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Lightweight End-to-End Neural Network Model for Automatic Heart Sound Classification

Tao Li, Yibo Yin, Kainan Ma, Sitao Zhang, Ming Liu
2021 Information  
In this study, we proposed an end-to-end lightweight neural network model that does not require heart sound segmentation and has very few parameters.  ...  These features were sent to the improved two-dimensional convolutional neural network (CNN) model for features learning and classification.  ...  Here, we developed a lightweight heart sound automatic classification model that does not require heart sound segmentation.  ... 
doi:10.3390/info12020054 fatcat:gxi3hzlmpzajrnflhxubzbkxs4

CardioXNet: A Novel Lightweight Deep Learning Framework for Cardiovascular Disease Classification Using Heart Sound Recordings

Samiul Based Shuvo, Shams Nafisa Ali, Soham Irtiza Swapnil, Mabrook S. Al-Rakhami, Abdu Gumaei
2021 IEEE Access  
In this article, we propose CardioXNet, a novel lightweight end-to-end CRNN architecture for automatic detection of five classes of cardiac auscultation namely normal, aortic stenosis, mitral stenosis,  ...  INDEX TERMS Phonocardiogram analysis, unsegmented heart sound, cardiovascular disease, lightweight CRNN architecture, deep learning, SqueezeNet.  ...  The major contribution of this work is the automatic end to end classification of valvular heart disease from PCG signals using a lightweight CRNN network with no manual feature extraction or preprocessing  ... 
doi:10.1109/access.2021.3063129 fatcat:io5rva7lnnay5hkuft6sllqkz4

CardioXNet: A Novel Lightweight CRNN Framework for Classifying Cardiovascular Diseases from Phonocardiogram Recordings [article]

Samiul Based Shuvo, Shams Nafisa Ali, Soham Irtiza Swapnil
2020 arXiv   pre-print
For resolving this issue, in this paper, we introduce CardioXNet,a novel lightweight CRNN architecture for automatic detection of five classes of cardiac auscultation namely normal, aortic stenosis, mitral  ...  The obtained results demonstrate that the proposed end-to-end architecture yields outstanding performance in all the evaluation metrics compared to the previous state-of-the-art methods with up to 99.6%  ...  The proposed lightweight CNN model has extremely low end to end time of 63.687(±0.06) ms.  ... 
arXiv:2010.01392v1 fatcat:7w3mv5mkozadjnbgjbtpsxdmtq

Automatic Detection of Heartbeats in Heart Sound Signals Using Deep Convolutional Neural Networks

Grega Vrbancic, Iztok Jr. Fister, Vili Podgorelec
2019 Elektronika ir Elektrotechnika  
In order to address these problems, we designed a new method for the segmentation of heart sound signals using deep convolutional neural networks, which works in a straightforward automatic manner and  ...  using deep neural networks.  ...  EXPERIMENTAL SETTINGS To test the proposed method for automatic signal segmentation, we used a collection of annotated heartbeat sound clips, initially prepared for Classifying Heart Sounds Challenge (  ... 
doi:10.5755/j01.eie.25.3.23680 fatcat:wwz24qfdxngprb6xnevpprztiu

Deep CardioSound-An Ensembled Deep Learning Model for Heart Sound MultiLabelling [article]

Li Guo, Steven Davenport, Yonghong Peng
2022 arXiv   pre-print
To further extend the landscape of the automatic heart sound diagnosis landscape, this work proposes a deep multilabel learning model that can automatically annotate heart sound recordings with labels  ...  Most of the current work is designed for single category based heard sound classification tasks.  ...  The reported work has extended the landscape of automatic cardiac auscultation from simple heart sound classification to multilabel learning.  ... 
arXiv:2204.07420v2 fatcat:i75lxe4ol5dkrhg33qdyghfdby

A Lightweight CNN Model for Detecting Respiratory Diseases from Lung Auscultation Sounds using EMD-CWT-based Hybrid Scalogram [article]

Samiul Based Shuvo, Shams Nafisa Ali, Soham Irtiza Swapnil, Taufiq Hasan, Mohammed Imamul Hassan Bhuiyan
2020 arXiv   pre-print
In this work, we propose a lightweight convolutional neural network (CNN) architecture to classify respiratory diseases using hybrid scalogram-based features of lung sounds.  ...  Listening to lung sounds through auscultation is vital in examining the respiratory system for abnormalities.  ...  We have calculated the time required for the end-to-end classification of an auscultation sound using our framework.  ... 
arXiv:2009.04402v1 fatcat:7jssczet6bcqtgcgblnt6ra4um

IEEE Access Special Section Editorial: Deep Learning for Computer-Aided Medical Diagnosis

Yu-Dong Zhang, Zhengchao Dong, Shui-Hua Wang, Carlo Cattani
2020 IEEE Access  
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).  ...  method, named deep CNN-BLSTM network model, to automatically detect AF heartbeats using ECG signals.  ... 
doi:10.1109/access.2020.2996690 fatcat:m6r36o6udrbfrnvwsca2uzu5dm

