Deep learning approach for automatic assessment of schizophrenia and bipolar disorder in patients using R-R intervals
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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.
Author summary
Mental health conditions such as schizophrenia and bipolar disorder can be challenging to diagnose, but early detection is crucial to better outcomes. In our study, we explored a simple but powerful idea: using heart rate variability (HRV), the natural fluctuations in time between heartbeats, as a potential indicator of these disorders. As HRV reflects the balance of the autonomic nervous system, research has shown that people with schizophrenia and bipolar disorder often have distinct HRV patterns, suggesting that heart activity analysis could support mental health screening. We tested whether a wearable heart monitor, similar to a fitness tracker, could help recognize these patterns using artificial intelligence. By analyzing heart activity records from 60 individuals, some with these conditions and others without, we trained several machine learning models to differentiate between them. Our approach successfully classified individuals with about 80% accuracy. Although not a replacement for clinical diagnosis, heart monitoring combined with artificial intelligence could help detect signs of mental illness, leading to faster interventions. Our work opens the door for further studies and, ultimately, the development of easy-to-use tools that could support doctors and improve patient care.
