Generic placeholder image

Current Neuropharmacology

Editor-in-Chief

ISSN (Print): 1570-159X
ISSN (Online): 1875-6190

Systematic Review Article

Medical Technology: A Systematic Review on Medical Devices Utilized for Epilepsy Prediction and Management

Author(s): Jen Sze Ong, Shuet Nee Wong, Alina Arulsamy, Jessica L. Watterson and Mohd. Farooq Shaikh*

Volume 20, Issue 5, 2022

Published on: 09 March, 2022

Page: [950 - 964] Pages: 15

DOI: 10.2174/1570159X19666211108153001

Price: $65

Abstract

Background: Epilepsy is a devastating neurological disorder that affects nearly 70 million people worldwide. Epilepsy causes uncontrollable, unprovoked and unpredictable seizures that reduce the quality of life of those afflicted, with 1-9 epileptic patient deaths per 1000 patients occurring annually due to sudden unexpected death in epilepsy (SUDEP). Predicting the onset of seizures and managing them may help patients from harming themselves and may improve their well-being. For a long time, electroencephalography (EEG) devices have been the mainstay for seizure detection and monitoring. This systematic review aimed to elucidate and critically evaluate the latest advancements in medical devices, besides EEG, that have been proposed for the management and prediction of epileptic seizures. A literature search was performed on three databases, PubMed, Scopus and EMBASE.

Methods: Following title/abstract screening by two independent reviewers, 27 articles were selected for critical analysis in this review.

Results: These articles revealed ambulatory, non-invasive and wearable medical devices, such as the in-ear EEG devices; the accelerometer-based devices and the subcutaneous implanted EEG devices might be more acceptable than traditional EEG systems. In addition, extracerebral signalbased devices may be more efficient than EEG-based systems, especially when combined with an intervention trigger. Although further studies may still be required to improve and validate these proposed systems before commercialization, these findings may give hope to epileptic patients, particularly those with refractory epilepsy, to predict and manage their seizures.

Conclusion: The use of medical devices for epilepsy may improve patients' independence and quality of life and possibly prevent sudden unexpected death in epilepsy (SUDEP).

Keywords: Electroencephalography, electrophysiology device, seizure prediction, wearable device, extracerebral signals, SUDEP.

