Detection of Insomnia using Electrocardiography and Electromyography

Insomnia is one of the serious sleep disorders. In this disease, a patient feels trouble falling asleep and stay awake for the whole night. Insomnia also co-morbid with other diseases like cancer, cardiovascular disease, asthma, Alzheimer's, Parkinson's disease. This research aims at a method for identifying insomnia using both electrocardiogram (ECG) and electromyogram (EMG) signals. The Cyclic Alternating Pattern (CAP) database from Physionet is used. Preprocessing by empirical mode decomposition (EMD) is carried out in this research. Features with high discriminative ability are extracted from signals to classify via logistic regression (LR), support vector machines (SVM), K-nearest neighbor (KNN), decision tree (DT), ensemble classifier (EC), and Naïve Bayes (NB) classification methods. we have achieved the highest accuracy of 100% on both ECG and EMG signals from our proposed methodology.