<![CDATA[Drowsiness is suggested as the most frequent factor in traffic and manufacture accidents. Therefore, a system which can early detect drowsiness is important for an effort to reduce accident number. This article presents a new method for drowsiness detection. The method uses electrocardiogram (ECG) and a Random Forest. Features of normal-to-normal interval (NNI) from ECG for the input of the detection system are investigated. The features include NNI characteristics in terms of time domain i.e the statictics of NNI, and in terms of frequency domain i.e. NNI signal characteristics in VLF until HF band. The level of drowsiness is categorized using Karolinska Sleepiness Scale (KSS) becoming two groups i.e. drowsy and awake. Classification algorithm used is the detection is Random Forest. In the Random Forest, the effect of the number of estimator and maximum feature to the detection performance is evaluated. The detection system is tested using data of drowsy study. The test show that the detection system performs 94.61%, 96.67% and 91.67% in terms of accuracy, sensitivity and specificity, respectively.]]>
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