Graphical Abstract

[1]
Fisher, R.S.; Acevedo, C.; Arzimanoglou, A.; Bogacz, A.; Cross, J.H.; Elger, C.E.; Engel, J., Jr; Forsgren, L.; French, J.A.; Glynn, M.; Hesdorffer, D.C.; Lee, B.I.; Mathern, G.W.; Moshé, S.L.; Perucca, E.; Scheffer, I.E.; Tomson, T.; Watanabe, M.; Wiebe, S. ILAE official report: a practical clinical definition of epilepsy. Epilepsia, 2014, 55(4), 475-482.
[http://dx.doi.org/10.1111/epi.12550] [PMID: 24730690]
[2]
World Health Organization. Epilepsy: World Health Organization (WHO); 2019 [cited 2021 28/5/2021]. Available from: https://www.who.int/news-room/fact-sheets/detail/epilepsy
[3]
Keezer, M.R.; Sisodiya, S.M.; Sander, J.W. Comorbidities of epilepsy: current concepts and future perspectives. Lancet Neurol., 2016, 15(1), 106-115.
[http://dx.doi.org/10.1016/S1474-4422(15)00225-2] [PMID: 26549780]
[4]
Stewart, M. An explanation for sudden death in epilepsy (SUDEP). J. Physiol. Sci., 2018, 68(4), 307-320.
[http://dx.doi.org/10.1007/s12576-018-0602-z] [PMID: 29542031]
[5]
Anderson, P. Wearable Device Clears a First 'Milestone' in Seizure Detection: Medscape; 2020. Available from: https://www. medscape.com/viewarticle/942344
[6]
Smith, S.J.M. EEG in the diagnosis, classification, and management of patients with epilepsy. J. Neurol. Neurosurg. Psychiatry, 2005, 76(Suppl. 2), ii2-ii7.
[http://dx.doi.org/10.1136/jnnp.2005.069245] [PMID: 15961864]
[7]
Wahab, A. Difficulties in treatment and management of epilepsy and challenges in new drug development. Pharmaceuticals (Basel), 2010, 3(7), 2090-2110.
[http://dx.doi.org/10.3390/ph3072090] [PMID: 27713344]
[8]
Kwan, P.; Arzimanoglou, A.; Berg, A.T.; Brodie, M.J.; Allen Hauser, W.; Mathern, G.; Moshé, S.L.; Perucca, E.; Wiebe, S.; French, J. Definition of drug resistant epilepsy: consensus proposal by the ad hoc Task Force of the ILAE Commission on Therapeutic Strategies. Epilepsia, 2010, 51(6), 1069-1077.
[http://dx.doi.org/10.1111/j.1528-1167.2009.02397.x] [PMID: 19889013]
[9]
Fisher, R.S. Therapeutic devices for epilepsy. Ann. Neurol., 2012, 71(2), 157-168.
[http://dx.doi.org/10.1002/ana.22621] [PMID: 22367987]
[10]
Kwan, P.; Brodie, M.J. Combination therapy in epilepsy: when and what to use. Drugs, 2006, 66(14), 1817-1829.
[http://dx.doi.org/10.2165/00003495-200666140-00004] [PMID: 17040113]
[11]
Mutanana, N.; Tsvere, M.; Chiweshe, M.K. General side effects and challenges associated with anti-epilepsy medication: A review of related literature. Afr. J. Prim. Health Care Fam. Med., 2020, 12(1), e1-e5.
[http://dx.doi.org/10.4102/phcfm.v12i1.2162] [PMID: 32634006]
[12]
Jette, N.; Reid, A.Y.; Wiebe, S. Surgical management of epilepsy. CMAJ, 2014, 186(13), 997-1004.
[http://dx.doi.org/10.1503/cmaj.121291] [PMID: 24914117]
[13]
An, S.; Kang, C.; Lee, H.W. Artificial intelligence and computational approaches for epilepsy. J. Epilepsy Res., 2020, 10(1), 8-17.
[http://dx.doi.org/10.14581/jer.20003] [PMID: 32983950]
[14]
van Andel, J.; Thijs, R.D.; de Weerd, A.; Arends, J.; Leijten, F. Non-EEG based ambulatory seizure detection designed for home use: What is available and how will it influence epilepsy care? Epilepsy Behav., 2016, 57(Pt A), 82-89.
