Detecting sustained attention during cognitive work using heart rate variability

Sustained attention is an important requirement when we are doing vigilant works. The detection of whether a worker is in sustained attention stage is important to maintain the safety of the worker. Thus this paper performs classification of sustained attention and non sustained attention phases based on the heart rate variability (HRV). To achieve this purpose several features are derived from time domain, frequency domain and nonlinear analysis from Electrocardiogram (ECG). Then linear discriminant analysis (LDA) with K-nearest neighbor (KNN) is adopted as the classifier. It was found that the proposed method is promising in classifying the sustained attention and non sustained attention with 98% of accuracy.