Diagnosing Schizophrenia from Activity Records using Hidden Markov Model Parameters
Boeker, Matthias; Riegler, Michael; Hammer, Hugo Lewi; Halvorsen, Pål; Fasmer, Ole Bernt; Jakobsen, Petter
Chapter
Accepted version
Permanent lenke
https://hdl.handle.net/11250/2988600Utgivelsesdato
2021Metadata
Vis full innførselSamlinger
- Department of Clinical Medicine [2086]
- Registrations from Cristin [10237]
Originalversjon
In: 34th International Symposium on Computer-Based Medical Systems (CBMS) https://doi.org/10.1109/CBMS52027.2021.00048Sammendrag
The diagnosis of Schizophrenia is mainly based on qualitative characteristics. With the usage of portable devices which measure activity of humans, the diagnosis of Schizophrenia can be enriched through quantitative features. The goal of this work is to classify between schizophrenic and non-schizophrenic subjects based on their measured activity over a certain amount of time. To do so, the periods in which a subject was resting or active were identified by the application of a Hidden Markov model (HMM). The trained model parameters of the HMM, such as the mean or variance of activity during the state of rest or activity, are used as classification features for a logistic regression model. Our results indicate that the features from the HMM are significant in classifying between schizophrenic and non-schizophrenic subjects. Moreover, the features outperform the features derived through other methods in literature in terms of goodness-of-fit and classification performance.