Classification of Normal/Abnormal Heart Sound Recordings

The papers below were presented at Computing in Cardiology 2016. Please cite this publication when referencing any of these papers. These papers have been made available by their authors under the terms of the Creative Commons Attribution License 3.0 (CCAL). We wish to thank all of the authors for their contributions.

The first of these papers is an introduction to the challenge topic, with a summary of the challenge results and a discussion of their implications.

Classification of Normal/Abnormal Heart Sound Recordings: the PhysioNet/Computing in Cardiology Challenge 2016
Gari D. Clifford, Chengyu Liu, Benjamin Moody, David Springer, Ikaro Silva, Qiao Li, Roger G. Mark

The following papers were presented by participants in the Challenge, who describe their approaches to the challenge problem.

Morphological Determination of Pathological PCG Signals by Time and Frequency Domain Analysis
Márton Áron Goda, Péter Hajas

PCG Classification Using a Neural Network Approach
Iga Grzegorczyk, Mateusz Soliński, Michał Łepek, Anna Perka, Jacek Rosiński, Joanna Rymko, Katarzyna Stępień, Jan Gierałtowski

Automatic Heart Sound Recording Classification using a Nested Set of Ensemble Algorithms
Masun Nabhan Homsi, Natasha Medina, Miguel Hernandez, Natacha Quintero, Gilberto Perpiñan, Andrea Quintana, Philip Warrick

Abnormal Heart Sounds Detected from Short Duration Unsegmented Phonocardiograms by Wavelet Entropy
Philip Langley, Alan Murray

Normal / Abnormal Heart Sound Recordings Classification Using Convolutional Neural Network
Tanachat Nilanon, Jiayu Yao, Junheng Hao, Sanjay Purushotham, Yan Liu

Heart Sound Classification Based on Temporal Alignment Techniques
José Javier González Ortiz, Cheng Perng Phoo, Jenna Wiens

Ensemble of Feature-based and Deep learning-based Classifiers for Detection of Abnormal Heart Sounds
Cristhian Potes, Saman Parvaneh, Asif Rahman, Bryan Conroy

Classifying Heart Sound Recordings using Deep Convolutional Neural Networks and Mel-Frequency Cepstral Coefficients
Jonathan Rubin, Rui Abreu, Anurag Ganguli, Saigopal Nelaturi, Ion Matei, Kumar Sricharan

Using Spectral Acoustic Features to Identify Abnormal Heart Sounds
Nicholas E. Singh-Miller, Natasha Singh-Miller

Heart Sound Classification Using Deep Structured Features
Michael Tschannen, Thomas Kramer, Gian Marti, Matthias Heinzmann, Thomas Wiatowski

A Novel Approach for Classification of Normal/Abnormal Phonocardiogram Recordings using Temporal Signal Analysis and Machine Learning
Sachin Vernekar, Saurabh Nair, Deepu Vijaysenan, Rohit Ranjan

Heart Sound Anomaly and Quality Detection using Ensemble of Neural Networks without Segmentation
Morteza Zabihi, Ali Bahrami Rad, Serkan Kiranyaz, Moncef Gabbouj, Aggelos K. Katsaggelos