1
|
Macea J, Swinnen L, Varon C, De Vos M, Van Paesschen W. Cardiorespiratory disturbances in focal impaired awareness seizures: Insights from wearable ECG monitoring. Epilepsy Behav 2024; 158:109917. [PMID: 38924968 DOI: 10.1016/j.yebeh.2024.109917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 06/06/2024] [Accepted: 06/22/2024] [Indexed: 06/28/2024]
Abstract
PURPOSE Seizures are characterized by periictal autonomic changes. Wearable devices could help improve our understanding of these phenomena through long-term monitoring. In this study, we used wearable electrocardiogram (ECG) data to evaluate differences between temporal and extratemporal focal impaired awareness (FIA) seizures monitored in the hospital and at home. We assessed periictal heart rate, respiratory rate, heart rate variability (HRV), and respiratory sinus arrhythmia (RSA). METHODS We extracted ECG signals across three time points - five minutes baseline and preictal, ten minutes postictal - and the seizure duration. After automatic Rpeak selection, we calculated the heart rate and estimated the respiratory rate using the ECG-derived respiration methodology. HRV was calculated in both time and frequency domains. To evaluate the influence of other modulators on the HRV after removing the respiratory influences, we recalculated the residual power in the high-frequency (HF) and low-frequency (LF) bands using orthogonal subspace projections. Finally, 5-minute and 30-second (ultra-short) ECG segments were used to calculate RSA using three different methods. Seizures from temporal and extratemporal origins were compared using mixed-effects models and estimated marginal means. RESULTS The mean preictal heart rate was 69.95 bpm (95 % CI 65.6 - 74.3), and it increased to 82 bpm, 95 % CI (77.51 - 86.47) and 84.11 bpm, 95 % CI (76.9 - 89.5) during the ictal and postictal periods. Preictal, ictal and postictal respiratory rates were 16.1 (95 % CI 15.2 - 17.1), 14.8 (95 % CI 13.4 - 16.2) and 15.1 (95 % CI 14 - 16.2), showing not statistically significant bradypnea. HRV analysis found a higher baseline power in the LF band, which was still significantly higher after removing the respiratory influences. Postictally, we found decreased power in the HF band and the respiratory influences in both frequency bands. The RSA analysis with the new methods confirmed the lower cardiorespiratory interaction during the postictal period. Additionally, using ultra-short ECG segments, we found that RSA decreases before the electroclinical seizure onset. No differences were observed in the studied parameters between temporal and extratemporal seizures. CONCLUSIONS We found significant increases in the ictal and postictal heart rates and lower respiratory rates. Isolating the respiratory influences on the HRV showed a postictal reduction of respiratory modulations on both LF and HF bands, suggesting a central role of respiratory influences in the periictal HRV, unlike the baseline measurements. We found a reduced cardiorespiratory interaction during the periictal period using other RSA methods, suggesting a blockade in vagal efferences before the electroclinical onset. These findings highlight the importance of respiratory influences in cardiac dynamics during seizures and emphasize the need to longitudinally assess HRV and RSA to gain insights into long-term autonomic dysregulation.
Collapse
Affiliation(s)
- Jaiver Macea
- Laboratory for Epilepsy Research, Leuven Brain Institute, Department of Neurosciences, KU Leuven, Leuven 3000, Belgium.
| | - Lauren Swinnen
- Laboratory for Epilepsy Research, Leuven Brain Institute, Department of Neurosciences, KU Leuven, Leuven 3000, Belgium.
| | - Carolina Varon
- STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, Leuven 3000, Belgium.
| | - Maarten De Vos
- STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, Leuven 3000, Belgium; Department of Development and Regeneration, KU Leuven, Leuven 3000, Belgium.
| | - Wim Van Paesschen
- Laboratory for Epilepsy Research, Leuven Brain Institute, Department of Neurosciences, KU Leuven, Leuven 3000, Belgium; Department of Neurology, Leuven University Hospitals, Leuven 3000, Belgium.
| |
Collapse
|
2
|
Acedo Reina E, Germany Morrison E, Dereli AS, Collard E, Raffoul R, Nonclercq A, El Tahry R. Vagus nerve electroneurogram-based detection of acute kainic acid induced seizures. Front Neurosci 2024; 18:1427308. [PMID: 39170680 PMCID: PMC11335647 DOI: 10.3389/fnins.2024.1427308] [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: 05/03/2024] [Accepted: 07/02/2024] [Indexed: 08/23/2024] Open
Abstract
Seizures produce autonomic symptoms, mainly sympathetic but also parasympathetic in origin. Within this context, the vagus nerve is a key player as it carries information from the different organs to the brain and vice versa. Hence, exploiting vagal neural traffic for seizure detection might be a promising tool to improve the efficacy of closed-loop Vagus Nerve Stimulation. This study developed a VENG detection algorithm that effectively detects seizures by emphasizing the loss of spontaneous rhythmicity associated with respiration in acute intrahippocampal Kainic Acid rat model. Among 20 induced seizures in six anesthetized rats, 13 were detected (sensitivity: 65%, accuracy: 92.86%), with a mean VENG-detection delay of 25.3 ± 13.5 s after EEG-based seizure onset. Despite variations in detection parameters, 7 out of 20 seizures exhibited no ictal VENG modifications and remained undetected. Statistical analysis highlighted a significant difference in Delta, Theta and Beta band evolution between detected and undetected seizures, in addition to variations in the magnitude of HR changes. Binomial logistic regression analysis confirmed that an increase in delta and theta band activity was associated with a decreased likelihood of seizure detection. This results suggest the possibility of distinct seizure spreading patterns between the two groups which may results in differential activation of the autonomic central network. Despite notable progress, limitations, particularly the absence of respiration recording, underscore areas for future exploration and refinement in closed-loop stimulation strategies for epilepsy management. This study constitutes the initial phase of a longitudinal investigation, which will subsequently involve reproducing these experiments in awake conditions with spontaneous recurrent seizures.
Collapse
Affiliation(s)
- Elena Acedo Reina
- Clinical Neuroscience, Institute of Neuroscience (IoNS), Université Catholique de Louvain, Brussels, Belgium
| | - Enrique Germany Morrison
- Clinical Neuroscience, Institute of Neuroscience (IoNS), Université Catholique de Louvain, Brussels, Belgium
- Walloon Excellence in Life Sciences and Biotechnology (WELBIO) Department, WEL Research Institute, Wavre, Belgium
| | - Ayse S. Dereli
- Clinical Neuroscience, Institute of Neuroscience (IoNS), Université Catholique de Louvain, Brussels, Belgium
| | - Elise Collard
- Clinical Neuroscience, Institute of Neuroscience (IoNS), Université Catholique de Louvain, Brussels, Belgium
| | - Romain Raffoul
- BEAMS Department, Université Libre de Bruxelles, Brussels, Belgium
| | | | - Riëm El Tahry
- Clinical Neuroscience, Institute of Neuroscience (IoNS), Université Catholique de Louvain, Brussels, Belgium
- Walloon Excellence in Life Sciences and Biotechnology (WELBIO) Department, WEL Research Institute, Wavre, Belgium
- Department of Neurology, Center for Refractory Epilepsy, Cliniques Universitaires Saint-Luc, Brussels, Belgium
| |
Collapse
|
3
|
Ricordeau F, Chouchou F, Pichot V, Roche F, Petitjean T, Gormand F, Bastuji H, Charbonnier E, Le Cam P, Stauffer E, Rheims S, Peter-Derex L. Impaired post-sleep apnea autonomic arousals in patients with drug-resistant epilepsy. Clin Neurophysiol 2024; 160:1-11. [PMID: 38367308 DOI: 10.1016/j.clinph.2024.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 12/20/2023] [Accepted: 02/04/2024] [Indexed: 02/19/2024]
Abstract
OBJECTIVE Sudden and unexpected deaths in epilepsy (SUDEP) pathophysiology may involve an interaction between respiratory dysfunction and sleep/wake state regulation. We investigated whether patients with epilepsy exhibit impaired sleep apnea-related arousals. METHODS Patients with drug-resistant (N = 20) or drug-sensitive (N = 20) epilepsy and obstructive sleep apnea, as well as patients with sleep apnea but without epilepsy (controls, N = 20) were included. We explored (1) the respiratory arousal threshold based on nadir oxygen saturation, apnea-hypopnea index, and fraction of hypopnea among respiratory events; (2) the cardiac autonomic response to apnea/hypopnea quantified as percentages of changes from the baseline in RR intervals (RRI), high (HF) and low (LF) frequency powers, and LF/HF. RESULTS The respiratory arousal threshold did not differ between groups. At arousal onset, RRI decreased (-9.42%) and LF power (179%) and LF/HF ratio (190%) increased. This was followed by an increase in HF power (118%), p < 0.05. The RRI decrease was lower in drug-resistant (-7.40%) than in drug-sensitive patients (-9.94%) and controls (-10.91%), p < 0.05. LF and HF power increases were higher in drug-resistant (188%/126%) than in drug-sensitive patients (172%/126%) and controls (177%/115%), p < 0.05. CONCLUSIONS Cardiac reactivity following sleep apnea is impaired in drug-resistant epilepsy. SIGNIFICANCE This autonomic dysfunction might contribute to SUDEP pathophysiology.
