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Short-segment Heart Sound Classification Using an Ensemble of Deep Convolutional Neural Networks

Fuad Noman, Chee-Ming Ting, Sh-Hussain Salleh, Hernando Ombao
<span title="">2019</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rc5jnc4ldvhs3dswicq5wk3vsq" style="color: black;">ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</a> </i> &nbsp;
This paper proposes a framework based on deep convolutional neural networks (CNNs) for automatic heart sound classification using short-segments of individual heart beats.  ...  We design a 1D-CNN that directly learns features from raw heart-sound signals, and a 2D-CNN that takes inputs of twodimensional time-frequency feature maps based on Mel-frequency cepstral coefficients  ...  We developed an ensemble of deep CNNs to classify normal and abnormal heart sounds based on shortsegment recordings of individual heart beats with promising performance.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icassp.2019.8682668">doi:10.1109/icassp.2019.8682668</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/icassp/NomanTSO19.html">dblp:conf/icassp/NomanTSO19</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/seal43qeqfds7bytruvfm6ljdi">fatcat:seal43qeqfds7bytruvfm6ljdi</a> </span>
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Recognizing Abnormal Heart Sounds Using Deep Learning [article]

Jonathan Rubin, Rui Abreu, Anurag Ganguli, Saigopal Nelaturi, Ion Matei, Kumar Sricharan
<span title="2017-10-19">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We describe an automated heart sound classification algorithm that combines the use of time-frequency heat map representations with a deep convolutional neural network (CNN).  ...  an ensemble approach.  ...  Conclusion The work presented here is one of the first to apply deep convolutional neural networks to the task of automated heart sound classification for recognizing normal and abnormal heart sounds.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1707.04642v2">arXiv:1707.04642v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zoya26koqjcylhfgv3dbwxs2dq">fatcat:zoya26koqjcylhfgv3dbwxs2dq</a> </span>
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IEEE Access Special Section Editorial: Deep Learning for Computer-Aided Medical Diagnosis

Yu-Dong Zhang, Zhengchao Dong, Shui-Hua Wang, Carlo Cattani
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
In the article, ''Design and application of a laconic heart sound neural network,'' by Cheng et al. the authors proposed a laconic heart sound neural network (LHSNN).  ...  In the article, ''Development of an automated screening system for retinopathy of prematurity using a deep neural network for wide-angle retinal images,'' by Zhang et al.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.2996690">doi:10.1109/access.2020.2996690</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/m6r36o6udrbfrnvwsca2uzu5dm">fatcat:m6r36o6udrbfrnvwsca2uzu5dm</a> </span>
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Deep Learning Methods for Heart Sounds Classification: A Systematic Review

Wei Chen, Qiang Sun, Xiaomin Chen, Gangcai Xie, Huiqun Wu, Chen Xu
<span title="2021-05-26">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4d3elkqvznfzho6ki7a35bt47u" style="color: black;">Entropy</a> </i> &nbsp;
on the convolutional neural network (CNN) and recurrent neural network (RNN) methods developed over the last five years.  ...  With the aim of improving the accuracy of heart sounds classification, an in-depth systematic review and an analysis of existing deep learning methods were performed in the present study, with an emphasis  ...  RNN Methods for Heart Sounds Classification RNNs are a family of neural networks specifically used for processing sequential data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/e23060667">doi:10.3390/e23060667</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34073201">pmid:34073201</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/q7l4nz3wcnhz3lpk6uvhonepa4">fatcat:q7l4nz3wcnhz3lpk6uvhonepa4</a> </span>
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An Ensemble of Transfer, Semi-supervised and Supervised Learning Methods for Pathological Heart Sound Classification

Ahmed Imtiaz Humayun, Md. Tauhiduzzaman Khan, Shabnam Ghaffarzadegan, Zhe Feng, Taufiq Hasan
<span title="2018-09-02">2018</span> <i title="ISCA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/trpytsxgozamtbp7emuvz2ypra" style="color: black;">Interspeech 2018</a> </i> &nbsp;
Our primary classification framework constitutes a convolutional neural network with 1D-CNN time-convolution (tConv) layers, which uses features transferred from a model trained on the 2016 Physionet Heart  ...  In this work, we propose an ensemble of classifiers to distinguish between various degrees of abnormalities of the heart using Phonocardiogram (PCG) signals acquired using digital stethoscopes in a clinical  ...  Previous research on automatic classification of heart sounds can be broadly classified into two areas: (i) PCG segmentation, i.e., detection of the first and second heart sounds (S1 and S2), and (ii)  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.21437/interspeech.2018-2413">doi:10.21437/interspeech.2018-2413</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/interspeech/HumayunKG0H18.html">dblp:conf/interspeech/HumayunKG0H18</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/itkm2oucejai7fk7pbuycy5s5u">fatcat:itkm2oucejai7fk7pbuycy5s5u</a> </span>
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Computational biology: deep learning