[http://dx.doi.org/10.1016/j.yebeh.2016.01.003] [PMID: 26926071]
[15]
Moher, D.; Shamseer, L.; Clarke, M.; Ghersi, D.; Liberati, A.; Petticrew, M.; Shekelle, P.; Stewart, L.A. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst. Rev., 2015, 4(1), 1.
[http://dx.doi.org/10.1186/2046-4053-4-1] [PMID: 25554246]
[16]
EPHPP EPHPP. Effective Public Healthcare Panacea Project EPHPP, 1999.
[17]
Sterne, J.A.C.; Savović, J.; Page, M.J.; Elbers, R.G.; Blencowe, N.S.; Boutron, I.; Cates, C.J.; Cheng, H.Y.; Corbett, M.S.; Eldridge, S.M.; Emberson, J.R.; Hernán, M.A.; Hopewell, S.; Hróbjartsson, A.; Junqueira, D.R.; Jüni, P.; Kirkham, J.J.; Lasserson, T.; Li, T.; McAleenan, A.; Reeves, B.C.; Shepperd, S.; Shrier, I.; Stewart, L.A.; Tilling, K.; White, I.R.; Whiting, P.F.; Higgins, J.P.T. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ, 2019, 366, l4898.
[http://dx.doi.org/10.1136/bmj.l4898] [PMID: 31462531]
[18]
Hooijmans, C.R.; Rovers, M.M.; de Vries, R.B.M.; Leenaars, M.; Ritskes-Hoitinga, M.; Langendam, M.W. SYRCLE’s risk of bias tool for animal studies. BMC Med. Res. Methodol., 2014, 14(1), 43.
[http://dx.doi.org/10.1186/1471-2288-14-43] [PMID: 24667063]
[19]
Abel, T.J.; Varela Osorio, R.; Amorim-Leite, R.; Mathieu, F.; Kahane, P.; Minotti, L.; Hoffmann, D.; Chabardes, S. Frameless robot-assisted stereoelectroencephalography in children: technical aspects and comparison with Talairach frame technique. J. Neurosurg. Pediatr., 2018, 22(1), 37-46.
[http://dx.doi.org/10.3171/2018.1.PEDS17435] [PMID: 29676681]
[20]
Arbune, A.A.; Conradsen, I.; Cardenas, D.P.; Whitmire, L.E.; Voyles, S.R.; Wolf, P.; Lhatoo, S.; Ryvlin, P.; Beniczky, S. Ictal quantitative surface electromyography correlates with postictal EEG suppression. Neurology, 2020, 94(24), e2567-e2576.
[http://dx.doi.org/10.1212/WNL.0000000000009492] [PMID: 32398358]
[21]
Gu, Y.; Cleeren, E.; Dan, J.; Claes, K.; Van Paesschen, W.; Van Huffel, S.; Hunyadi, B. Comparison between scalp EEG and behind-the-ear EEG for development of a wearable seizure detection system for patients with focal epilepsy. Sensors (Basel), 2017, 18(1), 29.
[http://dx.doi.org/10.3390/s18010029] [PMID: 29295522]
[22]
Weisdorf, S.; Duun-Henriksen, J.; Kjeldsen, M.J.; Poulsen, F.R.; Gangstad, S.W.; Kjaer, T.W. Ultra-long-term subcutaneous home monitoring of epilepsy-490 days of EEG from nine patients. Epilepsia, 2019, 60(11), 2204-2214.
[http://dx.doi.org/10.1111/epi.16360] [PMID: 31608435]
[23]
Sintotskiy, G.; Hinrichs, H. In-ear-EEG - a portable platform for home monitoring. J. Med. Eng. Technol., 2020, 44(1), 26-37.
[http://dx.doi.org/10.1080/03091902.2020.1713238] [PMID: 31971037]
[24]
Karthick, P.A.; Tanaka, H.; Khoo, H.M.; Gotman, J. Prediction of secondary generalization from a focal onset seizure in intracerebral EEG. Clin. Neurophysiol., 2018, 129(5), 1030-1040.
[http://dx.doi.org/10.1016/j.clinph.2018.02.122] [PMID: 29571121]
[25]
Duun-Henriksen, J.; Madsen, R.E.; Remvig, L.S.; Thomsen, C.E.; Sorensen, H.B.D.; Kjaer, T.W. Automatic detection of childhood absence epilepsy seizures: toward a monitoring device. Pediatr. Neurol., 2012, 46(5), 287-292.
[http://dx.doi.org/10.1016/j.pediatrneurol.2012.02.018] [PMID: 22520349]
