Abstract
The prediction of epileptic seizures in patients can help prevent many unwanted risks and excessive suffering. In this research, electrocardiography (ECG) signals for 7 patients under supervision and aged 31–48 years old, both male and female, were recorded. To predict recurrent seizures, we aimed to differentiate heart rate variation (HRV) signals in both seizure and seizure-free states. The evaluation of ECG signals alone was not able to predict recurrent seizures. Furthermore, we extracted geometrical features from our recordings. The results showed distinct differences between the two states with a high percentage of performance evaluation. Therefore, the proposed algorithm was successful and can be used to predict these epileptic seizures, thus decreasing the pain and suffering in patients. In this study, we have presented a new approach based on the geometric feature from extracted HRV analysis. We achieved a sensitivity of 100%, an accuracy of 90%, and a specificity of 88.33%, making our study practical for predicting seizures based on the pre-ictal state. With this approach, high-risk patients will be recognized, under control, and monitored sooner.
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References
Sirven, J.I.: Epilepsy: a spectrum disorder. Cold Spring Harb. Perspect. Med. 5(9), a022848 (2015)
Ogwang, R., Ningwa, A., Akun, P., Bangirana, P., Anguzu, R., Mazumder, R., Salamon, N., Henning, O.J., Newton, C.R., Abbo, C., Mwaka, A.D., Marsh, K., Idro, R.: Epilepsy in onchocerca volvulus sero-positive patients from northern uganda—clinical, EEG and brain ımaging features. Front. Neurol. 12, 687281 (2021)
Picot, M.C., Baldy-Moulinier, M., Daures, J.P., Dujols, P., Crespel, A.: The prevalence of epilepsy and pharmacoresistant epilepsy in adults: a population-based study in a Western European country. Epilepsia 49, 1230–1238 (2008)
Moridani, M.K., Farhadi, H.: Heart rate variability as a biomarker for epilepsy seizure prediction. Bratislava Med. J. 118(01), 3–8 (2017)
Devinsky, O., Hesdorffer, D.C., Thurman, D.J., Lhatoo, S., Richerson, G.: Sudden unexpected death in epilepsy: epidemiology, mechanisms, and prevention. Lancet Neurol. 15(10), 1075–1088 (2020)
Devinsky, O., Bundock, E., Hesdorffer, D., Donner, E., Moseley, B., Cihan, E., Hussain, F., Friedman, D.: Resolving ambiguities in SUDEP classification. Epilepsia 59(6), 1220–1233 (2018)
Van de Vel, A., Cuppens, K., Bonroy, B., Milosevic, M., Jansen, K., Van Huffel, S., Vanrumste, B., Cras, P., Lagae, L., Ceulemans, B.: Non-EEG seizure detection systems and potential SUDEP prevention: state of the art. Seizure 41, 141–153 (2016)
Stirling, R.E., Cook, M.J., Grayden, D.B., Karoly, P.J.: Seizure forecasting and cyclic control of seizures. Epilepsia 62(S1), S2–S14 (2020)
Van de Vel, A., Cuppens, K., Bonroy, B., Milosevic, M., Van Huffel, S., Vanrumste, B., Lagae, L., Ceulemans, B.: Long-term home monitoring of hypermotor seizures by patient-worn accelerometers. Epilepsy Behav. 26, 118–125 (2013)
Vandecasteele, K., De Cooman, T., Gu, Y., Cleeren, E., Claes, K., Paesschen, W.V., Huffel, S.V., Hunyadi, B.: Automated epileptic seizure detection based on wearable and PPG in a hospital environment. Sensors 17(10), 2338 (2017)
Park, Y., Luo, L., Parhi, K.K., Netoff, T.: Seizure prediction with spectral power of EEG using cost-sensitive support vector machines. Epilepsia 52(10), 1761–1770 (2011)
