Assessment of symptom severity in psychotic disorder patients based on heart rate variability and accelerometer mobility data

Kamil Książek; Wilhelm Masarczyk; Przemysław Głomb; Michał Romaszewski; Iga Stokłosa; Piotr Ścisło; Paweł Dębski; Robert Pudlo; Krisztián Buza; Piotr Gorczyca; Magdalena Piegza

doi:10.1016/j.compbiomed.2024.108544

Assessment of symptom severity in psychotic disorder patients based on heart rate variability and accelerometer mobility data

Comput Biol Med. 2024 May 3:176:108544. doi: 10.1016/j.compbiomed.2024.108544. Online ahead of print.

Affiliations

¹ Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, Gliwice, 44-100, Poland. Electronic address: kksiazek@iitis.pl.
² Department of Psychiatry, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Pyskowicka 49, Tarnowskie Góry, 42-612, Poland.
³ Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, Gliwice, 44-100, Poland.
⁴ Psychiatric Department of the Multidisciplinary Hospital, Tarnowskie Góry, 42-612, Poland.
⁵ Institute of Psychology, Humanitas University in Sosnowiec, Kilińskiego 43, Sosnowiec, 41-200, Poland.
⁶ Department of Psychoprophylaxis, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Pyskowicka 49, Tarnowskie Góry, 42-612, Poland.
⁷ Budapest Business University, Buzogány utca 10-12, Budapest, 1149, Hungary; BioIntelligence Group, Department of Mathematics-Informatics, Sapientia Hungarian University of Transylvania, Târgu Mureş, Romania.

PMID: 38723395
DOI: 10.1016/j.compbiomed.2024.108544

Abstract

Background: Advancement in mental health care requires easily accessible, efficient diagnostic and treatment assessment tools. Viable biomarkers could enable objectification and automation of the diagnostic and treatment process, currently dependent on a psychiatric interview. Available wearable technology and computational methods make it possible to incorporate heart rate variability (HRV), an indicator of autonomic nervous system (ANS) activity, into potential diagnostic and treatment assessment frameworks as a biomarker of disease severity in mental disorders, including schizophrenia and bipolar disorder (BD).

Method: We used a commercially available electrocardiography (ECG) chest strap with a built-in accelerometer, i.e. Polar H10, to record R-R intervals and physical activity of 30 hospitalized schizophrenia or BD patients and 30 control participants through ca. 1.5-2 h time periods. We validated a novel approach to data acquisition based on a flexible, patient-friendly and cost-effective setting. We analyzed the relationship between HRV and the Positive and Negative Syndrome Scale (PANSS) test scores, as well as the HRV and mobility coefficient. We also proposed a method of rest period selection based on R-R intervals and mobility data. The source code for reproducing all experiments is available on GitHub, while the dataset is published on Zenodo.

Results: Mean HRV values were lower in the patient compared to the control group and negatively correlated with the results of the PANSS general subcategory. For the control group, we also discovered the inversely proportional dependency between the mobility coefficient, based on accelerometer data, and HRV. This relationship was less pronounced for the treatment group.

Conclusions: HRV value itself, as well as the relationship between HRV and mobility, may be promising biomarkers in disease diagnostics. These findings can be used to develop a flexible monitoring system for symptom severity assessment.

Keywords: Accelerometer; Bipolar disorder; Heart rate variability; Mobility; PANSS; Polar H10; Schizophrenia.