epilepsy based on the evidence from both clinical and preclinical studies. We also discuss their possible anti-epileptic mechanisms on neural circuit level and propose perspectives on possible research directions for future studies.
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Affiliation(s)
- Zhongxia Li
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China; Zhejiang Rehabilitation Medical Center Department, The Third Affiliated Hospital, Zhejiang Chinese Medical University, Hangzhou, China
| | - Liying Chen
- Department of Pharmacy, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Cenglin Xu
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Zhong Chen
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Yi Wang
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China; Zhejiang Rehabilitation Medical Center Department, The Third Affiliated Hospital, Zhejiang Chinese Medical University, Hangzhou, China.
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28
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Abdel-Hamid L. An Efficient Machine Learning-Based Emotional Valence Recognition Approach Towards Wearable EEG. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23031255. [PMID: 36772295 PMCID: PMC9921881 DOI: 10.3390/s23031255] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 01/14/2023] [Accepted: 01/17/2023] [Indexed: 05/17/2023]
Abstract
Emotion artificial intelligence (AI) is being increasingly adopted in several industries such as healthcare and education. Facial expressions and tone of speech have been previously considered for emotion recognition, yet they have the drawback of being easily manipulated by subjects to mask their true emotions. Electroencephalography (EEG) has emerged as a reliable and cost-effective method to detect true human emotions. Recently, huge research effort has been put to develop efficient wearable EEG devices to be used by consumers in out of the lab scenarios. In this work, a subject-dependent emotional valence recognition method is implemented that is intended for utilization in emotion AI applications. Time and frequency features were computed from a single time series derived from the Fp1 and Fp2 channels. Several analyses were performed on the strongest valence emotions to determine the most relevant features, frequency bands, and EEG timeslots using the benchmark DEAP dataset. Binary classification experiments resulted in an accuracy of 97.42% using the alpha band, by that outperforming several approaches from literature by ~3-22%. Multiclass classification gave an accuracy of 95.0%. Feature computation and classification required less than 0.1 s. The proposed method thus has the advantage of reduced computational complexity as, unlike most methods in the literature, only two EEG channels were considered. In addition, minimal features concluded from the thorough analyses conducted in this study were used to achieve state-of-the-art performance. The implemented EEG emotion recognition method thus has the merits of being reliable and easily reproducible, making it well-suited for wearable EEG devices.
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Affiliation(s)
- Lamiaa Abdel-Hamid
- Department of Electronics & Communication, Faculty of Engineering, Misr International University (MIU), Heliopolis, Cairo P.O. Box 1 , Egypt
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Prieto-Avalos G, Sánchez-Morales LN, Alor-Hernández G, Sánchez-Cervantes JL. A Review of Commercial and Non-Commercial Wearables Devices for Monitoring Motor Impairments Caused by Neurodegenerative Diseases. BIOSENSORS 2022; 13:72. [PMID: 36671907 PMCID: PMC9856141 DOI: 10.3390/bios13010072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 12/24/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
Neurodegenerative diseases (NDDs) are among the 10 causes of death worldwide. The effects of NDDs, including irreversible motor impairments, have an impact not only on patients themselves but also on their families and social environments. One strategy to mitigate the pain of NDDs is to early identify and remotely monitor related motor impairments using wearable devices. Technological progress has contributed to reducing the hardware complexity of mobile devices while simultaneously improving their efficiency in terms of data collection and processing and energy consumption. However, perhaps the greatest challenges of current mobile devices are to successfully manage the security and privacy of patient medical data and maintain reasonable costs with respect to the traditional patient consultation scheme. In this work, we conclude: (1) Falls are most monitored for Parkinson's disease, while tremors predominate in epilepsy and Alzheimer's disease. These findings will provide guidance for wearable device manufacturers to strengthen areas of opportunity that need to be addressed, and (2) Of the total universe of commercial wearables devices that are available on the market, only a few have FDA approval, which means that there is a large number of devices that do not safeguard the integrity of the users who use them.