Collapse
Affiliation(s)
- François Ricordeau
- Centre for Sleep Medicine and Respiratory Diseases, Hospices Civils de Lyon, Lyon, France; Department of Functional Neurology and Epileptology, Hospices Civils de Lyon, Lyon, France
| | - Florian Chouchou
- IRISSE Laboratory (EA4075), UFR SHE, University of La Réunion, Le Tampon, France
| | - Vincent Pichot
- SAINBIOSE, INSERM U1059, Saint-Etienne Jean-Monnet University, Mines Saint-Etienne, France; Clinical Physiology and Exercise, Visas Center, Saint Etienne University Hospital, France
| | - Frédéric Roche
- SAINBIOSE, INSERM U1059, Saint-Etienne Jean-Monnet University, Mines Saint-Etienne, France; Clinical Physiology and Exercise, Visas Center, Saint Etienne University Hospital, France
| | - Thierry Petitjean
- Centre for Sleep Medicine and Respiratory Diseases, Hospices Civils de Lyon, Lyon, France
| | - Frédéric Gormand
- Centre for Sleep Medicine and Respiratory Diseases, Hospices Civils de Lyon, Lyon, France
| | - Hélène Bastuji
- Centre for Sleep Medicine and Respiratory Diseases, Hospices Civils de Lyon, Lyon, France; Lyon Neuroscience Research Center, CNRS UMR 5292 / INSERM U1028 and Lyon 1 University, Lyon, France
| | - Eléna Charbonnier
- Centre for Sleep Medicine and Respiratory Diseases, Hospices Civils de Lyon, Lyon, France
| | - Pierre Le Cam
- Centre for Sleep Medicine and Respiratory Diseases, Hospices Civils de Lyon, Lyon, France
| | - Emeric Stauffer
- Centre for Sleep Medicine and Respiratory Diseases, Hospices Civils de Lyon, Lyon, France; Inter-university Laboratoryof Human MovementBiology (LIBM) EA7424, Team « Vascular Biology and Red Blood Cell », Lyon 1 University, Lyon, France; Respiratory Functional Investigation & Physical Activity Department, Hospices Civils de Lyon, Lyon, France
| | - Sylvain Rheims
- Department of Functional Neurology and Epileptology, Hospices Civils de Lyon, Lyon, France; Lyon Neuroscience Research Center, CNRS UMR 5292 / INSERM U1028 and Lyon 1 University, Lyon, France; Lyon 1 University, Lyon, France
| | - Laure Peter-Derex
- Centre for Sleep Medicine and Respiratory Diseases, Hospices Civils de Lyon, Lyon, France; Lyon Neuroscience Research Center, CNRS UMR 5292 / INSERM U1028 and Lyon 1 University, Lyon, France; Lyon 1 University, Lyon, France.
| |
Collapse
|
4
|
Seth EA, Watterson J, Xie J, Arulsamy A, Md Yusof HH, Ngadimon IW, Khoo CS, Kadirvelu A, Shaikh MF. Feasibility of cardiac-based seizure detection and prediction: A systematic review of non-invasive wearable sensor-based studies. Epilepsia Open 2024; 9:41-59. [PMID: 37881157 PMCID: PMC10839362 DOI: 10.1002/epi4.12854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 10/21/2023] [Indexed: 10/27/2023] Open
Abstract
A reliable seizure detection or prediction device can potentially reduce the morbidity and mortality associated with epileptic seizures. Previous findings indicating alterations in cardiac activity during seizures suggest the usefulness of cardiac parameters for seizure detection or prediction. This study aims to examine available studies on seizure detection and prediction based on cardiac parameters using non-invasive wearable devices. The Embase, PubMed, and Scopus databases were used to systematically search according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Human studies that evaluated seizure detection or prediction based on cardiac parameters collected using wearable devices were included. The QUADAS-2 tool and proposed standards for validation for seizure detection devices were used for quality assessment. Twenty-four articles were identified and included in the analysis. Twenty studies evaluated seizure detection algorithms, and four studies focused on seizure prediction. Most studies used either a wrist-worn or chest-worn device for data acquisition. Among the seizure detection studies, cardiac parameters utilized for the algorithms mainly included heart rate (HR) (n = 11) or a combination of HR and heart rate variability (HRV) (n = 6). HR-based seizure detection studies collectively reported a sensitivity range of 56%-100% and a false alarm rate (FAR) of 0.02-8/h, with most studies performing retrospective validation of the algorithms. Three of the seizure prediction studies retrospectively validated multimodal algorithms, combining cardiac features with other physiological signals. Only one study prospectively validated their seizure prediction algorithm using HRV extracted from ECG data collected from a custom wearable device. These studies have demonstrated the feasibility of using cardiac parameters for seizure detection and prediction with wearable devices, with varying algorithmic performance. Many studies are in the proof-of-principle stage, and evidence for real-time detection or prediction is currently limited. Future studies should prioritize further refinement of the algorithm performance with prospective validation using large-scale longitudinal data. PLAIN LANGUAGE SUMMARY: This systematic review highlights the potential use of wearable devices, like wristbands, for detecting and predicting seizures via the measurement of heart activity. By reviewing 24 articles, it was found that most studies focused on using heart rate and changes in heart rate for seizure detection. There was a lack of studies looking at seizure prediction. The results were promising but most studies were not conducted in real-time. Therefore, more real-time studies are needed to verify the usage of heart activity-related wearable devices to detect seizures and even predict them, which will be beneficial to people with epilepsy.
Collapse
Affiliation(s)
- Eryse Amira Seth
- Neuropharmacology Research Laboratory, Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
- Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
| | - Jessica Watterson
- Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
- Department of Human‐Centred ComputingMonash UniversityMelbourneVictoriaAustralia
| | - Jue Xie
- Department of Human‐Centred ComputingMonash UniversityMelbourneVictoriaAustralia
| | - Alina Arulsamy
- Neuropharmacology Research Laboratory, Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
- Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
| | - Hadri Hadi Md Yusof
- Neuropharmacology Research Laboratory, Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
- Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
| | - Irma Wati Ngadimon
- Neuropharmacology Research Laboratory, Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
- Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
| | - Ching Soong Khoo
- Neurology Unit, Department of MedicineUniversiti Kebangsaan Malaysia Medical CentreKuala LumpurMalaysia
| | - Amudha Kadirvelu
- Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
| | - Mohd Farooq Shaikh
- Neuropharmacology Research Laboratory, Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
- Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
- School of Dentistry and Medical SciencesCharles Sturt UniversityOrangeNew South WalesAustralia
| |
Collapse
|
5
|
Mason F, Scarabello A, Taruffi L, Pasini E, Calandra-Buonaura G, Vignatelli L, Bisulli F. Heart Rate Variability as a Tool for Seizure Prediction: A Scoping Review. J Clin Med 2024; 13:747. [PMID: 38337440 PMCID: PMC10856437 DOI: 10.3390/jcm13030747] [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: 12/06/2023] [Revised: 01/04/2024] [Accepted: 01/22/2024] [Indexed: 02/12/2024] Open
Abstract
The most critical burden for People with Epilepsy (PwE) is represented by seizures, the unpredictability of which severely impacts quality of life. The design of real-time warning systems that can detect or even predict ictal events would enhance seizure management, leading to high benefits for PwE and their caregivers. In the past, various research works highlighted that seizure onset is anticipated by significant changes in autonomic cardiac control, which can be assessed through heart rate variability (HRV). This manuscript conducted a scoping review of the literature analyzing HRV-based methods for detecting or predicting ictal events. An initial search on the PubMed database returned 402 papers, 72 of which met the inclusion criteria and were included in the review. These results suggest that seizure detection is more accurate in neonatal and pediatric patients due to more significant autonomic modifications during the ictal transitions. In addition, conventional metrics are often incapable of capturing cardiac autonomic variations and should be replaced with more advanced methodologies, considering non-linear HRV features and machine learning tools for processing them. Finally, studies investigating wearable systems for heart monitoring denoted how HRV constitutes an efficient biomarker for seizure detection in patients presenting significant alterations in autonomic cardiac control during ictal events.
