Klasifikasi Sinyal EEG Dengan Power Spectra Density Berbasis Metode Welch Dan MLP Backpropagation

  • Nursuci Putri Husain Universitas Islam Makassar
  • Nurseno Bayu Aji Universitas Gajayana Malang
Keywords: Classification, Electroencephalogram, Power Spectra Density, Principle Component Analysis, Multi Layer Perceptron Backpropagation

Abstract

Electroencephalogram (EEG) signal is a signal that could become an information for study about disorders of brain function  such as Epilepsi. EEG that detected in epileptic seizures produce patterns that allow doctors to distinguish it from normal conditions. However, a visual analysis can not be done continuously. This study proposed a new hybrid method of EEG signal classification using Power Spectral Density (PSD) based on Welch method, Principle Component Analysis (PCA), and Multi Layer Perceptron Backpropagation.There are 3 main stages in this study, firstly preprocessing the dataset of EEG signals by Power Spectral Density (PSD) based on Welch method, then Principle Component Analysis (PCA) as a method of  dimensionallity reduction of the EEG signal data and the Multi Layer Perceptron Backpropagation for classifying a signal. Based on experimental results, the proposed method is successfully obtain high accuracy for the 80-20% training-testing partition (99.68%).

 

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References

Karyawan, Moch. Anang . dkk, , (Juli, 2011). Klasifikasi Sinyal Eeg Menggunakan Koefisien Autoregresif, F-Score, Dan Least Squares Support Vector Machine, jurnal TIF vol. 2, no.1.
Talwar, D. (2004). Primer of EEG with a Mini-Atlas 31, 378.
Teplan, M. (2002), Fundamentals of EEG measurements, Measmt. Sci. Rev., Vol. 2.
Übeylï, E. D. (2010). Least squares support vector machines employing modelbased me-thods coefficients for analysis of EEG signals, Expert Systems with Applications 37, (2010), 233–239.
Übeyli, E. D. (2009). Combined neural network model employing wavelet coefficients for EEG signals classification. Digital Signal Processing, 19(2), 297–308.
L. Ma, J. W. Minett, T. Blu, and W. S. Wang, (2015) “Resting State EEG-Based Biometrics for Individual Identification Using Convolutional Neural Networks,” pp. 2848–2851.
O. Faust, U. R. Acharya, H. Adeli, and A. Adeli, (2015) “Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis,” Seizure, vol. 26, pp. 56–64.
Dyah Titisari,dkk. (2013). Reduksi Suara Jantung Dari Instrumentasi Akuisisi Perekaman Suara Paru-Paru Pada Anak-Anak Menggunakan Butterworth Band Pass Filter. Seminar Nasional ke 8: Rekayasa Teknologi Industri dan Informasi.
Kemalasari, Ardik Wijayanto, Pramitra Joko R, Identifikasi Sinyal Suara Paru Berdasarkan Power Spectra Density Metode Welch Untuk Deteksi Kelainan Parenkim Paru , Jurusan Teknik Elektronika, Politeknik Elektronika Negeri Surabaya Kampus PENS-ITS Sukolilo, Surabaya.
Eko prasetyo, (2014). Data Mining Mengolah Data Menjadi Informasi Menggunakan Matlab, Yogyakarta, ANDI.
Andrzejak RG, Lehnertz K, Rieke C, Mormann F, David P, Elger CE (2001) Indications of nonlinear deterministic and finite dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state, Phys. Rev. E, 64, 061907.
V. Marpaung,(2005) “Depresi Pada Penderita Epilepsi Umum Dengan Kejang Tonik Klonik Dan epilepsi Parsial Sederhana,” pp. 1–25.
Published
2019-05-10
Section
Articles
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