A Simple and Effective Classifier for the Detection of Psychotic Disorders based on Heart Rate Variability Time Series

TytułA Simple and Effective Classifier for the Detection of Psychotic Disorders based on Heart Rate Variability Time Series
Publication TypeConference Proceedings
Rok publikacji2023
AutorzyBuza K, Książek K, Masarczyk W, Głomb P, Gorczyca P, Piegza M
Conference Name Information Technologies – Applications and Theory 2023
Volume3498
Date Published09/2023
PublisherKnižnicné a edicné centrum, Fakulta matematiky, fyziky a informatiky, Univerzita Komenského, Mlynská dolina, Bratislava
Conference LocationTatranské Matliare
ISBN978-80-8147-132-2
Słowa kluczowebipolar disorder, Classification, convolutional nearest neighbor, heart rate variability (HRV), schizophrenia
Abstract

In this paper, we focus on automated detection of schizophrenia and bipolar disorder. For this task, we describe a simple and effective classifier, i.e. convolutional nearest neighbor. It provides a data-driven and objective approach for the detection of schizophrenia and bipolar disorder based on heart rate variability time series. According to our results, our approach is able to distinguish whether the selected person belongs to the patient group with an accuracy of 85% and area under receiver-operator characteristic curve of 0.92.

URLhttps://ceur-ws.org/Vol-3498/paper28.pdf

Historia zmian

Data aktualizacji: 10/10/2023 - 09:59; autor zmian: Kamil Książek (kksiazek@iitis.pl)