[26]
Parvez, M.Z.; Paul, M. Seizure prediction using undulated global and local features. IEEE Trans. Biomed. Eng., 2017, 64(1), 208-217.
[http://dx.doi.org/10.1109/TBME.2016.2553131] [PMID: 27093309]
[27]
Kiral-Kornek, I.; Roy, S.; Nurse, E.; Mashford, B.; Karoly, P.; Carroll, T.; Payne, D.; Saha, S.; Baldassano, S.; O’Brien, T.; Grayden, D.; Cook, M.; Freestone, D.; Harrer, S. Epileptic seizure prediction using big data and deep learning: Toward a mobile system. EBioMedicine, 2018, 27, 103-111.
[http://dx.doi.org/10.1016/j.ebiom.2017.11.032] [PMID: 29262989]
[28]
Cook, M.J.; O’Brien, T.J.; Berkovic, S.F.; Murphy, M.; Morokoff, A.; Fabinyi, G.; D’Souza, W.; Yerra, R.; Archer, J.; Litewka, L.; Hosking, S.; Lightfoot, P.; Ruedebusch, V.; Sheffield, W.D.; Snyder, D.; Leyde, K.; Himes, D. Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study. Lancet Neurol., 2013, 12(6), 563-571.
[http://dx.doi.org/10.1016/S1474-4422(13)70075-9] [PMID: 23642342]
[29]
DiLorenzo, D.J.; Leyde, K.W.; Kaplan, D. Neural state monitoring in the treatment of epilepsy: Seizure prediction-conceptualization to first-in-man study. Brain Sci., 2019, 9(7), E156.
[http://dx.doi.org/10.3390/brainsci9070156] [PMID: 31266223]
[30]
Sareen, S.; Sood, S.K.; Gupta, S.K. An automatic prediction of epileptic seizures using cloud computing and wireless sensor networks. J. Med. Syst., 2016, 40(11), 226.
[http://dx.doi.org/10.1007/s10916-016-0579-1] [PMID: 27628727]
[31]
Velez, M.; Fisher, R.S.; Bartlett, V.; Le, S. Tracking generalized tonic-clonic seizures with a wrist accelerometer linked to an online database. Seizure, 2016, 39, 13-18.
[http://dx.doi.org/10.1016/j.seizure.2016.04.009] [PMID: 27205871]
[32]
Yamakawa, T.; Miyajima, M.; Fujiwara, K.; Kano, M.; Suzuki, Y.; Watanabe, Y.; Watanabe, S.; Hoshida, T.; Inaji, M.; Maehara, T. Wearable epileptic seizure prediction system with machine-learning-based anomaly detection of heart rate variability. Sensors (Basel), 2020, 20(14), 3987.
[http://dx.doi.org/10.3390/s20143987] [PMID: 32709064]
[33]
Boon, P.; Vonck, K.; van Rijckevorsel, K.; El Tahry, R.; Elger, C.E.; Mullatti, N.; Schulze-Bonhage, A.; Wagner, L.; Diehl, B.; Hamer, H.; Reuber, M.; Kostov, H.; Legros, B.; Noachtar, S.; Weber, Y.G.; Coenen, V.A.; Rooijakkers, H.; Schijns, O.E.; Selway, R.; Van Roost, D.; Eggleston, K.S.; Van Grunderbeek, W.; Jayewardene, A.K.; McGuire, R.M. A prospective, multicenter study of cardiac-based seizure detection to activate vagus nerve stimulation. Seizure, 2015, 32, 52-61.
[http://dx.doi.org/10.1016/j.seizure.2015.08.011] [PMID: 26552564]
[34]
Johansson, D.; Ohlsson, F.; Krýsl, D.; Rydenhag, B.; Czarnecki, M.; Gustafsson, N.; Wipenmyr, J.; McKelvey, T.; Malmgren, K. Tonic-clonic seizure detection using accelerometry-based wearable sensors: A prospective, video-EEG controlled study. Seizure, 2019, 65, 48-54.
[http://dx.doi.org/10.1016/j.seizure.2018.12.024] [PMID: 30611010]
[35]
Nasseri, M.; Nurse, E.; Glasstetter, M.; Böttcher, S.; Gregg, N.M.; Laks Nandakumar, A.; Joseph, B.; Pal Attia, T.; Viana, P.F.; Bruno, E.; Biondi, A.; Cook, M.; Worrell, G.A.; Schulze-Bonhage, A.; Dümpelmann, M.; Freestone, D.R.; Richardson, M.P.; Brinkmann, B.H. Signal quality and patient experience with wearable devices for epilepsy management. Epilepsia, 2020, 61(Suppl. 1), S25-S35.
