Assessment of Epileptic Seizures and Non-Epileptic Seizures via Wearable Sensors and Priori Detection of Epileptic Seizures

Year 2022, , 150 - 155, 30.04.2022

Ömer Faruk Ertuğrul , Yasin Sönmez , Necmettin Sezgin , Eşref Akıl

https://doi.org/10.17694/bajece.1054818

Cited By: 1

Abstract

Epilepsy is one the most prevalent neurological disorders whose causes are not exactly known. Diagnosis and treatment of epilepsy are closely related to the patient's story, and the most important indicator is the frequency and severity of seizures. Since the disease does not only affect the patients but also the lives of their environment seriously, it is very important to make the diagnosis and treatment correctly. However, sometimes misrecognition from patients and their relatives, unnecessary epilepsy treatment to the patient in non-epileptic seizures mixed with epileptic seizures, or increasing the dose of the drugs used for the patient are the situations frequently encountered.
The so-called video-EEG method is used in the detection and segregation of epileptic / non-epileptic seizures. In this method, the patient is kept in an environment where video recording is continuously taken until the seizure occurs, and EEG, EMG, and ECG records of the patient are taken. When the patient has a seizure, the seizure type is separated by examining these records. In this project, seizure detection and seizure type (epileptic / non-epileptic) detection is aimed to be done by using wearable sensors increasingly applied in the field of health. The achievable benefits from the project and data set will provide a different perspective on the epilepsy illness, as well as reduce the number of epilepsy patients who are not in fact epilepsy patients needing treatment, and keep epileptic seizure recordings constantly in the electronic environment so that the treatment processes are monitored more closely.

Keywords

Epilepsy, Epileptic Seizures, Non-Epileptic Seizures, Priori Detection of Epileptic Seizures, Priori Detection of Epileptic Seizures