William Jones, Kaur Alasoo, Dmytro Fishman, Leopold Parts
<span title="2017-11-14">2017</span> <i title="Portland Press Ltd."> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/zys6voe3srdovn3a4vlajre5h4" style="color: black;">Emerging Topics in Life Sciences</a> </i> &nbsp;
This exciting class of methods, based on artificial neural networks, quickly became popular due to its competitive performance in prediction problems.  ...  Deep learning is the trendiest tool in a computational biologist's toolbox.  ...  Aging.ai, which uses an ensemble of deep neural networks on 41 standardized blood test measurements, has been trained to predict an individual's chronological age [79] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1042/etls20160025">doi:10.1042/etls20160025</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33525807">pmid:33525807</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7289034/">pmcid:PMC7289034</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qnw2yndsp5aqlnxxshtaipzctu">fatcat:qnw2yndsp5aqlnxxshtaipzctu</a> </span>
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Deep Ensemble Detection of Congestive Heart Failure using Short-term RR Intervals

Ludi Wang, Wei Zhou, Qing Chang, Jiangen Chen, Xiaoguang Zhou
<span title="">2019</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
To solve these challenges, an ensemble method for CHF detection using short-term HRV data and deep neural networks was proposed.  ...  First, we extracted the expert features of RR intervals (RRIs) and then built a long short-term memory-convolutional neural network-based network to extract deep-learning (DL) features automatically.  ...  In this paper, we propose an ensemble classifier for CHF detection using short-term HRV data and deep neural networks.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2019.2912226">doi:10.1109/access.2019.2912226</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zxbktkeiafdzzouqsorzmsqzdm">fatcat:zxbktkeiafdzzouqsorzmsqzdm</a> </span>
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Feature-Based Fusion Using CNN for Lung and Heart Sound Classification

Zeenat Tariq, Sayed Khushal Shah, Yugyung Lee
<span title="2022-02-16">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/taedaf6aozg7vitz5dpgkojane" style="color: black;">Sensors</a> </i> &nbsp;
Finally, we built a fusion of three optimal convolutional neural network models by feeding the image feature vectors transformed from audio features.  ...  Lung or heart sound classification is challenging due to the complex nature of audio data, its dynamic properties of time, and frequency domains.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s22041521">doi:10.3390/s22041521</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/35214424">pmid:35214424</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8875944/">pmcid:PMC8875944</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ubb3zmzv5zbazpfgk5ykwur3fq">fatcat:ubb3zmzv5zbazpfgk5ykwur3fq</a> </span>
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Deep Learning in Cardiology

Paschalis Bizopoulos, Dimitrios Koutsouris
<span title="">2019</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/pjmncnm4zfdltlh5rxo2xd62xa" style="color: black;">IEEE Reviews in Biomedical Engineering</a> </i> &nbsp;
We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.  ...  The medical field is creating large amount of data that physicians are unable to decipher and use efficiently.  ...  + Deep Belief Network DNN + Deep Neural Network FCN + Fully Convolutional Network FNN + Fully Connected Network GAN + Generative Adversarial Network GRU + Gated Recurrent Unit LSTM + Long-Short  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/rbme.2018.2885714">doi:10.1109/rbme.2018.2885714</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pa47trmskvflvig5cotth265q4">fatcat:pa47trmskvflvig5cotth265q4</a> </span>
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Heartbeat Sound Signal Classification Using Deep Learning

Ali Raza, Arif Mehmood, Saleem Ullah, Maqsood Ahmad, Gyu Sang Choi, Byung-Won On
<span title="2019-11-05">2019</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/taedaf6aozg7vitz5dpgkojane" style="color: black;">Sensors</a> </i> &nbsp;
Heartbeat sound classification is still a challenging problem in heart sound segmentation and feature extraction.  ...  Then we applied a purposed model Recurrent Neural Network (RNN) that is based on Long Short-Term Memory (LSTM), Dropout, Dense and Softmax layer.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s19214819">doi:10.3390/s19214819</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31694339">pmid:31694339</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6864449/">pmcid:PMC6864449</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2qxgnqxdtbho7i4f2rgp47hydi">fatcat:2qxgnqxdtbho7i4f2rgp47hydi</a> </span>
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Cross-Domain Transfer Learning for PCG Diagnosis Algorithm

