Samuel Boudet1, Agathe Houzé de l'Aulnoit2, Romain Demailly2, Laurent Peyrodie3, Régis Beuscart4, Denis Houzé de l'Aulnoit2. 1. Univ Nord de France, UCLille, Faculté de Médecine et Maïeutique, Biomedical Signal Processing Unit (UTSB), F-59800, Lille, France. Electronic address: samuel.boudet@gmail.com. 2. Univ Nord de France, UCLille, Faculté de Médecine et Maïeutique, Biomedical Signal Processing Unit (UTSB), F-59800, Lille, France; Lille Catholic Hospital, Obstetrics Department, F-59020, Lille, France. 3. Yncréa École des hautes études d'ingénieur, Biomedical Signal Processing Unit (UTSB), 59800, Lille, France; I3MTO EA 4708 Orléans, France. 4. Univ Nord de France, CHU Lille, UDSL EA2694, F-59000, Lille, France.
Abstract
BACKGROUND: Automated fetal heart rate (FHR) analysis removes inter- and intra-expert variability, and is a promising solution for reducing the occurrence of fetal acidosis and the implementation of unnecessary medical procedures. The first steps in automated FHR analysis are determination of the baseline, and detection of accelerations and decelerations (A/D). We describe a new method in which a weighted median filter baseline (WMFB) is computed and A/Ds are then detected. METHOD: The filter weightings are based on the prior probability that the sampled FHR is in the baseline state or in an A/D state. This probability is computed by estimating the signal's stability at low frequencies and by progressively trimming the signal. Using a competition dataset of 90 previously annotated FHR recordings, we evaluated the WMFB method and 11 recently published literature methods against the ground truth of an expert consensus. The level of agreement between the WMFB method and the expert consensus was estimated by calculating several indices (primarily the morphological analysis discordance index, MADI). The agreement indices were then compared with the values for eleven other methods. We also compared the level of method-expert agreement with the level of interrater agreement. RESULTS: For the WMFB method, the MADI indicated a disagreement of 4.02% vs. the consensus; this value is significantly lower (p<10-13) than that calculated for the best of the 11 literature methods (7.27%, for Lu and Wei's empirical mode decomposition method). The level of inter-expert agreement (according to the MADI) and the level of WMFB-expert agreement did not differ significantly (p=0.22). CONCLUSION: The WMFB method reproduced the expert consensus analysis better than 11 other methods. No differences in performance between the WMFB method and individual experts were observed. The method Matlab source code is available under General Public Licence at http://utsb.univ-catholille.fr/fhr-wmfb.
BACKGROUND: Automated fetal heart rate (FHR) analysis removes inter- and intra-expert variability, and is a promising solution for reducing the occurrence of fetal acidosis and the implementation of unnecessary medical procedures. The first steps in automated FHR analysis are determination of the baseline, and detection of accelerations and decelerations (A/D). We describe a new method in which a weighted median filter baseline (WMFB) is computed and A/Ds are then detected. METHOD: The filter weightings are based on the prior probability that the sampled FHR is in the baseline state or in an A/D state. This probability is computed by estimating the signal's stability at low frequencies and by progressively trimming the signal. Using a competition dataset of 90 previously annotated FHR recordings, we evaluated the WMFB method and 11 recently published literature methods against the ground truth of an expert consensus. The level of agreement between the WMFB method and the expert consensus was estimated by calculating several indices (primarily the morphological analysis discordance index, MADI). The agreement indices were then compared with the values for eleven other methods. We also compared the level of method-expert agreement with the level of interrater agreement. RESULTS: For the WMFB method, the MADI indicated a disagreement of 4.02% vs. the consensus; this value is significantly lower (p<10-13) than that calculated for the best of the 11 literature methods (7.27%, for Lu and Wei's empirical mode decomposition method). The level of inter-expert agreement (according to the MADI) and the level of WMFB-expert agreement did not differ significantly (p=0.22). CONCLUSION: The WMFB method reproduced the expert consensus analysis better than 11 other methods. No differences in performance between the WMFB method and individual experts were observed. The method Matlab source code is available under General Public Licence at http://utsb.univ-catholille.fr/fhr-wmfb.
Authors: Maria Ribeiro; João Monteiro-Santos; Luísa Castro; Luís Antunes; Cristina Costa-Santos; Andreia Teixeira; Teresa S Henriques Journal: Front Med (Lausanne) Date: 2021-11-30