EEG signal classification for drowsiness detection using wavelet transform and support vector machine

Abstract

<span id="docs-internal-guid-ed628156-7fff-8934-2369-94f011b043ca"><span>There are several categories to detect and measure driver drowsiness such as physiological methods, subjective methods and behavioral methods. The most objective method for drowsiness detection is the physiological method. One of the physiological methods used is an electroencephalogram (EEG). In this research wavelet transform is used as a feature extraction and using support vector machine (SVM) as a classifier. We proposed an experiment of retrieval data which is designed by using modified-EAR and EEG signal. From the SVM training process, with the 5-fold cross validation, Quadratic kernel has the highest accuracy 84.5% then others. In testing Driving-2 process 7 respondents were detected as drowsiness class, and 3 respondents were detected as awake class. In the testing of Driving-3 process, 6 respondents were detected as drowsiness class, and 4 respondents were detected as awake class. </span></span>

Non-Destructive Inspection of Tile Debonding by DWT and MFCC of Tile-Tapping Sound with Machine versus Deep Learning Models

“…Research exists in other different but relevant domains, where machine learning (ML) was used to classify one-dimensional signals. For instance, in biomedicine, electrocardiographic (ECG) signals were analyzed to diagnose cardiovascular diseases [8]- [11]. Toulni [8] processed ECG signals with discrete wavelet transform (DWT).…”

“…Sun [10] proposed a novel ensemble multi-label classification model, which consisted of various elements, e.g., binary relevance, multi-label k-Nearest Neighbors (k-NN), hierarchical adaptive resonance associative map, twin SVM, sklearn (Scikit-Learn) embedder, and embedding classifier. Similarly, wavelet feature extraction and SVM classification was applied to electroencephalogram (EEG) for diagnosing purpose [11].…”