Collapse
Affiliation(s)
- Federico Mason
- Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy; (F.M.); (A.S.); (L.T.); (G.C.-B.); (F.B.)
| | - Anna Scarabello
- Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy; (F.M.); (A.S.); (L.T.); (G.C.-B.); (F.B.)
| | - Lisa Taruffi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy; (F.M.); (A.S.); (L.T.); (G.C.-B.); (F.B.)
| | - Elena Pasini
- IRCCS Institute of Neurological Sciences of Bologna, Full Member of the European Reference Network EpiCARE, 40139 Bologna, Italy;
| | - Giovanna Calandra-Buonaura
- Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy; (F.M.); (A.S.); (L.T.); (G.C.-B.); (F.B.)
- IRCCS Institute of Neurological Sciences of Bologna, Full Member of the European Reference Network EpiCARE, 40139 Bologna, Italy;
| | - Luca Vignatelli
- IRCCS Institute of Neurological Sciences of Bologna, Full Member of the European Reference Network EpiCARE, 40139 Bologna, Italy;
| | - Francesca Bisulli
- Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy; (F.M.); (A.S.); (L.T.); (G.C.-B.); (F.B.)
- IRCCS Institute of Neurological Sciences of Bologna, Full Member of the European Reference Network EpiCARE, 40139 Bologna, Italy;
| |
Collapse
|
6
|
Bhandare AM, Dale N. Neural correlate of reduced respiratory chemosensitivity during chronic epilepsy. Front Cell Neurosci 2023; 17:1288600. [PMID: 38193031 PMCID: PMC10773801 DOI: 10.3389/fncel.2023.1288600] [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: 09/04/2023] [Accepted: 11/27/2023] [Indexed: 01/10/2024] Open
Abstract
While central autonomic, cardiac, and/or respiratory dysfunction underlies sudden unexpected death in epilepsy (SUDEP), the specific neural mechanisms that lead to SUDEP remain to be determined. In this study, we took advantage of single-cell neuronal Ca2+ imaging and intrahippocampal kainic acid (KA)-induced chronic epilepsy in mice to investigate progressive changes in key cardiorespiratory brainstem circuits during chronic epilepsy. Weeks after induction of status epilepticus (SE), when mice were experiencing recurrent spontaneous seizures (chronic epilepsy), we observed that the adaptive ventilatory responses to hypercapnia were reduced for 5 weeks after SE induction with its partial recovery at week 7. These changes were paralleled by alterations in the chemosensory responses of neurons in the retrotrapezoid nucleus (RTN). Neurons that displayed adapting responses to hypercapnia were less prevalent and exhibited smaller responses over weeks 3-5, whereas neurons that displayed graded responses to hypercapnia became more prevalent by week 7. Over the same period, chemosensory responses of the presympathetic rostral ventrolateral medullary (RVLM) neurons showed no change. Mice with chronic epilepsy showed enhanced sensitivity to seizures, which invade the RTN and possibly put the chemosensory circuits at further risk of impairment. Our findings establish a dysfunctional breathing phenotype with its RTN neuronal correlate in mice with chronic epilepsy and suggest that the assessment of respiratory chemosensitivity may have the potential for identifying people at risk of SUDEP.
Collapse
Affiliation(s)
- Amol M. Bhandare
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
| | | |
Collapse
|
7
|
Abreu M, Carmo AS, Peralta AR, Sá F, Plácido da Silva H, Bentes C, Fred AL. PreEpiSeizures: description and outcomes of physiological data acquisition using wearable devices during video-EEG monitoring in people with epilepsy. Front Physiol 2023; 14:1248899. [PMID: 37881691 PMCID: PMC10597694 DOI: 10.3389/fphys.2023.1248899] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 09/04/2023] [Indexed: 10/27/2023] Open
Abstract
The PreEpiSeizures project was created to better understand epilepsy and seizures through wearable technologies. The motivation was to capture physiological information related to epileptic seizures, besides Electroencephalography (EEG) during video-EEG monitorings. If other physiological signals have reliable information of epileptic seizures, unobtrusive wearable technology could be used to monitor epilepsy in daily life. The development of wearable solutions for epilepsy is limited by the nonexistence of datasets which could validate these solutions. Three different form factors were developed and deployed, and the signal quality was assessed for all acquired biosignals. The wearable data acquisition was performed during the video-EEG of patients with epilepsy. The results achieved so far include 59 patients from 2 hospitals totaling 2,721 h of wearable data and 348 seizures. Besides the wearable data, the Electrocardiogram of the hospital is also useable, totalling 5,838 h of hospital data. The quality ECG signals collected with the proposed wearable is equated with the hospital system, and all other biosignals also achieved state-of-the-art quality. During the data acquisition, 18 challenges were identified, and are presented alongside their possible solutions. Though this is an ongoing work, there were many lessons learned which could help to predict possible problems in wearable data collections and also contribute to the epilepsy community with new physiological information. This work contributes with original wearable data and results relevant to epilepsy research, and discusses relevant challenges that impact wearable health monitoring.
Collapse
Affiliation(s)
- Mariana Abreu
- Instituto de Telecomunicações, Lisboa, Portugal
- Departamento de Bioengenharia, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| | - Ana Sofia Carmo
- Instituto de Telecomunicações, Lisboa, Portugal
- Departamento de Bioengenharia, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| | - Ana Rita Peralta
- Lab EEG-Sono, Centro Hospitalar Universitário Lisboa Norte, Hospital de Santa Maria, Lisboa, Portugal
| | - Francisca Sá
- Departamento Neurologia, Centro Hospitalar Lisboa Ocidental, Hospital Egas Moniz, Lisboa, Portugal
| | - Hugo Plácido da Silva
- Instituto de Telecomunicações, Lisboa, Portugal
- Departamento de Bioengenharia, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
- Lisbon Unit for Learning and Intelligent Systems (LUMLIS), A Unit of the European Laboratory for Learning and Intelligent Systems (ELLIS), Lisboa, Portugal
| | - Carla Bentes
- Lab EEG-Sono, Centro Hospitalar Universitário Lisboa Norte, Hospital de Santa Maria, Lisboa, Portugal
| | - Ana Luísa Fred
- Instituto de Telecomunicações, Lisboa, Portugal
- Departamento de Bioengenharia, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| |
Collapse
|
8
|
Wong S, Simmons A, Rivera-Villicana J, Barnett S, Sivathamboo S, Perucca P, Ge Z, Kwan P, Kuhlmann L, Vasa R, Mouzakis K, O'Brien TJ. EEG datasets for seizure detection and prediction- A review. Epilepsia Open 2023. [PMID: 36740244 DOI: 10.1002/epi4.12704] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Accepted: 01/28/2023] [Indexed: 02/07/2023] Open
Abstract
Electroencephalogram (EEG) datasets from epilepsy patients have been used to develop seizure detection and prediction algorithms using machine learning (ML) techniques with the aim of implementing the learned model in a device. However, the format and structure of publicly available datasets are different from each other, and there is a lack of guidelines on the use of these datasets. This impacts the generatability, generalizability, and reproducibility of the results and findings produced by the studies. In this narrative review, we compiled and compared the different characteristics of the publicly available EEG datasets that are commonly used to develop seizure detection and prediction algorithms. We investigated the advantages and limitations of the characteristics of the EEG datasets. Based on our study, we identified 17 characteristics that make the EEG datasets unique from each other. We also briefly looked into how certain characteristics of the publicly available datasets affect the performance and outcome of a study, as well as the influences it has on the choice of ML techniques and preprocessing steps required to develop seizure detection and prediction algorithms. In conclusion, this study provides a guideline on the choice of publicly available EEG datasets to both clinicians and scientists working to develop a reproducible, generalizable, and effective seizure detection and prediction algorithm.
