02630nas a2200241   4500000000100000000000100001008004100002260001200043100002000055700002200075700002300097700002400120700001900144700002400163700002000187700002500207700001900232700002100251245012200272490000700394520197300401022001402374       2025                            d  c09/20251 aKamil Książek1 aWilhelm Masarczyk1 aPrzemysław Głomb1 aMichał Romaszewski1 aKrisztian Buza1 aPrzemysław Sekuła1 aMichał Cholewa1 aKatarzyna Kołodziej1 aPiotr Gorczyca1 aMagdalena Piegza00aDeep learning approach for automatic assessment of schizophrenia and bipolar disorder in patients using R-R intervals0 v213 a<p>Schizophrenia and bipolar disorder are severe mental illnesses that significantly impact quality of life. These disorders are associated with autonomic nervous system dysfunction, which can be assessed through heart activity analysis. Heart rate variability (HRV) has shown promise as a potential biomarker for diagnostic support and early screening of those conditions. This study aims to develop and evaluate an automated classification method for schizophrenia and bipolar disorder using short-duration electrocardiogram (ECG) signals recorded with a low-cost wearable device. We conducted classification experiments using machine learning techniques to analyze R-R interval windows extracted from short ECG recordings. The study included 60 participants—30 individuals diagnosed with schizophrenia or bipolar disorder and 30 control subjects. We evaluated multiple machine learning models, including Support Vector Machines, XGBoost, multilayer perceptrons, Gated Recurrent Units, and ensemble methods. Two time window lengths (about 1 and 5 minutes) were evaluated. Performance was assessed using 5-fold cross-validation and leave-one-out cross-validation, with hyperparameter optimization and patient-level classification based on individual window decisions. Our method achieved classification accuracy of 83% for the 5-fold cross-validation and 80% for the leave-one-out scenario. Despite the complexity of our scenario, which mirrors real-world clinical settings, the proposed approach yielded performance comparable to advanced diagnostic methods reported in the literature. The results highlight the potential of short-duration HRV analysis as a cost-effective and accessible tool for aiding in the diagnosis of schizophrenia and bipolar disorder. Our findings support the feasibility of using wearable ECG devices and machine learning-based classification for psychiatric screening, paving the way for further research and clinical applications.</p>
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