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Carmo AS, Abreu M, Baptista MF, de Oliveira Carvalho M, Peralta AR, Fred A, Bentes C, da Silva HP. Automated algorithms for seizure forecast: a systematic review and meta-analysis. J Neurol 2024:10.1007/s00415-024-12655-z. [PMID: 39240346 DOI: 10.1007/s00415-024-12655-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2024] [Revised: 08/16/2024] [Accepted: 08/18/2024] [Indexed: 09/07/2024]
Abstract
This study aims to review the proposed methodologies and reported performances of automated algorithms for seizure forecast. A systematic review was conducted on studies reported up to May 10, 2024. Four databases and registers were searched, and studies were included when they proposed an original algorithm for automatic human epileptic seizure forecast that was patient specific, based on intraindividual cyclic distribution of events and/or surrogate measures of the preictal state and provided an evaluation of the performance. Two meta-analyses were performed, one evaluating area under the ROC curve (AUC) and another Brier Skill Score (BSS). Eighteen studies met the eligibility criteria, totaling 43 included algorithms. A total of 419 patients participated in the studies, and 19442 seizures were reported across studies. Of the analyzed algorithms, 23 were eligible for the meta-analysis with AUC and 12 with BSS. The overall mean AUC was 0.71, which was similar between the studies that relied solely on surrogate measures of the preictal state, on cyclic distributions of events, and on a combination of these. BSS was also similar for the three types of input data, with an overall mean BSS of 0.13. This study provides a characterization of the state of the art in seizure forecast algorithms along with their performances, setting a benchmark for future developments. It identified a considerable lack of standardization across study design and evaluation, leading to the proposal of guidelines for the design of seizure forecast solutions.
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Affiliation(s)
- Ana Sofia Carmo
- Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal.
- Instituto de Telecomunicações, Lisboa, Portugal.
| | - Mariana Abreu
- Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
- Instituto de Telecomunicações, Lisboa, Portugal
| | - Maria Fortuna Baptista
- Neurophysiology Monitoring Unit EEG/Sleep Laboratory, Hospital de Santa Maria, Unidade Local de Saúde Santa Maria, Lisboa, Portugal
- Centro de Estudos Egas Moniz. Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal
| | - Miguel de Oliveira Carvalho
- Neurophysiology Monitoring Unit EEG/Sleep Laboratory, Hospital de Santa Maria, Unidade Local de Saúde Santa Maria, Lisboa, Portugal
- Centro de Estudos Egas Moniz. Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal
| | - Ana Rita Peralta
- Neurophysiology Monitoring Unit EEG/Sleep Laboratory, Hospital de Santa Maria, Unidade Local de Saúde Santa Maria, Lisboa, Portugal
- Centro de Estudos Egas Moniz. Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal
| | - Ana Fred
- Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
- Instituto de Telecomunicações, Lisboa, Portugal
| | - Carla Bentes
- Neurophysiology Monitoring Unit EEG/Sleep Laboratory, Hospital de Santa Maria, Unidade Local de Saúde Santa Maria, Lisboa, Portugal
- Centro de Estudos Egas Moniz. Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal
| | - Hugo Plácido da Silva
- Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
- Instituto de Telecomunicações, Lisboa, Portugal
- LUMLIS The Lisbon ELLIS Unit | European Laboratory for Learning and Intelligent Systems, Lisboa, Portugal
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Huang W, Zong J, Zhang Y, Zhou Y, Zhang L, Wang Y, Shan Z, Xie Q, Li M, Pan S, Xiao Z. The Role of Circadian Rhythm in Neurological Diseases: A Translational Perspective. Aging Dis 2024; 15:1565-1587. [PMID: 37815902 PMCID: PMC11272204 DOI: 10.14336/ad.2023.0921] [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: 06/27/2023] [Accepted: 09/21/2023] [Indexed: 10/12/2023] Open
Abstract
Intrinsic biological clocks drive the circadian rhythm, which coordinates the physiological and pathophysiological processes in the body. Recently, a bidirectional relationship between circadian rhythms and several neurological diseases has been reported. Neurological diseases can lead to the disruption of circadian homeostasis, thereby increasing disease severity. Therefore, optimizing the current treatments through circadian-based approaches, including adjusted dosing, changing lifestyle, and targeted interventions, offer a promising opportunity for better clinical outcomes and precision medicine. In this review, we provide detailed implications of the circadian rhythm in neurological diseases through bench-to-bedside approaches. Furthermore, based on the unsatisfactory clinical outcomes, we critically discuss the potential of circadian-based interventions, which may encourage more studies in this discipline, with the hope of improving treatment efficacy in neurological diseases.
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Affiliation(s)
- Wanbin Huang
- Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Jiabin Zong
- Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Yu Zhang
- Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Yanjie Zhou
- Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Lily Zhang
- Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Yajuan Wang
- Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Zhengming Shan
- Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Qingfang Xie
- Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Ming Li
- Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Songqing Pan
- Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Zheman Xiao
- Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
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Muralidharan P, Sankaran R, Bendapudi P, Kumar CS, Kumar AA. AI in ECG: Validating an ambulatory semiology labeller and predictor. Epilepsy Res 2024; 204:107403. [PMID: 38944916 DOI: 10.1016/j.eplepsyres.2024.107403] [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: 04/17/2024] [Revised: 06/10/2024] [Accepted: 06/27/2024] [Indexed: 07/02/2024]
Abstract
OBJECTIVES Early prediction of epileptic seizures can help reduce morbidity and mortality. In this work, we explore using electrocardiographic (ECG) signal as input to a seizure prediction system and note that the performance can be improved by using selected signal processing techniques. METHODS We used frequency domain analysis with a deep neural network backend for all our experiments in this work. We further analysed the effect of the proposed system for different seizure semiologies and prediction horizons. We explored refining the signal using signal processing to enhance the system's performance. RESULTS Our final system using the Temple University Hospital's Seizure (TUHSZ) corpus gave an overall prediction accuracy of 84.02 %, sensitivity of 87.59 %, specificity of 81.9 %, and an area under the receiver operating characteristic curve (AUROC) of 0.9112. Notably, these results surpassed the state-of-the-art outcomes reported using the TUHSZ database; all findings are statistically significant. We also validated our study using the Siena scalp EEG database. Using the frequency domain data, our baseline system gave a performance of 75.17 %, 79.17 %, 70.04 % and 0.82 for prediction accuracy, sensitivity, specificity and AUROC, respectively. After selecting the optimal frequency band of 0.8-15 Hz, we obtained a performance of 80.49 %, 89.51 %, 75.23 % and 0.89 for prediction accuracy, sensitivity, specificity and AUROC, respectively which is an improvement of 5.32 %, 10.34 %, 5.19 % and 0.08 for prediction accuracy, sensitivity, specificity and AUROC, respectively. CONCLUSIONS The seizure information in ECG is concentrated in a narrow frequency band. Identifying and selecting that band can help improve the performance of seizure detection and prediction. SIGNIFICANCE EEG is susceptible to artefacts and is not preferred in a low-cost ambulatory device. ECG can be used in wearable devices (like chest bands) and is feasible for developing a low-cost ambulatory device for seizure prediction. Early seizure prediction can provide patients and clinicians with the required alert to take necessary precautions and prevent a fatality, significantly improving the patient's quality of life.
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Affiliation(s)
- Pooja Muralidharan
- Machine Intelligence Research Laboratory, Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu 641112, India
| | - Ravi Sankaran
- Department of Physical Medicine and Rehabilitation, Amrita Institute of Medical Sciences and Research Centre, Amrita Vishwa Vidyapeetham, Cochin, Kerala 682041, India
| | - Perraju Bendapudi
- Department of Neonatology, Amrita Institute of Medical Sciences and Research Centre, Amrita Vishwa Vidyapeetham, Cochin, Kerala 682041, India
| | - C Santhosh Kumar
- Machine Intelligence Research Laboratory, Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu 641112, India.
| | - A Anand Kumar
- Department of Neurology, Amrita Institute of Medical Sciences and Research Centre, Amrita Vishwa Vidyapeetham, Cochin, Kerala 682041, India
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Kerr WT, McFarlane KN, Figueiredo Pucci G. The present and future of seizure detection, prediction, and forecasting with machine learning, including the future impact on clinical trials. Front Neurol 2024; 15:1425490. [PMID: 39055320 PMCID: PMC11269262 DOI: 10.3389/fneur.2024.1425490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 06/03/2024] [Indexed: 07/27/2024] Open
Abstract
Seizures have a profound impact on quality of life and mortality, in part because they can be challenging both to detect and forecast. Seizure detection relies upon accurately differentiating transient neurological symptoms caused by abnormal epileptiform activity from similar symptoms with different causes. Seizure forecasting aims to identify when a person has a high or low likelihood of seizure, which is related to seizure prediction. Machine learning and artificial intelligence are data-driven techniques integrated with neurodiagnostic monitoring technologies that attempt to accomplish both of those tasks. In this narrative review, we describe both the existing software and hardware approaches for seizure detection and forecasting, as well as the concepts for how to evaluate the performance of new technologies for future application in clinical practice. These technologies include long-term monitoring both with and without electroencephalography (EEG) that report very high sensitivity as well as reduced false positive detections. In addition, we describe the implications of seizure detection and forecasting upon the evaluation of novel treatments for seizures within clinical trials. Based on these existing data, long-term seizure detection and forecasting with machine learning and artificial intelligence could fundamentally change the clinical care of people with seizures, but there are multiple validation steps necessary to rigorously demonstrate their benefits and costs, relative to the current standard.
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Affiliation(s)
- Wesley T. Kerr
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States
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Miron G, Halimeh M, Jeppesen J, Loddenkemper T, Meisel C. Autonomic biosignals, seizure detection, and forecasting. Epilepsia 2024. [PMID: 38837428 DOI: 10.1111/epi.18034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 05/17/2024] [Accepted: 05/22/2024] [Indexed: 06/07/2024]
Abstract
Wearable devices have attracted significant attention in epilepsy research in recent years for their potential to enhance patient care through improved seizure monitoring and forecasting. This narrative review presents a detailed overview of the current clinical state of the art while addressing how devices that assess autonomic nervous system (ANS) function reflect seizures and central nervous system (CNS) state changes. This includes a description of the interactions between the CNS and the ANS, including physiological and epilepsy-related changes affecting their dynamics. We first discuss technical aspects of measuring autonomic biosignals and considerations for using ANS sensors in clinical practice. We then review recent seizure detection and seizure forecasting studies, highlighting their performance and capability for seizure detection and forecasting using devices measuring ANS biomarkers. Finally, we address the field's challenges and provide an outlook for future developments.
