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Behbahani S, Jafarnia Dabanloo N, Nasrabadi AM, Dourado A. Epileptic seizure prediction based on features extracted from lagged Poincaré plots. Int J Neurosci 2024; 134:381-397. [PMID: 35892226 DOI: 10.1080/00207454.2022.2106435] [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: 06/24/2022] [Accepted: 07/14/2022] [Indexed: 10/16/2022]
Abstract
OBJECTIVE The present work proposes a new epileptic seizure prediction method based on lagged Poincaré plot analysis of heart rate (HR). METHODS In this article, the Poincaré plots with six different lags (1-6) were constructed for four episodes of heart rate variability (HRV) before the seizures. Moreover, two features were extracted based on lagged Poincare plots, which include the angle between the time series and the ellipse density fitted to the RR points. RESULTS The proposed method was applied to 16 epileptic patients with 170 seizures. The results included sensitivity of 80.42% for the angle feature and 75.19% for the density feature. The false-positive rate was 0.15/Hr, which indicates that the system has superiority over the random predictor. CONCLUSION The proposed HRV-based epileptic seizure prediction method has the potential to be used in daily life because HR can be measured easily by using a wearable sensor.
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Affiliation(s)
- Soroor Behbahani
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
| | - Nader Jafarnia Dabanloo
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | | | - Antonio Dourado
- Center for Informatics and Systems (CISUC), Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
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2
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Buchhalter J, Neuray C, Cheng JY, D’Cruz O, Datta AN, Dlugos D, French J, Haubenberger D, Hulihan J, Klein P, Komorowski RW, Kramer L, Lothe A, Nabbout R, Perucca E, der Ark PV. EEG Parameters as Endpoints in Epilepsy Clinical Trials- An Expert Panel Opinion Paper. Epilepsy Res 2022; 187:107028. [DOI: 10.1016/j.eplepsyres.2022.107028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 08/29/2022] [Accepted: 09/26/2022] [Indexed: 11/30/2022]
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Bongers J, Gutierrez-Quintana R, Stalin CE. The Prospects of Non-EEG Seizure Detection Devices in Dogs. Front Vet Sci 2022; 9:896030. [PMID: 35677934 PMCID: PMC9168902 DOI: 10.3389/fvets.2022.896030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 04/28/2022] [Indexed: 11/16/2022] Open
Abstract
The unpredictable nature of seizures is challenging for caregivers of epileptic dogs, which calls the need for other management strategies such as seizure detection devices. Seizure detection devices are systems that rely on non-electroencephalographic (non-EEG) ictal changes, designed to detect seizures. The aim for its use in dogs would be to provide owners with a more complete history of their dog's seizures and to help install prompt (and potentially life-saving) intervention. Although seizure detection via wearable intracranial EEG recordings is associated with a higher sensitivity in humans, there is robust evidence for reliable detection of generalized tonic-clonic seizures (GTCS) using non-EEG devices. Promising non-EEG changes described in epileptic humans, include heart rate variability (HRV), accelerometry (ACM), electrodermal activity (EDA), and electromyography (EMG). Their sensitivity and false detection rate to detect seizures vary, however direct comparison of studies is nearly impossible, as there are many differences in study design and standards for testing. A way to improve sensitivity and decrease false-positive alarms is to combine the different parameters thereby profiting from the strengths of each one. Given the challenges of using EEG in veterinary clinical practice, non-EEG ictal changes could be a promising alternative to monitor seizures more objectively. This review summarizes various seizure detection devices described in the human literature, discusses their potential use and limitations in veterinary medicine and describes what is currently known in the veterinary literature.
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Affiliation(s)
| | | | - Catherine Elizabeth Stalin
- Neurology and Neurosurgery Service, The School of Veterinary Medicine, College of Medicine, Veterinary Medicine and Life Sciences, University of Glasgow, Glasgow, United Kingdom
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4
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Nagata S, Fujiwara K, Kuga K, Ozaki H. Prediction of GABA receptor antagonist-induced convulsion in cynomolgus monkeys by combining machine learning and heart rate variability analysis. J Pharmacol Toxicol Methods 2021; 112:107127. [PMID: 34619314 DOI: 10.1016/j.vascn.2021.107127] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 09/30/2021] [Accepted: 09/30/2021] [Indexed: 12/19/2022]
Abstract
Drug-induced convulsion is a severe adverse event; however, no useful biomarkers for it have been discovered. We propose a new method for predicting drug-induced convulsions in monkeys based on heart rate variability (HRV) and a machine learning technique. Because autonomic nervous activities are altered around the time of a convulsion and such alterations affect HRV, they may be predicted by monitoring HRV. In the proposed method, anomalous changes in multiple HRV parameters are monitored by means of a convulsion prediction model, and convulsion alarms are issued when abnormal changes in HRV are detected. The convulsion prediction model is constructed based on multivariate statistical process control (MSPC), a well-known anomaly detection algorithm in machine learning. In this study, HRV data were collected from four cynomolgus monkeys administered with multiple doses of pentylenetetrazol (PTZ) and picrotoxin (PTX), which are GABA receptor antagonists, as convulsant agents. In addition, low doses of pilocarpine (PILO) were administered as a negative control. Twelve HRV parameters in three hours after drug administration were monitored by means of the prediction model. The number and duration of convulsion alarms from HRV increased at medium and high doses of PTZ and PTX (1/3 or 1/4 of convulsion dose). On the other hand, the frequency of convulsion alarms did not increase with PILO. Although vomiting was observed in all drugs, convulsion alarms were not associated with the vomiting. Thus, convulsion alarms can be used as a biomarker for convulsions induced by GABA receptor antagonists.
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Affiliation(s)
- Shoya Nagata
- Department of Material Process Engineering, Nagoya University, Nagoya, Japan
| | - Koichi Fujiwara
- Department of Material Process Engineering, Nagoya University, Nagoya, Japan.
| | - Kazuhiro Kuga
- Drug Safety Research and Evaluation, Takeda Pharmaceutical Company Ltd., Kanagawa, Japan
| | - Harushige Ozaki
- Drug Safety Research and Evaluation, Takeda Pharmaceutical Company Ltd., Kanagawa, Japan
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5
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Payne DE, Dell KL, Karoly PJ, Kremen V, Gerla V, Kuhlmann L, Worrell GA, Cook MJ, Grayden DB, Freestone DR. Identifying seizure risk factors: A comparison of sleep, weather, and temporal features using a Bayesian forecast. Epilepsia 2020; 62:371-382. [PMID: 33377501 DOI: 10.1111/epi.16785] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 11/15/2020] [Accepted: 11/16/2020] [Indexed: 11/30/2022]
Abstract
OBJECTIVE Most seizure forecasting algorithms have relied on features specific to electroencephalographic recordings. Environmental and physiological factors, such as weather and sleep, have long been suspected to affect brain activity and seizure occurrence but have not been fully explored as prior information for seizure forecasts in a patient-specific analysis. The study aimed to quantify whether sleep, weather, and temporal factors (time of day, day of week, and lunar phase) can provide predictive prior probabilities that may be used to improve seizure forecasts. METHODS This study performed post hoc analysis on data from eight patients with a total of 12.2 years of continuous intracranial electroencephalographic recordings (average = 1.5 years, range = 1.0-2.1 years), originally collected in a prospective trial. Patients also had sleep scoring and location-specific weather data. Histograms of future seizure likelihood were generated for each feature. The predictive utility of individual features was measured using a Bayesian approach to combine different features into an overall forecast of seizure likelihood. Performance of different feature combinations was compared using the area under the receiver operating curve. Performance evaluation was pseudoprospective. RESULTS For the eight patients studied, seizures could be predicted above chance accuracy using sleep (five patients), weather (two patients), and temporal features (six patients). Forecasts using combined features performed significantly better than chance in six patients. For four of these patients, combined forecasts outperformed any individual feature. SIGNIFICANCE Environmental and physiological data, including sleep, weather, and temporal features, provide significant predictive information on upcoming seizures. Although forecasts did not perform as well as algorithms that use invasive intracranial electroencephalography, the results were significantly above chance. Complementary signal features derived from an individual's historic seizure records may provide useful prior information to augment traditional seizure detection or forecasting algorithms. Importantly, many predictive features used in this study can be measured noninvasively.
