Publications from Heart Murmur Detection from Phonocardiogram Recordings: The George B. Moody PhysioNet Challenge 2022

The following papers were presented at the Computing in Cardiology Conference.

Heart Murmur Detection from Phonocardiogram Recordings: The George B. Moody PhysioNet Challenge 2022
Matthew Reyna¹, Yashar Kiarashinejad¹, Andoni Elola², Jorge Oliveira³, Francesco Renna⁴, Annie Gu¹, Nadi Sadr¹, Erick Andres Perez Alday¹, Ashish Sharma¹, Sandra Mattos⁵, Miguel Coimbra⁴, Reza Sameni¹, Ali Bahrami Rad¹, Gari Clifford^6
1Emory University, ²University of the Basque Country, ³Instituto de Telecomunicações, ⁴INESC TEC, Faculdade de Ciências da Universidade do Porto, ⁵Real Hospital Português, ⁶Emory University and Georgia Institute of Technology ¹Emory University, ²University of the Basque Country UPV/EHU, ³University of Michigan, ⁴Emory University and Georgia Institute of Technology
Detection of Heart Sound Murmurs and Clinical Outcome with Bidirectional Long Short-Term Memory Networks
Sofia Monteiro, Ana Fred, Hugo Silva
Instituto de Telecomunicações, Department of Bioengineering at Instituto Superior Técnico
Learning Time-Frequency Representations of Phonocardiogram for Murmur Detection
Jae-Man Shin¹, Seong-Yong Park¹, Hyun-Seok Kim², Woo-Young Seo², Sung-Hoon Kim^3
1Department of Anesthesiology and Pain Medicine, Asan Medical Center, ²Biomedical Engineering Research Center, Asan Institute for Life Science, Asan Medical Center, ³Department of Anesthesiology and Pain Medicine, Asan Medical Center, University of Ulsan College of Medicine
Outcome Prediction and Murmur Detection in Sets of Phonocardiograms by a Deep Learning-Based Ensemble Approach
Sven Festag¹, Gideon Stein², Tim Büchner³, Maha Shadaydeh³, Joachim Denzler², Cord Spreckelsen^1
1Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital, Germany, ²Computer Vision Group, Institute for Computer Science, Friedrich Schiller University Jena, ³Computer Vision Group, Institute for Computer Science, Friedrich Schiller University Jena, Germany
Detection of Heart Murmurs in Phonocardiograms with Parallel Hidden Semi-Markov Models
Andrew McDonald, Mark Gales, Anurag Agarwal
University of Cambridge
Multitask and Transfer Learning for Cardiac Abnormality Detection in Heart Sounds
João Costa¹, Rui Rodrigues², Paula Couto^2
1University Institute of Lisbon - ISCTE and CAMGSD - IST/ULisboa, ²DM-FCT NOVA
Using Mel-Spectrograms and 2D-CNNs to Detect Murmurs in Variable Length Phonocardiograms
Marius Knorr and Jan Bremer
University Medical Center Hamburg Eppendorf, Department of Cardiology, University Heart & Vascular Center Hamburg, Hamburg, Germany
Towards Uncertainty-Aware Murmur Detection in Heart Sounds via Tandem Learning
Erika Bondareva, Jing Han, Tong Xia, Cecilia Mascolo
University of Cambridge
Two-Stage Multitask-Learner for PCG Murmur Location Detection
Maurice Rohr¹, Benedikt Müller¹, Sebastian Dill¹, Gökhan Güney¹, Christoph Hoog Antink^2
1Technische Universität Darmstadt, ²TU Darmstadt
Transfer Learning in Heart Sound Classification using Mel spectrogram
Xin Li, Fernando Schlindwein, G. Andre Ng
University of Leicester
Classification of phonocardiograms using residual convolutional neural network and MLP
Guohui Peng, Haitao Zou, Jin Wang
Jiangsu University of Science and Technology
Ensemble Transformer-Based Neural Networks Detect Heart Murmur In Phonocardiogram Recordings
Mohanad Alkhodari¹, Syafiq Azman², Leontios Hadjileontiadis¹, Ahsan Khandoker^1
1Khalifa University, ²AIQ, ADNOC H.Q.