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Affiliation(s)
- Gadi Miron
- Computational Neurology, Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Mustafa Halimeh
- Computational Neurology, Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Jesper Jeppesen
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Tobias Loddenkemper
- Department of Neurology, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Christian Meisel
- Computational Neurology, Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
- Center for Stroke Research Berlin, Berlin, Germany
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Goldenholz DM, Eccleston C, Moss R, Westover MB. Prospective validation of a seizure diary forecasting falls short. Epilepsia 2024; 65:1730-1736. [PMID: 38606580 PMCID: PMC11166505 DOI: 10.1111/epi.17984] [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] [Revised: 03/13/2024] [Accepted: 04/02/2024] [Indexed: 04/13/2024]
Abstract
OBJECTIVE Recently, a deep learning artificial intelligence (AI) model forecasted seizure risk using retrospective seizure diaries with higher accuracy than random forecasts. The present study sought to prospectively evaluate the same algorithm. METHODS We recruited a prospective cohort of 46 people with epilepsy; 25 completed sufficient data entry for analysis (median = 5 months). We used the same AI method as in our prior study. Group-level and individual-level Brier Skill Scores (BSSs) compared random forecasts and simple moving average forecasts to the AI. RESULTS The AI had an area under the receiver operating characteristic curve of .82. At the group level, the AI outperformed random forecasting (BSS = .53). At the individual level, AI outperformed random in 28% of cases. At the group and individual level, the moving average outperformed the AI. If pre-enrollment (nonverified) diaries (with presumed underreporting) were included, the AI significantly outperformed both comparators. Surveys showed most did not mind poor-quality LOW-RISK or HIGH-RISK forecasts, yet 91% wanted access to these forecasts. SIGNIFICANCE The previously developed AI forecasting tool did not outperform a very simple moving average forecasting in this prospective cohort, suggesting that the AI model should be replaced.
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Affiliation(s)
- Daniel M. Goldenholz
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Celena Eccleston
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | | | - M. Brandon Westover
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- McCance Center for Brain Health, Boston, Massachusetts, USA
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Jeppesen J, Lin K, Melo HM, Pavei J, Marques JLB, Beniczky S, Walz R. Detection of seizures with ictal tachycardia, using heart rate variability and patient adaptive logistic regression machine learning methods: A hospital-based validation study. Epileptic Disord 2024; 26:199-208. [PMID: 38334223 DOI: 10.1002/epd2.20196] [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: 08/18/2023] [Revised: 12/19/2023] [Accepted: 01/08/2024] [Indexed: 02/10/2024]
Abstract
OBJECTIVE Automated seizure detection of focal epileptic seizures is needed for objective seizure quantification to optimize the treatment of patients with epilepsy. Heart rate variability (HRV)-based seizure detection using patient-adaptive threshold with logistic regression machine learning (LRML) methods has presented promising performance in a study with a Danish patient cohort. The objective of this study was to assess the generalizability of the novel LRML seizure detection algorithm by validating it in a dataset recorded from long-term video-EEG monitoring (LTM) in a Brazilian patient cohort. METHODS Ictal and inter-ictal ECG-data epochs recorded during LTM were analyzed retrospectively. Thirty-four patients had 107 seizures (79 focal, 28 generalized tonic-clonic [GTC] including focal-to-bilateral-tonic-clonic seizures) eligible for analysis, with a total of 185.5 h recording. Because HRV-based seizure detection is only suitable in patients with marked ictal autonomic change, patients with >50 beats/min change in heart rate during seizures were selected as responders. The patient-adaptive LRML seizure detection algorithm was applied to all elected ECG data, and results were computed separately for responders and non-responders. RESULTS The patient-adaptive LRML seizure detection algorithm yielded a sensitivity of 84.8% (95% CI: 75.6-93.9) with a false alarm rate of .25/24 h in the responder group (22 patients, 59 seizures). Twenty-five of the 26 GTC seizures were detected (96.2%), and 25 of the 33 focal seizures without bilateral convulsions were detected (75.8%). SIGNIFICANCE The study confirms in a new, independent external dataset the good performance of seizure detection from a previous study and suggests that the method is generalizable. This method seems useful for detecting both generalized and focal epileptic seizures. The algorithm can be embedded in a wearable seizure detection system to alert patients and caregivers of seizures and generate objective seizure counts helping to optimize the treatment of the patients.
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Affiliation(s)
- Jesper Jeppesen
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Katia Lin
- Medical Sciences Post-graduate Program, Federal University of Santa Catarina (UFSC), Florianópolis, SC, Brazil
- Neurology Division, Federal University of Santa Catarina (UFSC), Florianópolis, SC, Brazil
- Center for Applied Neurosciences (CeNAp), Federal University of Santa Catarina (UFSC), Florianópolis, SC, Brazil
| | | | - Jonatas Pavei
- Institute of Biomedical Engineering, Federal University of Santa Catarina, Florianópolis, SC, Brazil
| | - Jefferson Luiz Brum Marques
- Center for Applied Neurosciences (CeNAp), Federal University of Santa Catarina (UFSC), Florianópolis, SC, Brazil
- Institute of Biomedical Engineering, Federal University of Santa Catarina, Florianópolis, SC, Brazil
| | - Sándor Beniczky
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund, Denmark
| | - Roger Walz
- Medical Sciences Post-graduate Program, Federal University of Santa Catarina (UFSC), Florianópolis, SC, Brazil
- Neurology Division, Federal University of Santa Catarina (UFSC), Florianópolis, SC, Brazil
- Center for Applied Neurosciences (CeNAp), Federal University of Santa Catarina (UFSC), Florianópolis, SC, Brazil
- Graduate Program in Neuroscience, UFSC, Florianópolis, SC, Brazil
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Fu A, Lado FA. Seizure Detection, Prediction, and Forecasting. J Clin Neurophysiol 2024; 41:207-213. [PMID: 38436388 DOI: 10.1097/wnp.0000000000001045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2024] Open
Abstract
SUMMARY Among the many fears associated with seizures, patients with epilepsy are greatly frustrated and distressed over seizure's apparent unpredictable occurrence. However, increasing evidence have emerged over the years to support that seizure occurrence is not a random phenomenon as previously presumed; it has a cyclic rhythm that oscillates over multiple timescales. The pattern in rises and falls of seizure rate that varies over 24 hours, weeks, months, and years has become a target for the development of innovative devices that intend to detect, predict, and forecast seizures. This article will review the different tools and devices available or that have been previously studied for seizure detection, prediction, and forecasting, as well as the associated challenges and limitations with the utilization of these devices. Although there is strong evidence for rhythmicity in seizure occurrence, very little is known about the mechanism behind this oscillation. This article concludes with early insights into the regulations that may potentially drive this cyclical variability and future directions.
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Affiliation(s)
- Aradia Fu
- Department of Neurology, Zucker School of Medicine at Hofstra-Northwell, Great Neck, New York, U.S.A
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Vieluf S, Cantley S, Krishnan V, Loddenkemper T. Ultradian rhythms in accelerometric and autonomic data vary based on seizure occurrence in paediatric epilepsy patients. Brain Commun 2024; 6:fcae034. [PMID: 38454964 PMCID: PMC10919479 DOI: 10.1093/braincomms/fcae034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 07/18/2023] [Accepted: 02/09/2024] [Indexed: 03/09/2024] Open
Abstract
Ultradian rhythms are physiological oscillations that resonate with period lengths shorter than 24 hours. This study examined the expression of ultradian rhythms in patients with epilepsy, a disease defined by an enduring seizure risk that may vary cyclically. Using a wearable device, we recorded heart rate, body temperature, electrodermal activity and limb accelerometry in patients admitted to the paediatric epilepsy monitoring unit. In our case-control design, we included recordings from 29 patients with tonic-clonic seizures and 29 non-seizing controls. We spectrally decomposed each signal to identify cycle lengths of interest and compared average spectral power- and period-related markers between groups. Additionally, we related seizure occurrence to the phase of ultradian rhythm in patients with recorded seizures. We observed prominent 2- and 4-hour-long ultradian rhythms of accelerometry, as well as 4-hour-long oscillations in heart rate. Patients with seizures displayed a higher peak power in the 2-hour accelerometry rhythm (U = 287, P = 0.038) and a period-lengthened 4-hour heart rate rhythm (U = 291.5, P = 0.037). Those that seized also displayed greater mean rhythmic electrodermal activity (U = 261; P = 0.013). Most seizures occurred during the falling-to-trough quarter phase of accelerometric rhythms (13 out of 27, χ2 = 8.41, P = 0.038). Fluctuations in seizure risk or the occurrence of seizures may interrelate with ultradian rhythms of movement and autonomic function. Longitudinal assessments of ultradian patterns in larger patient samples may enable us to understand how such rhythms may improve the temporal precision of seizure forecasting models.
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Affiliation(s)
- Solveig Vieluf
- Division of Epilepsy and Clinical Neurophysiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Department of Medicine I, LMU University Hospital, LMU Munich, 81377 Munich, Germany
| | - Sarah Cantley
- Division of Epilepsy and Clinical Neurophysiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Vaishnav Krishnan
- Departments of Neurology, Neuroscience and Psychiatry & Behavioral Sciences, Baylor College of Medicine, Houston, TX 77030, USA
| | - Tobias Loddenkemper
- Division of Epilepsy and Clinical Neurophysiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
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Sanger ZT, Henry TR, Park MC, Darrow D, McGovern RA, Netoff TI. Neural signal data collection and analysis of Percept™ PC BrainSense recordings for thalamic stimulation in epilepsy. J Neural Eng 2024; 21:10.1088/1741-2552/ad1dc3. [PMID: 38211344 PMCID: PMC11299490 DOI: 10.1088/1741-2552/ad1dc3] [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: 05/18/2023] [Accepted: 01/11/2024] [Indexed: 01/13/2024]
Abstract
Deep brain stimulation (DBS) using Medtronic's Percept™ PC implantable pulse generator is FDA-approved for treating Parkinson's disease (PD), essential tremor, dystonia, obsessive compulsive disorder, and epilepsy. Percept™ PC enables simultaneous recording of neural signals from the same lead used for stimulation. Many Percept™ PC sensing features were built with PD patients in mind, but these features are potentially useful to refine therapies for many different disease processes. When starting our ongoing epilepsy research study, we found it difficult to find detailed descriptions about these features and have compiled information from multiple sources to understand it as a tool, particularly for use in patients other than those with PD. Here we provide a tutorial for scientists and physicians interested in using Percept™ PC's features and provide examples of how neural time series data is often represented and saved. We address characteristics of the recorded signals and discuss Percept™ PC hardware and software capabilities in data pre-processing, signal filtering, and DBS lead performance. We explain the power spectrum of the data and how it is shaped by the filter response of Percept™ PC as well as the aliasing of the stimulation due to digitally sampling the data. We present Percept™ PC's ability to extract biomarkers that may be used to optimize stimulation therapy. We show how differences in lead type affects noise characteristics of the implanted leads from seven epilepsy patients enrolled in our clinical trial. Percept™ PC has sufficient signal-to-noise ratio, sampling capabilities, and stimulus artifact rejection for neural activity recording. Limitations in sampling rate, potential artifacts during stimulation, and shortening of battery life when monitoring neural activity at home were observed. Despite these limitations, Percept™ PC demonstrates potential as a useful tool for recording neural activity in order to optimize stimulation therapies to personalize treatment.
