Abstract
Healthcare department represented in the detection and prediction of epileptic seizures and other chronic diseases play a significant effect on the environment and make people well. Therefore, it is interesting to understand how healthcare contributes to sustainable development Epilepsy is one of the dangerous and devastating diseases that affect the human nervous system. This disease may affect anyone at any age, leading to a delayed reactivity and loss of consciousness. Epileptic seizure detection is an emerging approach in the neurological processing of brain signals. In this paper, an automated method for detection of abrupt changes of Electroencephalogram (EEG) signals is presented. The basic idea of this method depends on the utilization of Fast Walsh Hadamard Transform (FWHT). The FWHT analyzes the EEG signals in the frequency domain and decomposes it into the Hadamard coefficients. Different signal attributes are extracted from the decomposed EEG signals. These attributes comprise: Kurtosis, skewness, mean curve length, and Hjorth activity. Finally, classification is implemented using a thresholding strategy to discriminate between seizure and healthy epochs. This method is tested on long-term EEG recordings from the available Physio-Net EEG dataset. The proposed method demonstrates a high classification performance in comparison with other previous methods. An average sensitivity of 98.59%, an average specificity of 96.26% and an average accuracy of 96.26% are achieved from the mean curve length feature with FWHT.
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References
Gulis G, Krishnankutty N, Boess ER, Lyhne I, Kørnøv L (2022) Environmental impact assessment, human health and the sustainable development goals. Int J Publ Health
Wang EY, Zafar JE, Lawrence CM, Gavin LF, Mishra S, Boateng A, Thiel CL, Dubrow R, Sherman JD (2021) Environmental emissions reduction of a preoperative evaluation center utilizing telehealth screening and standardized preoperative testing guidelines. Resour Conserv Recycl
Niknazar H, Maghooli K, Nasrabadi AM (2015) Epileptic seizure prediction using statistical behavior of local extrema and fuzzy logic system. Int J Comput Appl (2)
Riney K, Bogacz A, Somerville E, Hirsch E, Nabbout R, Scheffer IE, Zuberi SM, Alsaadi T, Jain S, French J, Specchio N (2022) International League Against Epilepsy classification and definition of epilepsy syndromes with onset at a variable age: position statement by the ILAE task force on nosology and definitions. Epilepsia 63(6):1443–1474
Alickovic E, Kevric J, Subasi A (2018) Performance evaluation of empirical mode decomposition, discrete wavelet transforms, and wavelet packed decomposition for automated epileptic seizure detection and prediction. Biomed Signal Process Control 39:94–102
Ullah I, Hussain M, Aboalsamh H (2018) An automated system for epilepsy detection using EEG brain signals based on deep learning approach. Expert Syst Appl 107:61–71
Peng P, Song Y, Yang L, Wei H (2022) Seizure prediction in EEG signals using STFT and domain adaptation. Front Neurosci 15
Teplan M (2002) Fundamentals of EEG measurement. Measur Sci Rev 2(2):1–1
The CHB-MIT database (online). https://physionet.org/content/chbmit/1.0.0/. Accessed 4 Apr 2021
Duch D, Wieczorek T, Biesiada J, Blachnik M (2004) Comparison of feature ranking methods based on information entropy. IEEE Int Joint Conf Neural Netw 2(4):1415–1419
Hussain L (2018) Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach. Cognitive Neuro-Dyn 12(3):271–294
Sharma R, Ram PB, Rajendra UA (2015) An integrated index for the identification of focal electroencephalogram signals using discrete wavelet transform and entropy measures. Entropy 17(8):5218–5240
Shakya N, Rahul D, Laxmi S (2021) Stress detection using EEG signal based on fast Walsh Hadamard transform and voting classifier
Subathra MSP, Mohammed MA, Maashi MS, Garcia-Zapirain B, Sairamya NJ, George ST (2020) Detection of focal and non-focal electroencephalogram signals using fast Walsh-Hadamard transform and artificial neural network. Sensors 17
Oh SH, Yu-Ri L, Hyoung NK (2014) A novel EEG feature extraction method using Hjorth parameter. Int J Electron Electr Eng 2:106–110
Büyükçakır B, Furkan E, Mutlu AY (2020) Hilbert vibration decomposition-based epileptic seizure prediction with neural network. Comput Biol Med
Mudhiganti PR (2012) A comparative analysis of feature extraction techniques for EEG signals from Alzheimer patients
Yahyaei R (2022) Fast EEG based biometrics via mean curve length. MS thesis. Middle East Technical University
Paranjpe MJ, Kakatkar MN (2013) Automated diabetic retinopathy severity classification using support vector machine. Int J Res Sci Adv Technol 3:86–91
Tsiouris KM, Markoula S, Konitsiotis D, Fotiadis DI (2018) A robust unsupervised epileptic seizure detection methodology to accelerate large EEG database evaluation. Biomed Signal Process Control 40:275–285
Prathap P, Aswathy TD (2017) EEG spectral feature-based seizure prediction using an efficient sparse classifier. In: IEEE international conference on intelligent computing, instrumentation and control technologies (ICICICT)
Behnam M, Hossein P (2016) Real-time seizure prediction using RLS filtering and interpolated histogram feature based on hybrid optimization algorithm of Bayesian classifier and Hunting search. In: Computer methods and programs in biomedicine, pp 115–136
Janjarasjitt S (2017) Performance of epileptic single-channel scalp EEG classifications using single wavelet-based features. Austr Phys Eng Sci Med 1:57–67
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El-Gindy, S.AE., Ahmed, A., Elsayed, S. (2024). Epileptic Seizure Detection Contribution in Healthcare Sustainability. In: Negm, A.M., Rizk, R.Y., Abdel-Kader, R.F., Ahmed, A. (eds) Engineering Solutions Toward Sustainable Development. IWBBIO 2023. Earth and Environmental Sciences Library. Springer, Cham. https://doi.org/10.1007/978-3-031-46491-1_30
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