Collapse
Affiliation(s)
- Sheng Wong
- Applied Artificial Intelligence Institute, Deakin University, Burwood, Victoria, Australia
| | - Anj Simmons
- Applied Artificial Intelligence Institute, Deakin University, Burwood, Victoria, Australia
| | | | - Scott Barnett
- Applied Artificial Intelligence Institute, Deakin University, Burwood, Victoria, Australia
| | - Shobi Sivathamboo
- Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia.,Department of Neurology, The Royal Melbourne Hospital, Parkville, Victoria, Australia.,Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.,Department of Neurology, Alfred Health, Melbourne, Victoria, Australia
| | - Piero Perucca
- Department of Neurology, The Royal Melbourne Hospital, Parkville, Victoria, Australia.,Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.,Department of Neurology, Alfred Health, Melbourne, Victoria, Australia.,Department of Medicine, Austin Health, The University of Melbourne, Heidelberg, Victoria, Australia.,Comprehensive Epilepsy Program, Austin Health, Heidelberg, Victoria, Australia
| | - Zongyuan Ge
- Monash eResearch Centre, Monash University, Clayton, Victoria, Australia
| | - Patrick Kwan
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.,Department of Neurology, Alfred Health, Melbourne, Victoria, Australia
| | - Levin Kuhlmann
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia.,Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Victoria, Australia
| | - Rajesh Vasa
- Applied Artificial Intelligence Institute, Deakin University, Burwood, Victoria, Australia
| | - Kon Mouzakis
- Applied Artificial Intelligence Institute, Deakin University, Burwood, Victoria, Australia
| | - Terence J O'Brien
- Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia.,Department of Neurology, The Royal Melbourne Hospital, Parkville, Victoria, Australia.,Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.,Department of Neurology, Alfred Health, Melbourne, Victoria, Australia
| |
Collapse
|
9
|
Rheims S, Sperling MR, Ryvlin P. Drug-resistant epilepsy and mortality-Why and when do neuromodulation and epilepsy surgery reduce overall mortality. Epilepsia 2022; 63:3020-3036. [PMID: 36114753 PMCID: PMC10092062 DOI: 10.1111/epi.17413] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 09/14/2022] [Accepted: 09/14/2022] [Indexed: 01/11/2023]
Abstract
Patients with drug-resistant epilepsy have an increased mortality rate, with the majority of deaths being epilepsy related and 40% due to sudden unexpected death in epilepsy (SUDEP). The impact of epilepsy surgery on mortality has been investigated since the 1970s, with increased interest in this field during the past 15 years. We systematically reviewed studies investigating mortality rate in patients undergoing epilepsy surgery or neuromodulation therapies. The quality of available evidence proved heterogenous and often limited by significant methodological issues. Perioperative mortality following epilepsy surgery was found to be <1%. Meta-analysis of studies that directly compared patients who underwent surgery to those not operated following presurgical evaluation showed that the former have a two-fold lower risk of death and a three-fold lower risk of SUDEP compared to the latter (odds ratio [OR] 0.40, 95% confidence interval [CI]: 0.29-0.56; p < .0001 for overall mortality and OR 0.32, 95% CI: 0.18-0.57; p < .001 for SUDEP). Limited data are available regarding the risk of death and SUDEP in patients undergoing neuromodulation therapies, although some evidence indicates that vagus nerve stimulation might be associated with a lower risk of SUDEP. Several key questions remain to be addressed in future studies, considering the need to better inform patients about the long-term benefit-risk ratio of epilepsy surgery. Dedicated long-term prospective studies will thus be required to provide more personalized information on the impact of surgery and/or neuromodulation on the risk of death and SUDEP.
Collapse
Affiliation(s)
- Sylvain Rheims
- Department of Functional Neurology and Epileptology, Hospices Civils de Lyon and University of Lyon, Lyon, France.,Lyon Neuroscience Research Center, INSERM U1028/CNRS UMR 5292 and Lyon 1 University, Lyon, France
| | - Mickael R Sperling
- Jefferson Comprehensive Epilepsy Center, Department of Neurology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Philippe Ryvlin
- Department of Clinical Neurosciences, Vaudois University Hospital Center, Lausanne, Switzerland
| |
Collapse
|
10
|
Ghazale PP, dos Santos Borges K, Gomes KP, Quintino C, Braga PPP, de Castro CH, Mendes EP, Scorza FA, Colugnati DB, Pansani AP. Alterations in aortic vasorelaxation in rats with epilepsy induced by the electrical amygdala kindling model. Epilepsy Res 2022; 182:106920. [DOI: 10.1016/j.eplepsyres.2022.106920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 03/14/2022] [Accepted: 04/01/2022] [Indexed: 11/03/2022]
|
11
|
Faria MT, Rodrigues S, Campelo M, Dias D, Rego R, Rocha H, Sá F, Tavares-Silva M, Pinto R, Pestana G, Oliveira A, Pereira J, Cunha JPS, Rocha-Gonçalves F, Gonçalves H, Martins E. Does the type of seizure influence heart rate variability changes? Epilepsy Behav 2022; 126:108453. [PMID: 34864377 DOI: 10.1016/j.yebeh.2021.108453] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 11/15/2021] [Accepted: 11/16/2021] [Indexed: 11/25/2022]
Abstract
OBJECTIVE Heart rate variability (HRV), an index of the autonomic cardiac activity, is decreased in patients with epilepsy, and a low HRV is associated with a higher risk of sudden death. Generalized tonic-clonic seizures are one of the most consistent risk factors for SUDEP, but the influence (and relative risk) of each type of seizure on cardiac function is still unknown. Our objective was to assess the impact of the type of seizure (focal to bilateral tonic-clonic seizure - FBTCS - versus non-FBTCS) on periictal HRV, in a group of patients with refractory epilepsy and both types of seizures. METHODS We performed a 48-hour Holter recording on 121 patients consecutively admitted to our Epilepsy Monitoring Unit. We only included patients with both FBTCS and non-FBTCS on the Holter recording and selected the first seizure of each type to analyze. To evaluate HRV parameters (AVNN, SDNN, RMSSD, pNN20, LF, HF, and LF/HF), we chose 5-min epochs pre- and postictally. RESULTS We included 14 patients, with a median age of 36 (min-max, 16-55) years and 64% were female. Thirty-six percent had cardiovascular risk factors, but no previously known cardiac disease. In the preictal period, there were no statistically significant differences in HRV parameters, between FBTCS and non-FBTCS. In the postictal period, AVNN, RMSSD, pNN20, LF, and HF were significantly lower, and LF/HF and HR were significantly higher in FBTCS. From preictal to postictal periods, FBTCS elicited a statistically significant rise in HR and LF/HF, and a statistically significant fall in AVNN, RMSSD, pNN20, and HF. Non-FBTCS only caused statistically significant changes in HR (decrease) and AVNN (increase). SIGNIFICANCE/CONCLUSION This work emphasizes the greater effect of FBTCS in autonomic cardiac function in patients with refractory epilepsy, compared to other types of seizures, with a significant reduction in vagal tonus, which may be associated with an increased risk of SUDEP.
Collapse
Affiliation(s)
- Maria Teresa Faria
- Nuclear Medicine Department, Centro Hospitalar Universitário de São João, E.P.E., Porto, Portugal.