Billeci, L., Marino, D., Insana, L., Vatti, G., Varanini, M.: Patient-specific seizure prediction based on heart rate variability and recurrence quantification analysis. PLOS ONE 13(9), e0204339 (2018)
Malarvili, M.B., Mesbah, M.: Newborn seizure detection based on heart rate variability. IEEE Trans. Biomed. Eng. 56(11), 2594–2603 (2009)
Ponnusamy, A., Marques, J.L., Reuber, M.: Heart rate variability measures as biomarkers in patients with psychogenic nonepileptic seizures: potential and limitations. Epilepsy Behav. 22, 685–691 (2011)
Waxenbaum, J.A., Reddy, V., Varacallo, M.: Anatomy, Autonomic Nervous System. StatPearls Publishing [Internet] (2021)
Mueller, S.G., Nei, M., Bateman, L.M., Knowlton, R., Laxer, K.D., Friedman, D., Devinsky, O., Goldman, A.M.: Brainstem network disruption: a pathway to sudden unexplained death in epilepsy? Hum. Brain Mapp. 39(12), 4820–4830 (2018)
Billman, G.E.: Heart rate variability—a historical perspective. Front. Physiol. 2, 86 (2011)
Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P.C., Mark, R., Stanley, H.E.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2002)
Leal, A., Pinto, M.F., Lopes, F., et al.: Heart rate variability analysis for the identification of the preictal interval in patients with drug-resistant epilepsy. Sci. Rep. 11, 5987 (2021)
Al-Aweel, I.C., Krishnamurthy, K.B., Hausdorff, J.M., Mietus, J.E., Ives, J.R., Blum, A.S., Schomer, D.L., Goldberger, A.L.: Postictal heart rate oscillations in partial epilepsy. Neurology 53(7), 1590–1592 (1999)
Fariha, M.A.Z., Ikeura, R., Hayakawa, S., Tsutsumi, S.: Analysis of Pan-Tompkins algorithm performance with noisy ECG signals. J. Phys.: Conf. Ser. 1532 (2019)
Fishman, M., Jacono, F.J., Park, S., Jamasebi, R., Thungtong, A., Loparo, K.A., Dick, T.E.: A method for analyzing temporal patterns of variability of a time series from Poincare plots. J. Appl. Physiol. 113(2), 297–306 (2012)
Karmakar, C.K., Khandoker, A.H., Gubbi, J., Palaniswami, M.: Complex correlation measure: a novel descriptor for Poincare plot. Biomed. Eng. Online 8(1), 17 (2009)
Tang, G., Li, C., Wang, M.: Polarization discriminated time‐bin phase‐encoding measurement‐device‐independent quantum key distribution. Quantum Eng. e79 (2021)
Whitney, R., Donner, E.J.: Risk factors for sudden unexpected death in epilepsy (SUDEP) and their mitigation. Curr. Treat. Options Neurol. 21(2), 7 (2019)
Baratloo, A., Hosseini, M., Negida, A., El Ashal, G.: Part 1: simple definition and calculation of accuracy, sensitivity and specificity. Emergency 3(2), 48–49 (2015)
Adam, E.E.B., Ammayappan, S.: Survey on medical imaging of electrical impedance tomography (EIT) by variable current pattern methods. J. ISMAC 3(2), 82–95 (2021)
Patcha, K., Ammayappan, S., Hamdan, Y.B.: Early prediction of autism spectrum disorder by computational approaches to fMRI analysis with early learning technique. J. Artif. Intell. Capsule Netw. 2(4), 207–216 (2020)
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Mahmoudi, N., Moridani, M.K., Khosroshahi, M., Moghadam, S.T. (2022). Epileptic Seizure Prediction Using Geometrical Features Extracted from HRV Signal. In: Suma, V., Fernando, X., Du, KL., Wang, H. (eds) Evolutionary Computing and Mobile Sustainable Networks. Lecture Notes on Data Engineering and Communications Technologies, vol 116. Springer, Singapore. https://doi.org/10.1007/978-981-16-9605-3_33
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