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Affiliation(s)
- Zachary T Sanger
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, United States of America
| | - Thomas R Henry
- Department of Neurology, University of Minnesota, Minneapolis, United States of America
| | - Michael C Park
- Department of Neurosurgery, University of Minnesota, Minneapolis, United States of America
- Department of Neurology, University of Minnesota, Minneapolis, United States of America
| | - David Darrow
- Department of Neurosurgery, University of Minnesota, Minneapolis, United States of America
| | - Robert A McGovern
- Department of Neurosurgery, University of Minnesota, Minneapolis, United States of America
| | - Theoden I Netoff
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, United States of America
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Biondi A, Simblett SK, Viana PF, Laiou P, Fiori AMG, Nurse E, Schreuder M, Pal DK, Richardson MP. Feasibility and acceptability of an ultra-long-term at-home EEG monitoring system (EEG@HOME) for people with epilepsy. Epilepsy Behav 2024; 151:109609. [PMID: 38160578 DOI: 10.1016/j.yebeh.2023.109609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 12/21/2023] [Accepted: 12/22/2023] [Indexed: 01/03/2024]
Abstract
BACKGROUND Recent technological advancements offer new ways to monitor and manage epilepsy. The adoption of these devices in routine clinical practice will strongly depend on patient acceptability and usability, with their perspectives being crucial. Previous studies provided feedback from patients, but few explored the experience of them using independently multiple devices independently at home. PURPOSE The study, assessed through a mixed methods design, the direct experiences of people with epilepsy independently using a non-invasive monitoring system (EEG@HOME) for an extended duration of 6 months, at home. We aimed to investigate factors affecting engagement, gather qualitative insights, and provide recommendations for future home epilepsy monitoring systems. MATERIALS AND METHODS Adults with epilepsy independently were trained to use a wearable dry EEG system, a wrist-worn device, and a smartphone app for seizure tracking and behaviour monitoring for 6 months at home. Monthly acceptability questionnaires (PSSUQ, SUS) and semi-structured interviews were conducted to explore participant experience. Adherence with the procedure, acceptability scores and systematic thematic analysis of the interviews, focusing on the experience with the procedure, motivation and benefits and opinion about the procedure were assessed. RESULTS Twelve people with epilepsy took part into the study for an average of 193.8 days (range 61 to 312) with a likelihood of using the system at six months of 83 %. The e-diary and the smartwatch were highly acceptable and preferred to a wearable EEG system (PSSUQ score of 1.9, 1.9, 2.4). Participants showed an acceptable level of adherence with all solutions (Average usage of 63 %, 66 %, 92 %) reporting more difficulties using the EEG twice a day and remembering to complete the daily behavioural questionnaires. Clear information and training, continuous remote support, perceived direct and indirect benefits and the possibility to have a flexible, tailored to daily routine monitoring were defined as key factors to ensure compliance with long-term monitoring systems. CONCLUSIONS EEG@HOME study demonstrated people with epilepsy' interest and ability in active health monitoring using new technologies. Remote training and support enable independent home use of new non-invasive technologies, but to ensure long term acceptability and usability systems will require to be integrated into patients' routines, include healthcare providers, and offer continuous support and personalized feedback.
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Affiliation(s)
- Andrea Biondi
- Department of Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience (IoPPN), King's College London, London, United Kingdom.
| | - Sara K Simblett
- Department of Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience (IoPPN), King's College London, London, United Kingdom; Department of Psychology, Institute of Psychiatry, Psychology & Neuroscience (IoPPN), King's College London, London, United Kingdom
| | - Pedro F Viana
- Department of Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience (IoPPN), King's College London, London, United Kingdom; Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | - Petroula Laiou
- Department of Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience (IoPPN), King's College London, London, United Kingdom
| | - Anna M G Fiori
- King's College Hospital NHS Foundation Trust, London, United Kingdom
| | - Ewan Nurse
- Seer Medical Inc, Melbourne, VIC, Australia; Department of Medicine, St. Vincent's Hospital Melbourne, The University of Melbourne, Melbourne, VIC, Australia
| | | | - Deb K Pal
- Department of Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience (IoPPN), King's College London, London, United Kingdom
| | - Mark P Richardson
- Department of Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience (IoPPN), King's College London, London, United Kingdom; NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, United Kingdom
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12
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Goldenholz DM, Eccleston C, Moss R, Westover MB. Prospective validation of a seizure diary forecasting falls short. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.11.24301175. [PMID: 38260666 PMCID: PMC10802655 DOI: 10.1101/2024.01.11.24301175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
OBJECTIVE Recently, a deep learning AI model forecasted seizure risk using retrospective seizure diaries with higher accuracy than random forecasts. The present study sought to prospectively evaluate the same algorithm. METHODS We recruited a prospective cohort of 46 people with epilepsy; 25 completed sufficient data entry for analysis (median 5 months). We used the same AI method as in our prior study. Group-level and individual-level Brier Skill Scores (BSS) compared random forecasts and simple moving average forecasts to the AI. RESULTS The AI had an AUC of 0.82. At the group level, the AI outperformed random forecasting (BSS=0.53). At the individual level, AI outperformed random in 28% of cases. At the group and individual level, the moving average outperformed the AI. If pre-enrollment (non-verified) diaries (with presumed under-reporting) were included, the AI significantly outperformed both comparators. Surveys showed most did not mind poor quality LOW-RISK or HIGH-RISK forecasts, yet 91% wanted access to these forecasts. SIGNIFICANCE The previously developed AI forecasting tool did not outperform a very simple moving average forecasting this prospective cohort, suggesting that the AI model should be replaced.
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Affiliation(s)
- Daniel M Goldenholz
- Dept. of Neurology, Beth Israel Deaconess Medical Center, Boston 02215 MA
- Dept. of Neurology, Harvard Medical School, Boston 02215 MA
| | - Celena Eccleston
- Dept. of Neurology, Beth Israel Deaconess Medical Center, Boston 02215 MA
- Dept. of Neurology, Harvard Medical School, Boston 02215 MA
| | | | - M Brandon Westover
- Dept. of Neurology, Beth Israel Deaconess Medical Center, Boston 02215 MA
- Dept. of Neurology, Harvard Medical School, Boston 02215 MA
- Dept. of Neurology, Massachusetts General Hospital, Boston 02114 MA
- McCance Center for Brain Health, Boston, 02114 MA
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13
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Warren AEL, Tobochnik S, Chua MMJ, Singh H, Stamm MA, Rolston JD. Neurostimulation for Generalized Epilepsy: Should Therapy be Syndrome-specific? Neurosurg Clin N Am 2024; 35:27-48. [PMID: 38000840 PMCID: PMC10676463 DOI: 10.1016/j.nec.2023.08.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2023]
Abstract
Current applications of neurostimulation for generalized epilepsy use a one-target-fits-all approach that is agnostic to the specific epilepsy syndrome and seizure type being treated. The authors describe similarities and differences between the 2 "archetypes" of generalized epilepsy-Lennox-Gastaut syndrome and Idiopathic Generalized Epilepsy-and review recent neuroimaging evidence for syndrome-specific brain networks underlying seizures. Implications for stimulation targeting and programming are discussed using 5 clinical questions: What epilepsy syndrome does the patient have? What brain networks are involved? What is the optimal stimulation target? What is the optimal stimulation paradigm? What is the plan for adjusting stimulation over time?
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Affiliation(s)
- Aaron E L Warren
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Steven Tobochnik
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Melissa M J Chua
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Hargunbir Singh
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michaela A Stamm
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - John D Rolston
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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14
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Baud MO, Proix T, Gregg NM, Brinkmann BH, Nurse ES, Cook MJ, Karoly PJ. Seizure forecasting: Bifurcations in the long and winding road. Epilepsia 2023; 64 Suppl 4:S78-S98. [PMID: 35604546 PMCID: PMC9681938 DOI: 10.1111/epi.17311] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/20/2022] [Accepted: 05/20/2022] [Indexed: 11/28/2022]
Abstract
To date, the unpredictability of seizures remains a source of suffering for people with epilepsy, motivating decades of research into methods to forecast seizures. Originally, only few scientists and neurologists ventured into this niche endeavor, which, given the difficulty of the task, soon turned into a long and winding road. Over the past decade, however, our narrow field has seen a major acceleration, with trials of chronic electroencephalographic devices and the subsequent discovery of cyclical patterns in the occurrence of seizures. Now, a burgeoning science of seizure timing is emerging, which in turn informs best forecasting strategies for upcoming clinical trials. Although the finish line might be in view, many challenges remain to make seizure forecasting a reality. This review covers the most recent scientific, technical, and medical developments, discusses methodology in detail, and sets a number of goals for future studies.
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Affiliation(s)
- Maxime O Baud
- Sleep-Wake-Epilepsy Center, Center for Experimental Neurology, NeuroTec, Department of Neurology, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland
- Wyss Center for Bio- and Neuro-Engineering, Geneva, Switzerland
| | - Timothée Proix
- Department of Basic Neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Nicholas M Gregg
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Benjamin H Brinkmann
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Ewan S Nurse
- Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Mark J Cook
- Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Philippa J Karoly
- Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
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15
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Kerr WT, McFarlane KN. Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist. Curr Neurol Neurosci Rep 2023; 23:869-879. [PMID: 38060133 DOI: 10.1007/s11910-023-01318-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/24/2023] [Indexed: 12/08/2023]
Abstract
PURPOSE OF REVIEW Machine Learning (ML) and Artificial Intelligence (AI) are data-driven techniques to translate raw data into applicable and interpretable insights that can assist in clinical decision making. Some of these tools have extremely promising initial results, earning both great excitement and creating hype. This non-technical article reviews recent developments in ML/AI in epilepsy to assist the current practicing epileptologist in understanding both the benefits and limitations of integrating ML/AI tools into their clinical practice. RECENT FINDINGS ML/AI tools have been developed to assist clinicians in almost every clinical decision including (1) predicting future epilepsy in people at risk, (2) detecting and monitoring for seizures, (3) differentiating epilepsy from mimics, (4) using data to improve neuroanatomic localization and lateralization, and (5) tracking and predicting response to medical and surgical treatments. We also discuss practical, ethical, and equity considerations in the development and application of ML/AI tools including chatbots based on Large Language Models (e.g., ChatGPT). ML/AI tools will change how clinical medicine is practiced, but, with rare exceptions, the transferability to other centers, effectiveness, and safety of these approaches have not yet been established rigorously. In the future, ML/AI will not replace epileptologists, but epileptologists with ML/AI will replace epileptologists without ML/AI.
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Affiliation(s)
- Wesley T Kerr
- Department of Neurology, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA.
