ECGPsychNet: an optimized hybrid ensemble model for automatic detection of psychiatric disorders using ECG signals

Background. Psychiatric disorders such as schizophrenia (SCZ), bipolar disorder (BD), and depression (DPR) are some of the leading causes of disability and suicide worldwide. The signs and symptoms of SCZ, BD, and DPR vary dynamically and do not have uniform detection strategies. The main causes of delays in the detection of psychiatric disorders are negligence by immediate caregivers, varying symptoms, stigma, and limited availability of physiological signals. Motivation. The brain functionality in the patients with SCZ, BD, and DPR changes compared to the normal cognition population. The brain–heart interaction plays a crucial role in tracking the changes in cardiac activities during such disorders. Therefore, this paper explores the application of electrocardiogram (ECG) signals for the detection of three psychiatric (SCZ, BD, and DPR) disorders. Method. This paper develops ECGPsychNet an ensemble decomposition and classification technique for the automated detection of SCZ, BD, and DPR using ECG signals. Three well-known decomposition techniques, empirical mode decomposition, variational mode decomposition, and tunable Q wavelet transform (TQWT), are used to decompose the ECG signals into various subbands (SBs). Various features are extracted from the different SBs and classified using optimizable ensemble techniques using two validation techniques. Results. The developed ECGPsychNet has obtained the highest classification accuracy of 98.15% using the features from the sixth SB of TQWT. Our proposed model has the highest detection rates of 98.96%, 96.04%, and 95.12% for SCZ, DPR, and BD. Conclusions. Our developed prototype is able to detect SCZ, DPR, and BD using ECG signals. However, the automated ECGPsychNet is ready to be tested with more datasets including different races and age groups.