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Affiliation(s)
- Guillermo Prieto-Avalos
- Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
| | - Laura Nely Sánchez-Morales
- CONACYT-Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
| | - Giner Alor-Hernández
- Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
| | - José Luis Sánchez-Cervantes
- CONACYT-Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
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30
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Jiang P, Gao F, Liu S, Zhang S, Zhang X, Xia Z, Zhang W, Jiang T, Zhu JL, Zhang Z, Shu Q, Snyder M, Li J. Longitudinally tracking personal physiomes for precision management of childhood epilepsy. PLOS DIGITAL HEALTH 2022; 1:e0000161. [PMID: 36812648 PMCID: PMC9931296 DOI: 10.1371/journal.pdig.0000161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 11/13/2022] [Indexed: 12/24/2022]
Abstract
Our current understanding of human physiology and activities is largely derived from sparse and discrete individual clinical measurements. To achieve precise, proactive, and effective health management of an individual, longitudinal, and dense tracking of personal physiomes and activities is required, which is only feasible by utilizing wearable biosensors. As a pilot study, we implemented a cloud computing infrastructure to integrate wearable sensors, mobile computing, digital signal processing, and machine learning to improve early detection of seizure onsets in children. We recruited 99 children diagnosed with epilepsy and longitudinally tracked them at single-second resolution using a wearable wristband, and prospectively acquired more than one billion data points. This unique dataset offered us an opportunity to quantify physiological dynamics (e.g., heart rate, stress response) across age groups and to identify physiological irregularities upon epilepsy onset. The high-dimensional personal physiome and activity profiles displayed a clustering pattern anchored by patient age groups. These signatory patterns included strong age and sex-specific effects on varying circadian rhythms and stress responses across major childhood developmental stages. For each patient, we further compared the physiological and activity profiles associated with seizure onsets with the personal baseline and developed a machine learning framework to accurately capture these onset moments. The performance of this framework was further replicated in another independent patient cohort. We next referenced our predictions with the electroencephalogram (EEG) signals on selected patients and demonstrated that our approach could detect subtle seizures not recognized by humans and could detect seizures prior to clinical onset. Our work demonstrated the feasibility of a real-time mobile infrastructure in a clinical setting, which has the potential to be valuable in caring for epileptic patients. Extension of such a system has the potential to be leveraged as a health management device or longitudinal phenotyping tool in clinical cohort studies.
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Affiliation(s)
- Peifang Jiang
- National Clinical Research Center for Child Health, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Feng Gao
- National Clinical Research Center for Child Health, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Sixing Liu
- SensOmics, Inc. Burlingame, California, United States of America
| | - Sai Zhang
- SensOmics, Inc. Burlingame, California, United States of America
| | - Xicheng Zhang
- National Clinical Research Center for Child Health, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, California, United States of America
| | - Zhezhi Xia
- National Clinical Research Center for Child Health, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Weiqin Zhang
- National Clinical Research Center for Child Health, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tiejia Jiang
- National Clinical Research Center for Child Health, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jason L. Zhu
- SensOmics, Inc. Burlingame, California, United States of America
| | - Zhaolei Zhang
- SensOmics, Inc. Burlingame, California, United States of America
- Donnelly Centre, Department of Computer Science and Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- * E-mail: (ZZ); (QS); (MS); (JL)
| | - Qiang Shu
- National Clinical Research Center for Child Health, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- * E-mail: (ZZ); (QS); (MS); (JL)
| | - Michael Snyder
- SensOmics, Inc. Burlingame, California, United States of America
- * E-mail: (ZZ); (QS); (MS); (JL)
| | - Jingjing Li
- SensOmics, Inc. Burlingame, California, United States of America
- * E-mail: (ZZ); (QS); (MS); (JL)
