ArticleSensors (Basel, Switzerland)2023
The Actigraphy-Based Identification of Premorbid Latent Liability of Schizophrenia and Bipolar Disorder.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- The Impact of Quantifying Human Locomotor Activity on Examining Sleep-Wake Cycles.Sensors (Basel, Switzerland) · 2025Article
- [Risk Prediction Performance of Blood Biomarkers for Bipolar Disorder With Psychotic Symptoms].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2025Article
- A new framework for mental illnesses diagnosis using wearable devices aided by improved convolutional neural network.Scientific reports · 2025Article
- The two ends of the spectrum: comparing chronic schizophrenia and premorbid latent schizotypy by actigraphy.BMC psychiatry · 2025Article
- A Machine-Learning-Based Analysis of Resting State Electroencephalogram Signals to Identify Latent Schizotypal and Bipolar Development in Healthy University Students.Diagnostics (Basel, Switzerland) · 2025Article
- Hierarchical organization of human physical activity.Scientific reports · 2024Article
- General spectral characteristics of human activity and its inherent scale-free fluctuations.Scientific reports · 2024Article
- Schizophrenia diagnosis based on diverse epoch size resting-state EEG using machine learning.PeerJ. Computer science · 2024Article
- Machine learning techniques in diagnostics and prediction of the clinical features of schizophrenia: a narrative review.Consortium psychiatricum · 2023Review
Corrections and comments
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Authors and funding
16 authors.
Funding
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.
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Registered trials
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