| | - Susana Rodrigues
- Institute for Systems Engineering and Computers Technology and Science (INESC TEC), Porto, Portugal
| | - Manuel Campelo
- Cardiology Department, Centro Hospitalar Universitário de São João, E.P.E., Porto, Portugal; Department of Medicine, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Duarte Dias
- Institute for Systems Engineering and Computers Technology and Science (INESC TEC), Porto, Portugal
| | - Ricardo Rego
- Neurophysiology Unit, Neurology Department, Centro Hospitalar Universitário de São João, E.P.E., Porto, Portugal
| | - Helena Rocha
- Neurophysiology Unit, Neurology Department, Centro Hospitalar Universitário de São João, E.P.E., Porto, Portugal
| | - Francisca Sá
- Neurophysiology Unit, Neurology Department, Centro Hospitalar Universitário de São João, E.P.E., Porto, Portugal
| | - Marta Tavares-Silva
- Cardiology Department, Centro Hospitalar Universitário de São João, E.P.E., Porto, Portugal; Department of Surgery and Physiology, Cardiovascular R&D Centre, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Roberto Pinto
- Cardiology Department, Centro Hospitalar Universitário de São João, E.P.E., Porto, Portugal
| | - Gonçalo Pestana
- Cardiology Department, Centro Hospitalar Universitário de São João, E.P.E., Porto, Portugal
| | - Ana Oliveira
- Nuclear Medicine Department, Centro Hospitalar Universitário de São João, E.P.E., Porto, Portugal
| | - Jorge Pereira
- Nuclear Medicine Department, Centro Hospitalar Universitário de São João, E.P.E., Porto, Portugal
| | - João Paulo Silva Cunha
- Institute for Systems Engineering and Computers Technology and Science (INESC TEC), Porto, Portugal; Faculty of Engineering, University of Porto, Porto, Portugal
| | | | - Hernâni Gonçalves
- Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal; CINTESIS, Faculty of Medicine, University of Porto, Portugal
| | - Elisabete Martins
- Cardiology Department, Centro Hospitalar Universitário de São João, E.P.E., Porto, Portugal; Department of Medicine, Faculty of Medicine, University of Porto, Porto, Portugal
| |
Collapse
|
12
|
Sivathamboo S, Friedman D, Laze J, Nightscales R, Chen Z, Kuhlmann L, Devore S, Macefield V, Kwan P, D'Souza W, Berkovic SF, Perucca P, O'Brien TJ, Devinsky O. Association of Short-term Heart Rate Variability and Sudden Unexpected Death in Epilepsy. Neurology 2021; 97:e2357-e2367. [PMID: 34649884 DOI: 10.1212/wnl.0000000000012946] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 09/29/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND AND OBJECTIVES We compared heart rate variability (HRV) in sudden unexpected death in epilepsy (SUDEP) cases and living epilepsy controls. METHODS This international, multicenter, retrospective, nested case-control study examined patients admitted for video-EEG monitoring (VEM) between January 1, 2003, and December 31, 2014, and subsequently died of SUDEP. Time domain and frequency domain components were extracted from 5-minute interictal ECG recordings during sleep and wakefulness from SUDEP cases and controls. RESULTS We identified 31 SUDEP cases and 56 controls. Normalized low-frequency power (LFP) during wakefulness was lower in SUDEP cases (median 42.5, interquartile range [IQR] 32.6-52.6) than epilepsy controls (55.5, IQR 40.7-68.9; p = 0.015, critical value = 0.025). In the multivariable model, normalized LFP was lower in SUDEP cases compared to controls (contrast -11.01, 95% confidence interval [CI] -20.29 to 1.73; p = 0.020, critical value = 0.025). There was a negative correlation between LFP and the latency to SUDEP, where each 1% incremental reduction in normalized LFP conferred a 2.7% decrease in the latency to SUDEP (95% CI 0.95-0.995; p = 0.017, critical value = 0.025). Increased survival duration from VEM to SUDEP was associated with higher normalized high-frequency power (HFP; p = 0.002, critical value = 0.025). The survival model with normalized LFP was associated with SUDEP (c statistic 0.66, 95% CI 0.55-0.77), which nonsignificantly increased with the addition of normalized HFP (c statistic 0.70, 95% CI 0.59-0.81; p = 0.209). CONCLUSIONS Reduced short-term LFP, which is a validated biomarker for sudden death, was associated with SUDEP. Increased HFP was associated with longer survival and may be cardioprotective in SUDEP. HRV quantification may help stratify individual SUDEP risk. CLASSIFICATION OF EVIDENCE This study provides Class III evidence that in patients with epilepsy, some measures of HRV are associated with SUDEP.
Collapse
Affiliation(s)
- Shobi Sivathamboo
- From the Department of Neuroscience, Central Clinical School (S.S., R.N., Z.C., M.B., V.M., P.K., P.P., T.J.O.), Clinical Epidemiology, School of Public Health and Preventive Medicine (Z.C., M.B.), and Department of Data Science and AI, Faculty of Information Technology (L.K.), Monash University; Department of Medicine (The Royal Melbourne Hospital) (S.S., R.N., Z.C., M.B., P.K., P.P., T.J.O.), The University of Melbourne; Department of Neurology (S.S., R.N., P.K., P.P., T.J.O.), The Royal Melbourne Hospital; Department of Neurology (S.S., R.N., P.K., P.P., T.J.O.), Alfred Health, Melbourne, Australia; Department of Neurology (D.F., J.L., S.D., O.D.), New York University Grossman School of Medicine, New York; Human Autonomic Neurophysiology (V.M.), Baker Heart and Diabetes Institute, Melbourne; Department of Medicine (W.D., M.D.C.B.), St. Vincent's Hospital, The University of Melbourne, Fitzroy; and Department of Medicine (S.F.B.), Austin Health, The University of Melbourne, Heidelberg, Australia
| | - Daniel Friedman
- From the Department of Neuroscience, Central Clinical School (S.S., R.N., Z.C., M.B., V.M., P.K., P.P., T.J.O.), Clinical Epidemiology, School of Public Health and Preventive Medicine (Z.C., M.B.), and Department of Data Science and AI, Faculty of Information Technology (L.K.), Monash University; Department of Medicine (The Royal Melbourne Hospital) (S.S., R.N., Z.C., M.B., P.K., P.P., T.J.O.), The University of Melbourne; Department of Neurology (S.S., R.N., P.K., P.P., T.J.O.), The Royal Melbourne Hospital; Department of Neurology (S.S., R.N., P.K., P.P., T.J.O.), Alfred Health, Melbourne, Australia; Department of Neurology (D.F., J.L., S.D., O.D.), New York University Grossman School of Medicine, New York; Human Autonomic Neurophysiology (V.M.), Baker Heart and Diabetes Institute, Melbourne; Department of Medicine (W.D., M.D.C.B.), St. Vincent's Hospital, The University of Melbourne, Fitzroy; and Department of Medicine (S.F.B.), Austin Health, The University of Melbourne, Heidelberg, Australia
| | - Juliana Laze
- From the Department of Neuroscience, Central Clinical School (S.S., R.N., Z.C., M.B., V.M., P.K., P.P., T.J.O.), Clinical Epidemiology, School of Public Health and Preventive Medicine (Z.C., M.B.), and Department of Data Science and AI, Faculty of Information Technology (L.K.), Monash University; Department of Medicine (The Royal Melbourne Hospital) (S.S., R.N., Z.C., M.B., P.K., P.P., T.J.O.), The University of Melbourne; Department of Neurology (S.S., R.N., P.K., P.P., T.J.O.), The Royal Melbourne Hospital; Department of Neurology (S.S., R.N., P.K., P.P., T.J.O.), Alfred Health, Melbourne, Australia; Department of Neurology (D.F., J.L., S.D., O.D.), New York University Grossman School of Medicine, New York; Human Autonomic Neurophysiology (V.M.), Baker Heart and Diabetes Institute, Melbourne; Department of Medicine (W.D., M.D.C.B.), St. Vincent's Hospital, The University of Melbourne, Fitzroy; and Department of Medicine (S.F.B.), Austin Health, The University of Melbourne, Heidelberg, Australia
| | - Russell Nightscales
- From the Department of Neuroscience, Central Clinical School (S.S., R.N., Z.C., M.B., V.M., P.K., P.P., T.J.O.), Clinical Epidemiology, School of Public Health and Preventive Medicine (Z.C., M.B.), and Department of Data Science and AI, Faculty of Information Technology (L.K.), Monash University; Department of Medicine (The Royal Melbourne Hospital) (S.S., R.N., Z.C., M.B., P.K., P.P., T.J.O.), The University of Melbourne; Department of Neurology (S.S., R.N., P.K., P.P., T.J.O.), The Royal Melbourne Hospital; Department of Neurology (S.S., R.N., P.K., P.P., T.J.O.), Alfred Health, Melbourne, Australia; Department of Neurology (D.F., J.L., S.D., O.D.), New York University Grossman School of Medicine, New York; Human Autonomic Neurophysiology (V.M.), Baker Heart and Diabetes Institute, Melbourne; Department of Medicine (W.D., M.D.C.B.), St. Vincent's Hospital, The University of Melbourne, Fitzroy; and Department of Medicine (S.F.B.), Austin Health, The University of Melbourne, Heidelberg, Australia
| | - Zhibin Chen
- From the Department of Neuroscience, Central Clinical School (S.S., R.N., Z.C., M.B., V.M., P.K., P.P., T.J.O.), Clinical Epidemiology, School of Public Health and Preventive Medicine (Z.C., M.B.), and Department of Data Science and AI, Faculty of Information Technology (L.K.), Monash University; Department of Medicine (The Royal Melbourne Hospital) (S.S., R.N., Z.C., M.B., P.K., P.P., T.J.O.), The University of Melbourne; Department of Neurology (S.S., R.N., P.K., P.P., T.J.O.), The Royal Melbourne Hospital; Department of Neurology (S.S., R.N., P.K., P.P., T.J.O.), Alfred Health, Melbourne, Australia; Department of Neurology (D.F., J.L., S.D., O.D.), New York University Grossman School of Medicine, New York; Human Autonomic Neurophysiology (V.M.), Baker Heart and Diabetes Institute, Melbourne; Department of Medicine (W.D., M.D.C.B.), St. Vincent's Hospital, The University of Melbourne, Fitzroy; and Department of Medicine (S.F.B.), Austin Health, The University of Melbourne, Heidelberg, Australia
