A Simple and Effective Classifier for the Detection of Psychotic Disorders based on Heart Rate Variability Time Series [1]
| Tytuł | A Simple and Effective Classifier for the Detection of Psychotic Disorders based on Heart Rate Variability Time Series |
| Publication Type | Conference Proceedings |
| Rok publikacji | 2023 |
| Autorzy | Buza K [2], Książek K [3], Masarczyk W [4], Głomb P [5], Gorczyca P [6], Piegza M [7] |
| Conference Name | Information Technologies – Applications and Theory 2023 |
| Volume | 3498 |
| Date Published | 09/2023 |
| Publisher | Knižnicné a edicné centrum, Fakulta matematiky, fyziky a informatiky, Univerzita Komenského, Mlynská dolina, Bratislava |
| Conference Location | Tatranské Matliare |
| ISBN | 978-80-8147-132-2 |
| Słowa kluczowe | bipolar disorder [8], Classification [9], convolutional nearest neighbor [10], heart rate variability (HRV) [11], schizophrenia [12] |
| 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. |
| URL | https://ceur-ws.org/Vol-3498/paper28.pdf [13] |