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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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Ji P, Wang Y, Peron T, Li C, Nagler J, Du J. Structure and function in artificial, zebrafish and human neural networks. Phys Life Rev 2023; 45:74-111. [PMID: 37182376 DOI: 10.1016/j.plrev.2023.04.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 04/20/2023] [Indexed: 05/16/2023]
Abstract
Network science provides a set of tools for the characterization of the structure and functional behavior of complex systems. Yet a major problem is to quantify how the structural domain is related to the dynamical one. In other words, how the diversity of dynamical states of a system can be predicted from the static network structure? Or the reverse problem: starting from a set of signals derived from experimental recordings, how can one discover the network connections or the causal relations behind the observed dynamics? Despite the advances achieved over the last two decades, many challenges remain concerning the study of the structure-dynamics interplay of complex systems. In neuroscience, progress is typically constrained by the low spatio-temporal resolution of experiments and by the lack of a universal inferring framework for empirical systems. To address these issues, applications of network science and artificial intelligence to neural data have been rapidly growing. In this article, we review important recent applications of methods from those fields to the study of the interplay between structure and functional dynamics of human and zebrafish brain. We cover the selection of topological features for the characterization of brain networks, inference of functional connections, dynamical modeling, and close with applications to both the human and zebrafish brain. This review is intended to neuroscientists who want to become acquainted with techniques from network science, as well as to researchers from the latter field who are interested in exploring novel application scenarios in neuroscience.
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Affiliation(s)
- Peng Ji
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Shanghai 200433, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
| | - Yufan Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, 320 Yue-Yang Road, Shanghai 200031, China
| | - Thomas Peron
- Institute of Mathematics and Computer Science, University of São Paulo, São Carlos 13566-590, São Paulo, Brazil.
| | - Chunhe Li
- Shanghai Center for Mathematical Sciences and School of Mathematical Sciences, Fudan University, Shanghai 200433, China; Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China.
| | - Jan Nagler
- Deep Dynamics, Frankfurt School of Finance & Management, Frankfurt, Germany; Centre for Human and Machine Intelligence, Frankfurt School of Finance & Management, Frankfurt, Germany
| | - Jiulin Du
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, 320 Yue-Yang Road, Shanghai 200031, China.
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Yang Y, Truong ND, Eshraghian JK, Nikpour A, Kavehei O. Weak self-supervised learning for seizure forecasting: a feasibility study. ROYAL SOCIETY OPEN SCIENCE 2022; 9:220374. [PMID: 35950196 PMCID: PMC9346358 DOI: 10.1098/rsos.220374] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 07/12/2022] [Indexed: 05/27/2023]
Abstract
This paper proposes an artificial intelligence system that continuously improves over time at event prediction using initially unlabelled data by using self-supervised learning. Time-series data are inherently autocorrelated. By using a detection model to generate weak labels on the fly, which are concurrently used as targets to train a prediction model on a time-shifted input data stream, this autocorrelation can effectively be harnessed to reduce the burden of manual labelling. This is critical in medical patient monitoring, as it enables the development of personalized forecasting models without demanding the annotation of long sequences of physiological signal recordings. We perform a feasibility study on seizure prediction, which is identified as an ideal test case, as pre-ictal brainwaves are patient-specific, and tailoring models to individual patients is known to improve forecasting performance significantly. Our self-supervised approach is used to train individualized forecasting models for 10 patients, showing an average relative improvement in sensitivity by 14.30% and a reduction in false alarms by 19.61% in early seizure forecasting. This proof-of-concept on the feasibility of using a continuous stream of time-series neurophysiological data paves the way towards a low-power neuromorphic neuromodulation system.
