The Actigraphy-Based Identification of Premorbid Latent Liability of Schizophrenia and Bipolar Disorder
Ádám Nagy,
József Dombi,
Martin Patrik Fülep,
Emese Rudics,
Emőke Adrienn Hompoth,
Zoltán Szabó,
András Dér,
András Búzás,
Zsolt János Viharos,
Anh Tuan Hoang,
Bálint Maczák,
Gergely Vadai,
Zoltán Gingl,
Szandra László,
Vilmos Bilicki,
István Szendi
Affiliations
Ádám Nagy
Department of Software Engineering, University of Szeged, 13 Dugonics Square, 6720 Szeged, Hungary
József Dombi
Department of Computer Algorithms and Artificial Intelligence, University of Szeged, 2 Árpád Square, 6720 Szeged, Hungary
Martin Patrik Fülep
Department of Software Engineering, University of Szeged, 13 Dugonics Square, 6720 Szeged, Hungary
Emese Rudics
Department of Software Engineering, University of Szeged, 13 Dugonics Square, 6720 Szeged, Hungary
Emőke Adrienn Hompoth
Department of Software Engineering, University of Szeged, 13 Dugonics Square, 6720 Szeged, Hungary
Zoltán Szabó
Department of Software Engineering, University of Szeged, 13 Dugonics Square, 6720 Szeged, Hungary
András Dér
ELKH Biological Research Centre, Institute of Biophysics, 62 Temesvári Boulevard, 6726 Szeged, Hungary
András Búzás
ELKH Biological Research Centre, Institute of Biophysics, 62 Temesvári Boulevard, 6726 Szeged, Hungary
Zsolt János Viharos
Institute for Computer Science and Control, Center of Excellence in Production Informatics and Control, Eötvös Lóránd Research Network (ELKH), Center of Excellence of the Hungarian Academy of Sciences (MTA), 13-17 Kende Street, 1111 Budapest, Hungary
Anh Tuan Hoang
Institute for Computer Science and Control, Center of Excellence in Production Informatics and Control, Eötvös Lóránd Research Network (ELKH), Center of Excellence of the Hungarian Academy of Sciences (MTA), 13-17 Kende Street, 1111 Budapest, Hungary
Bálint Maczák
Department of Technical Informatics, University of Szeged, 2 Árpád Square, 6720 Szeged, Hungary
Gergely Vadai
Department of Technical Informatics, University of Szeged, 2 Árpád Square, 6720 Szeged, Hungary
Zoltán Gingl
Department of Technical Informatics, University of Szeged, 2 Árpád Square, 6720 Szeged, Hungary
Szandra László
Doctoral School of Interdisciplinary Medicine, Department of Medical Genetics, University of Szeged, 4 Somogyi Béla Street, 6720 Szeged, Hungary
Vilmos Bilicki
Department of Software Engineering, University of Szeged, 13 Dugonics Square, 6720 Szeged, Hungary
István Szendi
Department of Software Engineering, University of Szeged, 13 Dugonics Square, 6720 Szeged, Hungary
(1) Background and Goal: Several studies have investigated the association of sleep, diurnal patterns, and circadian rhythms with the presence and with the risk states of mental illnesses such as schizophrenia and bipolar disorder. The goal of our study was to examine actigraphic measures to identify features that can be extracted from them so that a machine learning model can detect premorbid latent liabilities for schizotypy and bipolarity. (2) Methods: Our team developed a small wrist-worn measurement device that collects and identifies actigraphic data based on an accelerometer. The sensors were used by carefully selected healthy participants who were divided into three groups: Control Group (C), Cyclothymia Factor Group (CFG), and Positive Schizotypy Factor Group (PSF). From the data they collected, our team performed data cleaning operations and then used the extracted metrics to generate the feature combinations deemed most effective, along with three machine learning algorithms for categorization. (3) Results: By conducting the training, we were able to identify a set of mildly correlated traits and their order of importance based on the Shapley value that had the greatest impact on the detection of bipolarity and schizotypy according to the logistic regression, Light Gradient Boost, and Random Forest algorithms. (4) Conclusions: These results were successfully compared to the results of other researchers; we had a similar differentiation in features used by others, and successfully developed new ones that might be a good complement for further research. In the future, identifying these traits may help us identify people at risk from mental disorders early in a cost-effective, automated way.