Kuo-Kun Tseng, Chao Wang, Yu-Feng Huang, Guan-Rong Chen, Kai-Leung Yung, Wai-Hung Ip
<span title="2021-04-20">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/67lbgjadfzhv7axcc5zzsqmwo4" style="color: black;">Biosensors</a> </i> &nbsp;
Cardiechema is a way to reflect cardiovascular disease where the doctor uses a stethoscope to help determine the heart condition with a sound map.  ...  In this paper, phonocardiogram (PCG) is used as a diagnostic signal, and a deep learning diagnostic framework is proposed.  ...  Leung [34] used a neural network classifier to classify heart sounds; and Turkoglu [35] designed an expert system based on a BP neural network to successfully diagnose heart valve disease.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/bios11040127">doi:10.3390/bios11040127</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33923928">pmid:33923928</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/isufqgdn7rhurlvxitkvykpm54">fatcat:isufqgdn7rhurlvxitkvykpm54</a> </span>
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Phonocardiographic Sensing using Deep Learning for Abnormal Heartbeat Detection [article]

Siddique Latif, Muhammad Usman, Rajib Rana, Junaid Qadir
<span title="2020-07-28">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Cardiac auscultation involves expert interpretation of abnormalities in heart sounds using stethoscope.  ...  In this work, we propose a Recurrent Neural Networks (RNNs) based automated cardiac auscultation solution.  ...  A deep learning based approach was used in [23] for automatic recognition of abnormal heartbeat using a deep Convolutional Neural Network (CNN).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1801.08322v4">arXiv:1801.08322v4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/p3ewnmeohjc5nefzsenrdfc2zi">fatcat:p3ewnmeohjc5nefzsenrdfc2zi</a> </span>
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2020 Index IEEE Journal of Biomedical and Health Informatics Vol. 24

<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q2z26obphvchndqieqd65vltle" style="color: black;">IEEE journal of biomedical and health informatics</a> </i> &nbsp;
., A Globalized Model for Mapping Wearable Seismocardiogram Signals to Whole-Body Ballistocardiogram Signals Based on Deep Learning; JBHI May 2020 1296-1309 Herskovic, V., see Saint-Pierre, C., JBHI Jan  ...  2833-2843 Hong, H., see Xue, B., JBHI Feb. 2020 614-625 Hoog Antink, C., Mai, Y., Aalto, R., Bruser, C., Leonhardt, S., Oksala, N., and Vehkaoja, A., Ballistocardiography Can Estimate Beat-to-Beat Heart  ...  ., +, JBHI Feb. 2020 465-474 Falls Risk Classification of Older Adults Using Deep Neural Networks and Transfer Learning.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/jbhi.2020.3048808">doi:10.1109/jbhi.2020.3048808</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/iifrkwtzazdmboabdqii7x5ukm">fatcat:iifrkwtzazdmboabdqii7x5ukm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210228010254/https://ieeexplore.ieee.org/ielx7/6221020/9281055/09313843.pdf?tp=&amp;arnumber=9313843&amp;isnumber=9281055&amp;ref=" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/fa/77/fa778104828871222f338036eb55e083a3e1a9ad.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/jbhi.2020.3048808"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation [article]

Theekshana Dissanayake, Tharindu Fernando, Simon Denman, Sridha Sridharan, Houman Ghaemmaghami, Clinton Fookes
<span title="2020-09-29">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Experimental results demonstrate that the segmentation plays an essential role in abnormal heart sound classification.  ...  In this study, we explicitly examine the importance of heart sound segmentation as a prior step for heart sound classification, and then seek to apply the obtained insights to propose a robust classifier  ...  Hence, we use the segmentation model proposed by Fernando et al. [25] which has an accuracy of 97% using an Attention-based Long-Short Term Memory (LSTM) network.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2005.10480v2">arXiv:2005.10480v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/chd2ywgsivdbddcarmvrd4tszu">fatcat:chd2ywgsivdbddcarmvrd4tszu</a> </span>
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Phonocardiographic Sensing using Deep Learning for Abnormal Heartbeat Detection

Siddique Latif, Muhammad Usman, Rajib Rana, Junaid Qadir
<span title="">2018</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/blxwdtb2nfevpltlbto4vdqbte" style="color: black;">IEEE Sensors Journal</a> </i> &nbsp;
In this work, we propose a Recurrent Neural Networks (RNNs) based automated cardiac auscultation solution.  ...  Deep learning based cardiac auscultation is of significant interest to the healthcare community as it can help reducing the burden of manual auscultation with automated detection of abnormal heartbeats  ...  A deep learning based approach was used in [17] for automatic recognition of abnormal heartbeat using a deep Convolutional Neural Network (CNN).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/jsen.2018.2870759">doi:10.1109/jsen.2018.2870759</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/g7foup23rjgfzhczgregnh5iqi">fatcat:g7foup23rjgfzhczgregnh5iqi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190429043921/https://eprints.usq.edu.au/34789/1/Abnormal_Heartbeat.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b4/ab/b4ab569775e976b6e0072c2d64c73c2ba232ffc5.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/jsen.2018.2870759"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>
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