- Department of Biomedical Informatics, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA.
- Department of Neurology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
| | - Katherine N McFarlane
- Department of Neurology, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA
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16
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Andrzejak RG, Zaveri HP, Schulze‐Bonhage A, Leguia MG, Stacey WC, Richardson MP, Kuhlmann L, Lehnertz K. Seizure forecasting: Where do we stand? Epilepsia 2023; 64 Suppl 3:S62-S71. [PMID: 36780237 PMCID: PMC10423299 DOI: 10.1111/epi.17546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 02/06/2023] [Indexed: 02/14/2023]
Abstract
A lot of mileage has been made recently on the long and winding road toward seizure forecasting. Here we briefly review some selected milestones passed along the way, which were discussed at the International Conference for Technology and Analysis of Seizures-ICTALS 2022-convened at the University of Bern, Switzerland. Major impetus was gained recently from wearable and implantable devices that record not only electroencephalography, but also data on motor behavior, acoustic signals, and various signals of the autonomic nervous system. This multimodal monitoring can be performed for ultralong timescales covering months or years. Accordingly, features and metrics extracted from these data now assess seizure dynamics with a greater degree of completeness. Most prominently, this has allowed the confirmation of the long-suspected cyclical nature of interictal epileptiform activity, seizure risk, and seizures. The timescales cover daily, multi-day, and yearly cycles. Progress has also been fueled by approaches originating from the interdisciplinary field of network science. Considering epilepsy as a large-scale network disorder yielded novel perspectives on the pre-ictal dynamics of the evolving epileptic brain. In addition to discrete predictions that a seizure will take place in a specified prediction horizon, the community broadened the scope to probabilistic forecasts of a seizure risk evolving continuously in time. This shift of gears triggered the incorporation of additional metrics to quantify the performance of forecasting algorithms, which should be compared to the chance performance of constrained stochastic null models. An imminent task of utmost importance is to find optimal ways to communicate the output of seizure-forecasting algorithms to patients, caretakers, and clinicians, so that they can have socioeconomic impact and improve patients' well-being.
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Grants
- NIH NS109062 NIH HHS
- MR/N026063/1 Medical Research Council
- R01 NS109062 NINDS NIH HHS
- R01 NS094399 NINDS NIH HHS
- NIH NS094399 NIH HHS
- Medical Research Council Centre for Neurodevelopmental Disorders
- National Health and Medical Research Council
- National Institutes of Health
- University of Bern, the Inselspital, University Hospital Bern, the Alliance for Epilepsy Research, the Swiss National Science Foundation, UCB, FHC, the Wyss Center for bio‐ and neuro‐engineering, the American Epilepsy Society (AES), the CURE epilepsy Foundation, Ripple neuro, Sintetica, DIXI medical, UNEEG medical and NeuroPace.
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Affiliation(s)
- Ralph G. Andrzejak
- Department of Information and Communication TechnologiesUniversitat Pompeu FabraBarcelonaSpain
| | | | - Andreas Schulze‐Bonhage
- Epilepsy Center, NeurocenterUniversity Medical Center, University of FreiburgFreiburgGermany
| | - Marc G. Leguia
- Department of Information and Communication TechnologiesUniversitat Pompeu FabraBarcelonaSpain
| | - William C. Stacey
- Department of Neurology, Department of Biomedical EngineeringBioInterfaces Institute, University of MichiganAnn ArborMichiganUSA
- Division of NeurologyVA Ann Arbor Medical CenterAnn ArborMichiganUSA
| | - Mark P. Richardson
- School of NeuroscienceInstitute of Psychiatry Psychology and Neuroscience, King's College LondonLondonUK
| | - Levin Kuhlmann
- Department of Data Science and AI, Faculty of Information TechnologyMonash UniversityClaytonVictoriaAustralia
| | - Klaus Lehnertz
- Department of EpileptologyUniversity of Bonn Medical CentreBonnGermany
- Helmholtz Institute for Radiation and Nuclear PhysicsUniversity of BonnBonnGermany
- Interdisciplinary Center for Complex SystemsUniversity of BonnBonnGermany
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17
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Cousyn L, Dono F, Navarro V, Chavez M. Can heart rate variability identify a high-risk state of upcoming seizure? Epilepsy Res 2023; 197:107232. [PMID: 37783038 DOI: 10.1016/j.eplepsyres.2023.107232] [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: 05/23/2023] [Revised: 08/10/2023] [Accepted: 09/21/2023] [Indexed: 10/04/2023]
Abstract
Heart rate variability (HRV) is an accessible and convenient means to assess the sympathetic/parasympathetic balance. Autonomic dysfunctions may reflect a pro-ictal state and occur before the seizure onset. Previous studies have reported HRV-based models to identify preictal states in continuous electrocardiogram (EKG) monitoring. Here, we evaluated the ability of HRV metrics extracted from daily single resting-state periods to estimate the risk of upcoming seizure(s) using probabilistic forecasts. Daily standardized 10-min vigilance-controlled EKG periods were recorded in 15 patients with drug-resistant focal epilepsy who underwent intracerebral electroencephalography (EEG). Analyses of a total of 156 periods, based on machine learning approaches, suggested that HRV features can identify preictal states with a median AUC of 0.75 [0.68;0.99]. Pseudoprospective daily forecasts yielded a median Brier score of 0.3 [0.18;0.48]. About 60% of preictal days were correctly forecasted, while false positive predictions were noticed in 24% of interictal days. Daily resting HRV seems to capture information on autonomic variations that may reflect a pro-ictal state. The method could be embedded in an ambulatory clinical seizure prediction device, but additional modalities (prodromes, EEG-based features, etc.) should be associated to improve its performance.
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Affiliation(s)
- Louis Cousyn
- Paris Brain Institute (Inserm, CNRS, Sorbonne Université), Paris, France; AP-HP, Department of Neurology, Epilepsy Unit, Pitié-Salpêtrière Hospital, Paris, France.
| | - Fedele Dono
- Department of Neuroscience, Imaging and Clinical Sciences, "G. d'Annunzio" University of Chieti -Pescara, Chieti, Italy
| | - Vincent Navarro
- Paris Brain Institute (Inserm, CNRS, Sorbonne Université), Paris, France; AP-HP, Department of Neurology, Epilepsy Unit, Pitié-Salpêtrière Hospital, Paris, France
| | - Mario Chavez
- CNRS UMR-7225, Pitié-Salpêtrière Hospital, Paris, France
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18
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Ojemann WKS, Scheid BH, Mouchtaris S, Lucas A, LaRocque JJ, Aguila C, Ashourvan A, Caciagli L, Davis KA, Conrad EC, Litt B. Resting-state background features demonstrate multidien cycles in long-term EEG device recordings. Brain Stimul 2023; 16:1709-1718. [PMID: 37979654 DOI: 10.1016/j.brs.2023.11.005] [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: 06/19/2023] [Revised: 11/06/2023] [Accepted: 11/07/2023] [Indexed: 11/20/2023] Open
Abstract
BACKGROUND Longitudinal EEG recorded by implanted devices is critical for understanding and managing epilepsy. Recent research reports patient-specific, multi-day cycles in device-detected epileptiform events that coincide with increased likelihood of clinical seizures. Understanding these cycles could elucidate mechanisms generating seizures and advance drug and neurostimulation therapies. OBJECTIVE/HYPOTHESIS We hypothesize that seizure-correlated cycles are present in background neural activity, independent of interictal epileptiform spikes, and that neurostimulation may temporarily interrupt these cycles. METHODS We analyzed regularly-recorded seizure-free data epochs from 20 patients implanted with a responsive neurostimulation (RNS) device for at least 1.5 years, to explore the relationship between cycles in device-detected interictal epileptiform activity (dIEA), clinician-validated interictal spikes, background EEG features, and neurostimulation. RESULTS Background EEG features tracked the cycle phase of dIEA in all patients (AUC: 0.63 [0.56-0.67]) with a greater effect size compared to clinically annotated spike rate alone (AUC: 0.55 [0.53-0.61], p < 0.01). After accounting for circadian variation and spike rate, we observed significant population trends in elevated theta and beta band power and theta and alpha connectivity features at the cycle peaks (sign test, p < 0.05). In the period directly after stimulation we observe a decreased association between cycle phase and EEG features compared to background recordings (AUC: 0.58 [0.55-0.64]). CONCLUSIONS Our findings suggest that seizure-correlated dIEA cycles are not solely due to epileptiform discharges but are associated with background measures of brain state; and that neurostimulation may temporarily interrupt these cycles. These results may help elucidate mechanisms underlying seizure generation, provide new biomarkers for seizure risk, and facilitate monitoring, treating, and managing epilepsy with implantable devices.
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Affiliation(s)
- William K S Ojemann
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street, Philadelphia, PA, 19104, USA.
| | - Brittany H Scheid
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street, Philadelphia, PA, 19104, USA
| | - Sofia Mouchtaris
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street, Philadelphia, PA, 19104, USA
| | - Alfredo Lucas
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street, Philadelphia, PA, 19104, USA; University of Pennsylvania, Perelman School of Medicine, 3400 Civic Center Blvd, Philadelphia, PA, 19104, USA
| | - Joshua J LaRocque
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street, Philadelphia, PA, 19104, USA; Hospital of the University of Pennsylvania, Department of Neurology, 3400 Spruce St, Philadelphia, PA, 19104, USA
| | - Carlos Aguila
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street, Philadelphia, PA, 19104, USA
| | - Arian Ashourvan
- The University of Kansas, Department of Psychology, 1415 Jayhawk Blvd, Lawrence, KS, 66045, USA
| | - Lorenzo Caciagli
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street, Philadelphia, PA, 19104, USA
| | - Kathryn A Davis
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street, Philadelphia, PA, 19104, USA; Hospital of the University of Pennsylvania, Department of Neurology, 3400 Spruce St, Philadelphia, PA, 19104, USA
| | - Erin C Conrad
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street, Philadelphia, PA, 19104, USA; Hospital of the University of Pennsylvania, Department of Neurology, 3400 Spruce St, Philadelphia, PA, 19104, USA
| | - Brian Litt
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street, Philadelphia, PA, 19104, USA; Hospital of the University of Pennsylvania, Department of Neurology, 3400 Spruce St, Philadelphia, PA, 19104, USA
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19
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Sides K, Kilungeja G, Tapia M, Kreidl P, Brinkmann BH, Nasseri M. Analyzing physiological signals recorded with a wearable sensor across the menstrual cycle using circular statistics. FRONTIERS IN NETWORK PHYSIOLOGY 2023; 3:1227228. [PMID: 37928057 PMCID: PMC10621043 DOI: 10.3389/fnetp.2023.1227228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 09/19/2023] [Indexed: 11/07/2023]
Abstract
This study aims to identify the most significant features in physiological signals representing a biphasic pattern in the menstrual cycle using circular statistics which is an appropriate analytic method for the interpretation of data with a periodic nature. The results can be used empirically to determine menstrual phases. A non-uniform pattern was observed in ovulating subjects, with a significant periodicity (p< 0.05) in mean temperature, heart rate (HR), Inter-beat Interval (IBI), mean tonic component of Electrodermal Activity (EDA), and signal magnitude area (SMA) of the EDA phasic component in the frequency domain. In contrast, non-ovulating cycles displayed a more uniform distribution (p> 0.05). There was a significant difference between ovulating and non-ovulating cycles (p< 0.05) in temperature, IBI, and EDA but not in mean HR. Selected features were used in training an Autoregressive Integrated Moving Average (ARIMA) model, using data from at least one cycle of a subject, to predict the behavior of the signal in the last cycle. By iteratively retraining the algorithm on a per-day basis, the mean temperature, HR, IBI and EDA tonic values of the next day were predicted with root mean square error (RMSE) of 0.13 ± 0.07 (C°), 1.31 ± 0.34 (bpm), 0.016 ± 0.005 (s) and 0.17 ± 0.17 (μS), respectively.
