Publications citing Classification of Heart Sound Recordings: The PhysioNet/Computing in Cardiology Challenge 2016 v1.0.0

Deep Neural Networks for the Recognition and Classification of Heart Murmurs Using Neuromorphic Auditory Sensors
Juan Pedro Dominguez-Morales, Angel F. Jimenez-Fernandez, Manuel Domínguez-Morales et al. IEEE Transactions on Biomedical Circuits and Systems, 2017. Cites: paper

A Data-Driven Feature Extraction Method for Enhanced Phonocardiogram Segmentation
Francesco Renna, Jorge Oliveira, Miguel Tavares Coimbra. Computing in cardiology, 2017. Cites: paper

Coupling Analysis for Systolic, Diastolic and RR Interval Time Series Using Multivariable Fuzzy Measure Entropy
Lina Zhao, Shoushui Wei, Hong Xiang Tang et al. Computing in cardiology, 2017. Cites: paper

Nonlinear Analysis of Heart Sounds for the Detection of Cardiac Disorders Using Recurrence Quantification Analysis
Shadi Ghiasi, Mostafa Abdollahpur, nasimalsadat madani et al. Computing in cardiology, 2017. Cites: paper

Second Heart Sound Onset to Identify T-Wave Offset
Agnese Sbrollini, Marta Beghella Bartoli, Angela Agostinelli et al. Computing in cardiology, 2017. Cites: paper

A Deep Learning Approach for Valve Defect Recognition in Heart Acoustic Signal
Dariusz Kucharski, Dominik Grochala, Marcin Kajor et al. Advances in intelligent systems and computing, 2017. Cites: paper

Automatic heart activity diagnosis based on Gram polynomials and probabilistic neural networks
Francesco Beritelli, Giacomo Capizzi, Grazia Lo Sciuto et al. Biomedical Engineering Letters, 2017. Cites: paper

Best Paper Selection
William W.Y. Hsu, S. Park, Charles E. Kahn. Yearbook of Medical Informatics, 2017. Cites: paper

Chronic Heart Failure Detection from Heart Sounds Using a Stack of Machine-Learning Classifiers
Martin Gjoreski, Monika Simjanoska, Anton Gradišek et al. International Conference on Intelligent Environments (IE), 2017. Cites: paper

Performance of an open-source heart sound segmentation algorithm on eight independent databases
Chengyu Liu, David Springer, Gari D. Clifford. Physiological Measurement, 2017. Cites: paper

Sensor, Signal, and Imaging Informatics
William Hsu, Seoyoon Park, Charles E. Kahn. Yearbook of Medical Informatics, 2017. Cites: paper

DropConnected neural networks trained on time-frequency and inter-beat features for classifying heart sounds
Edmund Kay, Anurag Agarwal. Physiological Measurement, 2017. Cites: paper

Recognizing Abnormal Heart Sounds Using Deep Learning
Jonathan Rubin, Abreu, Rui, Anurag Ganguli et al. arXiv (Cornell University), 2017. Cites: paper

Recent advances in heart sound analysis
Gari D. Clifford, Chengyu Liu, Benjamin Moody et al. Physiological Measurement, 2017. Cites: paper

Analysis of phonocardiogram signals through proactive denoising using novel self-discriminant learner
Chetanya Puri, Rituraj Singh, Soma Bandyopadhyay et al. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017. Cites: paper

A robust dataset-agnostic heart disease classifier from Phonocardiogram
Rohan Banerjee, Anirban Dutta Choudhury, Parijat Deshpande et al. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017. Cites: paper

Normal and abnormal phonocardiogram classification based waveform and frequency features using LS-SVM
Yatao Zhang, Shoushui Wei. International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 2017. Cites: paper

Novel features from autocorrelation and spectrum to classify Phonocardiogram quality
Deepan Das, Rohan Banerjee, Anirban Dutta Choudhury et al. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017. Cites: paper

Pattern discovery and similarity assessment for robust heart sound segmentation
D. Nunes, P. Carvalho, Jorge M. O. Henriques et al. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017. Cites: paper

Pattern discovery and similarity assessment for robust Heart Sound Segmentation
Diogo Nunes, P. Carvalho, Jorge M. O. Henriques et al. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017. Cites: paper

Ensemble methods with outliers for phonocardiogram classification
Masun Nabhan Homsi, Philip A. Warrick. Physiological Measurement, 2017. Cites: paper

Detection of pathological heart sounds
Mostafa Abdollahpur, Ali Ghaffari, Shadi Ghiasi et al. Physiological Measurement, 2017. Cites: paper

Recognition of normal–abnormal phonocardiographic signals using deep convolutional neural networks and mel-frequency spectral coefficients
Vykintas Maknickas, Algirdas Maknickas. Physiological Measurement, 2017. Cites: paper

Bayesian LSTMs in medicine
Jos van der Westhuizen, Joan Lasenby. arXiv (Cornell University), 2017. Cites: paper

Combining sparse coding and time-domain features for heart sound classification
Bradley M. Whitaker, Pradyumna Byappanahalli Suresha, Chengyu Liu et al. Physiological Measurement, 2017. Cites: paper