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Affiliation(s)
- Daniel E Payne
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia.,Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Victoria, Australia
| | - Katrina L Dell
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Victoria, Australia
| | - Phillipa J Karoly
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia.,Graeme Clark Institute, The University of Melbourne, Melbourne, Victoria, Australia
| | - Vaclav Kremen
- Department of Neurology, Mayo Clinic, Rochester, MN, USA.,Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic
| | - Vaclav Gerla
- Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic
| | - Levin Kuhlmann
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Victoria, Australia.,Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia
| | | | - Mark J Cook
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Victoria, Australia.,Graeme Clark Institute, The University of Melbourne, Melbourne, Victoria, Australia
| | - David B Grayden
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia.,Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Victoria, Australia
| | - Dean R Freestone
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Victoria, Australia
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6
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Meisel C, El Atrache R, Jackson M, Schubach S, Ufongene C, Loddenkemper T. Machine learning from wristband sensor data for wearable, noninvasive seizure forecasting. Epilepsia 2020; 61:2653-2666. [PMID: 33040327 DOI: 10.1111/epi.16719] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 09/17/2020] [Accepted: 09/17/2020] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Seizure forecasting may provide patients with timely warnings to adapt their daily activities and help clinicians deliver more objective, personalized treatments. Although recent work has convincingly demonstrated that seizure risk assessment is in principle possible, these early approaches relied largely on complex, often invasive setups including intracranial electrocorticography, implanted devices, and multichannel electroencephalography, and required patient-specific adaptation or learning to perform optimally, all of which limit translation to broad clinical application. To facilitate broader adaptation of seizure forecasting in clinical practice, noninvasive, easily applicable techniques that reliably assess seizure risk without much prior tuning are crucial. Wristbands that continuously record physiological parameters, including electrodermal activity, body temperature, blood volume pulse, and actigraphy, may afford monitoring of autonomous nervous system function and movement relevant for such a task, hence minimizing potential complications associated with invasive monitoring and avoiding stigma associated with bulky external monitoring devices on the head. METHODS Here, we applied deep learning on multimodal wristband sensor data from 69 patients with epilepsy (total duration > 2311 hours, 452 seizures) to assess its capability to forecast seizures in a statistically significant way. RESULTS Using a leave-one-subject-out cross-validation approach, we identified better-than-chance predictability in 43% of the patients. Time-matched seizure surrogate data analyses indicated forecasting not to be driven simply by time of day or vigilance state. Prediction performance peaked when all sensor modalities were used, and did not differ between generalized and focal seizure types, but generally increased with the size of the training dataset, indicating potential further improvement with larger datasets in the future. SIGNIFICANCE Collectively, these results show that statistically significant seizure risk assessments are feasible from easy-to-use, noninvasive wearable devices without the need of patient-specific training or parameter optimization.
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Affiliation(s)
- Christian Meisel
- Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Berlin Institute of Health, Berlin, Germany.,Boston Children's Hospital, Boston, MA, USA
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7
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Stirling RE, Cook MJ, Grayden DB, Karoly PJ. Seizure forecasting and cyclic control of seizures. Epilepsia 2020; 62 Suppl 1:S2-S14. [PMID: 32712968 DOI: 10.1111/epi.16541] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 04/23/2020] [Accepted: 04/27/2020] [Indexed: 02/02/2023]
Abstract
Epilepsy is a unique neurologic condition characterized by recurrent seizures, where causes, underlying biomarkers, triggers, and patterns differ across individuals. The unpredictability of seizures can heighten fear and anxiety in people with epilepsy, making it difficult to take part in day-to-day activities. Epilepsy researchers have prioritized developing seizure prediction algorithms to combat episodic seizures for decades, but the utility and effectiveness of prediction algorithms has not been investigated thoroughly in clinical settings. In contrast, seizure forecasts, which theoretically provide the probability of a seizure at any time (as opposed to predicting the next seizure occurrence), may be more feasible. Many advances have been made over the past decade in the field of seizure forecasting, including improvements in algorithms as a result of machine learning and exploration of non-EEG-based measures of seizure susceptibility, such as physiological biomarkers, behavioral changes, environmental drivers, and cyclic seizure patterns. For example, recent work investigating periodicities in individual seizure patterns has determined that more than 90% of people have circadian rhythms in their seizures, and many also experience multiday, weekly, or longer cycles. Other potential indicators of seizure susceptibility include stress levels, heart rate, and sleep quality, all of which have the potential to be captured noninvasively over long time scales. There are many possible applications of a seizure-forecasting device, including improving quality of life for people with epilepsy, guiding treatment plans and medication titration, optimizing presurgical monitoring, and focusing scientific research. To realize this potential, it is vital to better understand the user requirements of a seizure-forecasting device, continue to advance forecasting algorithms, and design clear guidelines for prospective clinical trials of seizure forecasting.
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Affiliation(s)
- Rachel E Stirling
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Vic., Australia
| | - Mark J Cook
- Graeme Clark Institute & St Vincent's Hospital, The University of Melbourne, Melbourne, Vic., Australia
| | - David B Grayden
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Vic., Australia
| | - Philippa J Karoly
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Vic., Australia.,Graeme Clark Institute & St Vincent's Hospital, The University of Melbourne, Melbourne, Vic., Australia
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8
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Yamakawa T, Miyajima M, Fujiwara K, Kano M, Suzuki Y, Watanabe Y, Watanabe S, Hoshida T, Inaji M, Maehara T. Wearable Epileptic Seizure Prediction System with Machine-Learning-Based Anomaly Detection of Heart Rate Variability. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3987. [PMID: 32709064 PMCID: PMC7411877 DOI: 10.3390/s20143987] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 06/17/2020] [Accepted: 07/13/2020] [Indexed: 02/01/2023]
Abstract
A warning prior to seizure onset can help improve the quality of life for epilepsy patients. The feasibility of a wearable system for predicting epileptic seizures using anomaly detection based on machine learning is evaluated. An original telemeter is developed for continuous measurement of R-R intervals derived from an electrocardiogram. A bespoke smartphone app calculates the indices of heart rate variability in real time from the R-R intervals, and the indices are monitored using multivariate statistical process control by the smartphone app. The proposed system was evaluated on seven epilepsy patients. The accuracy and reliability of the R-R interval measurement, which was examined in comparison with the reference electrocardiogram, showed sufficient performance for heart rate variability analysis. The results obtained using the proposed system were compared with those obtained using the existing video and electroencephalogram assessments; it was noted that the proposed method has a sensitivity of 85.7% in detecting heart rate variability change prior to seizures. The false positive rate of 0.62 times/h was not significantly different from the healthy controls. The prediction performance and practical advantages of portability and real-time operation are demonstrated in this study.
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Affiliation(s)
- Toshitaka Yamakawa
- Division of Informatics and Energy, Faculty of Advanced Science and Technology, Kumamoto University, Kumamoto 860-8555, Japan
- Fuzzy Logic Systems Institute, Iizuka 820-0067, Japan
| | - Miho Miyajima
- Section of Liaison Psychiatry and Palliative Medicine, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (M.M.); (Y.S.)
| | - Koichi Fujiwara
- Graduate School of Engineering, Nagoya University, Nagoya 464-8603, Japan;
| | - Manabu Kano
- Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan;
| | - Yoko Suzuki
- Section of Liaison Psychiatry and Palliative Medicine, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (M.M.); (Y.S.)
| | | | - Satsuki Watanabe
- Department of Psychiatry, National Center Hospital of Neurology and Psychiatry, Kodaira 187-8553, Japan;
- Department of Psychiatry, Saitama Medical University Hospital, Saitama 350-0495, Japan
| | - Tohru Hoshida
- National Hospital Organization Nara Medical Center, Nara 619-1124, Japan;
| | - Motoki Inaji
- Department of Neurosurgery, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (M.I.); (T.M.)
| | - Taketoshi Maehara
- Department of Neurosurgery, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (M.I.); (T.M.)
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9
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Ufongene C, El Atrache R, Loddenkemper T, Meisel C. Electrocardiographic changes associated with epilepsy beyond heart rate and their utilization in future seizure detection and forecasting methods. Clin Neurophysiol 2020; 131:866-879. [PMID: 32066106 DOI: 10.1016/j.clinph.2020.01.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 01/21/2020] [Accepted: 01/24/2020] [Indexed: 12/22/2022]
Abstract
The ability to assess seizure risk may help provide timely warnings and more personalized treatment plans for people with epilepsy (PWE). ECG changes are commonly observed in epilepsy which make ECG a promising candidate to monitor seizure risk. Most ECG research in this domain has focused on heart rate-related changes. However, several studies have identified a range of other peri-ictal ECG parameter changes that may potentially prove useful for seizure detection and forecasting. Here, we offer a systematic review of ECG changes in epilepsy outside of heart rate. We performed the systematic literature review according to PRISMA guidelines using key words related to ECG, SUDEP and epilepsy. We identified and screened 502 abstracts, read 110 full papers, and included 24 papers in the final review. Our results suggest that PWE may be more prone to cardiac conduction abnormalities than healthy controls. During interictal periods, PWE were more likely to have abnormal QTc intervals, ST segment abnormalities, elevated T Waves, early repolarization (ER), increased P Wave dispersion and PR intervals when compared to controls. Apart from these baseline abnormalities, changes during the pre-ictal and ictal states have been reported, with arrhythmias, QTc prolongation and ST segment changes being the most common. A better understanding of these state-dependent changes may afford less-cumbersome and less-stigmatizing epilepsy monitoring tools in the future.
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10
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Ciarlone GE, Hinojo CM, Stavitzski NM, Dean JB. CNS function and dysfunction during exposure to hyperbaric oxygen in operational and clinical settings. Redox Biol 2019; 27:101159. [PMID: 30902504 PMCID: PMC6859559 DOI: 10.1016/j.redox.2019.101159] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Revised: 02/20/2019] [Accepted: 03/01/2019] [Indexed: 12/26/2022] Open
Abstract
Hyperbaric oxygen (HBO2) is breathed during hyperbaric oxygen therapy and during certain undersea pursuits in diving and submarine operations. What limits exposure to HBO2 in these situations is the acute onset of central nervous system oxygen toxicity (CNS-OT) following a latent period of safe oxygen breathing. CNS-OT presents as various non-convulsive signs and symptoms, many of which appear to be of brainstem origin involving cranial nerve nuclei and autonomic and cardiorespiratory centers, which ultimately spread to higher cortical centers and terminate as generalized tonic-clonic seizures. The initial safe latent period makes the use of HBO2 practical in hyperbaric and undersea medicine; however, the latent period is highly variable between individuals and within the same individual on different days, making it difficult to predict onset of toxic indications. Consequently, currently accepted guidelines for safe HBO2 exposure are highly conservative. This review examines the disorder of CNS-OT and summarizes current ideas on its underlying pathophysiology, including specific areas of the CNS and fundamental neural and redox signaling mechanisms that are thought to be involved in seizure genesis and propagation. In addition, conditions that accelerate the onset of seizures are discussed, as are current mitigation strategies under investigation for neuroprotection against redox stress while breathing HBO2 that extend the latent period, thus enabling safer and longer exposures for diving and medical therapies.
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Affiliation(s)
- Geoffrey E Ciarlone
- Undersea Medicine Department, Naval Medical Research Center, 503 Robert Grant Ave., Silver Spring, MD, USA
| | - Christopher M Hinojo
- Department of Molecular Pharmacology and Physiology, Hyperbaric Biomedical Research Laboratory, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
| | - Nicole M Stavitzski
- Department of Molecular Pharmacology and Physiology, Hyperbaric Biomedical Research Laboratory, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
| | - Jay B Dean
- Department of Molecular Pharmacology and Physiology, Hyperbaric Biomedical Research Laboratory, Morsani College of Medicine, University of South Florida, Tampa, FL, USA.
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Identifying signal-dependent information about the preictal state: A comparison across ECoG, EEG and EKG using deep learning. EBioMedicine 2019; 45:422-431. [PMID: 31300348 PMCID: PMC6642360 DOI: 10.1016/j.ebiom.2019.07.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 06/08/2019] [Accepted: 07/01/2019] [Indexed: 01/17/2023] Open
Abstract
Background The inability to reliably assess seizure risk is a major burden for epilepsy patients and prevents developing better treatments. Recent advances have paved the way for increasingly accurate seizure preictal state detection algorithms, primarily using electrocorticography (ECoG). To develop seizure forecasting for broad clinical and ambulatory use, however, less complex and invasive modalities are needed. Algorithms using scalp electroencephalography (EEG) and electrocardiography (EKG) have also achieved better than chance performance. But it remains unknown how much preictal information is in ECoG versus modalities amenable to everyday use – such as EKG and single channel EEG - and how to optimally extract that preictal information for seizure prediction. Methods We apply deep learning - a powerful method to extract information from complex data - on a large epilepsy data set containing multi-day, simultaneous recordings of EKG, ECoG, and EEG, using a variety of feature sets. We use the relative performance of our algorithms to compare the preictal information contained in each modality. Results We find that single-channel EKG contains a comparable amount of preictal information as scalp EEG with up to 21 channels and that preictal information is best extracted not with standard heart rate measures, but from the power spectral density. We report that preictal information is not preferentially contained in EEG or ECoG channels within the seizure onset zone. Conclusion Collectively, these insights may help to devise future prospective, minimally invasive long-term epilepsy monitoring trials with single-channel EKG as a particularly promising modality.
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Amengual-Gual M, Ulate-Campos A, Loddenkemper T. Status epilepticus prevention, ambulatory monitoring, early seizure detection and prediction in at-risk patients. Seizure 2019; 68:31-37. [DOI: 10.1016/j.seizure.2018.09.013] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Revised: 08/16/2018] [Accepted: 09/15/2018] [Indexed: 02/08/2023] Open
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Abstract
PURPOSE It has been challenging to detect early changes preceding seizure onset in patients with epilepsy. This study investigated the preictal discharges (PIDs) by intracranial electroencephalogram of 11 seizures from 7 patients with mesial temporal lobe epilepsy. METHODS The EEG segments consisting of 30 seconds before ictal onset and 5 seconds after ictal onset were selected for analysis. After PID detection, the amplitude and interval were measured. According to the timing of PID onset, the 30-second period preceding seizure onset was divided into two stages: before PID stage and PID stage. The autocorrelation coefficients during the two stages were calculated and compared. RESULTS Preictal discharge amplitude progressively increased, while PID interval gradually decreased toward seizure onset. The autocorrelation coefficients of PID channels were significantly higher during PID stage than before PID stage. There was an overlap between channels with PIDs and seizure onset channels (80.77%). CONCLUSIONS Preictal discharges emerge prior to ictal event, with a dynamic change and a spatial correlation with seizure onset zone. These findings deepen our understanding of seizure generation and help early prediction and localization of seizure onset zone.
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Fujiwara K, Abe E, Kamata K, Nakayama C, Suzuki Y, Yamakawa T, Hiraoka T, Kano M, Sumi Y, Masuda F, Matsuo M, Kadotani H. Heart Rate Variability-Based Driver Drowsiness Detection and Its Validation With EEG. IEEE Trans Biomed Eng 2018; 66:1769-1778. [PMID: 30403616 DOI: 10.1109/tbme.2018.2879346] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Driver drowsiness detection is a key technology that can prevent fatal car accidents caused by drowsy driving. The present work proposes a driver drowsiness detection algorithm based on heart rate variability (HRV) analysis and validates the proposed method by comparing with electroencephalography (EEG)-based sleep scoring. METHODS Changes in sleep condition affect the autonomic nervous system and then HRV, which is defined as an RR interval (RRI) fluctuation on an electrocardiogram trace. Eight HRV features are monitored for detecting changes in HRV by using multivariate statistical process control, which is a well known anomaly detection method. RESULT The performance of the proposed algorithm was evaluated through an experiment using a driving simulator. In this experiment, RRI data were measured from 34 participants during driving, and their sleep onsets were determined based on the EEG data by a sleep specialist. The validation result of the experimental data with the EEG data showed that drowsiness was detected in 12 out of 13 pre-N1 episodes prior to the sleep onsets, and the false positive rate was 1.7 times per hour. CONCLUSION The present work also demonstrates the usefulness of the framework of HRV-based anomaly detection that was originally proposed for epileptic seizure prediction. SIGNIFICANCE The proposed method can contribute to preventing accidents caused by drowsy driving.
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15
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Billeci L, Marino D, Insana L, Vatti G, Varanini M. Patient-specific seizure prediction based on heart rate variability and recurrence quantification analysis. PLoS One 2018; 13:e0204339. [PMID: 30252915 PMCID: PMC6155519 DOI: 10.1371/journal.pone.0204339] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Accepted: 09/05/2018] [Indexed: 12/24/2022] Open
Abstract
Epilepsy is often associated with modifications in autonomic nervous system, which usually precede the onset of seizures of several minutes. Thus, there is a great interest in identifying these modifications enough time in advance to prevent a dangerous effect and to intervene. In addition, these changes can be a risk factor for epileptic patients and can increase the possibility of death. Notably autonomic changes associated to seizures are highly depended of seizure type, localization and lateralization. The aim of this study was to develop a patient-specific approach to predict seizures using electrocardiogram (ECG) features. Specifically, from the RR series, both time and frequency variables and features obtained by the recurrence quantification analysis were used. The algorithm was applied in a dataset of 15 patients with 38 different types of seizures. A feature selection step, was used to identify those features that were more significant in discriminating preictal and interictal phases. A preictal interval of 15 minutes was selected. A support vector machine (SVM) classifier was then built to classify preictal and interictal phases. First, a classifier was set up to classify preictal and interictal segments of each patient and an average sensibility of 89.06% was obtained, with a number of false positive per hour (FP/h) of 0.41. Then, in those patients who had at least 3 seizures, a double-cross-validation approach was used to predict unseen seizures on the basis of a training on previous ones. The results were quite variable according to seizure type, achieving the best performance in patients with more stereotypical seizure. The results of the proposed approach show that it is feasible to predict seizure in advance, considering patient-specific, and possible seizure specific, characteristics.
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Affiliation(s)
- Lucia Billeci
- Institute of Clinical Physiology, National Research Council of Italy (CNR), Pisa, Italy
- * E-mail:
| | - Daniela Marino
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Laura Insana
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Giampaolo Vatti
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Maurizio Varanini
- Institute of Clinical Physiology, National Research Council of Italy (CNR), Pisa, Italy
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Fan X, Gaspard N, Legros B, Lucchetti F, Ercek R, Nonclercq A. Seizure evolution can be characterized as path through synaptic gain space of a neural mass model. Eur J Neurosci 2018; 48:3097-3112. [PMID: 30194874 DOI: 10.1111/ejn.14142] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 08/08/2018] [Accepted: 08/29/2018] [Indexed: 11/30/2022]
Abstract
Physiologically based models could facilitate better understanding of mechanisms underlying epileptic seizures. In this paper, we attempt to reveal the dynamic evolution of intracranial EEG activity during epileptic seizures based on synaptic gain identification procedure of a neural mass model. The distribution of average excitatory, slow and fast inhibitory synaptic gain in the parameter space and their temporal evolution, i.e., the path through the model parameter space, were analyzed in thirty seizures from ten temporal lobe epileptic patients. Results showed that the synaptic gain values located roughly on a plane before seizure onset, dispersed during seizure and returned to the plane when seizure terminated. Cluster analysis was performed on seizure paths and demonstrated consistency in synaptic gain evolution across different seizures from the individual patient. Furthermore, two patient groups were identified, each one corresponding to a specific synaptic gain evolution in the parameter space during a seizure. Results were validated by a bootstrapping approach based on comparison with random paths. The differences in the path revealed variations in EEG dynamics for patients despite showing identical seizure onset pattern. Our approach may have the potential to classify the epileptic patients into subgroups based on different mechanisms revealed by subtle changes in synaptic gains and further enable more robust decisions regarding treatment strategy.
