Evidence map›Paper›PMID 40968955›Full record

ArticleSensors (Basel, Switzerland)2025

A Novel Method for ECG-Free Heart Sound Segmentation in Patients with Severe Aortic Valve Disease.

Elza Abdessater, Paniz Balali, Jimmy Pawlowski, Jérémy Rabineau, Cyril Tordeur, Vitalie Faoro, Philippe van de Borne, Amin Hossein

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Elza AbdessaterLaboratory of Physics and Physiology, Department of Cardiology, Hôpital Erasme, Hôpital Universitaire de Bruxelles, 1070 Brussels, Belgium.ORCID 0009-0001-8586-7513
Paniz BalaliLaboratory of Physics and Physiology, Department of Cardiology, Hôpital Erasme, Hôpital Universitaire de Bruxelles, 1070 Brussels, Belgium.ORCID 0000-0001-6336-167X
Jimmy PawlowskiLaboratory of Physics and Physiology, Department of Cardiology, Hôpital Erasme, Hôpital Universitaire de Bruxelles, 1070 Brussels, Belgium.
Jérémy RabineauLaboratory of Physics and Physiology, Department of Cardiology, Hôpital Erasme, Hôpital Universitaire de Bruxelles, 1070 Brussels, Belgium.ORCID 0000-0002-7946-075X
Cyril TordeurLaboratory of Physics and Physiology, Department of Cardiology, Hôpital Erasme, Hôpital Universitaire de Bruxelles, 1070 Brussels, Belgium.ORCID 0000-0002-6098-7463
Vitalie FaoroLaboratory of Physics and Physiology, Department of Cardiology, Hôpital Erasme, Hôpital Universitaire de Bruxelles, 1070 Brussels, Belgium.
Philippe van de BorneLaboratory of Physics and Physiology, Department of Cardiology, Hôpital Erasme, Hôpital Universitaire de Bruxelles, 1070 Brussels, Belgium.ORCID 0000-0002-5693-1045
Amin HosseinLaboratory of Physics and Physiology, Department of Cardiology, Hôpital Erasme, Hôpital Universitaire de Bruxelles, 1070 Brussels, Belgium.

Funding

Fonds de la Recherche Scientifique Mandat Aspirant F.R.S.- FNRS FC 04370Fonds Erasme No grant numberFonds pour la chirurgie cardiaque No grant numberPRODEX contract number PEA 4000110826
6 · The paper itself

Abstract

Severe aortic valve diseases (AVD) cause changes in heart sounds, making phonocardiogram (PCG) analyses challenging. This study presents a novel method for segmenting heart sounds without relying on an electrocardiogram (ECG), specifically targeting patients with severe AVD. Our algorithm enhances traditional Hidden Semi-Markov Models by incorporating signal envelope calculations and statistical tests to improve the detection of the first and second heart sounds (S1 and S2). We evaluated the method on the PhysioNet/CinC 2016 Challenge dataset and a newly acquired AVD-specific dataset. The method was tested on a total of 27,400 cardiac cycles. The proposed approach outperformed the existing methods, achieving a higher sensitivity and positive predictive value for S2, especially in the presence of severe heart murmurs. Notably, in patients with severe aortic stenosis, our proposed ECG-free method improved S2 sensitivity from 41% to 70%.

Indexed as

Aortic ValveAortic Valve DiseaseAortic Valve StenosisElectrocardiographyHeart SoundsAlgorithmsFemaleHumansMalePhonocardiographySignal Processing, Computer-Assistedaortic regurgitationaortic stenosisautomatic detectionphonocardiographytelemedicinevalvular heart disease

Identifiers

PMID40968955
PMCPMC12157725

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.