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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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Chougale A, Vedante S, Kulkarni G, Patnawar S. Recent Progress on Biosensors for the Early Detection of Neurological Disorders. ChemistrySelect 2022. [DOI: 10.1002/slct.202203155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Affiliation(s)
- Amit Chougale
- Department of Chemical Engineering University of Adelaide SA Australia 5000
| | - Shruti Vedante
- Department of Chemical Engineering University of Adelaide SA Australia 5000
| | - Guruprasad Kulkarni
- Department of Biotechnology Kolhapur Institute of Technology's College of Engineering Kolhapur Maharashtra India 416234
| | - Sneha Patnawar
- Department of Biotechnology Kolhapur Institute of Technology's College of Engineering Kolhapur Maharashtra India. 416234
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Ren Z, Han X, Wang B. The performance evaluation of the state-of-the-art EEG-based seizure prediction models. Front Neurol 2022; 13:1016224. [PMID: 36504642 PMCID: PMC9732735 DOI: 10.3389/fneur.2022.1016224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 11/09/2022] [Indexed: 11/26/2022] Open
Abstract
The recurrent and unpredictable nature of seizures can lead to unintentional injuries and even death. The rapid development of electroencephalogram (EEG) and Artificial Intelligence (AI) technologies has made it possible to predict seizures in real-time through brain-machine interfaces (BCI), allowing advanced intervention. To date, there is still much room for improvement in predictive seizure models constructed by EEG using machine learning (ML) and deep learning (DL). But, the most critical issue is how to improve the performance and generalization of the model, which involves some confusing conceptual and methodological issues. This review focuses on analyzing several factors affecting the performance of seizure prediction models, focusing on the aspects of post-processing, seizure occurrence period (SOP), seizure prediction horizon (SPH), and algorithms. Furthermore, this study presents some new directions and suggestions for building high-performance prediction models in the future. We aimed to clarify the concept for future research in related fields and improve the performance of prediction models to provide a theoretical basis for future applications of wearable seizure detection devices.
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Affiliation(s)
- Zhe Ren
- Department of Neurology, Zhengzhou University People's Hospital, Zhengzhou, China,Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, China
| | - Xiong Han
- Department of Neurology, Zhengzhou University People's Hospital, Zhengzhou, China,Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, China,*Correspondence: Xiong Han
| | - Bin Wang
- Department of Neurology, Zhengzhou University People's Hospital, Zhengzhou, China,Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, China
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Meisel C, Holtkamp M, Vock S. Ambulantes Langzeit-Video-EEG als neuer diagnostischer Ansatz in Deutschland: Ergebnisse einer Machbarkeitsstudie. DER NERVENARZT 2022:10.1007/s00115-022-01412-0. [DOI: 10.1007/s00115-022-01412-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/13/2022] [Indexed: 11/23/2022]
Abstract
ZusammenfassungDas Langzeit-Video-EEG als Goldstandarddiagnostik bei Epilepsie und anderen Anfallsleiden ist in Deutschland derzeit nur in wenigen spezialisierten Zentren zur stationären Überwachung verfügbar. Diese begrenzten Überwachungskapazitäten und der damit verbundene hohe Zeit- und Arbeitsaufwand führen bundesweit zu einer erheblichen Wartezeit für diese wichtige Diagnostik. Neue, tragbare Sensortechnologien und automatisierte Datenanalysemethoden schaffen Möglichkeiten für Video-EEG-Langzeituntersuchungen nach dem Goldstandard im ambulanten Bereich, die dazu beitragen können, diese Barriere zu überwinden. Hier berichten wir über die Ergebnisse einer Machbarkeitsstudie an einem Zentrum, in der das ambulante Langzeit-Video-EEG (ALVEEG) als diagnostischer Weg in Deutschland eingeführt wurde. In dem neuen Diagnostikpfad soll der Einsatz innovativer, tragbarer Video-EEG-Monitoringsysteme zusammen mit einer durch künstliche Intelligenz unterstützten Datenanalyse den von Anfallsleiden betroffenen Patienten einen schnelleren, effizienteren und sektorübergreifenden Zugang zu einer Goldstandarddiagnostik in der häuslichen Umgebung ermöglichen. Die Diagnostik wurde von Patienten und Klinikern gut angenommen und könnte eine ergänzende Option zur stationären Überwachung darstellen, um die derzeitigen Engpässe in Diagnostik und Versorgung zu beseitigen.
