Đorđević, Natalija

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  • Đorđević, Natalija (1)
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Author's Bibliography

Photoplethysmogram as a source of biomarkers for AI-based diagnosis of heart failure

Tadić, Predrag; Petrović, Jovana; Đorđević, Natalija; Ivanović, Marija; Lazović, Aleksandar; Vukčević, Vladan; Ristić, Arsen; Hadžievski, Ljupčo

(Belgrade : Institute of Physics, 2023) 

TY  - CONF
AU  - Tadić, Predrag
AU  - Petrović, Jovana
AU  - Đorđević, Natalija
AU  - Ivanović, Marija
AU  - Lazović, Aleksandar
AU  - Vukčević, Vladan
AU  - Ristić, Arsen
AU  - Hadžievski, Ljupčo
PY  - 2023
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/13044
AB  - We present our progress on the “Multi-SENSor SysteM and ARTificial intelligence in service of heart failure diagnosis (SensSmart)” project, which was introduced at the last year’s edition of the Workshop [1]. The goal of the SensSmart project is to enable early diagnosis of heart failure, through the development of: 1) a multi-sensor polycardiograph apparatus (PCG) that produces simultaneous acquisition of the subject’s electrocardiogram (ECG), photoplethysmogram (PPG), heart sounds, and heart movements, and 2) AI-assisted analysis of the acquired signals. This presentation is going to focus on the acquisition and processing of PPG signals. PPG is obtained by using a pulse oximeter which illuminates the skin and measures the changes in light absorption, thereby enabling the detection of blood volume changes in the vessels. Our PCG apparatus measures the blood flow through the brachial, radial, and carotid arteries. During each heartbeat, the generated waveform typically exhibits several characteristic points [2]. The magnitudes and time distances between these points are useful indicators of many cardiac conditions, including heart failure [3]. However, the inter-patient variability of the PPG waveform makes it challenging to derive simple rule-based diagnostic procedures. This has led many researchers to turn to statistical or machine learning methods for processing of PPG signals [4].  In this presentation, we give an overview of AI-based signal processing methods for PPG, and present some preliminary results and challenges in extracting features from real-world signals obtained using our PCG.
PB  - Belgrade : Institute of Physics
C3  - 16th Photonics Workshop : Book of abstracts
T1  - Photoplethysmogram as a source of biomarkers  for AI-based diagnosis of heart failure
SP  - 24
EP  - 24
UR  - https://hdl.handle.net/21.15107/rcub_vinar_13044
ER  - 
@conference{
author = "Tadić, Predrag and Petrović, Jovana and Đorđević, Natalija and Ivanović, Marija and Lazović, Aleksandar and Vukčević, Vladan and Ristić, Arsen and Hadžievski, Ljupčo",
year = "2023",
abstract = "We present our progress on the “Multi-SENSor SysteM and ARTificial intelligence in service of heart failure diagnosis (SensSmart)” project, which was introduced at the last year’s edition of the Workshop [1]. The goal of the SensSmart project is to enable early diagnosis of heart failure, through the development of: 1) a multi-sensor polycardiograph apparatus (PCG) that produces simultaneous acquisition of the subject’s electrocardiogram (ECG), photoplethysmogram (PPG), heart sounds, and heart movements, and 2) AI-assisted analysis of the acquired signals. This presentation is going to focus on the acquisition and processing of PPG signals. PPG is obtained by using a pulse oximeter which illuminates the skin and measures the changes in light absorption, thereby enabling the detection of blood volume changes in the vessels. Our PCG apparatus measures the blood flow through the brachial, radial, and carotid arteries. During each heartbeat, the generated waveform typically exhibits several characteristic points [2]. The magnitudes and time distances between these points are useful indicators of many cardiac conditions, including heart failure [3]. However, the inter-patient variability of the PPG waveform makes it challenging to derive simple rule-based diagnostic procedures. This has led many researchers to turn to statistical or machine learning methods for processing of PPG signals [4].  In this presentation, we give an overview of AI-based signal processing methods for PPG, and present some preliminary results and challenges in extracting features from real-world signals obtained using our PCG.",
publisher = "Belgrade : Institute of Physics",
journal = "16th Photonics Workshop : Book of abstracts",
title = "Photoplethysmogram as a source of biomarkers  for AI-based diagnosis of heart failure",
pages = "24-24",
url = "https://hdl.handle.net/21.15107/rcub_vinar_13044"
}
Tadić, P., Petrović, J., Đorđević, N., Ivanović, M., Lazović, A., Vukčević, V., Ristić, A.,& Hadžievski, L.. (2023). Photoplethysmogram as a source of biomarkers  for AI-based diagnosis of heart failure. in 16th Photonics Workshop : Book of abstracts
Belgrade : Institute of Physics., 24-24.
https://hdl.handle.net/21.15107/rcub_vinar_13044
Tadić P, Petrović J, Đorđević N, Ivanović M, Lazović A, Vukčević V, Ristić A, Hadžievski L. Photoplethysmogram as a source of biomarkers  for AI-based diagnosis of heart failure. in 16th Photonics Workshop : Book of abstracts. 2023;:24-24.
https://hdl.handle.net/21.15107/rcub_vinar_13044 .
Tadić, Predrag, Petrović, Jovana, Đorđević, Natalija, Ivanović, Marija, Lazović, Aleksandar, Vukčević, Vladan, Ristić, Arsen, Hadžievski, Ljupčo, "Photoplethysmogram as a source of biomarkers  for AI-based diagnosis of heart failure" in 16th Photonics Workshop : Book of abstracts (2023):24-24,
https://hdl.handle.net/21.15107/rcub_vinar_13044 .