Drowsiness detection using radial basis function network with electrocardiographic RR interval statistical feature

• Published in: Journal of physics. Conference series Vol. 1153; no. 1; pp. 12049 - 12054
• Main Authors: Maftukhaturrizqoh, Olivia, Nuryani, Nuryani, Darmanto, Darmanto
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.02.2019
• Subjects: Artificial neural networks; Classifiers; Cluster analysis; Clustering; Feature extraction; Neural networks; Radial basis function; Sleepiness; Traffic accidents; Vector quantization
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/1153/1/012049

Drowsiness detection is important since its strong relation with traffic accident. A study for drowsiness level detection using Artificial Neural Network (ANN) method has been conducted. It utilizes electrocardigraphic RR interval statistical features and Radial Basis Function Artificial Neural Network or RBF Network as classifier. Drowsiness levels are defined by Karonlinska Sleep Scale (KSS) which simplified into two classes, alert and drowsy classes. The main parameter of the RBFN are centers and width which are tuned using k-means clustering. A gradient descent is utilized to determine the output weight. The classifier is evaluated by using DROZY database which are collected from 14 subjects; each of them in different drowsiness levels. Feature extraction stage is conducted by segmenting the 10-min data into 30-seconds and it get the RR interval statistical feature. This study is conducted by varying the number of features as the input of RBFN. The method has been evaluated using 5fold cross validation with best performance 81.96%, 84.77%, 76.90% of accuracy, sensitivity, and specifity respectively.