01285nas a2200205   4500000000100000000000100001008004100002260015900043100001900202700002000221700002200241700002300263700001900286700002100305245012300326856004500449490000900494520055400503022002201057       2023                            d  c09/2023bKnižnicné a edicné centrum, Fakulta matematiky, fyziky a informatiky, Univerzita Komenského, Mlynská dolina, BratislavaaTatranské Matliare1 aKrisztian Buza1 aKamil Książek1 aWilhelm Masarczyk1 aPrzemysław Głomb1 aPiotr Gorczyca1 aMagdalena Piegza00aA Simple and Effective Classifier for the Detection of Psychotic Disorders based on Heart Rate Variability Time Series  uhttps://ceur-ws.org/Vol-3498/paper28.pdf0 v34983 a<p>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.</p>
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