Passive Fetal Monitoring by Advanced Signal Processing Methods in Fetal Phonocardiography

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F20%3A10246154" target="_blank" >RIV/61989100:27240/20:10246154 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9288669" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9288669</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2020.3043496" target="_blank" >10.1109/ACCESS.2020.3043496</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Passive Fetal Monitoring by Advanced Signal Processing Methods in Fetal Phonocardiography

Popis výsledku v původním jazyce

Fetal phonocardiography (fPCG) is a non-invasive technique for detection of fetal heart sounds (fHSs), murmurs and vibrations. This acoustic recording is passive and provides an alternative low-cost method to ultrasonographic cardiotocography (CTG). Unfortunately, the fPCG signal is often disturbed by the wide range of artifacts that make it difficult to obtain significant diagnostic information from this signal. The study focuses on the filtering of an fPCG signal containing three types of noise (ambient noise, Gaussian noise, and movement artifacts of the mother and the fetus) having different amplitudes. Three advanced signal processing methods: empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and adaptive wavelet transform (AWT) were tested and compared. The evaluation of the extraction was performed by determining the accuracy of S1 sounds detection and by determining the fetal heart rate (fHR). The evaluation of the effectiveness of the method was performed using signal-to-noise ratio (SNR), mean error of heart interval measurement (|ΔTi|), and the statistical parameters of accuracy (ACC), sensitivity (SE), positive predictive value (PPV), and harmonic mean between SE and PPV (F1). Using the EMD method, ACC &gt; 95% was achieved in 7 out of 12 types and levels of interference with average values of ACC = 88.73%, SE = 91.57%, PPV = 94.80% and F1 = 93.12%. Using the EEMD method, ACC &gt; 95% was achieved in 9 out of 12 types and levels of interference with average values of ACC = 97.49%, SE = 97.89%, PPV = 99.53% and F1 = 98.69%. In this study, the best results were achieved using the AWT method, which provided ACC &gt; 95% in all 12 types and levels of interference with average values of ACC = 99.34%, SE = 99.49%, PPV = 99.85% a F1 = 99.67%. CCBY

Název v anglickém jazyce

Passive Fetal Monitoring by Advanced Signal Processing Methods in Fetal Phonocardiography

Popis výsledku anglicky

Fetal phonocardiography (fPCG) is a non-invasive technique for detection of fetal heart sounds (fHSs), murmurs and vibrations. This acoustic recording is passive and provides an alternative low-cost method to ultrasonographic cardiotocography (CTG). Unfortunately, the fPCG signal is often disturbed by the wide range of artifacts that make it difficult to obtain significant diagnostic information from this signal. The study focuses on the filtering of an fPCG signal containing three types of noise (ambient noise, Gaussian noise, and movement artifacts of the mother and the fetus) having different amplitudes. Three advanced signal processing methods: empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and adaptive wavelet transform (AWT) were tested and compared. The evaluation of the extraction was performed by determining the accuracy of S1 sounds detection and by determining the fetal heart rate (fHR). The evaluation of the effectiveness of the method was performed using signal-to-noise ratio (SNR), mean error of heart interval measurement (|ΔTi|), and the statistical parameters of accuracy (ACC), sensitivity (SE), positive predictive value (PPV), and harmonic mean between SE and PPV (F1). Using the EMD method, ACC &gt; 95% was achieved in 7 out of 12 types and levels of interference with average values of ACC = 88.73%, SE = 91.57%, PPV = 94.80% and F1 = 93.12%. Using the EEMD method, ACC &gt; 95% was achieved in 9 out of 12 types and levels of interference with average values of ACC = 97.49%, SE = 97.89%, PPV = 99.53% and F1 = 98.69%. In this study, the best results were achieved using the AWT method, which provided ACC &gt; 95% in all 12 types and levels of interference with average values of ACC = 99.34%, SE = 99.49%, PPV = 99.85% a F1 = 99.67%. CCBY

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

<a href="/cs/project/EF16_019%2F0000867" target="_blank" >EF16_019/0000867: Centrum výzkumu pokročilých mechatronických systémů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

—

Svazek periodika

2020

Číslo periodika v rámci svazku

8

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

21

Strana od-do

—

Kód UT WoS článku

000600842600001

EID výsledku v databázi Scopus

2-s2.0-85097929169