A novel insomnia identification method based on Hjorth parameters

In this work, we present a &-means classifier using Hjorth parameters extracted from the central electroencephalogram (EEG) signals to accurately detect insomnia. To develop and test our classifier we used data from thirty six subjects: 18 patients diagnosed with primary insomnia (10 females, 8 males) and 18 controls (10 females, 8 males). The main findings of our work can be summarized as follows: 1) the Hjorth parameters, particularly the mobility and the complexity, accurately quantify the differences between the EEG sleep from insomnia patients and controls; 2) these differences can be observed across both C3 and C4 central channels; and 3) a k-means classifier based on Hjorth parameters extracted from the C3 channel is able to accurately detect epochs from insomnia patients with a Cohen's Kappa of 0.83, sensitivity of 91.9% and specificity of 91%.