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Affiliation(s)
- Krystal Sides
- School of Engineering, University of North Florida, Jacksonville, FL, United States
| | - Grentina Kilungeja
- School of Engineering, University of North Florida, Jacksonville, FL, United States
| | - Matthew Tapia
- School of Engineering, University of North Florida, Jacksonville, FL, United States
| | - Patrick Kreidl
- School of Engineering, University of North Florida, Jacksonville, FL, United States
| | - Benjamin H. Brinkmann
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Mona Nasseri
- School of Engineering, University of North Florida, Jacksonville, FL, United States
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, United States
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20
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Cui J, Balzekas I, Nurse E, Viana P, Gregg N, Karoly P, Stirling RE, Worrell G, Richardson MP, Freestone DR, Brinkmann BH. Perceived seizure risk in epilepsy: Chronic electronic surveys with and without concurrent electroencephalography. Epilepsia 2023; 64:2421-2433. [PMID: 37303239 PMCID: PMC10526687 DOI: 10.1111/epi.17678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Revised: 06/07/2023] [Accepted: 06/07/2023] [Indexed: 06/13/2023]
Abstract
OBJECTIVE Previous studies suggested that patients with epilepsy might be able to forecast their own seizures. This study aimed to assess the relationships between premonitory symptoms, perceived seizure risk, and future and recent self-reported and electroencephalographically (EEG)-confirmed seizures in ambulatory patients with epilepsy in their natural home environments. METHODS Long-term e-surveys were collected from patients with and without concurrent EEG recordings. Information obtained from the e-surveys included medication adherence, sleep quality, mood, stress, perceived seizure risk, and seizure occurrences preceding the survey. EEG seizures were identified. Univariate and multivariate generalized linear mixed-effect regression models were used to estimate odds ratios (ORs) for the assessment of the relationships. Results were compared with the seizure forecasting classifiers and device forecasting literature using a mathematical formula converting OR to equivalent area under the curve (AUC). RESULTS Fifty-four subjects returned 10 269 e-survey entries, with four subjects acquiring concurrent EEG recordings. Univariate analysis revealed that increased stress (OR = 2.01, 95% confidence interval [CI] = 1.12-3.61, AUC = .61, p = .02) was associated with increased relative odds of future self-reported seizures. Multivariate analysis showed that previous self-reported seizures (OR = 5.37, 95% CI = 3.53-8.16, AUC = .76, p < .001) were most strongly associated with future self-reported seizures, and high perceived seizure risk (OR = 3.34, 95% CI = 1.87-5.95, AUC = .69, p < .001) remained significant when prior self-reported seizures were added to the model. No correlation with medication adherence was found. No significant association was found between e-survey responses and subsequent EEG seizures. SIGNIFICANCE Our results suggest that patients may tend to self-forecast seizures that occur in sequential groupings and that low mood and increased stress may be the result of previous seizures rather than independent premonitory symptoms. Patients in the small cohort with concurrent EEG showed no ability to self-predict EEG seizures. The conversion from OR to AUC values facilitates direct comparison of performance between survey and device studies involving survey premonition and forecasting.
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Affiliation(s)
- Jie Cui
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
- Mayo College of Medicine and Science, Mayo Clinic, Rochester, Minnesota, USA
| | - Irena Balzekas
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Ewan Nurse
- Seer Medical, Melbourne, Australia
- Department of Medicine, St. Vincent’s Hospital Melbourne, University of Melbourne, Melbourne, Australia
| | - Pedro Viana
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, UK
- Faculty of Medicine, University of Lisbon, Portugal
| | - Nicholas Gregg
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Philippa Karoly
- Department of Medicine, St. Vincent’s Hospital Melbourne, University of Melbourne, Melbourne, Australia
| | - Rachel E Stirling
- Seer Medical, Melbourne, Australia
- Department of Medicine, St. Vincent’s Hospital Melbourne, University of Melbourne, Melbourne, Australia
| | - Gregory Worrell
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Mark P Richardson
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, UK
| | | | - Benjamin H Brinkmann
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
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21
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Ojemann WK, Scheid BH, Mouchtaris S, Lucas A, LaRocque JJ, Aguila C, Ashourvan A, Caciagli L, Davis KA, Conrad EC, Litt B. Resting-state background features demonstrate multidien cycles in long-term EEG device recordings. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.07.05.23291521. [PMID: 37461688 PMCID: PMC10350154 DOI: 10.1101/2023.07.05.23291521] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
Background Longitudinal EEG recorded by implanted devices is critical for understanding and managing epilepsy. Recent research reports patient-specific, multi-day cycles in device-detected epileptiform events that coincide with increased likelihood of clinical seizures. Understanding these cycles could elucidate mechanisms generating seizures and advance drug and neurostimulation therapies. Objective/Hypothesis We hypothesize that seizure-correlated cycles are present in background neural activity, independent of interictal epileptiform spikes, and that neurostimulation may disrupt these cycles. Methods We analyzed regularly-recorded seizure-free data epochs from 20 patients implanted with a responsive neurostimulation (RNS) device for at least 1.5 years, to explore the relationship between cycles in device-detected interictal epileptiform activity (dIEA), clinician-validated interictal spikes, background EEG features, and neurostimulation. Results Background EEG features tracked the cycle phase of dIEA in all patients (AUC: 0.63 [0.56 - 0.67]) with a greater effect size compared to clinically annotated spike rate alone (AUC: 0.55 [0.53-0.61], p < 0.01). After accounting for circadian variation and spike rate, we observed significant population trends in elevated theta and beta band power and theta and alpha connectivity features at the cycle peaks (sign test, p < 0.05). In the period directly after stimulation we observe a decreased association between cycle phase and EEG features compared to background recordings (AUC: 0.58 [0.55-0.64]). Conclusions Our findings suggest that seizure-correlated dIEA cycles are not solely due to epileptiform discharges but are associated with background measures of brain state; and that neurostimulation may disrupt these cycles. These results may help elucidate mechanisms underlying seizure generation, provide new biomarkers for seizure risk, and facilitate monitoring, treating, and managing epilepsy with implantable devices.
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Affiliation(s)
- William K.S. Ojemann
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street Philadelphia, PA 19104
| | - Brittany H. Scheid
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street Philadelphia, PA 19104
| | - Sofia Mouchtaris
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street Philadelphia, PA 19104
| | - Alfredo Lucas
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street Philadelphia, PA 19104
- University of Pennsylvania, Perelman School of Medicine, 3400 Civic Center Blvd, Philadelphia, PA 19104
| | - Joshua J. LaRocque
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street Philadelphia, PA 19104
- Hospital of the University of Pennsylvania, Department of Neurology, 3400 Spruce St, Philadelphia, PA 19104
| | - Carlos Aguila
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street Philadelphia, PA 19104
| | - Arian Ashourvan
- The University of Kansas, Department of Psychology, 1415 Jayhawk Blvd. Lawrence, KS 66045
| | - Lorenzo Caciagli
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street Philadelphia, PA 19104
| | - Kathryn A. Davis
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street Philadelphia, PA 19104
- Hospital of the University of Pennsylvania, Department of Neurology, 3400 Spruce St, Philadelphia, PA 19104
| | - Erin C. Conrad
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street Philadelphia, PA 19104
- Hospital of the University of Pennsylvania, Department of Neurology, 3400 Spruce St, Philadelphia, PA 19104
| | - Brian Litt
- University of Pennsylvania, Department of Bioengineering, 210 S. 33rd Street Philadelphia, PA 19104
- Hospital of the University of Pennsylvania, Department of Neurology, 3400 Spruce St, Philadelphia, PA 19104
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22
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Sidorenko L, Sidorenko I, Gapelyuk A, Wessel N. Pathological Heart Rate Regulation in Apparently Healthy Individuals. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1023. [PMID: 37509970 PMCID: PMC10378381 DOI: 10.3390/e25071023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 06/30/2023] [Accepted: 06/30/2023] [Indexed: 07/30/2023]
Abstract
Cardiovascular diseases are the leading cause of morbidity and mortality in adults worldwide. There is one common pathophysiological aspect present in all cardiovascular diseases-dysfunctional heart rhythm regulation. Taking this aspect into consideration for cardiovascular risk predictions opens important research perspectives, allowing for the development of preventive treatment techniques. The aim of this study was to find out whether certain pathologically appearing signs in the heart rate variability (HRV) of an apparently healthy person, even with high HRV, can be defined as biomarkers for a disturbed cardiac regulation and whether this can be treated preventively by a drug-free method. This multi-phase study included 218 healthy subjects of either sex, who consecutively visited the physician at Gesundheit clinic because of arterial hypertension, depression, headache, psycho-emotional stress, extreme weakness, disturbed night sleep, heart palpitations, or chest pain. In study phase A, baseline measurement to identify individuals with cardiovascular risks was done. Therefore, standard HRV, as well as the new cardiorhythmogram (CRG) method, were applied to all subjects. The new CRG analysis used here is based on the recently introduced LF drops and HF counter-regulation. Regarding the mechanisms of why these appear in a steady-state cardiorhythmmogram, they represent non-linear event-based dynamical HRV biomarkers. The next phase of the study, phase B, tested whether the pathologically appearing signs identified via CRG in phase A could be clinically influenced by drug-free treatment. In order to validate the new CRG method, it was supported by non-linear HRV analysis in both phase A and in phase B. Out of 218 subjects, the pathologically appearing signs could be detected in 130 cases (60%), p < 0.01, by the new CRG method, and by the standard HRV analysis in 40 cases (18%), p < 0.05. Thus, the CRG method was able to detect 42% more cases with pathologically appearing cardiac regulation. In addition, the comparative CRG analysis before and after treatment showed that the pathologically appearing signs could be clinically influenced without the use of medication. After treatment, the risk group decreased eight-fold-from 130 people to 16 (p < 0.01). Therefore, progression of the detected pathological signs to structural cardiac pathology or arrhythmia could be prevented in most of the cases. However, in the remaining risk group of 16 apparently healthy subjects, 8 people died due to all-cause mortality. In contrast, no other subject in this study has died so far. The non-linear parameter which is able to quantify the changes in CRGs before versus after treatment is FWRENYI4 (symbolic dynamic feature); it decreased from 2.85 to 2.53 (p < 0.001). In summary, signs of pathological cardiac regulation can be identified by the CRG analysis of apparently healthy subjects in the early stages of development of cardiac pathology. Thus, our method offers a sensitive biomarker for cardiovascular risks. The latter can be influenced by non-drug treatments (acupuncture) to stop the progression into structural cardiac pathologies or arrhythmias in most but not all of the patients. Therefore, this could be a real and easy-to-use supplemental method, contributing to primary prevention in cardiology.