[http://dx.doi.org/10.1111/epi.16527] [PMID: 32497269]
[36]
Forooghifar, F.; Aminifar, A.; Cammoun, L.; Wisniewski, I.; Ciumas, C.; Ryvlin, P. A self-aware epilepsy monitoring system for real-time epileptic seizure detection. Mob. Netw. Appl., 2019.
[http://dx.doi.org/10.1007/s11036-019-01322-7]
[37]
Bonnet, S.; Jallon, P.; Bourgerette, A.; Antonakios, M.; Guillemaud, R.; Caritu, Y. An Ethernet motion-sensor based alarm system for epilepsy monitoring. IRBM, 2011, 32(2), 155-157.
[http://dx.doi.org/10.1016/j.irbm.2011.01.021]
[38]
Davis, K.A.; Sturges, B.K.; Vite, C.H.; Ruedebusch, V.; Worrell, G.; Gardner, A.B.; Leyde, K.; Sheffield, W.D.; Litt, B. A novel implanted device to wirelessly record and analyze continuous intracranial canine EEG. Epilepsy Res., 2011, 96(1-2), 116-122.
[http://dx.doi.org/10.1016/j.eplepsyres.2011.05.011] [PMID: 21676591]
[39]
Brinkmann, B.H.; Patterson, E.E.; Vite, C.; Vasoli, V.M.; Crepeau, D.; Stead, M.; Howbert, J.J.; Cherkassky, V.; Wagenaar, J.B.; Litt, B.; Worrell, G.A. Forecasting seizures using intracranial EEG measures and SVM in naturally occurring canine epilepsy. PLoS One, 2015, 10(8), e0133900.
[http://dx.doi.org/10.1371/journal.pone.0133900] [PMID: 26241907]
[40]
Coles, L.D.; Patterson, E.E.; Sheffield, W.D.; Mavoori, J.; Higgins, J.; Michael, B.; Leyde, K.; Cloyd, J.C.; Litt, B.; Vite, C.; Worrell, G.A. Feasibility study of a caregiver seizure alert system in canine epilepsy. Epilepsy Res., 2013, 106(3), 456-460.
[http://dx.doi.org/10.1016/j.eplepsyres.2013.06.007] [PMID: 23962794]
[41]
Raghunathan, S.; Gupta, S.K.; Markandeya, H.S.; Roy, K.; Irazoqui, P.P. A hardware-algorithm co-design approach to optimize seizure detection algorithms for implantable applications. J. Neurosci. Methods, 2010, 193(1), 106-117.
[http://dx.doi.org/10.1016/j.jneumeth.2010.08.008] [PMID: 20713084]
[42]
Jiang, Z.; Zhao, W. Optimal selection of customized features for implementing seizure detection in wearable electroencephalography sensor. IEEE Sens. J., 2020, 20(21), 12941-12949.
[http://dx.doi.org/10.1109/JSEN.2020.3003733]
[43]
Ibrahim, S.; Majzoub, S. Adaptive epileptic seizure prediction based on EEG synchronization. J. Biomimetics. Biomater. Biomed. Eng., 2017, 33, 52-58.
[http://dx.doi.org/10.4028/www.scientific.net/JBBBE.33.52]
[44]
Djoufack Nkengfack, L.C.; Tchiotsop, D.; Atangana, R.; Louis-Door, V.; Wolf, D. EEG signals analysis for epileptic seizures detection using polynomial transforms, linear discriminant analysis and support vector machines. Biomed. Signal Process. Control, 2020, 62, 102141.
[http://dx.doi.org/10.1016/j.bspc.2020.102141]
[45]
Ranga, V.; Gupta, S.; Meena, J.; Agrawal, P. Automated human mind reading using EEG signals for seizure detection. J. Med. Eng. Technol., 2020, 44(5), 237-246.
[http://dx.doi.org/10.1080/03091902.2020.1791988] [PMID: 32657667]
[46]
Bennett, E.E.; VanBuren, J.; Holubkov, R.; Bratton, S.L. Presence of Invasive Devices and Risks of Healthcare-Associated Infections and Sepsis. J. Pediatr. Intensive Care, 2018, 7(4), 188-195.