| | - Levin Kuhlmann
- From the Department of Neuroscience, Central Clinical School (S.S., R.N., Z.C., M.B., V.M., P.K., P.P., T.J.O.), Clinical Epidemiology, School of Public Health and Preventive Medicine (Z.C., M.B.), and Department of Data Science and AI, Faculty of Information Technology (L.K.), Monash University; Department of Medicine (The Royal Melbourne Hospital) (S.S., R.N., Z.C., M.B., P.K., P.P., T.J.O.), The University of Melbourne; Department of Neurology (S.S., R.N., P.K., P.P., T.J.O.), The Royal Melbourne Hospital; Department of Neurology (S.S., R.N., P.K., P.P., T.J.O.), Alfred Health, Melbourne, Australia; Department of Neurology (D.F., J.L., S.D., O.D.), New York University Grossman School of Medicine, New York; Human Autonomic Neurophysiology (V.M.), Baker Heart and Diabetes Institute, Melbourne; Department of Medicine (W.D., M.D.C.B.), St. Vincent's Hospital, The University of Melbourne, Fitzroy; and Department of Medicine (S.F.B.), Austin Health, The University of Melbourne, Heidelberg, Australia
| | - Sasha Devore
- From the Department of Neuroscience, Central Clinical School (S.S., R.N., Z.C., M.B., V.M., P.K., P.P., T.J.O.), Clinical Epidemiology, School of Public Health and Preventive Medicine (Z.C., M.B.), and Department of Data Science and AI, Faculty of Information Technology (L.K.), Monash University; Department of Medicine (The Royal Melbourne Hospital) (S.S., R.N., Z.C., M.B., P.K., P.P., T.J.O.), The University of Melbourne; Department of Neurology (S.S., R.N., P.K., P.P., T.J.O.), The Royal Melbourne Hospital; Department of Neurology (S.S., R.N., P.K., P.P., T.J.O.), Alfred Health, Melbourne, Australia; Department of Neurology (D.F., J.L., S.D., O.D.), New York University Grossman School of Medicine, New York; Human Autonomic Neurophysiology (V.M.), Baker Heart and Diabetes Institute, Melbourne; Department of Medicine (W.D., M.D.C.B.), St. Vincent's Hospital, The University of Melbourne, Fitzroy; and Department of Medicine (S.F.B.), Austin Health, The University of Melbourne, Heidelberg, Australia
| | - Vaughan Macefield
- From the Department of Neuroscience, Central Clinical School (S.S., R.N., Z.C., M.B., V.M., P.K., P.P., T.J.O.), Clinical Epidemiology, School of Public Health and Preventive Medicine (Z.C., M.B.), and Department of Data Science and AI, Faculty of Information Technology (L.K.), Monash University; Department of Medicine (The Royal Melbourne Hospital) (S.S., R.N., Z.C., M.B., P.K., P.P., T.J.O.), The University of Melbourne; Department of Neurology (S.S., R.N., P.K., P.P., T.J.O.), The Royal Melbourne Hospital; Department of Neurology (S.S., R.N., P.K., P.P., T.J.O.), Alfred Health, Melbourne, Australia; Department of Neurology (D.F., J.L., S.D., O.D.), New York University Grossman School of Medicine, New York; Human Autonomic Neurophysiology (V.M.), Baker Heart and Diabetes Institute, Melbourne; Department of Medicine (W.D., M.D.C.B.), St. Vincent's Hospital, The University of Melbourne, Fitzroy; and Department of Medicine (S.F.B.), Austin Health, The University of Melbourne, Heidelberg, Australia
| | - Patrick Kwan
- From the Department of Neuroscience, Central Clinical School (S.S., R.N., Z.C., M.B., V.M., P.K., P.P., T.J.O.), Clinical Epidemiology, School of Public Health and Preventive Medicine (Z.C., M.B.), and Department of Data Science and AI, Faculty of Information Technology (L.K.), Monash University; Department of Medicine (The Royal Melbourne Hospital) (S.S., R.N., Z.C., M.B., P.K., P.P., T.J.O.), The University of Melbourne; Department of Neurology (S.S., R.N., P.K., P.P., T.J.O.), The Royal Melbourne Hospital; Department of Neurology (S.S., R.N., P.K., P.P., T.J.O.), Alfred Health, Melbourne, Australia; Department of Neurology (D.F., J.L., S.D., O.D.), New York University Grossman School of Medicine, New York; Human Autonomic Neurophysiology (V.M.), Baker Heart and Diabetes Institute, Melbourne; Department of Medicine (W.D., M.D.C.B.), St. Vincent's Hospital, The University of Melbourne, Fitzroy; and Department of Medicine (S.F.B.), Austin Health, The University of Melbourne, Heidelberg, Australia
| | - Wendyl D'Souza
- From the Department of Neuroscience, Central Clinical School (S.S., R.N., Z.C., M.B., V.M., P.K., P.P., T.J.O.), Clinical Epidemiology, School of Public Health and Preventive Medicine (Z.C., M.B.), and Department of Data Science and AI, Faculty of Information Technology (L.K.), Monash University; Department of Medicine (The Royal Melbourne Hospital) (S.S., R.N., Z.C., M.B., P.K., P.P., T.J.O.), The University of Melbourne; Department of Neurology (S.S., R.N., P.K., P.P., T.J.O.), The Royal Melbourne Hospital; Department of Neurology (S.S., R.N., P.K., P.P., T.J.O.), Alfred Health, Melbourne, Australia; Department of Neurology (D.F., J.L., S.D., O.D.), New York University Grossman School of Medicine, New York; Human Autonomic Neurophysiology (V.M.), Baker Heart and Diabetes Institute, Melbourne; Department of Medicine (W.D., M.D.C.B.), St. Vincent's Hospital, The University of Melbourne, Fitzroy; and Department of Medicine (S.F.B.), Austin Health, The University of Melbourne, Heidelberg, Australia
| | - Samuel F Berkovic
- From the Department of Neuroscience, Central Clinical School (S.S., R.N., Z.C., M.B., V.M., P.K., P.P., T.J.O.), Clinical Epidemiology, School of Public Health and Preventive Medicine (Z.C., M.B.), and Department of Data Science and AI, Faculty of Information Technology (L.K.), Monash University; Department of Medicine (The Royal Melbourne Hospital) (S.S., R.N., Z.C., M.B., P.K., P.P., T.J.O.), The University of Melbourne; Department of Neurology (S.S., R.N., P.K., P.P., T.J.O.), The Royal Melbourne Hospital; Department of Neurology (S.S., R.N., P.K., P.P., T.J.O.), Alfred Health, Melbourne, Australia; Department of Neurology (D.F., J.L., S.D., O.D.), New York University Grossman School of Medicine, New York; Human Autonomic Neurophysiology (V.M.), Baker Heart and Diabetes Institute, Melbourne; Department of Medicine (W.D., M.D.C.B.), St. Vincent's Hospital, The University of Melbourne, Fitzroy; and Department of Medicine (S.F.B.), Austin Health, The University of Melbourne, Heidelberg, Australia
| | - Piero Perucca
- From the Department of Neuroscience, Central Clinical School (S.S., R.N., Z.C., M.B., V.M., P.K., P.P., T.J.O.), Clinical Epidemiology, School of Public Health and Preventive Medicine (Z.C., M.B.), and Department of Data Science and AI, Faculty of Information Technology (L.K.), Monash University; Department of Medicine (The Royal Melbourne Hospital) (S.S., R.N., Z.C., M.B., P.K., P.P., T.J.O.), The University of Melbourne; Department of Neurology (S.S., R.N., P.K., P.P., T.J.O.), The Royal Melbourne Hospital; Department of Neurology (S.S., R.N., P.K., P.P., T.J.O.), Alfred Health, Melbourne, Australia; Department of Neurology (D.F., J.L., S.D., O.D.), New York University Grossman School of Medicine, New York; Human Autonomic Neurophysiology (V.M.), Baker Heart and Diabetes Institute, Melbourne; Department of Medicine (W.D., M.D.C.B.), St. Vincent's Hospital, The University of Melbourne, Fitzroy; and Department of Medicine (S.F.B.), Austin Health, The University of Melbourne, Heidelberg, Australia
| | - Terence J O'Brien
- From the Department of Neuroscience, Central Clinical School (S.S., R.N., Z.C., M.B., V.M., P.K., P.P., T.J.O.), Clinical Epidemiology, School of Public Health and Preventive Medicine (Z.C., M.B.), and Department of Data Science and AI, Faculty of Information Technology (L.K.), Monash University; Department of Medicine (The Royal Melbourne Hospital) (S.S., R.N., Z.C., M.B., P.K., P.P., T.J.O.), The University of Melbourne; Department of Neurology (S.S., R.N., P.K., P.P., T.J.O.), The Royal Melbourne Hospital; Department of Neurology (S.S., R.N., P.K., P.P., T.J.O.), Alfred Health, Melbourne, Australia; Department of Neurology (D.F., J.L., S.D., O.D.), New York University Grossman School of Medicine, New York; Human Autonomic Neurophysiology (V.M.), Baker Heart and Diabetes Institute, Melbourne; Department of Medicine (W.D., M.D.C.B.), St. Vincent's Hospital, The University of Melbourne, Fitzroy; and Department of Medicine (S.F.B.), Austin Health, The University of Melbourne, Heidelberg, Australia
| | - Orrin Devinsky
- From the Department of Neuroscience, Central Clinical School (S.S., R.N., Z.C., M.B., V.M., P.K., P.P., T.J.O.), Clinical Epidemiology, School of Public Health and Preventive Medicine (Z.C., M.B.), and Department of Data Science and AI, Faculty of Information Technology (L.K.), Monash University; Department of Medicine (The Royal Melbourne Hospital) (S.S., R.N., Z.C., M.B., P.K., P.P., T.J.O.), The University of Melbourne; Department of Neurology (S.S., R.N., P.K., P.P., T.J.O.), The Royal Melbourne Hospital; Department of Neurology (S.S., R.N., P.K., P.P., T.J.O.), Alfred Health, Melbourne, Australia; Department of Neurology (D.F., J.L., S.D., O.D.), New York University Grossman School of Medicine, New York; Human Autonomic Neurophysiology (V.M.), Baker Heart and Diabetes Institute, Melbourne; Department of Medicine (W.D., M.D.C.B.), St. Vincent's Hospital, The University of Melbourne, Fitzroy; and Department of Medicine (S.F.B.), Austin Health, The University of Melbourne, Heidelberg, Australia.