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Affiliation(s)
- Yikai Yang
- School of Biomedical Engineering, and the Australian Research Council Training Centre for Innovative BioEngineering, Faculty of EngineeringThe University of Sydney Nano Institute, Sydney, New South Wales 2006, Australia
| | - Nhan Duy Truong
- School of Biomedical Engineering, and the Australian Research Council Training Centre for Innovative BioEngineering, Faculty of EngineeringThe University of Sydney Nano Institute, Sydney, New South Wales 2006, Australia
- The University of Sydney Nano Institute, Sydney, New South Wales 2006, Australia
| | - Jason K. Eshraghian
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Armin Nikpour
- Faculty of Medicine and Health, Central Clinical School, The University of Sydney, Sydney, New South Wales 2006, Australia
- Comprehensive Epilepsy Service and Department of Neurology, Royal Prince Alfred Hospital, Camperdown, New South Wales 2050, Australia
| | - Omid Kavehei
- School of Biomedical Engineering, and the Australian Research Council Training Centre for Innovative BioEngineering, Faculty of EngineeringThe University of Sydney Nano Institute, Sydney, New South Wales 2006, Australia
- The University of Sydney Nano Institute, Sydney, New South Wales 2006, Australia
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Moss A, Moss E, Moss R, Moss L, Chiang S, Crino P. A Patient Perspective on Seizure Detection and Forecasting. Front Neurol 2022; 13:779551. [PMID: 35222243 PMCID: PMC8874203 DOI: 10.3389/fneur.2022.779551] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Accepted: 01/20/2022] [Indexed: 11/24/2022] Open
Affiliation(s)
- Aria Moss
- Northern Virginia Community College, Alexandria, VA, United States
- *Correspondence: Aria Moss
| | - Evan Moss
- W. T. Woodson High School, Fairfax, VA, United States
| | - Robert Moss
- Seizure Tracker, LLC, Springfield, VA, United States
| | - Lisa Moss
- Seizure Tracker, LLC, Springfield, VA, United States
- TSC Alliance, Silver Spring, MD, United States
| | - Sharon Chiang
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Peter Crino
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD, United States
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Ngarka L, Siewe Fodjo JN, Aly E, Masocha W, Njamnshi AK. The Interplay Between Neuroinfections, the Immune System and Neurological Disorders: A Focus on Africa. Front Immunol 2022; 12:803475. [PMID: 35095888 PMCID: PMC8792387 DOI: 10.3389/fimmu.2021.803475] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 12/13/2021] [Indexed: 12/31/2022] Open
Abstract
Neurological disorders related to neuroinfections are highly prevalent in Sub-Saharan Africa (SSA), constituting a major cause of disability and economic burden for patients and society. These include epilepsy, dementia, motor neuron diseases, headache disorders, sleep disorders, and peripheral neuropathy. The highest prevalence of human immunodeficiency virus (HIV) is in SSA. Consequently, there is a high prevalence of neurological disorders associated with HIV infection such as HIV-associated neurocognitive disorders, motor disorders, chronic headaches, and peripheral neuropathy in the region. The pathogenesis of these neurological disorders involves the direct role of the virus, some antiretroviral treatments, and the dysregulated immune system. Furthermore, the high prevalence of epilepsy in SSA (mainly due to perinatal causes) is exacerbated by infections such as toxoplasmosis, neurocysticercosis, onchocerciasis, malaria, bacterial meningitis, tuberculosis, and the immune reactions they elicit. Sleep disorders are another common problem in the region and have been associated with infectious diseases such as human African trypanosomiasis and HIV and involve the activation of the immune system. While most headache disorders are due to benign primary headaches, some secondary headaches are caused by infections (meningitis, encephalitis, brain abscess). HIV and neurosyphilis, both common in SSA, can trigger long-standing immune activation in the central nervous system (CNS) potentially resulting in dementia. Despite the progress achieved in preventing diseases from the poliovirus and retroviruses, these microbes may cause motor neuron diseases in SSA. The immune mechanisms involved in these neurological disorders include increased cytokine levels, immune cells infiltration into the CNS, and autoantibodies. This review focuses on the major neurological disorders relevant to Africa and neuroinfections highly prevalent in SSA, describes the interplay between neuroinfections, immune system, neuroinflammation, and neurological disorders, and how understanding this can be exploited for the development of novel diagnostics and therapeutics for improved patient care.
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Affiliation(s)
- Leonard Ngarka
- Brain Research Africa Initiative (BRAIN), Yaoundé, Cameroon
- Neuroscience Lab, Faculty of Medicine & Biomedical Sciences, The University of Yaoundé I, Yaoundé, Cameroon
- Department of Neurology, Yaoundé Central Hospital, Yaoundé, Cameroon
| | - Joseph Nelson Siewe Fodjo
- Brain Research Africa Initiative (BRAIN), Yaoundé, Cameroon
- Global Health Institute, University of Antwerp, Antwerp, Belgium
| | - Esraa Aly
- Department of Pharmacology and Therapeutics, Faculty of Pharmacy, Kuwait University, Safat, Kuwait
| | - Willias Masocha
- Department of Pharmacology and Therapeutics, Faculty of Pharmacy, Kuwait University, Safat, Kuwait
| | - Alfred K. Njamnshi
- Brain Research Africa Initiative (BRAIN), Yaoundé, Cameroon
- Neuroscience Lab, Faculty of Medicine & Biomedical Sciences, The University of Yaoundé I, Yaoundé, Cameroon
- Department of Neurology, Yaoundé Central Hospital, Yaoundé, Cameroon
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Truong ND, Yang Y, Maher C, Kuhlmann L, McEwan A, Nikpour A, Kavehei O. Seizure Susceptibility Prediction in Uncontrolled Epilepsy. Front Neurol 2021; 12:721491. [PMID: 34589049 PMCID: PMC8474878 DOI: 10.3389/fneur.2021.721491] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 07/28/2021] [Indexed: 12/01/2022] Open
Abstract
Epileptic seizure forecasting, combined with the delivery of preventative therapies, holds the potential to greatly improve the quality of life for epilepsy patients and their caregivers. Forecasting seizures could prevent some potentially catastrophic consequences such as injury and death in addition to several potential clinical benefits it may provide for patient care in hospitals. The challenge of seizure forecasting lies within the seemingly unpredictable transitions of brain dynamics into the ictal state. The main body of computational research on determining seizure risk has been focused solely on prediction algorithms, which involves a challenging issue of balancing sensitivity and false alarms. There have been some studies on identifying potential biomarkers for seizure forecasting; however, the questions of “What are the true biomarkers for seizure prediction” or even “Is there a valid biomarker for seizure prediction?” are yet to be fully answered. In this paper, we introduce a tool to facilitate the exploration of the potential biomarkers. We confirm using our tool that interictal slowing activities are a promising biomarker for epileptic seizure susceptibility prediction.