[http://dx.doi.org/10.1055/s-0038-1656535] [PMID: 31073493]
[47]
Zack, M.M.; Kobau, R. National and state estimates of the numbers of adults and children with active epilepsy - United States, 2015. MMWR Morb. Mortal. Wkly. Rep., 2017, 66(31), 821-825.
[http://dx.doi.org/10.15585/mmwr.mm6631a1] [PMID: 28796763]
[48]
Whitney, R.; Donner, E.J. Risk factors for sudden unexpected death in epilepsy (SUDEP) and their mitigation. Curr. Treat. Options Neurol., 2019, 21(2), 7.
[http://dx.doi.org/10.1007/s11940-019-0547-4] [PMID: 30758730]
[49]
Faber, J.; Fonseca, L.M. How sample size influences research outcomes. Dental Press J. Orthod., 2014, 19(4), 27-29.
[http://dx.doi.org/10.1590/2176-9451.19.4.027-029.ebo] [PMID: 25279518]
[50]
Grone, B.P.; Baraban, S.C. Animal models in epilepsy research: legacies and new directions. Nat. Neurosci., 2015, 18(3), 339-343.
[http://dx.doi.org/10.1038/nn.3934] [PMID: 25710835]
[51]
Berger, H. Über das Elektrenkephalogramm des Menschen. Arch. Psychiatr. Nervenkr., 1929, 87(1), 527-570.
[http://dx.doi.org/10.1007/BF01797193]
[52]
Viglione, S.S.; Walsh, G.O. Proceedings: Epileptic seizure prediction. Electroencephalogr. Clin. Neurophysiol., 1975, 39(4), 435-436.
[PMID: 51767]
[53]
McLane, H.C.; Berkowitz, A.L.; Patenaude, B.N.; McKenzie, E.D.; Wolper, E.; Wahlster, S.; Fink, G.; Mateen, F.J. Availability, accessibility, and affordability of neurodiagnostic tests in 37 countries. Neurology, 2015, 85(18), 1614-1622.
[http://dx.doi.org/10.1212/WNL.0000000000002090] [PMID: 26446063]
[54]
Iasemidis, L.D. Epileptic seizure prediction and control. IEEE Trans. Biomed. Eng., 2003, 50(5), 549-558.
[http://dx.doi.org/10.1109/TBME.2003.810705] [PMID: 12769431]
[55]
Pinto, M.F.; Leal, A.; Lopes, F.; Dourado, A.; Martins, P.; Teixeira, C.A. A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction. Sci. Rep., 2021, 11(1), 3415.
[http://dx.doi.org/10.1038/s41598-021-82828-7] [PMID: 33564050]
[56]
Nagaraj, V.; Lee, S.T.; Krook-Magnuson, E.; Soltesz, I.; Benquet, P.; Irazoqui, P.P.; Netoff, T.I. Future of seizure prediction and intervention: closing the loop. J. Clin. Neurophysiol., 2015, 32(3), 194-206.
[http://dx.doi.org/10.1097/WNP.0000000000000139] [PMID: 26035672]
[57]
Lieb, J.P.; Walsh, G.O.; Babb, T.L.; Walter, R.D.; Crandall, P.H. A comparison of EEG seizure patterns recorded with surface and depth electrodes in patients with temporal lobe epilepsy. Epilepsia, 1976, 17(2), 137-160.
[http://dx.doi.org/10.1111/j.1528-1157.1976.tb03392.x] [PMID: 947745]
[58]
Tao, J.X.; Baldwin, M.; Ray, A.; Hawes-Ebersole, S.; Ebersole, J.S. The impact of cerebral source area and synchrony on recording scalp electroencephalography ictal patterns. Epilepsia, 2007, 48(11), 2167-2176.
[http://dx.doi.org/10.1111/j.1528-1167.2007.01224.x] [PMID: 17662060]
[59]
Ali, A.; Wu, S.; Issa, N.P.; Rose, S.; Towle, V.L.; Warnke, P.; Tao, J.X. Association of sleep with sudden unexpected death in epilepsy. Epilepsy Behav., 2017, 76, 1-6.
[http://dx.doi.org/10.1016/j.yebeh.2017.08.021] [PMID: 28917499]
[60]