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Affiliation(s)
- Ludmila Sidorenko
- Department of Molecular Biology and Human Genetics, State University of Medicine and Pharmacy, "Nicolae Testemitanu", Stefan cel Mare Str. 165, MD-2004 Chisinau, Moldova
| | - Irina Sidorenko
- Medical Center "Gesundheit", Mihai Kogalniceanu Str. 45/2, MD-2009 Chisinau, Moldova
| | - Andrej Gapelyuk
- Cardiovascular Physics, Humboldt-Universität zu Berlin, D-10099 Berlin, Germany
| | - Niels Wessel
- Cardiovascular Physics, Humboldt-Universität zu Berlin, D-10099 Berlin, Germany
- MSB Medical School Berlin GmbH, D-14197 Berlin, Germany
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23
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Xiong W, Stirling RE, Payne DE, Nurse ES, Kameneva T, Cook MJ, Viana PF, Richardson MP, Brinkmann BH, Freestone DR, Karoly PJ. Forecasting seizure likelihood from cycles of self-reported events and heart rate: a prospective pilot study. EBioMedicine 2023; 93:104656. [PMID: 37331164 PMCID: PMC10300292 DOI: 10.1016/j.ebiom.2023.104656] [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: 08/18/2022] [Revised: 05/30/2023] [Accepted: 05/31/2023] [Indexed: 06/20/2023] Open
Abstract
BACKGROUND Seizure risk forecasting could reduce injuries and even deaths in people with epilepsy. There is great interest in using non-invasive wearable devices to generate forecasts of seizure risk. Forecasts based on cycles of epileptic activity, seizure times or heart rate have provided promising forecasting results. This study validates a forecasting method using multimodal cycles recorded from wearable devices. METHOD Seizure and heart rate cycles were extracted from 13 participants. The mean period of heart rate data from a smartwatch was 562 days, with a mean of 125 self-reported seizures from a smartphone app. The relationship between seizure onset time and phases of seizure and heart rate cycles was investigated. An additive regression model was used to project heart rate cycles. The results of forecasts using seizure cycles, heart rate cycles, and a combination of both were compared. Forecasting performance was evaluated in 6 of 13 participants in a prospective setting, using long-term data collected after algorithms were developed. FINDINGS The results showed that the best forecasts achieved a mean area under the receiver-operating characteristic curve (AUC) of 0.73 for 9/13 participants showing performance above chance during retrospective validation. Subject-specific forecasts evaluated with prospective data showed a mean AUC of 0.77 with 4/6 participants showing performance above chance. INTERPRETATION The results of this study demonstrate that cycles detected from multimodal data can be combined within a single, scalable seizure risk forecasting algorithm to provide robust performance. The presented forecasting method enabled seizure risk to be estimated for an arbitrary future period and could be generalised across a range of data types. In contrast to earlier work, the current study evaluated forecasts prospectively, in subjects blinded to their seizure risk outputs, representing a critical step towards clinical applications. FUNDING This study was funded by an Australian Government National Health & Medical Research Council and BioMedTech Horizons grant. The study also received support from the Epilepsy Foundation of America's 'My Seizure Gauge' grant.
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Affiliation(s)
- Wenjuan Xiong
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, Australia
| | - Rachel E Stirling
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia; Seer Medical, Melbourne, Australia
| | | | - Ewan S Nurse
- Seer Medical, Melbourne, Australia; Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, Melbourne, Australia
| | - Tatiana Kameneva
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, Australia; Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia
| | - Mark J Cook
- Seer Medical, Melbourne, Australia; Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, Melbourne, Australia; Graeme Clark Institute, The University of Melbourne, Melbourne, Australia
| | - Pedro F Viana
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Centre for Epilepsy, King's College Hospital NHS Foundation Trust, London, UK; Centro de Estudos Egas Moniz, Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | - Mark P Richardson
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Centre for Epilepsy, King's College Hospital NHS Foundation Trust, London, UK; NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, UK
| | - Benjamin H Brinkmann
- Bioelectronics Neurophysiology and Engineering Lab, Mayo Clinic, Rochester, MN, USA
| | | | - Philippa J Karoly
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia; Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, Melbourne, Australia; Graeme Clark Institute, The University of Melbourne, Melbourne, Australia.
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24
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Gregg NM, Pal Attia T, Nasseri M, Joseph B, Karoly P, Cui J, Stirling RE, Viana PF, Richner TJ, Nurse ES, Schulze-Bonhage A, Cook MJ, Worrell GA, Richardson MP, Freestone DR, Brinkmann BH. Seizure occurrence is linked to multiday cycles in diverse physiological signals. Epilepsia 2023; 64:1627-1639. [PMID: 37060170 PMCID: PMC10733995 DOI: 10.1111/epi.17607] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 04/03/2023] [Accepted: 04/03/2023] [Indexed: 04/16/2023]
Abstract
OBJECTIVE The factors that influence seizure timing are poorly understood, and seizure unpredictability remains a major cause of disability. Work in chronobiology has shown that cyclical physiological phenomena are ubiquitous, with daily and multiday cycles evident in immune, endocrine, metabolic, neurological, and cardiovascular function. Additionally, work with chronic brain recordings has identified that seizure risk is linked to daily and multiday cycles in brain activity. Here, we provide the first characterization of the relationships between the cyclical modulation of a diverse set of physiological signals, brain activity, and seizure timing. METHODS In this cohort study, 14 subjects underwent chronic ambulatory monitoring with a multimodal wrist-worn sensor (recording heart rate, accelerometry, electrodermal activity, and temperature) and an implanted responsive neurostimulation system (recording interictal epileptiform abnormalities and electrographic seizures). Wavelet and filter-Hilbert spectral analyses characterized circadian and multiday cycles in brain and wearable recordings. Circular statistics assessed electrographic seizure timing and cycles in physiology. RESULTS Ten subjects met inclusion criteria. The mean recording duration was 232 days. Seven subjects had reliable electroencephalographic seizure detections (mean = 76 seizures). Multiday cycles were present in all wearable device signals across all subjects. Seizure timing was phase locked to multiday cycles in five (temperature), four (heart rate, phasic electrodermal activity), and three (accelerometry, heart rate variability, tonic electrodermal activity) subjects. Notably, after regression of behavioral covariates from heart rate, six of seven subjects had seizure phase locking to the residual heart rate signal. SIGNIFICANCE Seizure timing is associated with daily and multiday cycles in multiple physiological processes. Chronic multimodal wearable device recordings can situate rare paroxysmal events, like seizures, within a broader chronobiology context of the individual. Wearable devices may advance the understanding of factors that influence seizure risk and enable personalized time-varying approaches to epilepsy care.
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Affiliation(s)
- Nicholas M Gregg
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Minnesota, Rochester, USA
| | - Tal Pal Attia
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Minnesota, Rochester, USA
| | - Mona Nasseri
- School of Engineering, University of North Florida, Florida, Jacksonville, USA
| | - Boney Joseph
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Minnesota, Rochester, USA
| | - Philippa Karoly
- Graeme Clark Institute for Biomedical Engineering, University of Melbourne, Victoria, Parkville, Australia
| | - Jie Cui
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Minnesota, Rochester, USA
| | - Rachel E Stirling
- Seer Medical, Victoria, Melbourne, Australia
- Department of Biomedical Engineering, University of Melbourne, Victoria, Melbourne, Australia
| | - Pedro F Viana
- School of Neuroscience, King's College London, London, UK
- Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | - Thomas J Richner
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Minnesota, Rochester, USA
| | - Ewan S Nurse
- Seer Medical, Victoria, Melbourne, Australia
- Department of Medicine, St. Vincent's Hospital Melbourne, University of Melbourne, Victoria, Fitzroy, Australia
| | | | - Mark J Cook
- Department of Medicine, St. Vincent's Hospital Melbourne, University of Melbourne, Victoria, Fitzroy, Australia
| | - Gregory A Worrell
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Minnesota, Rochester, USA
| | | | | | - Benjamin H Brinkmann
- Department of Neurology, Bioelectronics Neurophysiology and Engineering Laboratory, Mayo Clinic, Minnesota, Rochester, USA
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25
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Cui J, Balzekas I, Nurse E, Viana P, Gregg N, Karoly P, Worrell G, Richardson MP, Freestone DR, Brinkmann BH. Perceived seizure risk in epilepsy â€" Chronic electronic surveys with and without concurrent EEG. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.23.23287561. [PMID: 37034596 PMCID: PMC10081426 DOI: 10.1101/2023.03.23.23287561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
Objective Previous studies suggested that patients with epilepsy might be able to fore-cast their own seizures. We sought to assess the relationships of premonitory symptoms and perceived seizure risk with future and recent self-reported and EEG-confirmed seizures in the subjects living with epilepsy in their natural home environments. Methods We collected long-term e-surveys from ambulatory patients with and without concurrent EEG recordings. Information obtained from the e-surveys included medication compliance, sleep quality, mood, stress, perceived seizure risk and seizure occurrences preceding the survey. EEG seizures were identified. Univariate and multivariate generalized linear mixed-effect regression models were used to estimate odds ratios (ORs) for the assessment of the relationships. Results were compared with device seizure forecasting literature using a mathematical formula converting OR to equivalent area under the curve (AUC). Results Sixty-nine subjects returned 12,590 e-survey entries, with four subjects acquiring concurrent EEG recordings. Univariate analysis revealed increased stress (OR = 2.52, 95% CI = [1.52, 4.14], p < 0.001) and decreased mood (0.32, [0.13, 0.82], 0.02) were associated with increased relative odds of future self-reported seizures. On multivariate analysis, previous self-reported seizures (4.24, [2.69, 6.68], < 0.001) were most strongly associated with future self-reported seizures, and high perceived seizure risk (3.30, [1.97, 5.52], < 0.001) remained significant when prior self-reported seizures were added to the model. No significant association was found between e-survey responses and subsequent EEG seizures. Significance It appears that patients may tend to self-forecast seizures that occur in sequential groupings. Our results suggest that low mood and increased stress may be the result of previous seizures rather than independent premonitory symptoms. Patients in the small cohort with concurrent EEG showed no ability to self-predict EEG seizures. The conversion from OR to AUC values facilitates direct comparison of performance between survey and device studies involving survey premonition and forecasting. Key points Long-term e-surveys data and concurrent EEG signals were collected across three study sites to assess the ability of the patients to self-forecast their seizures.Patients may tend to self-forecast self-reported seizures that occur in sequential groupings.Factors, such as mood and stress, may not be independent premonitory symptoms but may be the consequence of recent seizures.No ability to self-forecast EEG confirmed seizures was observed in a small cohort with concurrent EEG validation.A mathematic relation between OR and AUC provides a means to compare forecasting performance between survey and device studies.
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Affiliation(s)
- Jie Cui
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
- Mayo College of Medicine and Science, Mayo Clinic, Rochester, Minnesota, USA
| | - Irena Balzekas
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Ewan Nurse
- Seer Medical, Melbourne, Australia
- Department of Medicine, St. Vincent’s Hospital Melbourne, University of Melbourne, Melbourne, Australia
| | - Pedro Viana
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, UK
- Faculty of Medicine, University of Lisbon, Portugal
| | - Nicholas Gregg
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Philippa Karoly
- Department of Medicine, St. Vincent’s Hospital Melbourne, University of Melbourne, Melbourne, Australia
| | - Gregory Worrell
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Mark P Richardson
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, UK
| | | | - Benjamin H. Brinkmann
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
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26
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Ceolini E, Ghosh A. Common multi-day rhythms in smartphone behavior. NPJ Digit Med 2023; 6:49. [PMID: 36959382 PMCID: PMC10036334 DOI: 10.1038/s41746-023-00799-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 03/10/2023] [Indexed: 03/25/2023] Open
Abstract
The idea that abnormal human activities follow multi-day rhythms is found in ancient beliefs on the moon to modern clinical observations in epilepsy and mood disorders. To explore multi-day rhythms in healthy human behavior our analysis includes over 300 million smartphone touchscreen interactions logging up to 2 years of day-to-day activities (N401 subjects). At the level of each individual, we find a complex expression of multi-day rhythms where the rhythms occur scattered across diverse smartphone behaviors. With non-negative matrix factorization, we extract the scattered rhythms to reveal periods ranging from 7 to 52 days - cutting across age and gender. The rhythms are likely free-running - instead of being ubiquitously driven by the moon - as they did not show broad population-level synchronization even though the sampled population lived in northern Europe. We propose that multi-day rhythms are a common trait, but their consequences are uniquely experienced in day-to-day behavior.
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Affiliation(s)
- Enea Ceolini
- Cognitive Psychology Unit, Institute of Psychology, Leiden University, Leiden, The Netherlands
| | - Arko Ghosh
- Cognitive Psychology Unit, Institute of Psychology, Leiden University, Leiden, The Netherlands.
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27
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Reynolds A, Vranic-Peters M, Lai A, Grayden DB, Cook MJ, Peterson A. Prognostic interictal electroencephalographic biomarkers and models to assess antiseizure medication efficacy for clinical practice: A scoping review. Epilepsia 2023; 64:1125-1174. [PMID: 36790369 DOI: 10.1111/epi.17548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 02/12/2023] [Accepted: 02/13/2023] [Indexed: 02/16/2023]
Abstract
Antiseizure medication (ASM) is the primary treatment for epilepsy. In clinical practice, methods to assess ASM efficacy (predict seizure freedom or seizure reduction), during any phase of the drug treatment lifecycle, are limited. This scoping review identifies and appraises prognostic electroencephalographic (EEG) biomarkers and prognostic models that use EEG features, which are associated with seizure outcomes following ASM initiation, dose adjustment, or withdrawal. We also aim to summarize the population and context in which these biomarkers and models were identified and described, to understand how they could be used in clinical practice. Between January 2021 and October 2022, four databases, references, and citations were systematically searched for ASM studies investigating changes to interictal EEG or prognostic models using EEG features and seizure outcomes. Study bias was appraised using modified Quality in Prognosis Studies criteria. Results were synthesized into a qualitative review. Of 875 studies identified, 93 were included. Biomarkers identified were classed as qualitative (visually identified by wave morphology) or quantitative. Qualitative biomarkers include identifying hypsarrhythmia, centrotemporal spikes, interictal epileptiform discharges (IED), classifying the EEG as normal/abnormal/epileptiform, and photoparoxysmal response. Quantitative biomarkers were statistics applied to IED, high-frequency activity, frequency band power, current source density estimates, pairwise statistical interdependence between EEG channels, and measures of complexity. Prognostic models using EEG features were Cox proportional hazards models and machine learning models. There is promise that some quantitative EEG biomarkers could be used to assess ASM efficacy, but further research is required. There is insufficient evidence to conclude any specific biomarker can be used for a particular population or context to prognosticate ASM efficacy. We identified a potential battery of prognostic EEG biomarkers, which could be combined with prognostic models to assess ASM efficacy. However, many confounders need to be addressed for translation into clinical practice.
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Affiliation(s)
- Ashley Reynolds
- Department of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurosciences, St. Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - Michaela Vranic-Peters
- Department of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurosciences, St. Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - Alan Lai
- Department of Neurosciences, St. Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - David B Grayden
- Department of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurosciences, St. Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia.,Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Mark J Cook
- Department of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurosciences, St. Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia.,Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Andre Peterson
- Department of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurosciences, St. Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia.,Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
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28
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Xiong W, Kameneva T, Lambert E, Cook MJ, Richardson MP, Nurse ES. Forecasting psychogenic non-epileptic seizure likelihood from ambulatory EEG and ECG. J Neural Eng 2022; 19. [PMID: 36270501 DOI: 10.1088/1741-2552/ac9c97] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 10/21/2022] [Indexed: 12/24/2022]
Abstract
Objective.Critical slowing features (variance and autocorrelation) of long-term continuous electroencephalography (EEG) and electrocardiography (ECG) data have previously been used to forecast epileptic seizure onset. This study tested the feasibility of forecasting non-epileptic seizures using the same methods. In doing so, we examined if long-term cycles of brain and cardiac activity are present in clinical physiological recordings of psychogenic non-epileptic seizures (PNES).Approach.Retrospectively accessed ambulatory EEG and ECG data from 15 patients with non-epileptic seizures and no background of epilepsy were used for developing the forecasting system. The median period of recordings was 161 h, with a median of 7 non-epileptic seizures per patient. The phases of different cycles (5 min, 1 h, 6 h, 12 h, 24 h) of EEG and RR interval (RRI) critical slowing features were investigated. Forecasters were generated using combinations of the variance and autocorrelation of both EEG and the RRI of the ECG at each of the aforementioned cycle lengths. Optimal forecasters were selected as those with the highest area under the receiver-operator curve (AUC).Main results.It was found that PNES events occurred in the rising phases of EEG feature cycles of 12 and 24 h in duration at a rate significantly above chance. We demonstrated that the proposed forecasters achieved performance significantly better than chance in 8/15 of patients, and the mean AUC of the best forecaster across patients was 0.79.Significance.To our knowledge, this is the first study to retrospectively forecast non-epileptic seizures using both EEG and ECG data. The significance of EEG in the forecasting models suggests that cyclic EEG features of non-epileptic seizures exist. This study opens the potential of seizure forecasting beyond epilepsy, into other disorders of episodic loss of consciousness or dissociation.
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Affiliation(s)
- Wenjuan Xiong
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, Australia
| | - Tatiana Kameneva
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, Australia.,Iverson Health Innovation Institute, Swinburne University of Technology, Melbourne, Australia.,Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia
| | - Elisabeth Lambert
- Iverson Health Innovation Institute, Swinburne University of Technology, Melbourne, Australia.,School of Health Sciences, Swinburne University of Technology, Melbourne, Australia
| | - Mark J Cook
- Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, Melbourne, Australia.,Graeme Clark Institute, The University of Melbourne, Melbourne, Australia
| | - Mark P Richardson
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Centre for Epilepsy, King's College Hospital NHS Foundation Trust, London, United Kingdom
| | - Ewan S Nurse
- Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, Melbourne, Australia.,Seer Medical, Melbourne, Australia
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29
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Kerr WT, Brandt C, Ngo LY, Patten A, Cheng JY, Kramer L, French JA. Time to exceed pre-randomization monthly seizure count for perampanel in participants with primary generalized tonic-clonic seizures: A potential clinical end point. Epilepsia 2022; 63:2994-3004. [PMID: 36106379 PMCID: PMC9828687 DOI: 10.1111/epi.17411] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 09/13/2022] [Accepted: 09/13/2022] [Indexed: 01/12/2023]
Abstract
OBJECTIVE To evaluate the exploratory time to exceed pre-randomization seizure count (T-PSC) in the determination of efficacy of adjunctive perampanel in participants with primary generalized tonic-clonic (PGTC) seizures in generalized-onset epilepsy. METHODS In this multicenter, double-blind study (ClinicalTrials.gov identifier: NCT01393743), participants ≥12 years of age with treatment-resistant idiopathic generalized epilepsy were randomized to receive placebo or adjunctive perampanel (≤8 mg/day) across a 17-week double-blind treatment phase (4-week titration; 13-week maintenance). We evaluated the pre-planned exploratory end point of the T-PSC using a Kaplan-Meier analysis. We also re-evaluated the correspondence of the primary end points of median percent seizure frequency change (MPC) and 50% responder rate (50RR) calculated at T-PSC and at the end of the trial. RESULTS The exploratory end point of median T-PSC on placebo was 43 days and >120 days on perampanel (log-rank p < .001). The primary end points calculated at T-PSC did not differ significantly from the end points at the end of the trial (MPC -31% vs -42% at T-PSC; 50RR 32% vs 51% at T-PSC). After T-PSC was reached, participants had a median (interquartile range) of 5 (3-13) additional seizures on placebo and 5 (2-10) on perampanel. SIGNIFICANCE The exploratory end point of T-PSC demonstrated the effectiveness of perampanel despite a shorter duration of monitoring. The seizures that occurred after T-PSC did not influence the conclusions of the trial; therefore, T-PSC may be a viable alternative to traditional trial end points that reduces the risk to participants.
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Affiliation(s)
- Wesley T. Kerr
- Department of NeurologyUniversity of MichiganAnn ArborMichiganUSA
| | - Christian Brandt
- Bethel Epilepsy CenterUniversity Hospital for EpileptologyBielefeldGermany
| | - Leock Y. Ngo
- Department of NeurologyEisai Inc.NutleyNew JerseyUSA
| | | | | | - Lynn Kramer
- Department of NeurologyEisai Inc.NutleyNew JerseyUSA
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Laiou P, Biondi A, Bruno E, Viana PF, Winston JS, Rashid Z, Ranjan Y, Conde P, Stewart C, Sun S, Zhang Y, Folarin A, Dobson RJB, Schulze-Bonhage A, Dümpelmann M, Richardson MP. Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence. Biomedicines 2022; 10:2662. [PMID: 36289925 PMCID: PMC9599905 DOI: 10.3390/biomedicines10102662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 10/10/2022] [Accepted: 10/11/2022] [Indexed: 12/02/2022] Open
Abstract
Epilepsy is one of the most common neurological disorders, characterized by the occurrence of repeated seizures. Given that epilepsy is considered a network disorder, tools derived from network neuroscience may confer the valuable ability to quantify the properties of epileptic brain networks. In this study, we use well-established brain network metrics (i.e., mean strength, variance of strength, eigenvector centrality, betweenness centrality) to characterize the temporal evolution of epileptic functional networks over several days prior to seizure occurrence. We infer the networks using long-term electroencephalographic recordings from 12 people with epilepsy. We found that brain network metrics are variable across days and show a circadian periodicity. In addition, we found that in 9 out of 12 patients the distribution of the variance of strength in the day (or even two last days) prior to seizure occurrence is significantly different compared to the corresponding distributions on all previous days. Our results suggest that brain network metrics computed fromelectroencephalographic recordings could potentially be used to characterize brain network changes that occur prior to seizures, and ultimately contribute to seizure warning systems.
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Affiliation(s)
- Petroula Laiou
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Andrea Biondi
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Elisa Bruno
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Pedro F. Viana
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
- Faculty of Medicine, University of Lisbon, 1649-028 Lisbon, Portugal
| | - Joel S. Winston
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Zulqarnain Rashid
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Yatharth Ranjan
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Pauline Conde
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Callum Stewart
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Shaoxiong Sun
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Yuezhou Zhang
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Amos Folarin
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
- Institute of Health Informatics, University College London, London WC1E 6BT, UK
- NIHR Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, King’s College London, London SE5 8AF, UK
- Health Data Research UK London, University College London, London WC1E 6BT, UK
- NIHR Biomedical Research Centre, University College London Hospitals NHS Foundation Trust, London W1T 7DN, UK
| | - Richard J. B. Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
- Institute of Health Informatics, University College London, London WC1E 6BT, UK
- NIHR Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, King’s College London, London SE5 8AF, UK
- Health Data Research UK London, University College London, London WC1E 6BT, UK
- NIHR Biomedical Research Centre, University College London Hospitals NHS Foundation Trust, London W1T 7DN, UK
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
| | - Matthias Dümpelmann
- Epilepsy Center, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
- Department of Microsystems Engineering (IMTEK), University of Freiburg, 79110 Freiburg, Germany
| | - Mark P. Richardson
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
- NIHR Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, King’s College London, London SE5 8AF, UK
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Kreitlow BL, Li W, Buchanan GF. Chronobiology of epilepsy and sudden unexpected death in epilepsy. Front Neurosci 2022; 16:936104. [PMID: 36161152 PMCID: PMC9490261 DOI: 10.3389/fnins.2022.936104] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 06/27/2022] [Indexed: 11/24/2022] Open
Abstract
Epilepsy is a neurological disease characterized by spontaneous, unprovoked seizures. Various insults render the brain hyperexcitable and susceptible to seizure. Despite there being dozens of preventative anti-seizure medications available, these drugs fail to control seizures in nearly 1 in 3 patients with epilepsy. Over the last century, a large body of evidence has demonstrated that internal and external rhythms can modify seizure phenotypes. Physiologically relevant rhythms with shorter periodic rhythms, such as endogenous circadian rhythms and sleep-state, as well as rhythms with longer periodicity, including multidien rhythms and menses, influence the timing of seizures through poorly understood mechanisms. The purpose of this review is to discuss the findings from both human and animal studies that consider the effect of such biologically relevant rhythms on epilepsy and seizure-associated death. Patients with medically refractory epilepsy are at increased risk of sudden unexpected death in epilepsy (SUDEP). The role that some of these rhythms play in the nocturnal susceptibility to SUDEP will also be discussed. While the involvement of some of these rhythms in epilepsy has been known for over a century, applying the rhythmic nature of such phenomenon to epilepsy management, particularly in mitigating the risk of SUDEP, has been underutilized. As our understanding of the physiological influence on such rhythmic phenomenon improves, and as technology for chronic intracranial epileptiform monitoring becomes more widespread, smaller and less invasive, novel seizure-prediction technologies and time-dependent chronotherapeutic seizure management strategies can be realized.
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Affiliation(s)
- Benjamin L. Kreitlow
- Medical Scientist Training Program, University of Iowa, Iowa City, IA, United States
- Interdisciplinary Graduate Program in Neuroscience, University of Iowa, Iowa City, IA, United States
- Iowa Neuroscience Institute, University of Iowa, Iowa City, IA, United States
- Department of Neurology, University of Iowa, Iowa City, IA, United States
- Carver College of Medicine, University of Iowa, Iowa City, IA, United States
| | - William Li
- Department of Neurology, University of Iowa, Iowa City, IA, United States
- Carver College of Medicine, University of Iowa, Iowa City, IA, United States
| | - Gordon F. Buchanan
- Medical Scientist Training Program, University of Iowa, Iowa City, IA, United States
- Interdisciplinary Graduate Program in Neuroscience, University of Iowa, Iowa City, IA, United States
- Iowa Neuroscience Institute, University of Iowa, Iowa City, IA, United States
- Department of Neurology, University of Iowa, Iowa City, IA, United States
- Carver College of Medicine, University of Iowa, Iowa City, IA, United States
- *Correspondence: Gordon F. Buchanan, ; orcid.org/0000-0003-2371-4455
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Seizure-related differences in biosignal 24-h modulation patterns. Sci Rep 2022; 12:15070. [PMID: 36064877 PMCID: PMC9445076 DOI: 10.1038/s41598-022-18271-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 08/08/2022] [Indexed: 11/08/2022] Open
Abstract
A seizure likelihood biomarker could improve seizure monitoring and facilitate adjustment of treatments based on seizure risk. Here, we tested differences in patient-specific 24-h-modulation patterns of electrodermal activity (EDA), peripheral body temperature (TEMP), and heart rate (HR) between patients with and without seizures. We enrolled patients who underwent continuous video-EEG monitoring at Boston Children's Hospital to wear a biosensor. We divided patients into two groups: those with no seizures and those with at least one seizure during the recording period. We assessed the 24-h modulation level and amplitude of EDA, TEMP, and HR. We performed machine learning including physiological and clinical variables. Subsequently, we determined classifier performance by cross-validated machine learning. Patients with seizures (n = 49) had lower EDA levels (p = 0.031), EDA amplitudes (p = 0.045), and trended toward lower HR levels (p = 0.060) compared to patients without seizures (n = 68). Averaged cross-validated classification accuracy was 69% (AUC-ROC: 0.75). Our results show the potential to monitor and forecast risk for epileptic seizures based on changes in 24-h patterns in wearable recordings in combination with clinical variables. Such biomarkers might be applicable to inform care, such as treatment or seizure injury risk during specific periods, scheduling diagnostic tests, such as admission to the epilepsy monitoring unit, and potentially other neurological and chronic conditions.
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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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Wheless JW, Friedman D, Krauss GL, Rao VR, Sperling MR, Carrazana E, Rabinowicz AL. Future Opportunities for Research in Rescue Treatments. Epilepsia 2022; 63 Suppl 1:S55-S68. [PMID: 35822912 PMCID: PMC9541657 DOI: 10.1111/epi.17363] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 06/16/2022] [Accepted: 07/11/2022] [Indexed: 11/30/2022]
Abstract
Clinical studies of rescue medications for seizure clusters are limited and are designed to satisfy regulatory requirements, which may not fully consider the needs of the diverse patient population that experiences seizure clusters or utilize rescue medication. The purpose of this narrative review is to examine the factors that contribute to, or may influence the quality of, seizure cluster research with a goal of improving clinical practice. We address five areas of unmet needs and provide advice for how they could enhance future trials of seizure cluster treatments. The topics addressed in this article are: (1) unaddressed end points to pursue in future studies, (2) roles for devices to enhance rescue medication clinical development programs, (3) tools to study seizure cluster prediction and prevention, (4) the value of other designs for seizure cluster studies, and (5) unique challenges of future trial paradigms for seizure clusters. By focusing on novel end points and technologies with value to patients, caregivers, and clinicians, data obtained from future studies can benefit the diverse patient population that experiences seizure clusters, providing more effective, appropriate care as well as alleviating demands on health care resources.
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Affiliation(s)
- James W Wheless
- Le Bonheur Children's Hospital, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Daniel Friedman
- New York University Grossman School of Medicine, New York, New York, USA
| | - Gregory L Krauss
- Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Vikram R Rao
- University of California, San Francisco, California, USA
| | | | - Enrique Carrazana
- Neurelis, San Diego, California, USA.,John A. Burns School of Medicine, University of Hawaii, Honolulu, Hawaii, USA
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35
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Verrier RL, Pang TD, Nearing BD, Schachter SC. Prolonged QT Interval Predicts All-Cause Mortality in Epilepsy Patients: Diagnostic and Therapeutic Implications. Heart Rhythm 2022; 19:585-587. [PMID: 35033664 DOI: 10.1016/j.hrthm.2022.01.015] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 01/11/2022] [Accepted: 01/11/2022] [Indexed: 11/29/2022]
Affiliation(s)
- Richard L Verrier
- Beth Israel Deaconess Medical Center, Department of Medicine, Harvard Medical School, Boston MA, USA.
| | - Trudy D Pang
- Beth Israel Deaconess Medical Center, Department of Neurology, Harvard Medical School, Boston MA, USA
| | - Bruce D Nearing
- Beth Israel Deaconess Medical Center, Department of Medicine, Harvard Medical School, Boston MA, USA
| | - Steven C Schachter
- Beth Israel Deaconess Medical Center, Department of Neurology, Harvard Medical School, Boston MA, USA; Beth Israel Deaconess Medical Center, Massachusetts General Hospital, Department of Neurology, Harvard Medical School, Boston MA, USA; Consortia for Improving Medicine with Innovation & Technology (CIMIT), Boston MA, USA
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36
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Proix T, Baud M. Human multidien rhythms: Commentary for: "Multiday cycles of heart rate are associated with seizure likelihood: An observational cohort study". EBioMedicine 2021; 74:103698. [PMID: 34800901 PMCID: PMC8605401 DOI: 10.1016/j.ebiom.2021.103698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 11/03/2021] [Indexed: 11/12/2022] Open
Affiliation(s)
- Timothée Proix
- Department of Basic Neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland.
| | - Maxime Baud
- Sleep-Wake-Epilepsy Center, NeuroTec and Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland; Wyss Center for Bio and Neuroengineering, Geneva, Switzerland.
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