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.
BT - Information Technologies – Applications and Theory 2023 CY - Tatranské Matliare DA - 09/2023 LA - eng N2 -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.
PB - Knižnicné a edicné centrum, Fakulta matematiky, fyziky a informatiky, Univerzita Komenského, Mlynská dolina, Bratislava PP - Tatranské Matliare PY - 2023 T2 - Information Technologies – Applications and Theory 2023 TI - A Simple and Effective Classifier for the Detection of Psychotic Disorders based on Heart Rate Variability Time Series UR - https://ceur-ws.org/Vol-3498/paper28.pdf VL - 3498 SN - 978-80-8147-132-2 ER -