Classification of heart murmurs using an ensemble of residual CNNs
Petr Nejedly¹, Jan Pavlus¹, Radovan Smisek², Zuzana Koscova³, Eniko Vargova³, Ivo Viscor³, Pavel Jurak³, Filip Plesinger^3
1Institute of Scientific Instruments of the Czech Academy of Science, ²Brno University of Technology, Faculty of Electrical Engineering and Communication, Department of Biomedical Engineering, ³Institute of Scientific Instruments of the CAS
Convolutional neural network aproach for heart murmur detection in auscultation signals using wavelet transform based features
Robertas Petrolis¹, Renata Paukstaitiene¹, Gabriele Rudokaite², Andrius Macas², Arturas Grigaliunas¹, Algimantas Krisciukaitis^1
1Department of Physics, Mathematics and Biophysics, Lithuanian University of Health Sciences., ²Department of Anaesthesiology, Medical Academy, Lithuanian University of Health Sciences
Detection of Murmurs from Heart Sound Recordings with Deep Residual Networks
Lei Hu, Wenjie Cai, Xinyue Li, Jia Li
University of Shanghai for Science and Technology
Classification of Murmurs in PCG Using Combined Frequency Domain and Physician Inspired Features
Julia Ding¹, Jing-Jing Li², Max Xu^3
1Emory University, ²University of California, Berkeley, ³Georgia Institute of Technology
Murmur Identification Using Supervised Constrastive Learning
Ľubomír Antoni¹, Erik Bruoth¹, Alexander Szabari¹, Gabriela Vozáriková¹, Peter Bugata², Peter Bugata Jr², Dávid Gajdoš², Dávid Hudák², Vladimíra Kmečová², Monika Staňková^2
1Pavol Jozef Šafárik University, ²VSL Software, a.s.
Deep Learning Based Heart Murmur Detection using Frequency-time Domain Features of Heartbeat Sounds
Jungguk Lee¹, Taein Kang¹, Narin Kim¹, Soyul Han¹, Hyejin Won¹, Wuming Gong², Il-Youp Kwak^1
1Chung-Ang University, ²University of Minnesota, Lillehei heart institute
Murmur Classification with U-net State Prediction
Sanghoon Choi, Hyo-Chang Seo, Kyungmin Choi, Giwon Yoon, Segyeong Joo
Asan medical center
Classification of Phonocardiogram Recordings using Vision Transformer Architecture
Joonyeob Kim, Gibeom Park, Bongwon Suh
Seoul National University
Phonocardiogram Classification Using 1-Dimensional Inception Time Convolutional Neural Networks
Bjørn-Jostein Singstad¹, Lars Bongo², Markus Johnsen³, Johan Ravn³, Antony Gitau⁴, Henrik Schirmer^5
1Simula Research Laboratory, ²The Arctic University of Norway, ³Medsensio, ⁴Kenyatta University, ⁵Department of Cardiology, Akershus University Hospital
Heart Murmur Detection from Phonocardiogram Based on Residual Neural Network with Classes Distinguished Focal Loss
Pan Xia¹, Yicheng Yao¹, Changyu Liu¹, Hao Zhang¹, Lirui Xu¹, Yuqi Wang², Lidong Du¹, Yusi Zhu³, Zhen Fang^1
1School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, ²Aerospace Information Research Institute, Chinese Academy of Sciences, ³School of Physics and Electronic Information, Yunnan Normal University
Searching for Effective Neural Network Architectures for Heart Murmur Detection from Phonocardiogram
Hao WEN¹ and Jingsu Kang^2
1Beihang University, ²Tianjin Medical University
Heart Murmur Detection and Clinical Outcome Prediction using Multilayer Perceptron Classifier
Kiarash Jalali, Mohammad Amin Saket, Saman Noorzadeh
Shahid Beheshti University
An LSTM-based Listener for Early Detection of Heart Disease
Philip Gemke¹, Nicolai Spicher², Tim Kacprowski^1
1Division Data Science in Biomedicine, PLRI of TU BS and MHH, BRICS, TU BS, ²Department of Medical Informatics, University Medical Center Göttingen
A Lightweight Robust Approach for Automatic Heart Murmurs and Clinical Outcomes Classification from Phonocardiogram Recordings
Hui Lu¹, Julia Yip², Tobias Steigleder², Stefan Grießhammer³, Maria Heckel⁴, Naga Venkata Sai Jitin Jami⁵, Bjoern Eskofier⁵, Christoph Ostgathe², Alexander Koelpin^6
1Brandenburg University of Technology, ²Palliative Medicine, Universitätsklinikum Erlangen, ³Palliative Medicine, Universitätsklinikum Erlangen,, ⁴Palliative Medicine, Universit�tsklinikum Erlangen, ⁵Friedrich-Alexander-Universität Erlangen-Nürnberg, ⁶Institute of High-Frequency Technology, Hamburg University of Technology
Heart Murmur Detection of PCG Using ResNet with Selective Kernel Convolution
Yonghao Gao, Lihong Qiao, Zhixiang Li
Chongqing University of Posts and Telecommunications
Transformer embedded with learnable filters for heart murmur detection
Pengfei Fan, Yucheng Shu, Yiming Han
