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All Journal PROSIDING SEMINAR NASIONAL MEDIA ELEKTRIKA CIRCUIT: Jurnal Ilmiah Pendidikan Teknik Elektro Jurnal ICT : Information Communication & Technology International Journal of Informatics and Computation Prosiding Seminar Nasional Unimus
Siswandari Noertjahjani
Program Studi Elektro, Universitas Muhammadiyah Semarang
Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Health Professions Nursing Public Health Other

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Journal : PROSIDING SEMINAR NASIONAL

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AUTOMATIC ABNORMAL WAVES DETECTION FROM THE ELECTROENCEPHALOGRAM OF EPILEPSY WITH DWT Noertjahjani, Siswandari
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2014: PROSIDING SEMINAR NASIONAL HASIL - HASIL PENELITIAN & PENGABDIAN
Publisher : Universitas Muhammadiyah Semarang

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Abstract

This paper proposes a feature extraction and recognition algorithm for interictal and ictal EEG signals using Discrit Wavelet Transform (DWT).Patients seizure consist 4 Males, 6 Females ages 3-35 years. Clinical status epileptic without seizure 10 Males, 18 Female, ages 10-40 years. Clinical status non epileptic 8 Male, 5 Female ages 8-42 years. Numerical data were acquired with EEG system at Karyadi hospital Semarang 2008-2013. The categorization is confirmed by Fast Fourier Transform (FFT) analysis. The dataset includes waves such as sharp, spike through DWT ( For this a mother daubechies 7, coiflets 1 and coiflets 5) of EEG records.The experimental results show that this algorithm can achieve the sensitivity of 94.00% and pecificity of 93.75% for interictal and ictal EEGs,and the sensitivity of 92.50% and specificity of 92.75%, total accuracy of 91.21% for normal and ictal EEGs on data sets.Besides,the experiment with interictal and ictal EEGs from karyadi Hospital data set also yields sensitivity of 90.05% . specificity of 95% and total accuracy of 94.63% .Automatic seizure detection is very helpful to review prolonged EEGs.The research carried out so far was to find the prospect of this digital signal processing on EEG waves to support the doctors work in this field.
DETEKSI EPILEPSI DENGAN PCA Siswandari Noertjahjani; Aris Kiswanto; Heri Dwi Santosa
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2017: Prosiding Seminar Nasional Publikasi Hasil-Hasil Penelitian dan Pengabdian Masyarakat
Publisher : Universitas Muhammadiyah Semarang

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The main purpose of this study is to early detection of symptoms of epilepsy symptoms on the introduction of normal EEG signaling patterns with epilepsy (abnormal) EEG signals. There are 5 characteristics of statistics used are mean, variant, kurtosis, entropy, skweness. Electrodes used in EEGs usually have 19 channels: FP1, FP2, F7, F3, F2, F4, F8, C3, CZ, C4, P3, P4, PZ, O1 and OZ. While in this research only use FP1 electrode with 2 second signal cutting. Extraction of normal wave characteristics and epilepsy using PCA (principle componen analysis). PCA method is very appropriate to use if the existing datahas a large number of variables and has a correlation between variables such as EEG signals.  The calculation of the principal component analysis is based on the calculation of eigenvalues and eigenvectorsexpressing the dissemination of data from a dataset and capable of reducing the high dimension to a low dimension, without losing the information contained in the original data.Keywords-epilepsy, EEG, FP1
Co-Authors Aisyah Lahdji Andini, Rizka Aris Kiswanto Aris Kiswanto Aris, Kiswanto Hendriansyah, Hendriansyah Heri Dwi Santosa Ibrahim, Maulana Julaipah Julaipah Muhammad, Sam'an Radiktyo Nindyo Sumarno Syafiq Abdul Zaki Tito Pinandita Yanuarita Tursinawati Yuris Bakhtiar Yuriz Bakhtiar Zainal Muttaqin