References

• [1] Akbulut, F. P., Akan, A. 2015. “Akıllı Giyilebilir Hasta Takip Sistemleri”, Vogue, 15(18), 440-443.
• [2] Araz, N. Ç., Yılmaz, K., Ölmez, A., Kılınç, M. 2009. “Epilepsi Ayırıcı Tanısında üç Olgu ile Kardiovasküler Nedenler”, ADÜ Tıp Fakültesi Dergisi, 10(2), 37 – 40.
• [3] Arıkanoğlu, A. 2011. “Epileptik nöbet ve psödonöbetlerin ayırıcı tanısına güncel yaklaşım”, Journal of Clinical and Experimental Investigations, 2(3), 330-334.
• [4] Behbahani, S., Dabanloo, N. J., Nasrabadi, A. M., Attarodi, G., Teixeira, C. A., Dourado, A. 2012. “Epileptic seizure behaviour from the perspective of heart rate variability”, IEEE Computing in Cardiology (CinC), 117-120.
• [5] Bodde, N. M. G., Brooks, J. L., Baker, G. A., Boon, P. A., Hendriksen, J. G., Aldenkamp, A. P. 2009. “Psychogenic non-epileptic seizures—diagnostic issues: a critical review”, Clinical neurology and neurosurgery, 111(1), 1-9.
• [6] Cragar, D. E., Berry, D. T. R., Fakhoury, T. A., Cibula, J. E., Schmitt, F. A. 2002. “A Review of Diagnostic Techniques in the Differential Diagnosis of Epileptic and Nonepileptic Seizures”, Neuropsychology Review, 12(1), 31-63.
• [7] Cuthill, F. M., Espie, C. A. 2005. “Sensitivity and specificity of procedures for the differential diagnosis of epileptic and non-epileptic seizures: a systematic review”, Seizure, 14(5), 293-303.
• [8] Çakıl, D., İnanır, S., Baykan, H., Aygün, H., Kozan, R. 2013. “Epilepsi ayırıcı tanısında psikojenik non-epileptik nöbetler”, Göztepe Tıp Dergisi, 28(1), 41-47.
• [9] D’Alessio, L, Giagante, B., Oddo, S., Wa, W. S., Solıs, P., Consalvo, D., Kochen, S. 2006. “Psychiatric disorders in patients with psychogenic non-epileptic seizures, with and without comorbid epilepsy”, Seizure, 15(5), 333-339.
• [10] Ertuğrul, Ö.F. (2016-a). Determining the Order of Risk Factors in Diagnosing Heart Disease by Extreme Learning Machine, International Conference on Natural Science and Engineering (ICNASE’16), 10-19.
• [11] Ertuğrul, Ö. F. (2016-b). Forecasting electricity load by a novel recurrent extreme learning machines approach. International Journal of Electrical Power & Energy Systems. 78: 429–435, DOI:10.1016/j.ijepes.2015.12.006.
• [12] Ertuğrul, Ö. F. (2016-c). Aşırı Öğrenme Makineleri ile Biyolojik Sinyallerin Gizli Kaynaklarına Ayrıştırılması. Dicle Üniversitesi Mühendislik Dergisi, 7:41-40.
• [13] Ertuğrul, Ö. F., Altun, Ş. (Baskıda). Developing Correlations by Extreme Learning Machine for Calculating Higher Heating Values of Waste Frying Oils from their Physical Properties. Neural Computing and Applications. DOI: 10.1007/s00521-016-2233-8.
• [14] Ertuğrul, Ö. F., Kaya, Y. (Baskıda). Determining the Optimal Number of Body-Worn Sensors for Human Activity Recognition. Soft Computing, DOI: 10.1007/s00500-016-2100-7.
• [15] Ertuğrul, Ö. F., Kaya, Y., Tekin, R. (2016-a). A novel approach for SEMG signal classification with adaptive local binary patterns. Medical & Biological Engineering & Computing, 54(7): 1137-1146, DOI: 10.1007/s11517-015-1443-z.
• [16] Ertuğrul, Ö.F., Tağluk, M.E., Kaya, Y. (2012). Enerji İletim Hatlarında Wigner Ville Dağılımı, Gri Düzey Eş Oluşum Matrisi Ve Örüntü Tanıma Yöntemleri İle Arıza Analizi. 20. Sinyal İşleme ve İletişim Uygulamaları Kurultayı (SIU2012), 1-4.
• [17] Ertuğrul, Ö.F., Tağluk, M.E., Kaya, Y., Tekin, R. (2013). EMG Sinyallerinin Aşırı Öğrenme Makinesi ile Sınıflandırılması. 21. Sinyal İşleme ve İletişim Uygulamaları Kurultayı (SIU2013), 1-4.
• [18] Gonzalez-Velldn, B., Sanei, S., Chambers, J. A. 2003. “Support vector machines for seizure detection”, 3rd IEEE International Symposium on Signal Processing and Information Technology (ISSPIT 2003), 126-129.
• [19] Gubbi, J., Kusmakar, S., Rao, A. S., Yan, B., O’Brien, T. J., Palaniswami M. 2015. “Automatic Detection and Classification of Convulsive Psychogenic Non-epileptic Seizures Using a Wearable Device”, IEEE Journal of Biomedical and Health Informatics, doi:10.1109/JBHI.2015.2446539.
• [20] Hinich, M.J. and Clay, C.S., ―The application of the discrete Fourier transform in the estimation of power spectra, coherence and bispectra of geophysical data‖, Reviews of Geophysics, 6(3):347-363, 1968.
• [21] Huang G. B., Zhu Q. Y. Siew C. K. (2006). Extreme learning machine: Theory and applications. Neurocomputing, 70:489–501.