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Affiliation(s)
- Nhan Duy Truong
- Australian Research Council Training Centre for Innovative BioEngineering, School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, NSW, Australia.,The University of Sydney Nano Institute, Sydney, NSW, Australia
| | - Yikai Yang
- Australian Research Council Training Centre for Innovative BioEngineering, School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, NSW, Australia
| | - Christina Maher
- Australian Research Council Training Centre for Innovative BioEngineering, School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, NSW, Australia
| | - Levin Kuhlmann
- Faculty of Information Technology, Monash University, Melbourne, VIC, Australia.,Department of Medicine - St. Vincent's Hospital Melbourne, The University of Melbourne, Fitzroy, VIC, Australia
| | - Alistair McEwan
- Australian Research Council Training Centre for Innovative BioEngineering, School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, NSW, Australia
| | - Armin Nikpour
- Comprehensive Epilepsy Service and Department of Neurology at the Royal Prince Alfred Hospital, Sydney, NSW, Australia.,Faculty of Medicine and Health, Central Clinical School, The University of Sydney, Sydney, NSW, Australia
| | - Omid Kavehei
- Australian Research Council Training Centre for Innovative BioEngineering, School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, NSW, Australia.,The University of Sydney Nano Institute, Sydney, NSW, Australia
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Turco F, Giorgi FS, Maestri M, Morganti R, Benedetto A, Milano C, Pizzanelli C, Menicucci D, Gemignani A, Fornai F, Siciliano G, Bonanni E. Prolonged and short epileptiform discharges have an opposite relationship with the sleep-wake cycle in patients with JME: Implications for EEG recording protocols. Epilepsy Behav 2021; 122:108226. [PMID: 34352666 DOI: 10.1016/j.yebeh.2021.108226] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 07/15/2021] [Accepted: 07/16/2021] [Indexed: 12/12/2022]
Abstract
In a recent study, we found that during 20.55 ± 1.60 h of artifact-free ambulatory EEG recordings, epileptiform discharges (EDs) longer than 2.68 s occurred exclusively in patients with Juvenile Myoclonic Epilepsy (JME) who experienced seizure recurrence within a year after the EEG. Here we expanded this analysis, exploring whether long EDs (>2.68 s), and short ones, were uniformly distributed during the day. Lastly, we evaluated the temporal distribution of seizure relapses. By Friedman test, we demonstrated that hourly frequencies of both short and long EDs were dependent on the hours of day and sleep-wake cycle factors, with an opposite trend. Short EDs were found mostly during the night (with two peaks at 1 AM and 6 AM), and sleep, dropping at the wake onset (p < 0.001). Conversely, long EDs surged at the wake onset (0.001), remaining frequent during the whole wake period, when compared to sleep (p = 0.002). Of note, this latter pattern mirrored that of seizures, which occurred exclusively during the wake period, and in 9 out of 13 cases at the wake onset. We therefore suggested that short and long EDs could reflect distinct pathophysiological phenomena. Extended wake EEG recordings, possibly including the awakening, could be extremely useful in clinical practice, as well as in further studies, with the ambitious goal of predicting seizure recurrences.
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Affiliation(s)
- Francesco Turco
- Department of Clinical and Experimental Medicine, Neurological Clinic, University of Pisa, Italy; Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Pisa, Italy.
| | - Filippo Sean Giorgi
- Department of Clinical and Experimental Medicine, Neurological Clinic, University of Pisa, Italy; Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Pisa, Italy
| | - Michelangelo Maestri
- Department of Clinical and Experimental Medicine, Neurological Clinic, University of Pisa, Italy
| | | | - Alessandro Benedetto
- Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Pisa, Italy
| | - Chiara Milano
- Department of Clinical and Experimental Medicine, Neurological Clinic, University of Pisa, Italy
| | - Chiara Pizzanelli
- Department of Clinical and Experimental Medicine, Neurological Clinic, University of Pisa, Italy
| | - Danilo Menicucci
- Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, Italy
| | - Angelo Gemignani
- Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, Italy
| | - Francesco Fornai
- Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Pisa, Italy; IRCCS Neuromed, Pozzilli, Italy
| | - Gabriele Siciliano
- Department of Clinical and Experimental Medicine, Neurological Clinic, University of Pisa, Italy; Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Pisa, Italy
| | - Enrica Bonanni
- Department of Clinical and Experimental Medicine, Neurological Clinic, University of Pisa, Italy; Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Pisa, Italy
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