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Naslov:Advancing AI-Based depression detection : a preliminary study on feature optimization and model robustness
Avtorji:ID Zorko, Albert (Avtor)
Datoteke:.pdf RAZ_Zorko_Albert_2025.pdf (12,52 MB)
MD5: 2EBEEB33F12E23AE224DC803CB606675
 
Jezik:Angleški jezik
Vrsta gradiva:Neznano
Tipologija:1.08 - Objavljeni znanstveni prispevek na konferenci
Organizacija:FIŠ - Fakulteta za informacijske študije v Novem mestu
Opis:This study constitutes the second part of our investigation presented at ITIS 2023, which explores the search for objective physiological biomarkers for major depressive disorder (MDD). Moving beyond the established role of Heart Rate Variability (HRV), this preliminary research focuses on Pulse-Respiratory Coupling (PRC) – the coordination between cardiac and respiratory rhythms. We hypothesize that depression, characterized by autonomic nervous system (ANS) dysregulation, disrupts this coupling. A group of 73 subjects (healthy controls, untreated depressed patients, and patients treated with tricyclic antidepressants) were submitted to simultaneous electrocardiogram (EKG) and respiratory recording. Analysis revealed a distinct degradation of PRC in the depressed group, manifesting as a loss of synchronous patterns observed in healthy subjects. Machine learning models were trained on features derived from PRC timing. The k-Nearest Neighbors algorithm achieved a promising classification accuracy of 97.3% in distinguishing depressed from healthy individuals, outperforming other classifiers like Random Forest (95.9%) and Support Vector Machine (95.9%). While these results are preliminary and require validation in larger cohorts, they strongly suggest that PRC is a sensitive, non-invasive marker of ANS dysfunction in depression. This work underscores the potential of integrating multi-system physiological analysis with artificial intelligence to create objective aids for psychiatric diagnosis.
Ključne besede:major depressive disorder, physiological biomarkers, pulse-respiratory coupling, heart rate variability, autonomic nervous system, machine learning
Status publikacije:Objavljeno
Verzija publikacije:Recenzirani rokopis
Datum objave:16.12.2026
Leto izida:2025
Št. strani:Str. [12-21]
PID:20.500.12556/ReVIS-13032 Novo okno
UDK:616.89-008.454:612:004.8
COBISS.SI-ID:264894723 Novo okno
Opomba:Nasl. z nasl. zaslona; Opis vira z dne 15. 1. 2026;
Datum objave v ReVIS:22.01.2026
Število ogledov:317
Število prenosov:4
Metapodatki:XML DC-XML DC-RDF
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Gradivo je del monografije

Naslov:16th International Conference on Information Technologies and Information Society : ITIS 2025
Uredniki:Maruša Gorišek, Tea Golob, Teja Štrempfel
Kraj izida:Novo mesto
Založnik:Faculty of information studies
Leto izida:2025
ISBN:978-961-96549-2-7
COBISS.SI-ID:263628291 Novo okno

Sekundarni jezik

Jezik:Slovenski jezik
Ključne besede:huda depresivna motnja, fiziološki biomarkerjipulzno‑respiratorno povezovanje, variabilnost srčnega utripa, avtonomni živčni sistem, strojno učenje


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