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Affiliation(s)
- Xiaoya Fan
- Bio, Electro And Mechanical Systems (BEAMS), Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Nicolas Gaspard
- Department of Neurology, Hôpital Erasme, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Benjamin Legros
- Department of Neurology, Hôpital Erasme, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Federico Lucchetti
- Bio, Electro And Mechanical Systems (BEAMS), Université Libre de Bruxelles (ULB), Brussels, Belgium.,Laboratoire de Neurophysiologie Sensorielle et Cognitive, Hôpital Brugmann, Brussels, Belgium
| | - Rudy Ercek
- Laboratories of Image, Signal Processing and Acoustics (LISA), Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Antoine Nonclercq
- Bio, Electro And Mechanical Systems (BEAMS), Université Libre de Bruxelles (ULB), Brussels, Belgium
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Baumgartner C, Koren JP, Rothmayer M. Automatic Computer-Based Detection of Epileptic Seizures. Front Neurol 2018; 9:639. [PMID: 30140254 PMCID: PMC6095028 DOI: 10.3389/fneur.2018.00639] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Accepted: 07/17/2018] [Indexed: 11/28/2022] Open
Abstract
Automatic computer-based seizure detection and warning devices are important for objective seizure documentation, for SUDEP prevention, to avoid seizure related injuries and social embarrassments as a consequence of seizures, and to develop on demand epilepsy therapies. Automatic seizure detection systems can be based on direct analysis of epileptiform discharges on scalp-EEG or intracranial EEG, on the detection of motor manifestations of epileptic seizures using surface electromyography (sEMG), accelerometry (ACM), video detection systems and mattress sensors and finally on the assessment of changes of physiologic parameters accompanying epileptic seizures measured by electrocardiography (ECG), respiratory monitors, pulse oximetry, surface temperature sensors, and electrodermal activity. Here we review automatic seizure detection based on scalp-EEG, ECG, and sEMG. Different seizure types affect preferentially different measurement parameters. While EEG changes accompany all types of seizures, sEMG and ACM are suitable mainly for detection of seizures with major motor manifestations. Therefore, seizure detection can be optimized by multimodal systems combining several measurement parameters. While most systems provide sensitivities over 70%, specificity expressed as false alarm rates still needs to be improved. Patients' acceptance and comfort of a specific device are of critical importance for its long-term application in a meaningful clinical way.
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Affiliation(s)
- Christoph Baumgartner
- Department of Neurology, General Hospital Hietzing with Neurological Center Rosenhügel, Vienna, Austria.,Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology, Vienna, Austria.,Medical Faculty, Sigmund Freud University, Vienna, Austria
| | - Johannes P Koren
- Department of Neurology, General Hospital Hietzing with Neurological Center Rosenhügel, Vienna, Austria.,Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology, Vienna, Austria
| | - Michaela Rothmayer
- Department of Neurology, General Hospital Hietzing with Neurological Center Rosenhügel, Vienna, Austria
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Vorkapić M, Useinović N, Janković M, Hrnčić D. Heart rate variability processing in epilepsy: The role in detection and prediction of seizures and SUDEP. MEDICINSKI PODMLADAK 2018. [DOI: 10.5937/mp69-18553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022] Open
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Epileptic Seizure Prediction Using CSP and LDA for Scalp EEG Signals. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2017; 2017:1240323. [PMID: 29225615 PMCID: PMC5684608 DOI: 10.1155/2017/1240323] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Revised: 08/15/2017] [Accepted: 10/04/2017] [Indexed: 11/17/2022]
Abstract
This paper presents a patient-specific epileptic seizure predication method relying on the common spatial pattern- (CSP-) based feature extraction of scalp electroencephalogram (sEEG) signals. Multichannel EEG signals are traced and segmented into overlapping segments for both preictal and interictal intervals. The features extracted using CSP are used for training a linear discriminant analysis classifier, which is then employed in the testing phase. A leave-one-out cross-validation strategy is adopted in the experiments. The experimental results for seizure prediction obtained from the records of 24 patients from the CHB-MIT database reveal that the proposed predictor can achieve an average sensitivity of 0.89, an average false prediction rate of 0.39, and an average prediction time of 68.71 minutes using a 120-minute prediction horizon.
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Behbahani S, Dabanloo NJ, Nasrabadi AM, Dourado A. Prediction of epileptic seizures based on heart rate variability. Technol Health Care 2017; 24:795-810. [PMID: 27315150 DOI: 10.3233/thc-161225] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Until now, different approaches have been published to resolve the problem of predicting epileptic seizures. The results are reminiscent of a substantial need for improvements in these methods to reach the stage of the clinical application. Our aim is to develop a reliable epileptic seizure prediction algorithm based on the Heart Rate Variability (HRV) analysis. METHODS We analyzed the HRV of sixteen epileptic patients with a total of 170 seizures, to predict the occurrence of seizures based on the dynamic changes of Electrocardiogram (ECG) during the pre-ictal period. Time and frequency-domain features were computed forthe consecutive time windows with a length of five minutes. An adaptive decision threshold method was used for raising alarms. Predictions were made when selected features exceeded the decision thresholds. RESULTS For the seizure occurrence period (SOP) of 4:30 minutes, and intervention time (IT) of 110 Sec, the presented method showed an average sensitivity of 78.59%, and average false prediction rate of 0.21/Hr, which indicates that the system has superiority to the random predictor. CONCLUSION The proposed approach shows a potential in the monitoring of epileptic patients and improving their life quality. The overall performance of the algorithm is a step forward for clinical implementation.
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Affiliation(s)
- Soroor Behbahani
- Department of Electrical Engineering, Islamic Azad University, South Tehran Branch, Iran
| | - Nader Jafarnia Dabanloo
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | | | - Antonio Dourado
- Center for Informatics and Systems (CISUC), Department of Informatics Engineering, University of Coimbra, Portugal
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Ulate-Campos A, Coughlin F, Gaínza-Lein M, Fernández IS, Pearl P, Loddenkemper T. Automated seizure detection systems and their effectiveness for each type of seizure. Seizure 2016; 40:88-101. [DOI: 10.1016/j.seizure.2016.06.008] [Citation(s) in RCA: 134] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2016] [Revised: 05/23/2016] [Accepted: 06/07/2016] [Indexed: 01/08/2023] Open
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Boon P, Vonck K, van Rijckevorsel K, Tahry RE, Elger CE, Mullatti N, Schulze-Bonhage A, Wagner L, Diehl B, Hamer H, Reuber M, Kostov H, Legros B, Noachtar S, Weber YG, Coenen VA, Rooijakkers H, Schijns OE, Selway R, Van Roost D, Eggleston KS, Van Grunderbeek W, Jayewardene AK, McGuire RM. A prospective, multicenter study of cardiac-based seizure detection to activate vagus nerve stimulation. Seizure 2015; 32:52-61. [DOI: 10.1016/j.seizure.2015.08.011] [Citation(s) in RCA: 101] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2015] [Revised: 08/09/2015] [Accepted: 08/13/2015] [Indexed: 10/23/2022] Open
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Jeppesen J, Beniczky S, Johansen P, Sidenius P, Fuglsang-Frederiksen A. Using Lorenz plot and Cardiac Sympathetic Index of heart rate variability for detecting seizures for patients with epilepsy. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2014:4563-6. [PMID: 25571007 DOI: 10.1109/embc.2014.6944639] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Tachycardia is often seen during epileptic seizures, but it also occurs during physical exercise. In order to assess whether focal epileptic seizures can be detected by short term moving window Heart Rate Variability (HRV) analysis, we modified the geometric HRV method, Lorenz plot, to consist of only 30, 50 or 100 R-R intervals per analyzed window. From each window we calculated the longitudinal (L) and transverse (T) variability of Lorenz plot to retrieve the Cardiac Sympathetic Index (CSI) as (L/T) and "Modified CSI" (described in methods), and compared the maximum during the patient's epileptic seizures with that during the patient's own exercise and non-seizure sessions as control. All five analyzed patients had complex partial seizures (CPS) originating in the temporal lobe (11 seizures) during their 1-5 days long term video-EEG monitoring. All CPS with electroencephalographic correlation were selected for the HRV analysis. The CSI and Modified CSI were correspondently calculated after each heart beat depicting the prior 30, 50 and 100 R-R intervals at the time. CSI (30, 50 and 100) and Modified CSI (100) showed a higher maximum peak during seizures than exercise/non-seizure (121-296%) for 4 of the 5 patients within 4 seconds before till 60 seconds after seizure onset time even though exercise maximum HR exceeded that of the seizures. The results indicate a detectable, sudden and inordinate shift towards sympathetic overdrive in the sympathovagal balance of the autonomic nervous system just around seizure-onset for certain patients. This new modified moving window Lorenz plot method seems promising way of constructing a portable ECG-based epilepsy alarm for certain patients with epilepsy who needs aid during seizure.
