CNN-GRU for Drowsiness Detection from Electrocardiogram Signal
DOI:
https://doi.org/10.21512/comtech.v16i2.12755Keywords:
Internet of Things, deep learning, ECG, drowsiness detection, edge computingAbstract
Drowsiness is a problem that needs to be addressed to improve road safety. To minimize this safety issue, driving-monitoring systems have been implemented in current car models, and electrocardiography (ECG) is one of the most commonly used driving monitoring techniques. ECG data are modeled using a deep neural network, including a Bidirectional Gated Recurrent Unit (Bi-GRU). However, the accuracy for classifying Wake-Sleep is under 80% and Wake-NREM-REM reaches less than 68%. To address this issue, ECG data from the MESA and SHHS datasets are modeled using a combination of a Convolutional Neural Network (CNN) and a Bi-GRU, referred to as CNN-GRU. This model incorporated Batch Normalization and RMSProp to achieve improved accuracy in classifying drivers' conditions. It operates in two computing sectors: cloud computing (Google Colaboratory, also known as Colab) and edge computing (utilizing an AMD Ryzen 5 4600H processor laptop). Those computing sectors focused on a case where no internet connectivity occurred to process the classification. Those classifications achieved accuracy rates of 82.88% and 81.78% for Wake-Sleep classification in cloud- and edge-computing, respectively. Additionally, it achieved 71.01% (Colab) and 68.85% (edge-computing) accuracy in Wake-NREM-REM classification. This result indicates that CNN-GRU achieved better performance, surpassing the previous Bi-GRU model, which only achieved 80.42% (Colab) and 76.2% (edge-computing) for Wake-Sleep, and 68.85% (Colab) and 66.43% for Wake-NREM-REM.
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