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(Scopus ID : 57189350561
Universitas Kristen Maranatha, Indonesia)(2) Dedeh Supantini
(Universitas Kristen Maranatha, Indonesia)(3) Decky Gunawan
(Universitas Kristen Maranatha, Indonesia)(4) Dennis Harnandi
(Universitas Kristen Maranatha, Indonesia)(5) Diana Chandrasasmita
(Universitas Kristen Maranatha, Indonesia)*corresponding author
AbstractSleep Disorders like insomnia is one of the main health problems. Sleep deficiencies can increase the risk of diabetes, hypertension and cognitive disorders and behavior. The brain produces electrical signals, when someone is doing any activity such as moving, waking up, sleeping, etc. This electrical signal can be recorded using an electroencephalogram (EEG). In this study, brain signals are read with EEG Digital Laboratory of Clinical Neurophysiology Imanuel Hospital. The EEG signal results will be classified using Machine Learning Support Vector Machine (SVM). EEG signal data was obtained from Immanuel Hospital in Bandung. Conditions to be classified are the condition of waking, drowsiness (stage-1), and sleep (stage-2). Extraction of features using discrete wavelet transform Daubechies DB4. The decomposition level used in this study is Level-1 and Level-2. Based on the tests that have been carried out, the best parameter values obtained are C 10, Gamma 1, and Kernel Poly. Based on these parameters, the accuracy value was 78.8% for level-1, and 76.6% for level-2.
Keywordselectroencephalogram; Support Vector Machine; Discrete Wavelet transform; brain; signal
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DOIhttps://doi.org/10.29099/ijair.v6i2.447 |
Article metrics10.29099/ijair.v6i2.447 Abstract views : 500 | PDF views : 172 |
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