Heart sounds analysis using probability assessment
Filip Plešinger, Ivo Viščor, Josef Halámek et al. Physiological Measurement, 2017. Cites: paper

Heart sound classification from unsegmented phonocardiograms
Philip Langley, Alan Murray. Physiological Measurement, 2017. Cites: paper

Heart sound classification based on scaled spectrogram and tensor decomposition
Wenjie Zhang, Jiqing Han, Shiwen Deng. Expert Systems with Applications, 2017. Cites: paper

A novel signal acquisition platform of human cardiovascular information with noninvasive method
Longcong Chen, Shaoxi Cai, Bo Yi Li et al. Review of Scientific Instruments, 2017. Cites: paper

Noise detection in smartphone phonocardiogram
Deepan Das, Rohan Banerjee, Anirban Dutta Choudhury et al. IEEE International Conference on Acoustics Speech and Signal Processing, 2017. Cites: paper

Classifying Heart Sounds Using Images of MFCC and Temporal Features
Diogo Marcelo Nogueira, Carlos Abreu Ferreira, Alípio Jorge. Lecture notes in computer science, 2017. Cites: paper

Evaluation of Algorithms for Automatic Classification of Heart Sound Signals
Ricardo Enrique Pérez-Guzmán, Rodolfo García−Bermúdez, Fernando Rojas et al. Lecture notes in computer science, 2017. Cites: paper

Precision/Recall Trade-Off Analysis in Abnormal/Normal Heart Sound Classification
Jeevith Bopaiah, Ramakanth Kavuluru. Lecture notes in computer science, 2017. Cites: paper

Sensor, Signal, and Imaging Informatics
William W.Y. Hsu, Seoyoon Park, Charles E. Kahn. Yearbook of Medical Informatics, 2017. Cites: paper

Towards Wide Learning: Experiments in Healthcare
Snehasis Banerjee, Tanushyam Chattopadhyay, Swagata Biswàs et al. arXiv (Cornell University), 2016. Cites: paper

Multivariable Fuzzy Measure Entropy Analysis for Heart Rate Variability and Heart Sound Amplitude Variability
Lina Zhao, Shoushui Wei, Hong Xiang Tang et al. Entropy, 2016. Cites: paper

Abnormal Heart Sounds Detected from Short Duration Unsegmented Phonocardiograms by Wavelet Entropy
Philip Langley, Alan Murray. Computing in cardiology, 2016. Cites: paper

Abnormal Heart Sounds detection based on the Scaled Time:Frequency Representation and Feature Selection
Wenjie Zhang, Shiwen Deng, Jiqing Han. Computing in cardiology, 2016. Cites: paper

A Novel Approach for Classification of Normal/Abnormal Phonocardiogram Recordings using Temporal Signal Analysis and Machine Learning
Sachin Vernekar, Saurabh Nair, Deepu Vijayasenan et al. Computing in cardiology, 2016. Cites: paper

A Tensor Approach to Heart Sound Classification
Ignacio J. Diaz-Bobillo. Computing in cardiology, 2016. Cites: paper

Automated Classification of Normal and Abnormal Heart Sounds using Support Vector Machines
Anthony Bouril, Darya Aleinikava, María S. Guillem et al. Computing in cardiology, 2016. Cites: paper

Automatic Heart Sound Recording Classification using a Nested Set of Ensemble Algorithms
Masun Nabhan Homsi, Natasha Medina, Miguel Hernandez et al. Computing in cardiology, 2016. Cites: paper

Can Electrocardiogram Classification be Applied to Phonocardiogram Data? :: An Analysis Using Recurrent Neural Networks
Christopher Schölzel, Andreas Dominik. Computing in cardiology, 2016. Cites: paper

Characteristics of Phonocardiography Waveforms that Influence Automatic Feature Recognition
Scott Stainton, Matthew Dyson, Charalampos C. Tsimenidis et al. Computing in cardiology, 2016. Cites: paper

Classification of Acoustic Physiological Signals Based on Deep Learning Neural Networks with Augmented Features
Te-chung Yang, Haowei Hsieh. Computing in cardiology, 2016. Cites: paper

Classification of Heart Sound Recordings Using Convolution Neural Network
Heechang Ryu, Jinkyoo Park, Hayong Shin. Computing in cardiology, 2016. Cites: paper

Classification of Normal/Abnormal Heart Sound Recordings based on Multi:Domain Features and Back Propagation Neural Network
Hong Xiang Tang, Huaming Chen, Ting Li et al. Computing in cardiology, 2016. Cites: paper

Classification of Normal/Abnormal Heart Sound Recordings: the PhysioNet/Computing in Cardiology Challenge 2016
Gari D. Clifford, Chengyu Liu, David Springer et al. Computing in cardiology, 2016. Cites: paper

Classification of Normal and Abnormal Heart Sound Recordings through Robust Feature Selection
Chetanya Puri, Arijit Ukil, Soma Bandyoapdhyay et al. Computing in cardiology, 2016. Cites: paper

Classifying Heart Sound Recordings using Deep Convolutional Neural Networks and Mel:Frequency Cepstral Coefficients
Jonathan Rubin, Rui Abreu, Anurag Ganguli et al. Computing in cardiology, 2016. Cites: paper

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