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Detection of epileptic seizures with a modified heart rate variability algorithm based on Lorenz plot. Seizure 2015; 24:1-7. [DOI: 10.1016/j.seizure.2014.11.004] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2014] [Revised: 11/06/2014] [Accepted: 11/08/2014] [Indexed: 11/18/2022] Open
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Abstract
Epilepsy is the second most common neurological disorder, affecting 0.6-0.8% of the world's population. In this neurological disorder, abnormal activity of the brain causes seizures, the nature of which tend to be sudden. Antiepileptic Drugs (AEDs) are used as long-term therapeutic solutions that control the condition. Of those treated with AEDs, 35% become resistant to medication. The unpredictable nature of seizures poses risks for the individual with epilepsy. It is clearly desirable to find more effective ways of preventing seizures for such patients. The automatic detection of oncoming seizures, before their actual onset, can facilitate timely intervention and hence minimize these risks. In addition, advance prediction of seizures can enrich our understanding of the epileptic brain. In this study, drawing on the body of work behind automatic seizure detection and prediction from digitised Invasive Electroencephalography (EEG) data, a prediction algorithm, ASPPR (Advance Seizure Prediction via Pre-ictal Relabeling), is described. ASPPR facilitates the learning of predictive models targeted at recognizing patterns in EEG activity that are in a specific time window in advance of a seizure. It then exploits advanced machine learning coupled with the design and selection of appropriate features from EEG signals. Results, from evaluating ASPPR independently on 21 different patients, suggest that seizures for many patients can be predicted up to 20 minutes in advance of their onset. Compared to benchmark performance represented by a mean S1-Score (harmonic mean of Sensitivity and Specificity) of 90.6% for predicting seizure onset between 0 and 5 minutes in advance, ASPPR achieves mean S1-Scores of: 96.30% for prediction between 1 and 6 minutes in advance, 96.13% for prediction between 8 and 13 minutes in advance, 94.5% for prediction between 14 and 19 minutes in advance, and 94.2% for prediction between 20 and 25 minutes in advance.
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Affiliation(s)
- Negin Moghim
- Heriot-Watt University, Edinburgh, United Kingdom
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Time-Variant, Frequency-Selective, Linear and Nonlinear Analysis of Heart Rate Variability in Children With Temporal Lobe Epilepsy. IEEE Trans Biomed Eng 2014; 61:1798-808. [DOI: 10.1109/tbme.2014.2307481] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Eggleston KS, Olin BD, Fisher RS. Ictal tachycardia: the head-heart connection. Seizure 2014; 23:496-505. [PMID: 24698385 DOI: 10.1016/j.seizure.2014.02.012] [Citation(s) in RCA: 130] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2014] [Revised: 02/21/2014] [Accepted: 02/22/2014] [Indexed: 11/15/2022] Open
Abstract
Epileptic seizures can lead to changes in autonomic function affecting the sympathetic, parasympathetic, and enteric nervous systems. Changes in cardiac signals are potential biomarkers that may provide an extra-cerebral indicator of ictal onset in some patients. Heart rate can be measured easily when compared to other biomarkers that are commonly associated with seizures (e.g., long-term EEG), and therefore it has become an interesting parameter to explore for detecting seizures. Understanding the prevalence and magnitude of heart rate changes associated with seizures, as well as the timing of such changes relative to seizure onset, is fundamental to the development and use of cardiac based algorithms for seizure detection. We reviewed 34 articles that reported the prevalence of ictal tachycardia in patients with epilepsy. Scientific literature supports the occurrence of significant increases in heart rate associated with ictal events in a large proportion of patients with epilepsy (82%) using concurrent electroencephalogram (EEG) and electrocardiogram (ECG). The average percentage of seizures associated with significant heart rate changes was similar for generalized (64%) and partial onset seizures (71%). Intra-individual variability was noted in several articles, with the majority of studies reporting significant increase in heart rate during seizures originating from the temporal lobe. Accurate detection of seizures is likely to require an adjustable threshold given the variability in the magnitude of heart rate changes associated with seizures within and across patients.
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Affiliation(s)
| | - Bryan D Olin
- Cyberonics, Inc., Houston, TX 77058, United States
| | - Robert S Fisher
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA 94305, United States
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Piper D, Schiecke K, Leistritz L, Pester B, Benninger F, Feucht M, Ungureanu M, Strungaru R, Witte H. Synchronization analysis between heart rate variability and EEG activity before, during, and after epileptic seizure. ACTA ACUST UNITED AC 2014; 59:343-55. [DOI: 10.1515/bmt-2013-0139] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2013] [Accepted: 02/28/2014] [Indexed: 11/15/2022]
Abstract
AbstractAn innovative concept for synchronization analysis between heart rate (HR) components and rhythms in EEG envelopes is represented; it applies time-variant analyses to heart rate variability (HRV) and EEG, and it was tested in children with temporal lobe epilepsy (TLE). After a removal of ocular and movement-related artifacts, EEG band activity was computed by means of the frequency-selective Hilbert transform providing envelopes of frequency bands. Synchronization between HRV and EEG envelopes was quantified by Morlet wavelet coherence. A surrogate data approach was adapted to test for statistical significance of time-variant coherences. Using this processing scheme, significant coherence values between a HRV low-frequency sub-band (0.08–0.12 Hz) and the EEG δ envelope (1.5–4 Hz) occurring both in the preictal and early postictal periods of a seizure can be shown. Investigations were performed for all electrodes at 20-s intervals and for selected electrode pairs (T3÷C3, T4÷C4) in a time-variant mode. Synchronization was more pronounced in the group of right hemispheric TLE patients than in the left hemispheric group. Such a group-specific augmentation of synchronization confirms the hypothesis of a right hemispheric lateralization of sympathetic cardiac control of the low-frequency HRV components.