• [22] Kanas, V. G., Zacharaki E. I., Pippa, E., Tsirka, V., Koutroumanidis, M., Megalooikonomou, V. 2015. “Classification of epileptic and non-epileptic events using tensor decomposition”, IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE), 1-5.
• [23] Kaya, Y., Ertuğrul, Ö. F. (2016). A novel approach for spam email detection based on shifted binary patterns. Security and Communication Networks, 9(10): 1216–1225.
• [24] Kaya, Y., Ertuğrul, Ö. F., Tekin, R. (2012). Epileptik EEG İşaretlerinin Sınıflandırılmasında Karar Kuralları ve Karar Ağaçlarının Kullanılması. Yaşam Bilimleri Dergisi, 1(2):403-413.
• [25] Kotsopoulos, I. A. W., de Krom, M. C. T. F. M., Kessels, F. G. H., Lodder, J., Troost, J., Twellaar, M., van Merode, T., Knottnerus, A. J. 2003. “The diagnosis of epileptic and non-epileptic seizures”, Epilepsy research, 57(1), 59-67.
• [26] Kusmakar, S., Gubbi, J., Rao, A. S., Yan, B., O’Brien, T. J., Palaniswami, M., 2015-b. “Classification of convulsive psychogenic non-epileptic seizures using histogram of oriented motion of accelerometry signals”, 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 586-589.
• [27] Kusmakar, S., Gubbi, J., Yan, B., O’Brien, T. J., Palaniswami, M. 2015-a. “Classification of convulsive psychogenic non-epileptic seizures using muscle transforms obtained from accelerometry signal”, 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 582-585.
• [28] Nei, M. 2009. “Cardiac Effects of Seizures”, Epilepsy Currents, 9:(4), 91–95.
• [29] Nikias, C.L. and Petropulu, A.P., Higher order spectral analysis: A nonlinear signal processing framework, Englewood Cliffs, NJ: Prentice-Hall, 1993. [30] Ning, T. and Bronzino, J.D., Bispectral analysis of the rat EEG during various vigilance states‖, IEEE Trans Biomed Eng, 36(4):497–499, 1989.
• [31] Pippa, E., Zacharaki, E. I., Mporas, I., Megalooikonomou, V., Tsirka, V., Richardson, M., Koutroumanidis, M., 2014. “Classification of epileptic and non-epileptic EEG events”, IEEE 4th International Conference on Wireless Mobile Communication and Healthcare (Mobihealth), 87-90.
• [32] Raghuveer, M.R. and Nikias, C.L., Bispectrum estimation: A parametric approach‖, IEEE Trans. Acoust., Speech, Signal Processing, 33(4):1113-1230, 1985.
• [33] Reuber, M. 2008. “Psychogenic nonepileptic seizures: answers and questions”, Epilepsy & Behavior, 12(4), 622-635.
• [34] Reuber, M., Elger, C. E. 2003. “Psychogenic nonepileptic seizures: review and update”, Epilepsy & Behavior, 4(3), 205-216.
• [35] Sezgin, N., Estimation and classification of sleep apnea in adults by developed preprocessing–neural network models, PhD thesis, Inonu University, Malatya, Turkey, 2010.
• [36] Sezgin, N., Tağluk, M. E., Ertuğrul, Ö. F., Kaya, Y. (2013). Epileptik EEG İşaretlerinin İkiz-spektrum Analizi. 21. Sinyal İşleme ve İletişim Uygulamaları Kurultayı (SIU2013), 1-4.
• [37] Sigl, J.C. and Chamoun, N.G., An introduction of bispectral analysis for the electroencephalogram‖, Journal of Clinical Monitoring, 10(6):392-404, 1994.
• [38] Tagluk M.E., Sezgin N., A new approach for estimation of obstructive sleep apnea syndrome‖ Expert Systems with Applications, 38(5);5346-5351, 2011.
• [39] Tatlı, B., Aydınlı, N., Çalışkan, M., Özmen, M. 2004. “Non epileptik paroksismal olaylar: olgu sunumları ile derleme”, Türk Pediatri Arşivi, 39(2), 58-64.
• [40] Turner, K., Piazzini, A., Chiesa, V., Barbieri, V., Vignoli, A., Gardella, E., Tisi, G., Scarone, S., Canevini, M. P., Gambini, O. 2011. “Patients with epilepsy and patients with psychogenic non-epileptic seizures: video-EEG, clinical and neuropsychological evaluation”, Seizure, 20(9), 706-710.
• [41] Xu, P., Xiong, X., Xue, Q., Li, P., Zhang, R., Wang, Z., Valdes-Sosa, P. A., Wang, Y., Yao, D. 2014. “Differentiating between psychogenic nonepileptic seizures and epilepsy based on common spatial pattern of weighted EEG resting networks”, IEEE Transactions on Biomedical Engineering, 61(6), 1747-1755.
• [42] Yadav, R., Agarwal, R., Swamy, M. N. S., 2009. “A new improved model-based seizure detection using statistically optimal null filter”, Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2009), 1318-1322.
• [43] Zijlmans, M., Flanagan, D., & Gotman, J. (2002). Heart rate changes and ECG abnormalities during epileptic seizures: prevalence and definition of an objective clinical sign. Epilepsia, 43(8), 847-854.