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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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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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Esmaeili B, Vieluf S, Dworetzky BA, Reinsberger C. The Potential of Wearable Devices and Mobile Health Applications in the Evaluation and Treatment of Epilepsy. Neurol Clin 2022; 40:729-739. [DOI: 10.1016/j.ncl.2022.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
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Smart Consumer Wearables as Digital Diagnostic Tools: A Review. Diagnostics (Basel) 2022; 12:diagnostics12092110. [PMID: 36140511 PMCID: PMC9498278 DOI: 10.3390/diagnostics12092110] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/26/2022] [Accepted: 08/29/2022] [Indexed: 11/17/2022] Open
Abstract
The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services. These devices, which carry different types of sensors, have emerged as personalized digital diagnostic tools. Data from such devices have enabled the prediction and detection of various physiological as well as psychological conditions and diseases. In this review, we have focused on the diagnostic applications of wrist-worn wearables to detect multiple diseases such as cardiovascular diseases, neurological disorders, fatty liver diseases, and metabolic disorders, including diabetes, sleep quality, and psychological illnesses. The fruitful usage of wearables requires fast and insightful data analysis, which is feasible through machine learning. In this review, we have also discussed various machine-learning applications and outcomes for wearable data analyses. Finally, we have discussed the current challenges with wearable usage and data, and the future perspectives of wearable devices as diagnostic tools for research and personalized healthcare domains.
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Dash DP, Kolekar MH, Chakraborty C, Khosravi MR. Review of Machine and Deep Learning Techniques in Epileptic Seizure Detection using Physiological Signals and Sentiment Analysis. ACM T ASIAN LOW-RESO 2022. [DOI: 10.1145/3552512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Abstract
Epilepsy is one of the significant neurological disorders affecting nearly 65 million people worldwide. The repeated seizure is characterized as epilepsy. Different algorithms were proposed for efficient seizure detection using intracranial and surface EEG signals. In the last decade, various machine learning techniques based on seizure detection approaches were proposed. This paper discusses different machine learning and deep learning techniques for seizure detection using intracranial and surface EEG signals. A wide range of machine learning techniques such as support vector machine (SVM) classifiers, artificial neural network (ANN) classifier, and deep learning techniques such as a convolutional neural network (CNN) classifier, long-short term memory (LSTM) network for seizure detection are compared in this paper. The effectiveness of time-domain features, frequency domain features, and time-frequency domain features are discussed along with different machine learning techniques. Along with EEG, other physiological signals such as electrocardiogram are used to enhance seizure detection accuracy which are discussed in this paper. In recent years deep learning techniques based on seizure detection have found good classification accuracy. In this paper, an LSTM deep learning-network-based approach is implemented for seizure detection and compared with state-of-the-art methods. The LSTM based approach achieved 96.5% accuracy in seizure-nonseizure EEG signal classification. Apart from analyzing the physiological signals, sentiment analysis also has potential to detect seizure.
Impact Statement-
This review paper gives a summary of different research work related to epileptic seizure detection using machine learning and deep learning techniques. Manual seizure detetion is time consuming and requires expertise. So the artificial intelligence techniques such as machine learning and deep learning techniques are used for automatic seizure detection. Different physiological signals are used for seizure detection. Different researchers are working on developing automatic seizure detection using EEG, ECG, accelerometer, sentiment analysis. There is a need for a review paper that can discuss previous techniques and give further research direction. We have discussed different techniques for seizure detection with an accuracy comparison table. It can help the researcher to get an overview of both surface and intracranial EEG-based seizure detection approaches. The new researcher can easily compare different models and decide the model they want to start working on. A deep learning model is discussed to give a practical application of seizure detection. Sentiment analysis is another dimension of seizure detection and summerizing it will give a new prospective to the reader.
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Ray J, Wijesekera L, Cirstea S. Machine learning and clinical neurophysiology. J Neurol 2022; 269:6678-6684. [PMID: 35907045 DOI: 10.1007/s00415-022-11283-9] [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: 06/13/2022] [Revised: 07/05/2022] [Accepted: 07/09/2022] [Indexed: 11/29/2022]
Abstract
Clinical neurophysiology constructs a wealth of dynamic information pertaining to the integrity and function of both central and peripheral nervous systems. As with many technological fields, there has been an explosion of data in neurophysiology over recent years, and this requires considerable analysis by experts. Computational algorithms and especially advances in machine learning (ML) have the ability to assist with this task and potentially reveal hidden insights. In this update article, we will provide a brief overview where such technology is being applied in clinical neurophysiology and possible future directions.
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Affiliation(s)
- Julian Ray
- Department of Clinical Neurophysiology, Addenbrooke's Hospital, Cambridge University Hospitals Neurosciences, Cambridge, UK.
| | - Lokesh Wijesekera
- Department of Clinical Neurophysiology, Addenbrooke's Hospital, Cambridge University Hospitals Neurosciences, Cambridge, UK
| | - Silvia Cirstea
- Department of Clinical Neurophysiology, Addenbrooke's Hospital, Cambridge University Hospitals Neurosciences, Cambridge, UK
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Chen F, Chen I, Zafar M, Sinha SR, Hu X. Seizures detection using multimodal signals: a scoping review. Physiol Meas 2022; 43. [PMID: 35724654 DOI: 10.1088/1361-6579/ac7a8d] [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: 11/25/2021] [Accepted: 06/20/2022] [Indexed: 11/12/2022]
Abstract
INTRODUCTION Epileptic seizures are common neurological disorders in the world, impacting 50 million people globally. Around 30% of patients with seizures suffer from refractory epilepsy, where seizures are not controlled by medications. The unpredictability of seizures makes it essential to have a continuous seizure monitoring system outside clinical settings for the purpose of minimizing patients' injuries and providing additional pathways for evaluation and treatment follow-up. Autonomic changes related to seizure events have been extensively studied and attempts made to apply them for seizure detection and prediction tasks. This scoping review aims to depict current research activities associated with the implementation of portable, wearable devices for seizure detection or prediction and inform future direction in continuous seizure tracking in ambulatory settings. METHODS Overall methodology framework includes 5 essential stages: research questions identification, relevant studies identification, selection of studies, data charting and summarizing the findings. A systematic searching strategy guided by systematic reviews and meta-analysis (PRISMA) was implemented to identify relevant records on two databases (PubMed, IEEE). RESULTS A total of 30 articles were included in our final analysis. Most of the studies were conducted off-line and employed consumer-graded wearable device. ACM is the dominant modality to be used in seizure detection, and widely deployed algorithms entail Support Vector Machine, Random Forest and threshold-based approach. The sensitivity ranged from 33.2% to 100% for single modality with a false alarm rate (FAR) ranging from 0.096 /day to 14.8 /day. Multimodality has a sensitivity ranging from 51% to 100% with FAR ranging from 0.12/day - 17.7/day. CONCLUSION The overall performance in seizure detection system based on non-cerebral physiological signals is promising, especially for the detection of motor seizures and seizures accompanied with intense ictal autonomic changes.