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Sahoo SS, Jayapandian C, Garg G, Kaffashi F, Chung S, Bozorgi A, Chen CH, Loparo K, Lhatoo SD, Zhang GQ. Heart beats in the cloud: distributed analysis of electrophysiological 'Big Data' using cloud computing for epilepsy clinical research. J Am Med Inform Assoc 2013; 21:263-71. [PMID: 24326538 DOI: 10.1136/amiajnl-2013-002156] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
OBJECTIVE The rapidly growing volume of multimodal electrophysiological signal data is playing a critical role in patient care and clinical research across multiple disease domains, such as epilepsy and sleep medicine. To facilitate secondary use of these data, there is an urgent need to develop novel algorithms and informatics approaches using new cloud computing technologies as well as ontologies for collaborative multicenter studies. MATERIALS AND METHODS We present the Cloudwave platform, which (a) defines parallelized algorithms for computing cardiac measures using the MapReduce parallel programming framework, (b) supports real-time interaction with large volumes of electrophysiological signals, and (c) features signal visualization and querying functionalities using an ontology-driven web-based interface. Cloudwave is currently used in the multicenter National Institute of Neurological Diseases and Stroke (NINDS)-funded Prevention and Risk Identification of SUDEP (sudden unexplained death in epilepsy) Mortality (PRISM) project to identify risk factors for sudden death in epilepsy. RESULTS Comparative evaluations of Cloudwave with traditional desktop approaches to compute cardiac measures (eg, QRS complexes, RR intervals, and instantaneous heart rate) on epilepsy patient data show one order of magnitude improvement for single-channel ECG data and 20 times improvement for four-channel ECG data. This enables Cloudwave to support real-time user interaction with signal data, which is semantically annotated with a novel epilepsy and seizure ontology. DISCUSSION Data privacy is a critical issue in using cloud infrastructure, and cloud platforms, such as Amazon Web Services, offer features to support Health Insurance Portability and Accountability Act standards. CONCLUSION The Cloudwave platform is a new approach to leverage of large-scale electrophysiological data for advancing multicenter clinical research.
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Affiliation(s)
- Satya S Sahoo
- Division of Medical Informatics, School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
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Responsive neurostimulation for the treatment of medically intractable epilepsy. Brain Res Bull 2013; 97:39-47. [PMID: 23735806 DOI: 10.1016/j.brainresbull.2013.05.010] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2013] [Revised: 05/10/2013] [Accepted: 05/16/2013] [Indexed: 01/17/2023]
Abstract
With an annual incidence of 50/100,000 people, nearly 1% of the population suffers from epilepsy. Treatment with antiepileptic medication fails to achieve seizure remission in 20-30% of patients. One treatment option for refractory epilepsy patients who would not otherwise be surgical candidates is electrical stimulation of the brain, which is a rapidly evolving and reversible adjunctive therapy. Therapeutic stimulation can involve direct stimulation of the brain nuclei or indirect stimulation of peripheral nerves. There are three stimulation modalities that have class I evidence supporting their uses: vagus nerve stimulation (VNS), stimulation of the anterior nuclei of the thalamus (ANT), and, the most recently developed, responsive neurostimulation (RNS). While the other treatment modalities outlined deliver stimulation regardless of neuronal activity, the RNS administers stimulation only if triggered by seizure activity. The lower doses of stimulation provided by such responsive devices can not only reduce power consumption, but also prevent adverse reactions caused by continuous stimulation, which include the possibility of habituation to long-term stimulation. RNS, as an investigational treatment for medically refractory epilepsy, is currently under review by the FDA. Eventually systems may be developed to enable activation by neurochemical triggers or to wirelessly transmit any information gathered. We review the mechanisms, the current status, the target options, and the prospects of RNS for the treatment of medically intractable epilepsy.
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Klatt J, Feldwisch-Drentrup H, Ihle M, Navarro V, Neufang M, Teixeira C, Adam C, Valderrama M, Alvarado-Rojas C, Witon A, Le Van Quyen M, Sales F, Dourado A, Timmer J, Schulze-Bonhage A, Schelter B. The EPILEPSIAE database: An extensive electroencephalography database of epilepsy patients. Epilepsia 2012; 53:1669-76. [DOI: 10.1111/j.1528-1167.2012.03564.x] [Citation(s) in RCA: 66] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Valderrama M, Alvarado C, Nikolopoulos S, Martinerie J, Adam C, Navarro V, Le Van Quyen M. Identifying an increased risk of epileptic seizures using a multi-feature EEG–ECG classification. Biomed Signal Process Control 2012. [DOI: 10.1016/j.bspc.2011.05.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Delamont RS, Walker MC. Pre-ictal autonomic changes. Epilepsy Res 2011; 97:267-72. [PMID: 22050981 DOI: 10.1016/j.eplepsyres.2011.10.016] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2011] [Revised: 10/09/2011] [Accepted: 10/10/2011] [Indexed: 01/11/2023]
Abstract
Autonomic measures frequently alter with seizure activity and with brain state and so theoretically, there could be pre-ictal changes in autonomic function. However, there are considerable confounders. First, the measurement of autonomic function is not straightforward; heart rate and measures derived form heart rate have been those that have used the most in assessing changes in autonomic function. Second, autonomic function can vary considerably over the 24h cycle and can change suddenly depending on internal and external stimuli (e.g. fear, pain) and so any measures of changes in autonomic function will lose specificity. Third, changes in autonomic function in response to seizures, depends upon the individual, seizure type and spread of the seizure and even then can vary from seizure to seizure in the same individual. The idea that there will be well-defined, unique autonomic changes that occur in the pre-ictal period is very unlikely. These factors make it unlikely that autonomic function monitoring can be used successfully as a means of seizure prediction. However, in sleep, changes in autonomic function relate to changes in arousal state and since such states and the transition between such states may predict seizure occurrence in certain individuals, autonomic function could be a helpful determinant of seizure risk at certain stages of sleep. This hypothesis has, however, yet to be tested.
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Affiliation(s)
- Robert S Delamont
- Institute of Epileptology, Dept of Neurology, King's College Hospital, London SE5 9RS, UK.
