Resources
7 results for "phonocardiogram"
Challenge
Open Access
Heart Murmur Detection from Phonocardiogram Recordings: The George B. Moody PhysioNet Challenge 2022
Congenital heart diseases affect about 1% of newborns, representing an important morbidity and mortality factor for several severe conditions, including advanced heart failure [1]. In a 2019 survey, it was estimated that congenital heart diseases af…
Database
Open Access
The CirCor DigiScope Phonocardiogram Dataset
A total number of 5272 heart sound recordings were collected from the main four auscultation locations of 1568 subjects, aged between 0 and 21 years (mean ± STD = 6.1 ± 4.3 years), with a duration between 4.8 to 80.4 seconds (mean ± STD = 22.9 ± 7.4…
Database
Open Access
EPHNOGRAM: A Simultaneous Electrocardiogram and Phonocardiogram Database
The electro-phono-cardiogram (EPHNOGRAM) project focused on the development of low-cost and low-power devices for recording simultaneous electrocardiogram (ECG) and phonocardiogram (ECG) data, with auxiliary channels for capturing environmental audi…
Database
Open Access
Simulated Fetal Phonocardiograms
This data set is a series of synthetic fetal phonocardiographic signals (PCGs) relative to different fetal states and recording conditions.
Database
Open Access
Shiraz University Fetal Heart Sounds Database
The Shiraz University (SU) fetal heart sounds database (SUFHSDB) contains fetal and maternal phonocardiogram (PCG) recordings from 109 pregnant women in single and twin pregnancies. The recordings were made at Hafez Hospital of Shiraz University of …
Software
Open Access
Logistic Regression-HSMM-based Heart Sound Segmentation
The identification of the exact positions of the first and second heart sounds within a phonocardiogram (PCG), or heart sound segmentation, is an essential step in the automatic analysis of heart sound recordings, allowing for the classification of …
Challenge
Open Access
Classification of Heart Sound Recordings: The PhysioNet/Computing in Cardiology Challenge 2016
The 2016 PhysioNet/CinC Challenge aims to encourage the development of algorithms to classify heart sound recordings collected from a variety of clinical or nonclinical (such as in-home visits) environments. The aim is to identify, from a single sho…