Chongqing University of Posts and Telecommunications
Beat-wise Uncertainty Learning for Murmur Detection in Heart Sounds
Xingyao Wang¹, Foli Fan¹, Hongxiang Gao¹, Shuo Zhang¹, Chenxi Yang¹, Jianqing Li¹, Chengyu Liu^2
1State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, China, ²Southeast University
ACQuA: Anomaly Classification with Quasi-Attractors
William Rudman, Jack Merullo, Laura Mercurio, Carsten Eickhoff
Brown University
Heart Murmur Detection Using Ensemble of Deep Learning Classifiers for Phonocardiograms Recorded from Multiple Auscultation Locations
Saman Parvaneh, Zaniar Ardalan, Joomyung Song, Kathan Vyas, Cristhian Potes
Edwards Lifesciences
Maiby's Algorithm: A Two-stage Deep Learning Approach for Murmur Detection in Mel Spectrograms for Automatic Auscultation of Congenital Heart Disease
Matheus Araujo¹, Dewen Zeng², Joao Palotti³, Xinrong Hu², Yiyu Shi², Lee Pyles⁴, Quan Ni^5
1Cleveland Clinic Foundation, ²University of Notre Dame, ³Qatar Computing Research Institute, ⁴West Virginia University, ⁵One Heart Health
Heart Murmur Detection Using Wavelet Time Scattering and Support Vector Machines
Adrian Cornely and Grace Mirsky
Benedictine University
Exploring a Segmentation-Classification Deep Learning-based Heart Murmurs Detector
Daniel Eneriz¹, Antonio Rodriguez-Almeida², Himar Fabelo³, Samuel Ortega⁴, Francisco Balea-Fernandez⁵, Nicolás Medrano¹, Belén Calvo¹, Gustavo Callico^2
1Group of Electronic Design Aragon Institute of Engineering Research (I3A) University of Zaragoza (UZ), ²Institute for Applied Microelectronics(IUMA) University of Las Palmas de Gran Canaria (ULPGC), ³Fundación Canaria Instituto de Investigación Sanitaria de Canarias (FIISC), IUMA, ULPGC, ⁴Norwegian Institute of Food Fisheries and Aquaculture Research, Nofima, ⁵Dept. of Psychology, Sociology and Social Work, IUMA, University of Las Palmas de Gran Canaria (ULPGC)
Heart Murmur Detection in Phonocardiographic Signals Using Breathing Noise Suppression
Kristóf Müller and Dr. Márton Áron Goda
Pázmány Péter Catholic University - Faculty of Information Technology and Bionics
Listen2YourHeart: A Self-Supervised Approach for Detecting Murmur in Heart-Beat Sounds
Aristotelis Ballas¹, Vasileios Papapanagiotou², Anastasios Delopoulos², Christos Diou^3
1Harokopio University of Athens, Department of Informatics and Telematics, ²Aristotle University of Thessaloniki, ³Harokopio University of Athens
Multi-Task Prediction of Murmur and Outcome from Heart Sound Recordings
Yale Chang¹, Luoluo Liu¹, Corneliu Antonescu^2
1Philips, ²Banner Health
A Fusion of Handcrafted Features and Deep Learning Classifiers for Heart Murmur Detection
Zaria Imran¹, Ethan Grooby¹, Chiranjibi Sitaula¹, Vinayaka Malgi², Sunil Aryal², Faezeh Marzbanrad^1
1Monash University, ²Deakin University
Modified Variable Kernel Length ResNets for Heart Murmur Detection and Clinical Outcome Prediction Using Phonocardiogram Recordings
Vijay Vignesh Venkataramani, Akshit Garg, U. Deva Priyakumar
International Institute of Information Technology, Hyderabad
Murmur Detection and Clinical Outcome Classification Using a VGG-like Network and Combined Time-Frequency Representations of PCG Signals
Zhongrui Bai¹, Baiju Yan², Xianxiang Chen³, Yirong Wu³, Peng Wang^3
1School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, ²Shanghai Jiao Tong University, ³Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS)
Two-stage Detection of Murmurs from Phonocardiograms using Deep and One-class Methods
Sara Summerton¹, Danny Wood¹, Darcy Murphy¹, Oliver Redfern², Matt Benatan³, Matti Kaisti⁴, David Wong^1
1University of Manchester, ²University of Oxford, ³Independent Researcher, ⁴University of Turku, Department of Computing
Dual Bayesian ResNet: A Deep Learning Approach to Heart Murmur Detection
Felix Krones¹, Benjamin Walker¹, Adam Mahdi¹, Ivan Kiskin², Terry Lyons¹, Guy Parsons^3
1Oxford University, ²University of Surrey, People-Centred Institute of AI, Surrey Sleep Research Centre, ³Intensive Care Registrar, Thames Valley Deanery, NIHR Academic Clinical
Phonocardiographic Murmur Detection by Scattering-Recurrent Networks
Philip Warrick¹ and Jonathan Afilalo^2
1PeriGen Canada, McGill University, ²McGill University
Hierarchical Multi-Scale Convolutional Network for Murmurs Detection on PCG Signals
Yujia Xu, Xinqi Bao, Hak-Keung Lam, Ernest Kamavuako
King's College London