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Harreby KR, Sevcencu C, Struijk JJ. Ictal and peri-ictal changes in cervical vagus nerve activity associated with cardiac effects. Med Biol Eng Comput 2011; 49:1025-33. [DOI: 10.1007/s11517-011-0782-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2010] [Accepted: 04/16/2011] [Indexed: 11/28/2022]
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Early seizure detection in rats based on vagus nerve activity. Med Biol Eng Comput 2010; 49:143-51. [DOI: 10.1007/s11517-010-0683-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2010] [Accepted: 08/13/2010] [Indexed: 10/19/2022]
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Nilsen KB, Haram M, Tangedal S, Sand T, Brodtkorb E. Is elevated pre-ictal heart rate associated with secondary generalization in partial epilepsy? Seizure 2010; 19:291-5. [DOI: 10.1016/j.seizure.2010.03.003] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2009] [Revised: 02/16/2010] [Accepted: 03/18/2010] [Indexed: 10/19/2022] Open
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Abstract
Studies with heart rate variability have revealed interictal autonomic alterations in patients with epilepsy. In addition, epilepsy is frequently associated with ictal tachycardia or bradycardia, which sometimes precedes the onset of seizures. Ictal tachycardia is sometimes associated with electrocardiography (ECG) morphologic changes and ictal bradycardia often progresses to asystole. Such cardiac manifestations of seizures have been hypothesized as possible causes for sudden unexplained death in epilepsy (SUPEP). The present review relates to interictal and ictal cardiac manifestations of epilepsy with focus on heart rate, heart rate variability, and ECG changes. Aspects of the supporting mechanisms are discussed and attention is drawn to the interaction between central and peripheral effects, interictal autonomic conditions, ictal autonomic discharges, and administration of antiepileptic drugs in shaping the ictal cardiac changes. Because these interactions are complex and not totally understood, closer surveillance of patients and more experimental work is necessary to elucidate the mechanistic support of autonomic and cardiac changes in epilepsy, and to design better strategies to avoid their undesirable effects. It is also suggested that some of these changes could be used as predictors or markers for the onset of seizures.
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Affiliation(s)
- Cristian Sevcencu
- Center for Sensory-Motor Interaction, Department of Health Science and Technology, Aalborg University, Denmark.
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40
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Vanzetta I, Grinvald A. Coupling between neuronal activity and microcirculation: implications for functional brain imaging. HFSP JOURNAL 2008; 2:79-98. [PMID: 19404475 PMCID: PMC2645573 DOI: 10.2976/1.2889618] [Citation(s) in RCA: 65] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2007] [Accepted: 02/11/2008] [Indexed: 01/12/2023]
Abstract
In the neocortex, neurons with similar response properties are often clustered together in column-like structures, giving rise to what has become known as functional architecture-the mapping of various stimulus feature dimensions onto the cortical sheet. At least partially, we owe this finding to the availability of several functional brain imaging techniques, both post-mortem and in-vivo, which have become available over the last two generations, revolutionizing neuroscience by yielding information about the spatial organization of active neurons in the brain. Here, we focus on how our understanding of such functional architecture is linked to the development of those functional imaging methodologies, especially to those that image neuronal activity indirectly, through metabolic or haemodynamic signals, rather than directly through measurement of electrical activity. Some of those approaches allow exploring functional architecture at higher spatial resolution than others. In particular, optical imaging of intrinsic signals reaches the striking detail of approximately 50 mum, and, together with other methodologies, it has allowed characterizing the metabolic and haemodynamic responses induced by sensory-evoked neuronal activity. Here, we review those findings about the spatio-temporal characteristics of neurovascular coupling and discuss their implications for functional brain imaging, including position emission tomography, and non-invasive neuroimaging techniques, such as funtional magnetic resonance imaging, applicable also to the human brain.
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Affiliation(s)
- Ivo Vanzetta
- Department of Neurobiology, The Weizmann Institute of Science, 76100 Rehovot, Israel
- Institut de Neurosciences Cognitives de la Méditerranée, CNRS UMR 6193, Aix-Marseille Université, 13402 Marseille Cedex 20, France
| | - Amiram Grinvald
- Department of Neurobiology, The Weizmann Institute of Science, 76100 Rehovot, Israel
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41
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Lehnertz K, Mormann F, Osterhage H, Müller A, Prusseit J, Chernihovskyi A, Staniek M, Krug D, Bialonski S, Elger CE. State-of-the-Art of Seizure Prediction. J Clin Neurophysiol 2007; 24:147-53. [PMID: 17414970 DOI: 10.1097/wnp.0b013e3180336f16] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
Abstract
SUMMARY Although there are numerous studies exploring basic neuronal mechanisms that are likely to be associated with seizures, to date no definite information is available as to how, when, or why a seizure occurs in humans. The fact that seizures occur without warning in the majority of cases is one of the most disabling aspects of epilepsy. If it were possible to identify preictal precursors from the EEG of epilepsy patients, therapeutic possibilities and quality of life could improve dramatically. The last three decades have witnessed a rapid increase in the development of new EEG analysis techniques that appear to be capable of defining seizure precursors. Since the 1970s, studies on seizure prediction have advanced from preliminary descriptions of preictal phenomena and proof of principle studies via controlled studies to studies on continuous multiday recordings. At present, it is unclear whether prospective algorithms can predict seizures. If prediction algorithms are to be used in invasive seizure intervention techniques in humans, they must be proven to perform considerably better than a random predictor. The authors present an overview of the field of seizure prediction, its history, accomplishments, recent controversies, and potential for future development.
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Affiliation(s)
- Klaus Lehnertz
- Department of Epileptology, University of Bonn, Bonn, Germany.
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42
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Greene BR, de Chazal P, Boylan GB, Connolly S, Reilly RB. Electrocardiogram Based Neonatal Seizure Detection. IEEE Trans Biomed Eng 2007; 54:673-82. [PMID: 17405374 DOI: 10.1109/tbme.2006.890137] [Citation(s) in RCA: 41] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
A method for the detection of seizures in the newborn using the electrocardiogram (ECG) signal is presented. Using a database of eight recordings, a method was developed for automatically annotating each 1-min epoch as "nonseizure" or "seizure". The system uses a linear discriminant classifier to process 41 heartbeat timing interval features. Performance assessment of the method showed that on a patient-specific basis an average accuracy of 70.5% was achieved in detecting seizures with associated sensitivity of 62.2% and specificity of 71.8%. On a patient-independent basis the average accuracy was 68.3% with sensitivity of 54.6% and specificity of 77.3%. Shifting the decision threshold for the patient-independent classifier allowed an increase in sensitivity to 78.4% at the expense of decreased specificity (51.6%), leading to increased false detections. The results of our ECG-based method are comparable with those reported for EEG-based neonatal seizure detection systems and offer the benefit of an easier acquisition methodology for seizure detection.
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Affiliation(s)
- Barry R Greene
- School of Electrical, Electronic & Mechanical Engineering, University College Dublin, Ireland.
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43
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Bermudez T, Lowe D, Arlaud-Lamborelle AM. Schemes for fusion of EEG and ECG towards temporal lobe epilepsy diagnostics. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2007; 2007:5132-5135. [PMID: 18003161 DOI: 10.1109/iembs.2007.4353495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
The Brain-Heart system can be considered as a coupled dynamical control system in which bioenergetic processes in the brain have an autonomic influence in the heart. Hence, temporal lobe epilepsy (TLE) can induce a modification of the cardiac rhythm: in the case of fully developed temporal lobe epilepsy, this modification tends to consist of tachycardia. In this paper we investigate this phenomenon through the introduction of a biomarker based on electroencephalogram (EEG) recordings, sensitive to fully-developed TLE seizures but also partial non-TLE seizures, whose diagnostic towards TLE is reinforced through data fusion by a biomarker based on electrocardiogram (ECG) recordings. The different schemes of information fusion are investigated as part of the uncertainty reduction problem.
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Affiliation(s)
- Thomas Bermudez
- Neural Computing Research Group, Aston University, Birmingham, United Kingdom.
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