Skupch, A.M.; Dollfuß, P.; Fürbaß, F.; Hartmann, M.; Perko, H.; Pataraia, E. EEG artifact detection using spatial distribution of rhythmicity. APCBEE Procedia, 2013, 7, 16-20.
[http://dx.doi.org/10.1016/j.apcbee.2013.08.005]
[61]
Jiang, X.; Bian, G-B.; Tian, Z. Removal of artifacts from EEG signals: A review. Sensors (Basel), 2019, 19(5), 987.
[http://dx.doi.org/10.3390/s19050987] [PMID: 30813520]
[62]
O’Regan, S.; Faul, S.; Marnane, W. Automatic detection of EEG artefacts arising from head movements using EEG and gyroscope signals. Med. Eng. Phys., 2013, 35(7), 867-874.
[http://dx.doi.org/10.1016/j.medengphy.2012.08.017] [PMID: 23018030]
[63]
Park, H.J.; Jeong, D.U.; Park, K.S. Automated detection and elimination of periodic ECG artifacts in EEG using the energy interval histogram method. IEEE Trans. Biomed. Eng., 2002, 49(12 Pt 2), 1526-1533.
[http://dx.doi.org/10.1109/TBME.2002.805482] [PMID: 12549734]
[64]
Lazarou, I.; Nikolopoulos, S.; Petrantonakis, P.C.; Kompatsiaris, I.; Tsolaki, M. EEG-Based Brain-Computer Interfaces for Communication and Rehabilitation of People with Motor Impairment: A Novel Approach of the 21 st Century. Front. Hum. Neurosci., 2018, 12, 14.
[http://dx.doi.org/10.3389/fnhum.2018.00014] [PMID: 29472849]
[65]
Hoppe, C.; Feldmann, M.; Blachut, B.; Surges, R.; Elger, C.E.; Helmstaedter, C. Novel techniques for automated seizure registration: Patients’ wants and needs. Epilepsy Behav., 2015, 52(Pt A), 1- 7.
[http://dx.doi.org/10.1016/j.yebeh.2015.08.006] [PMID: 26366675]
[66]
Osorio, I.; Schachter, S. Extracerebral detection of seizures: a new era in epileptology? Epilepsy Behav., 2011, 22(Suppl. 1), S82-S87.
[http://dx.doi.org/10.1016/j.yebeh.2011.09.012] [PMID: 22078524]
[67]
Al-Eidan, R.M.; Al-Khalifa, H.; Al-Salman, A.M. A review of wrist-worn wearable: Sensors, models, and challenges. J. Sens., 2018, 2018, 5853917.
[http://dx.doi.org/10.1155/2018/5853917]
[68]
Balli, S.; Sağbaş Ensar, A.; Peker, M. Human activity recognition from smart watch sensor data using a hybrid of principal component analysis and random forest algorithm. Meas. Control, 2019, 52(1-2), 37-45.
[http://dx.doi.org/10.1177/0020294018813692]
[69]
Akyüz, E.; Üner, A.K.; Köklü, B.; Arulsamy, A.; Shaikh, M.F. Cardiorespiratory findings in epilepsy: A recent review on outcomes and pathophysiology. J. Neurosci. Res., 2021, 99(9), 2059-2073.
[http://dx.doi.org/10.1002/jnr.24861] [PMID: 34109651]
[70]
Leutmezer, F.; Schernthaner, C.; Lurger, S.; Pötzelberger, K.; Baumgartner, C. Electrocardiographic changes at the onset of epileptic seizures. Epilepsia, 2003, 44(3), 348-354.
[http://dx.doi.org/10.1046/j.1528-1157.2003.34702.x] [PMID: 12614390]
[71]
Seyal, M.; Bateman, L.M.; Albertson, T.E.; Lin, T-C.; Li, C-S. Respiratory changes with seizures in localization-related epilepsy: analysis of periictal hypercapnia and airflow patterns. Epilepsia, 2010, 51(8), 1359-1364.
[http://dx.doi.org/10.1111/j.1528-1167.2009.02518.x] [PMID: 20163438]
[72]
Shao, M.; Zhou, Z.; Bin, G.; Bai, Y.; Wu, S. A wearable electrocardiogram telemonitoring system for atrial fibrillation detection. Sensors (Basel), 2020, 20(3), 606.
[http://dx.doi.org/10.3390/s20030606] [PMID: 31979184]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy