A Hybrid Approach for Sleep States Detection Using Blood Pressure and EEG Signal

Drowsiness Detection Using Brain Signal Recognition Deep Neural Network (BSRDNN)

“…To provide a fair comparison, we evaluated the performance of the proposed BSRDNN with the performance of other approaches on a benchmark consisting of MIT/BIH-PED data. Table 5 shows a detailed comparison of the metrics of our proposed BSRDNN model work with the work of the authors [16][17][18][19][20][21][22][23][24][25][26][27][34][35][36]. Table 6 shows the comparison between the studies [28][29][30]37] that proposed deep learning architectures based on 1D-CNN for drowsiness detection and our BSRDNN.…”

“…This improvement is often achieved by devising new and novel methods for extracting features or by combining multiple features from different physiological signals across various domains. Researchers have explored handcraftedengineered feature extraction methods [16][17][18][19][20][21][22][23][24][25] and fully automated approaches [26][27][28][29][30][31][32][33] using deep learning techniques. Upon reviewing existing literature and comparing works on the MIT/BIH-PED dataset for drowsiness detection, several studies stand out.…”

“…An ensemble-boosted tree classifier was utilised to obtain a higher accuracy of 97.19%. A unique hybrid technique was created to extract combination features from biomedical signals using blood and MIT/BIH-PED signals; the researchers achieved 99.01% accuracy [17]. A combination of MIT/BIH-PED and EOG hybrid novel feature extraction was used; the researchers utilised a gradient boosting decision tree (GBDT) as a classifier to achieve 91.34% accuracy [34].…”

“…To address limitations found in previous studies, we present a new deep learning architecture called BSRDNN that is built on a 1D-CNN and an ANN for drowsiness detection using single-channel EEG data from MIT/BIH-PED. The BSRDNN processes single-channel EEG data instead of using hybrid physiological signals to reduce complexity, computation time and mathematical operations resulting from pre-processing and feature extraction [17,[34][35][36]. It can extract features without converting them into 2D images [26,27] or relying on handcrafted-engineered feature extraction methods [16][17][18][19][20][21][22][23][24][25].…”

“…The BSRDNN processes single-channel EEG data instead of using hybrid physiological signals to reduce complexity, computation time and mathematical operations resulting from pre-processing and feature extraction [17,[34][35][36]. It can extract features without converting them into 2D images [26,27] or relying on handcrafted-engineered feature extraction methods [16][17][18][19][20][21][22][23][24][25].…”