| | | |
Collapse
|
13
|
Autonomic manifestations of epilepsy: emerging pathways to sudden death? Nat Rev Neurol 2021; 17:774-788. [PMID: 34716432 DOI: 10.1038/s41582-021-00574-w] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/24/2021] [Indexed: 12/24/2022]
Abstract
Epileptic networks are intimately connected with the autonomic nervous system, as exemplified by a plethora of ictal (during a seizure) autonomic manifestations, including epigastric sensations, palpitations, goosebumps and syncope (fainting). Ictal autonomic changes might serve as diagnostic clues, provide targets for seizure detection and help us to understand the mechanisms that underlie sudden unexpected death in epilepsy (SUDEP). Autonomic alterations are generally more prominent in focal seizures originating from the temporal lobe, demonstrating the importance of limbic structures to the autonomic nervous system, and are particularly pronounced in focal-to-bilateral and generalized tonic-clonic seizures. The presence, type and severity of autonomic features are determined by the seizure onset zone, propagation pathways, lateralization and timing of the seizures, and the presence of interictal autonomic dysfunction. Evidence is mounting that not all autonomic manifestations are linked to SUDEP. In addition, experimental and clinical data emphasize the heterogeneity of SUDEP and its infrequent overlap with sudden cardiac death. Here, we review the spectrum and diagnostic value of the mostly benign and self-limiting autonomic manifestations of epilepsy. In particular, we focus on presentations that are likely to contribute to SUDEP and discuss how wearable devices might help to prevent SUDEP.
Collapse
|
14
|
Heart rate variability in patients with refractory epilepsy: The influence of generalized convulsive seizures. Epilepsy Res 2021; 178:106796. [PMID: 34763267 DOI: 10.1016/j.eplepsyres.2021.106796] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 10/05/2021] [Accepted: 10/15/2021] [Indexed: 11/23/2022]
Abstract
OBJECTIVE Patients with epilepsy, mainly drug-resistant, have reduced heart rate variability (HRV), linked to an increased risk of sudden death in various other diseases. In this context, it could play a role in SUDEP. Generalized convulsive seizures (GCS) are one of the most consensual risk factors for SUDEP. Our objective was to assess the influence of GCS in HRV parameters in patients with drug-resistant epilepsy. METHODS We prospectively evaluated 121 patients with refractory epilepsy admitted to our Epilepsy Monitoring Unit. All patients underwent a 48-hour Holter recording. Only patients with GCS were included (n = 23), and we selected the first as the index seizure. We evaluated HRV (AVNN, SDNN, RMSSD, pNN50, LF, HF, and LF/HF) in 5-min epochs (diurnal and nocturnal baselines; preictal - 5 min before the seizure; ictal; postictal - 5 min after the seizure; and late postictal - >5 h after the seizure). These data were also compared with normative values from a healthy population (controlling for age and gender). RESULTS We included 23 patients, with a median age of 36 (min-max, 16-55) years and 65% were female. Thirty percent had cardiovascular risk factors, but no previously known cardiac disease. HRV parameters AVNN, RMSSD, pNN50, and HF were significantly lower in the diurnal than in the nocturnal baseline, whereas the opposite occurred with LF/HF and HR. Diurnal baseline parameters were inferior to the normative population values (which includes only diurnal values). We found significant differences in HRV parameters between the analyzed periods, especially during the postictal period. All parameters but LF/HF suffered a reduction in that period. LF/HF increased in that period but did not reach statistical significance. Visually, there was a tendency for a global reduction in our patients' HRV parameters, namely AVNN, RMSSD, and pNN50, in each period, comparing with those from a normative healthy population. No significant differences were found in HRV between diurnal and nocturnal seizures, between temporal lobe and extra-temporal-lobe seizures, between seizures with and without postictal generalized EEG suppression, or between seizures of patients with and without cardiovascular risk factors. SIGNIFICANCE/CONCLUSION Our work reinforces the evidence of autonomic cardiac dysfunction in patients with refractory epilepsy, at baseline and mainly in the postictal phase of a GCS. Those changes may have a role in some SUDEP cases. By identifying patients with worse autonomic cardiac function, HRV could fill the gap of a lacking SUDEP risk biomarker.
Collapse
|
15
|
Akyüz E, Üner AK, Köklü B, Arulsamy A, Shaikh MF. Cardiorespiratory findings in epilepsy: A recent review on outcomes and pathophysiology. J Neurosci Res 2021; 99:2059-2073. [PMID: 34109651 DOI: 10.1002/jnr.24861] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 04/16/2021] [Accepted: 05/06/2021] [Indexed: 12/17/2022]
Abstract
Epilepsy is a debilitating disorder of uncontrollable recurrent seizures that occurs as a result of imbalances in the brain excitatory and inhibitory neuronal signals, that could stem from a range of functional and structural neuronal impairments. Globally, nearly 70 million people are negatively impacted by epilepsy and its comorbidities. One such comorbidity is the effect epilepsy has on the autonomic nervous system (ANS), which plays a role in the control of blood circulation, respiration and gastrointestinal function. These epilepsy-induced impairments in the circulatory and respiratory systems may contribute toward sudden unexpected death in epilepsy (SUDEP). Although, various hypotheses have been proposed regarding the role of epilepsy on ANS, the linking pathological mechanism still remains unclear. Channelopathies and seizure-induced damages in ANS-control brain structures were some of the causal/pathological candidates of cardiorespiratory comorbidities in epilepsy patients, especially in those who were drug resistant. However, emerging preclinical research suggest that neurotransmitter/receptor dysfunction and synaptic changes in the ANS may also contribute to the epilepsy-related autonomic disorders. Thus, pathological mechanisms of cardiorespiratory dysfunction should be elucidated by considering the modifications in anatomy and physiology of the autonomic system caused by seizures. In this regard, we present a comprehensive review of the current literature, both clinical and preclinical animal studies, on the cardiorespiratory findings in epilepsy and elucidate the possible pathological mechanisms of these findings, in hopes to prevent SUDEP especially in patients who are drug resistant.
