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All Journal BERKALA FISIKA Jurnal Matematika & Sains Journal of Medical Physics and Biophysics Makara Journal of Science Indonesian Journal of Physics (IJP) Wahana Fisika : Jurnal Fisika dan Terapannya
Idam Arif
Institut Teknologi Bandung
Astronomy Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Physics

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Analisis Spektral Daya dan Koherensi EEG Pada Anak Penderita Autism Spectrum Disorders (ASD) Nita Handayani; Sra Harke Pratama; Siti Nurul Khotimah; Idam Arif; Freddy Haryanto
Wahana Fisika Vol 2, No 2 (2017): Desember
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/wafi.v2i2.9374

Abstract

Autism Spectrum Disorders (ASD) adalah kondisi neurodevelopmental yang berkaitan dengan defisit dalam fungsi eksekutif, emosi, bahasa, dan komunikasi sosial. Beberapa teknik neuroimaging dan neurofisiologi digunakan untuk memahami hubungan antara fungsionalitas otak dan perilaku autis. Quantitative Electroencephalography (QEEG) adalah sebuah teknik non-invasif yang dapat digunakan untuk memberikan gambaran fungsionalitas otak melalui beberapa besaran fisis yang dikaji. Pada paper ini akan dibahas tentang karakteristik sinyal listrik otak pada penderita austis berdasarkan analisis QEEG.  Perekaman sinyal otak menggunakan Emotiv Epoc 14 channel (AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T7, FC6, F4, F8, AF4) dan 2 channel referensi (CMS dan DRL). Jumlah subjek uji dalam penelitian sebanyak 6 anak penderita autis dan 5 anak sehat sebagai kontrol dengan rentang usia antara 10-15 tahun. Perekaman otak dilakukan pada kondisi rileks dan mata terutup selama 15 menit. Metode analisis data meliputi pre-processing data EEG untuk menghilangkan noise dan artefak, perhitungan spektral daya menggunakan periodogram Welch, dan analisis konektivitas fungsional otak dengan menghitung besarnya koherensi intra-hemisphere dan inter-hemisphere. Dari hasil studi diperoleh bahwa pada anak autis terjadi peningkatan spektral daya pada pita delta dan penurunan spektral daya pada pita alpha dibandingkan dengan subjek kontrol. Analisis konektivitas fungsional otak pada anak autis menunjukkan nilai koherensi intra-hemisphere dan inter-hemisphere yang lebih rendah pada pita delta dan theta, khususnya pada area frontal. QEEG dapat digunakan untuk karakterisasi sinyal otak pada penderita autis dan membedakannya dari subjek normal.      Kata Kunci   :  Retardasi Mental; Spektral Daya; Koherensi; EEG;  Sinyal Otak Autism Spectrum disorder (ASD) is a neurodevelopmental disorder associated with deficits in executive function, emotions, language, and social communication. Several neuroimaging and neurophysiology techniques are used to understand the relationship between brain functionality and autistic behavior. Quantitative Electroencephalography (QEEG) is a non-invasive technique that can be used to illustrate the functionality of the brain through the analysis of several physical quantities. This paper will discuss about the characteristics of electrical brain signals in austistic children based on QEEG analysis. Recording of brain signals using  Emotiv Epoc 14-channels (AF3, F7, F1, O2, P8, T7, FC6, F4, F8, AF4) and 2 reference channels (CMS and DRL). The number of test subjects in the study were 6 autistic children and 5 healthy children as controls with an age range between 10-15 years old. Brain recording performed on resting state and eyes closed for 15 minutes. The methods of analysis data includes pre-processing EEGs data to remove noise and artifacts, power spectral analysis using Welch Periodogram, and brain functional connectivity analysis by calculating the magnitude of intra-hemisphere and inter-hemisphere coherences. The results of the study found that an increased of power spectral in the delta band and a decreased of power spectral in the alpha band in autistic children compared to control subjects. Analysis of functional connectivity of the brain in autistic children shows lower intra-hemisphere and inter-hemisphere coherences in the delta and theta bands, particularly in the frontal area. QEEG can be used to characterized brain signals in autistic children and differentiated them from the normal subjects.          Keywords  : Mental Retardation; Power Spectral; Coherence; EEG; Brain Signal
Multiclass Classification of Covid-19 CT Scan Images With VGG-16 Architecture Using Transfer Learning System Tan, Nurlaila; Arif, Idam
Indonesian Journal of Physics Vol 35 No 1 (2024): vol 35 no 1 2024
Publisher : Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itb.ijp.2024.35.1.4

Abstract

COVID-19 is a respiratory disease caused by the coronavirus. The most common test technique used today for COVID-19 diagnosis is real-time reverse transcription-polymerase chain reaction (RT-PCR). However, compared to RT-PCR, radiological imaging such as X-rays and computer tomography (CT) may be a more precise, useful, and faster technology for COVID-19 classification. X-rays are more accessible because they are widely available in all hospitals in the world and are cheaper than CT scans, but the classification of COVID-19 using CT scan images is more sensitive than X-rays. Therefore, CT scan images can be used for the early detection of COVID-19 patients. One of them is using the deep learning method. In this study, a CNN algorithm with a VGG-16 architecture will be selected to classify COVID-19, intermediate, and non-COVID CT scan images using 2481 image datasets. First, pre-processing is done by resizing the image, converting the image channel into RGB, and dividing the dataset into a training dataset and a testing dataset. Then, the convolution process is continued by utilizing the pre-trained VGG-16 model from ImageNet. The results of testing the data with 97% accuracy were obtained. It is concluded that the model used to classify COVID-19, intermediate, and non-COVID CT scan images is effective and produces good results.
Co-Authors Astalini Astalini Bergas Bimo Branarto Choirul Anam AM Diponegoro Delianis Pringgenis Eko Hidayanto Freddy Haryanto Freddy Haryanto Geoff Dougherty Handayani, Nita Nita Handayani Octolia Tobigasa Tambunan Poppy Intan Tjahaja Poppy Intan Tjahaja Pratama, Sra Harke Pringgenis, Delianis Ratnawulan . Rena Widita Siti Nurul Khotimah Siti Nurul Khotimah Siti Nurul Khotimah Siti Nurul Khotimah Siti Nurul Khotimah Sparisoma Viridi Sra Harke Pratama Tan, Nurlaila The Houw Liong