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Affiliation(s)
- Fangyi Chen
- Biomedical Engineering, Duke University, 305 Teer Engineering Building Box 90271, Durham, North Carolina, 27708, UNITED STATES
| | - Ina Chen
- Biomedical Engineering, Duke University, 305 Teer Engineering Building Box 90271, Durham, North Carolina, 27708, UNITED STATES
| | - Muhammad Zafar
- Department of Paediatrics, Neurology, School of Medicine, Duke University, Duke University Medical Center Greenspace, Durham, North Carolina, 27710, UNITED STATES
| | - Saurabh Ranjan Sinha
- Duke Comprehensive Epilepsy Center, Department of Neurology, School of Medicine, Duke University, 295 Hanes Hse, 330 Trent Drive, Durham, North Carolina, 27710, UNITED STATES
| | - Xiao Hu
- Duke University, 4223 Interprofessional Education Building 307 Trent Drive, Durham, North Carolina, 27710, UNITED STATES
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Naganur V, Sivathamboo S, Chen Z, Kusmakar S, Antonic-Baker A, O'Brien TJ, Kwan P. Automated seizure detection with non-invasive wearable devices: A systematic review and meta-analysis. Epilepsia 2022; 63:1930-1941. [PMID: 35545836 PMCID: PMC9545631 DOI: 10.1111/epi.17297] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 05/08/2022] [Accepted: 05/09/2022] [Indexed: 11/29/2022]
Abstract
Objective This study was undertaken to review the reported performance of noninvasive wearable devices in detecting epileptic seizures and psychogenic nonepileptic seizures (PNES). Methods We conducted a systematic review and meta‐analysis of studies reported up to November 15, 2021. We included studies that used video‐electroencephalographic (EEG) monitoring as the gold standard to determine the sensitivity and false alarm rate (FAR) of noninvasive wearables for automated seizure detection. Results Twenty‐eight studies met the criteria for the systematic review, of which 23 were eligible for meta‐analysis. These studies (1269 patients in total, median recording time = 52.9 h per patient) investigated devices for tonic–clonic seizures using wrist‐worn and/or ankle‐worn devices to measure three‐dimensional accelerometry (15 studies), and/or wearable surface devices to measure electromyography (eight studies). The mean sensitivity for detecting tonic–clonic seizures was .91 (95% confidence interval [CI] = .85–.96, I2 = 83.8%); sensitivity was similar between the wrist‐worn (.93) and surface devices (.90). The overall FAR was 2.1/24 h (95% CI = 1.7–2.6, I2 = 99.7%); FAR was higher in wrist‐worn (2.5/24 h) than in wearable surface devices (.96/24 h). Three of the 23 studies also detected PNES; the mean sensitivity and FAR from these studies were 62.9% and .79/24 h, respectively. Four studies detected both focal and tonic–clonic seizures, and one study detected focal seizures only; the sensitivities ranged from 31.1% to 93.1% in these studies. Significance Reported noninvasive wearable devices had high sensitivity but relatively high FARs in detecting tonic–clonic seizures during limited recording time in a video‐EEG setting. Future studies should focus on reducing FAR, detection of other seizure types and PNES, and longer recording in the community.
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Affiliation(s)
- Vaidehi Naganur
- Department of Neuroscience, Central Clinical School, Monash University, 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, Australia.,Department of Neurology, Alfred Health, Melbourne, 3004, Victoria, Australia
| | - Shobi Sivathamboo
- Department of Neuroscience, Central Clinical School, Monash University, 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, Australia.,Department of Neurology, Alfred Health, Melbourne, 3004, Victoria, Australia
| | - Zhibin Chen
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, 3004, Victoria, Australia.,Department of Medicine, The Royal Melbourne Hospital), The University of Melbourne, 3000, Victoria, Australia.,Chronic Disease and Ageing, School of Public Health and Preventive Medicine, Monash University, Melbourne, 3004, Australia
| | - Shitanshu Kusmakar
- Department of Electrical and Electronic Engineering, The University of Melbourne, Victoria, Australia
| | - Ana Antonic-Baker
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, 3004, Victoria, Australia
| | - Terence J O'Brien
- Department of Neuroscience, Central Clinical School, Monash University, 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, Australia.,Department of Neurology, Alfred Health, Melbourne, 3004, Victoria, Australia
| | - Patrick Kwan
- Department of Neuroscience, Central Clinical School, Monash University, 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, Australia.,Department of Neurology, Alfred Health, Melbourne, 3004, Victoria, Australia
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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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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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Ambulatory seizure forecasting with a wrist-worn device using long-short term memory deep learning. Sci Rep 2021; 11:21935. [PMID: 34754043 PMCID: PMC8578354 DOI: 10.1038/s41598-021-01449-2] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 10/22/2021] [Indexed: 11/30/2022] Open
Abstract
The ability to forecast seizures minutes to hours in advance of an event has been verified using invasive EEG devices, but has not been previously demonstrated using noninvasive wearable devices over long durations in an ambulatory setting. In this study we developed a seizure forecasting system with a long short-term memory (LSTM) recurrent neural network (RNN) algorithm, using a noninvasive wrist-worn research-grade physiological sensor device, and tested the system in patients with epilepsy in the field, with concurrent invasive EEG confirmation of seizures via an implanted recording device. The system achieved forecasting performance significantly better than a random predictor for 5 of 6 patients studied, with mean AUC-ROC of 0.80 (range 0.72–0.92). These results provide the first clear evidence that direct seizure forecasts are possible using wearable devices in the ambulatory setting for many patients with epilepsy.