Collapse
Affiliation(s)
- Enes Akyüz
- Department of Biophysics, Faculty of Medicine, Yozgat Bozok University, Yozgat, Turkey
| | - Arda Kaan Üner
- Faculty of Medicine, Yozgat Bozok University, Yozgat, Turkey
| | - Betül Köklü
- Faculty of Medicine, Namık Kemal University, Tekirdağ, Turkey
| | - Alina Arulsamy
- Neuropharmacology Research Laboratory, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway, Malaysia
| | - Mohd Farooq Shaikh
- Neuropharmacology Research Laboratory, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway, Malaysia
| |
Collapse
|
16
|
Stewart M, Bain AR. Assessment of respiratory effort with EMG extracted from ECG recordings during prolonged breath holds: Insights into obstructive apnea and extreme physiology. Physiol Rep 2021; 9:e14873. [PMID: 34042313 PMCID: PMC8157791 DOI: 10.14814/phy2.14873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 04/19/2021] [Accepted: 04/21/2021] [Indexed: 11/24/2022] Open
Abstract
Breath holding divers display extraordinary voluntary control over involuntary reactions during apneic episodes. After an initial easy phase to the breath hold, this voluntary control is applied against the increasing involuntary effort to inspire. We quantified an electromyographic (EMG) signal associated with respiratory movements derived from broad bandpass ECG recordings taken from experienced breath holding divers during prolonged dry breath holds. We sought to define their relationship to involuntary respiratory movements and compare these signals with what is known to occur in obstructive sleep apnea (OSA) and epileptic seizures. ECG and inductance plethysmography records from 14 competitive apneists (1 female) were analyzed. ECG records were analyzed for intervals and the EMG signal was extracted from a re‐filtered version of the original broad bandpass signal and ultimately enveloped with a Hilbert transform. EMG burst magnitude, quantified as an area measure, increased over the course of the struggle phase, correlated with inductance plethysmography measures, and corresponded to significant variance in heart rate variability. We conclude that an EMG signal extracted from the ECG can complement plethysmography during breath holds and may help quantify involuntary effort, as reported previously for obstructive sleep apnea. Further, given the resemblance between cardiac and respiratory features of the breath hold struggle phase to obstructive apnea that can occur during sleep or in association with epileptic seizure activity, the struggle phase may be a useful simulation of obstructive apnea for controlled experimentation that can help clarify aspects of acute and chronic apnea‐associated physiology.
Collapse
Affiliation(s)
- Mark Stewart
- Department of Physiology & Pharmacology, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Anthony R Bain
- Department of Kinesiology, Faculty of Human Kinetics, University of Windsor, Windsor, ON, Canada
| |
Collapse
|
17
|
Singh J, Lanzarini E, Santosh P. Autonomic Characteristics of Sudden Unexpected Death in Epilepsy in Children-A Systematic Review of Studies and Their Relevance to the Management of Epilepsy in Rett Syndrome. Front Neurol 2021; 11:632510. [PMID: 33613425 PMCID: PMC7892970 DOI: 10.3389/fneur.2020.632510] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 12/28/2020] [Indexed: 01/21/2023] Open
Abstract
Aim: To systematically identify and critically appraise studies that investigate the autonomic characteristics of Sudden Unexpected Death in Epilepsy (SUDEP) in the pediatric population. We also wanted to explore how this information would be relevant to the management of epilepsy in patients with Rett Syndrome. Method: Using PRISMA guidelines, a systematic review of PubMed, Scopus, Cochrane, PsycINFO, Embase, and Web of Science databases was performed to identify eligible studies. After extracting data from the included studies, a thematic analysis was undertaken to identify emerging themes. A quality appraisal was also done to assess the quality of the included studies. Results: The systematic search revealed 41 records, and 15 full-text articles on the autonomic characteristics of SUDEP in children were included in the final analysis. Following thematic analysis, three themes were identified (I) modulation in sympathovagal tone, (II) pre- and post-ictal autonomic changes, and (III) other markers of autonomic dysregulation in children with epilepsy. Modulation in sympathovagal tone emerged as the theme with the highest frequency followed by pre- and post-ictal autonomic changes. While the themes provide additional insight into the management of epilepsy in the Rett Syndrome population, the quality of evidence concerning the autonomic characteristics of SUDEP in the pediatric population was low and underscores the importance of much needed research in this area. Conclusion: The mechanism of SUDEP in the pediatric population is complex and involves an interplay between several components of the autonomic nervous system. While direct clinical inferences regarding pediatric SUDEP could not be made, the thematic analysis does suggest that in vulnerable populations such as Rett Syndrome, where there is already a pervasive autonomic dysregulation, pro-active surveillance of the autonomic profile in this patient group would be useful to better manage epilepsy and reduce the SUDEP risk.
Collapse
Affiliation(s)
- Jatinder Singh
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Centre for Interventional Paediatric Psychopharmacology and Rare Diseases, South London and Maudsley NHS Foundation Trust, London, United Kingdom.,Centre for Personalised Medicine in Rett Syndrome, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Evamaria Lanzarini
- Child and Adolescent Neuropsychiatry Unit, Infermi Hospital, Rimini, Italy
| | - Paramala Santosh
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Centre for Interventional Paediatric Psychopharmacology and Rare Diseases, South London and Maudsley NHS Foundation Trust, London, United Kingdom.,Centre for Personalised Medicine in Rett Syndrome, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| |
Collapse
|
18
|
Lucchesi M, Silverman JB, Sundaram K, Kollmar R, Stewart M. Proposed Mechanism-Based Risk Stratification and Algorithm to Prevent Sudden Death in Epilepsy. Front Neurol 2021; 11:618859. [PMID: 33569036 PMCID: PMC7868441 DOI: 10.3389/fneur.2020.618859] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Accepted: 12/30/2020] [Indexed: 12/13/2022] Open
Abstract
Sudden Unexpected Death in Epilepsy (SUDEP) is the leading cause of death in young adults with uncontrolled seizures. First aid guidance to prevent SUDEP, though, has not been previously published because the rarity of monitored cases has made the underlying mechanism difficult to define. This starkly contrasts with the first aid guidelines for sudden cardiac arrest that have been developed based on retrospective studies and expert consensus and the discussion of resuscitation challenges in various American Heart Association certificate courses. However, an increasing amount of evidence from documented SUDEP cases and near misses and from animal models points to a consistent sequence of events that starts with sudden airway occlusion and suggests a mechanistic basis for enhancing seizure first aid. In monitored cases, this sudden airway occlusion associated with seizure activity can be accurately inferred from inductance plethysmography or (depending on recording bandwidth) from electromyographic (EMG) bursts that are associated with inspiratory attempts appearing on the electroencephalogram (EEG) or the electrocardiogram (ECG). In an emergency setting or outside a hospital, seizure first aid can be improved by (1) keeping a lookout for sudden changes in airway status during a seizure, (2) distinguishing thoracic and abdominal movements during attempts to inspire from effective breathing, (3) applying a simple maneuver, the laryngospasm notch maneuver, that may help with airway management when aggressive airway management is unavailable, (4) providing oxygen early as a preventative step to reduce the risk of death, and (5) performing cardiopulmonary resuscitation before the limited post-ictal window of opportunity closes. We propose that these additions to first aid protocols can limit progression of any potential SUDEP case and prevent death. Risk stratification can be improved by recognition of airway occlusion, attendant hypoxia, and need for resuscitation.
Collapse
Affiliation(s)
- Michael Lucchesi
- Department of Emergency Medicine, State University of New York Health Sciences University, Brooklyn, NY, United States
| | - Joshua B Silverman
- Department of Otolaryngology, North Shore Long Island Jewish Medical Center, New Hyde Park, NY, United States
| | - Krishnamurthi Sundaram
- Department of Otolaryngology, State University of New York Health Sciences University, Brooklyn, NY, United States
| | - Richard Kollmar
- Department of Otolaryngology, State University of New York Health Sciences University, Brooklyn, NY, United States.,Department of Cell Biology, State University of New York Health Sciences University, Brooklyn, NY, United States
| | - Mark Stewart
- Department of Neurology, State University of New York Health Sciences University, Brooklyn, NY, United States.,Department of Physiology & Pharmacology, State University of New York Health Sciences University, Brooklyn, NY, United States
| |
Collapse
|
19
|
Abstract
PURPOSE OF REVIEW Epilepsy is associated with autonomic dysfunction. Here, we provide an up-to-date review on measures of interictal autonomic function, focusing on heart rate variability (HRV), baroreflex sensitivity (BRS) and electrodermal activity (EDA). RECENT FINDINGS Resting HRV, BRS and EDA are altered in patients with epilepsy compared with healthy controls. A larger body of work is available for HRV compared with BRS and EDA, and points to interictal HRV derangements across a wide range of epilepsies, including focal, generalized, and combined generalized and focal epilepsies. HRV alterations are most pronounced in temporal lobe epilepsy, Dravet syndrome and drug-resistant and chronic epilepsies. There are conflicting data on the effect of antiseizure medications on measures of interictal autonomic function. However, carbamazepine has been associated with decreased HRV. Epilepsy surgery and vagus nerve stimulation do not appear to have substantial impact on measures of interictal autonomic function but well designed studies are lacking. SUMMARY Patients with epilepsy, particularly those with longstanding uncontrolled seizures, have measurable alterations of resting autonomic function. These alterations may be relevant to the increased risk of premature mortality in epilepsy, including sudden unexpected death in epilepsy, which warrants investigation in future research.
Collapse
|