The Actigraphy-Based Identification of Premorbid Latent Liability of Schizophrenia and Bipolar Disorder

Abstract

(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.

Hierarchical organization of human physical activity

“…It is conspicuous that non-random features prevalently appear in Range 2 (between ca. 20 and 100 min), in agreement with our former results established for a different set of subjects 41 . Although the appearance of these events is fuzzy, they might well obey the “20 min—100 min rule” 18 .…”

“…The use of actigraphy in sleep medicine is prevalent 5 , 39 , and it is considered a valuable tool, e.g., in polysomnography 40 . However, unlike most of the related studies, here we were focusing rather on the possible existence of nocturnal rhythms than on other usual measures like the sleep length or fragmentation index 41 . Hence, an evaluation of the nocturnal time series analogous to that of the corresponding daytime data was performed by CW-analysis (for more details, see “ Methods ”).…”

“…Nevertheless, already at the present state of knowledge, it is reasonable to assume that malfunctions of the cognitive mechanism should cause alterations of HPA. In fact, in case of mental disorders, like schizophrenia, bipolar psychosis, or action disorganization syndrome, characteristic anomalies in the activity patterns have been reported 41 , 47 , 52 – 54 . Stress has also been shown to disorganize rhythms on the ultradian scale and beyond, in various organisms including humans 55 , 56 .…”

“…Stress has also been shown to disorganize rhythms on the ultradian scale and beyond, in various organisms including humans 55 , 56 . Given these premises, the utilization of the sophisticated statistical methods employed in our analysis is anticipated to markedly enhance the diagnostic methodology for psychiatric and neurological disorders 41 .…”

General spectral characteristics of human activity and its inherent scale-free fluctuations

“…One of the most common applications of actigraphy is the description and analysis of the sleep quality 2 and the circadian rhythm 3 of the measured subjects. Actigraphy is also utilized in psychiatric examinations 4 , 5 , i.e., to distinguish between similar mental diseases or to recognize behavioural disorders. Besides therapeutic applications, actigraphy is also employed to study human activity patterns 6 – 8 .…”