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46
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Onorati F, Regalia G, Caborni C, LaFrance WC, Blum AS, Bidwell J, De Liso P, El Atrache R, Loddenkemper T, Mohammadpour-Touserkani F, Sarkis RA, Friedman D, Jeschke J, Picard R. Prospective Study of a Multimodal Convulsive Seizure Detection Wearable System on Pediatric and Adult Patients in the Epilepsy Monitoring Unit. Front Neurol 2021; 12:724904. [PMID: 34489858 PMCID: PMC8418082 DOI: 10.3389/fneur.2021.724904] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 07/27/2021] [Indexed: 12/03/2022] Open
Abstract
Background: Using machine learning to combine wrist accelerometer (ACM) and electrodermal activity (EDA) has been shown effective to detect primarily and secondarily generalized tonic-clonic seizures, here termed as convulsive seizures (CS). A prospective study was conducted for the FDA clearance of an ACM and EDA-based CS-detection device based on a predefined machine learning algorithm. Here we present its performance on pediatric and adult patients in epilepsy monitoring units (EMUs). Methods: Patients diagnosed with epilepsy participated in a prospective multi-center clinical study. Three board-certified neurologists independently labeled CS from video-EEG. The Detection Algorithm was evaluated in terms of Sensitivity and false alarm rate per 24 h-worn (FAR) on all the data and on only periods of rest. Performance were analyzed also applying the Detection Algorithm offline, with a less sensitive but more specific parameters configuration (“Active mode”). Results: Data from 152 patients (429 days) were used for performance evaluation (85 pediatric aged 6–20 years, and 67 adult aged 21–63 years). Thirty-six patients (18 pediatric) experienced a total of 66 CS (35 pediatric). The Sensitivity (corrected for clustered data) was 0.92, with a 95% confidence interval (CI) of [0.85-1.00] for the pediatric population, not significantly different (p > 0.05) from the adult population's Sensitivity (0.94, CI: [0.89–1.00]). The FAR on the pediatric population was 1.26 (CI: [0.87–1.73]), higher (p < 0.001) than in the adult population (0.57, CI: [0.36–0.81]). Using the Active mode, the FAR decreased by 68% while reducing Sensitivity to 0.95 across the population. During rest periods, the FAR's were 0 for all patients, lower than during activity periods (p < 0.001). Conclusions: Performance complies with FDA's requirements of a lower bound of CI for Sensitivity higher than 0.7 and of a FAR lower than 2, for both age groups. The pediatric FAR was higher than the adult FAR, likely due to higher pediatric activity. The high Sensitivity and precision (having no false alarms) during sleep might help mitigate SUDEP risk by summoning caregiver intervention. The Active mode may be advantageous for some patients, reducing the impact of the FAR on daily life. Future work will examine the performance and usability outside of EMUs.
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Affiliation(s)
| | | | | | - W Curt LaFrance
- Division of Neuropsychiatry and Behavioral Neurology, Rhode Island Hospital, Brown University, Providence, RI, United States
| | - Andrew S Blum
- Department of Neurology, Rhode Island Hospital, Brown University, Providence, RI, United States
| | | | - Paola De Liso
- Department of Neuroscience, Bambino Gesù Children's Hospital, Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy
| | - Rima El Atrache
- Department of Neurology, Boston Children's Hospital, Boston, MA, United States
| | - Tobias Loddenkemper
- Department of Neurology, Boston Children's Hospital, Boston, MA, United States
| | | | - Rani A Sarkis
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, United States
| | - Daniel Friedman
- Department of Neurology, New York University Langone Medical Center, New York, NY, United States
| | - Jay Jeschke
- Department of Neurology, New York University Langone Medical Center, New York, NY, United States
| | - Rosalind Picard
- Empatica, Inc., Boston, MA, United States.,MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA, United States
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