Deep Belief Networks Based Brain Activity Classification Using EEG from Slow Cortical Potentials in Stroke

Abstract

An electroencephalogram (EEG) is an electrical activity which is recorded from the scalp over the sensorimotor cortex during vigilance or sleeping conditions of subjects. It can be used to detect potential problems associated with brain disorders. The aim of this study is assessing the clinical usefulness of EEG which is recorded from slow cortical potentials (SCP) training in stroke patients using Deep belief network (DBN) which has a greedy layer wise training using Restricted Boltzmann Machines based unsupervised weight and bias evaluation and neural network based supervised training. EEGs are recorded during eight SCP neurofeedback sessions from two stroke patients with a sampling rate of 256 Hz. All EEGs are filtered with a low pass filter. Hilbert-Huang Transform is applied to the trails and various numbers of Instinct Mode Functions (IMFs) are obtained. High order statistics and standard statistics are extracted from IMFs to create the dataset. The proposed DBN-based brain activity classification has discriminated positivity and negativity tasks in stroke patients and has achieved high rates of 90.30%, 96.58%, and 91.15%, for sensitivity, selectivity, and accuracy, respectively.

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“…The researches on early diagnosis of COPD by distinguishing healthy lung sounds and pathological lung sounds, and detecting different severities of the COPD are the pioneer approaches in signal analysis. The literature usually focuses on cardiac diseases using ECG [8], [24], [25], neurological disorders using EEG [26], disabled activities using EMG. It is a recent area analyzing musical characteristic auscultation signals including lung, tracheal, vesicular, and bronchial sounds for detecting abnormalities on respiratory and cardiopulmonary diseases.…”

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“…DBN network is a deep learning network that consists of several unsupervised restricted Boltzmann machines and a supervised backpropagation network. DBN had achieved good results in disease diagnosis by using physiological signals such as electrocardiogram [ 34 ], electroencephalogram [ 35 ], and lung sounds [ 19 , 20 , 28 ].…”

Durağan Durum Görsel Uyaran Potansiyellerinden Fourier Dönüşümü ile Üç Farklı Frekansın Kestirimi

“…TP (incelenen uyartım frekansını doğru tahmin etme durumu), TN (tahmin edilen uyartım frekansının incelenen uyartım frekansından farklı olduğunu doğru tahmin durumu), FP (farklı bir uyartım frekansını yanlışlıkla incelenen frekans olduğuna karar verme durumu), FN (incelenen uyartım frekansını yanlışlıkla farklı bir frekans olduğuna karar verme durumu)'dir. Bu değerlere bağlı olarak sınıflandırıcı performansını gösteren doğruluk değeri hesaplanır [16], [17], [18], [19]:…”