Automated COVID-19 and Heart Failure Detection Using DNA Pattern Technique with Cough Sounds

Mehmet Ali Kobat, Tarik Kivrak, Prabal Datta Barua, Turker Tuncer, Sengul Dogan, Ru-San Tan, Edward J. Ciaccio, U. Rajendra Acharya
2021 Diagnostics  
To the best our knowledge, this is the first work to automatically classify healthy subjects, HF and COVID-19 patients using cough sounds signals.  ...  Our proposed model attained an accuracy of 100.0%, 99.38%, and 99.49% for Case 1, Case 2, and Case 3, respectively.  ...  We aimed to study the feasibility of feature generation when utilizing these DNA patterns, as well as the diagnostic performance of the DNA pattern-based model, for automatic classification of cough sounds  ... 
doi:10.3390/diagnostics11111962 pmid:34829308 pmcid:PMC8620352 fatcat:a5kjsbsy3zblxhn27ernd3onry

Papers by Title

2021 2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)  
Cardiodynamic Status Monitoring: Multi-scale Modeling of the Heart Sound Traffic Agent Trajectory Prediction Using a Time Sequence Deep Learning Model with Trajectory Mapping for Autonomous Driving U 3  ...  Gender and Accent Recognition using English Keywords A IoT System for Vehicle Tracking using Long Range Wide Area Network A Lightweight Fine-Grained Action Recognition Network for Basketball Foul Detection  ... 
doi:10.1109/icce-tw52618.2021.9602951 fatcat:x5phgfzzl5cafcxul53thv5w7e

A Visual Domain Transfer Learning Approach for Heartbeat Sound Classification [article]

Uddipan Mukherjee, Sidharth Pancholi
2021 arXiv   pre-print
approaches for automated heart sound classification.  ...  This research proposes to convert cleansed and normalized heart sound into visual mel scale spectrograms and then using visual domain transfer learning approaches to automatically extract features and  ...  MobileNetV2 MobileNet focused on building lightweight and efficient visual domain deep neural networks introduced by [14] .  ... 
arXiv:2107.13237v2 fatcat:w5u2xass6bhgjohnbrxebkedam

Robust and Interpretable Temporal Convolution Network for Event Detection in Lung Sound Recordings [article]

Tharindu Fernando, Sridha Sridharan, Simon Denman, Houman Ghaemmaghami, Clinton Fookes
2021 arXiv   pre-print
overhead resulting in a robust lightweight network.The lightweight nature of our model allows it to be deployed in end-user devices such as smartphones, and it has the ability to generate predictions  ...  framework for lung sound event detection.  ...  The authors would also like to thank M3DICINE HOLDINGS PTY LTD for providing access to the M3DICINE lung sound database.  ... 
arXiv:2106.15835v1 fatcat:lwsht3575jf3ppoy2pg6olrnli

Analysis of Gastrointestinal Acoustic Activity Using Deep Neural Networks

Jakub Ficek, Kacper Radzikowski, Jan Krzysztof Nowak, Osamu Yoshie, Jaroslaw Walkowiak, Robert Nowak
2021 Sensors  
This article proposes a novel methodology for the analysis of BS using hybrid convolutional and recursive neural networks. It is one of the first methods of using deep learning to be widely explored.  ...  Our algorithm can detect bowel sounds with an accuracy >93%. Moreover, we have achieved a very high specificity (>97%), crucial in diagnosis.  ...  The authors would like to thank patients for their participation in the study and Hanna Wieli ńska-Wiśniewska (Poznan) for collecting the data.  ... 
doi:10.3390/s21227602 pmid:34833679 pmcid:PMC8618847 fatcat:jhkmpbicm5hkzi4cpdourbybhi

Bonseyes AI Pipeline – bringing AI to you. End-to-end integration of data, algorithms and deployment tools [article]

Miguel de Prado, Jing Su, Rabia Saeed, Lorenzo Keller, Noelia Vallez, Andrew Anderson, David Gregg, Luca Benini, Tim Llewellynn, Nabil Ouerhani, Rozenn Dahyot and, Nuria Pazos
2020 arXiv   pre-print
By removing the integration barriers and lowering the required expertise, we can interconnect the different stages of tools and provide a modular end-to-end development of AI products for embedded devices  ...  Besides, we integrate our deployment framework, LPDNN, into the AI pipeline and present its lightweight architecture and deployment capabilities for embedded devices.  ...  End-to-end AI Pipeline Based on the previous concepts, we propose an end-to-end AI pipeline to develop and deploy Deep Neural Network solutions on embedded devices.  ... 
arXiv:1901.05049v3 fatcat:dgs4zxtccne5nd4zvt4dqys44y

Feature-Based Fusion Using CNN for Lung and Heart Sound Classification

Zeenat Tariq, Sayed Khushal Shah, Yugyung Lee
2022 Sensors  
The FDC-FS framework aims to effectively transfer learning from three different deep neural network models built from audio datasets.  ...  In this paper, we propose a novel feature-based fusion network called FDC-FS for classifying heart and lung sounds.  ...  Acknowledgments: The co-author, Yugyung Lee, would like to acknowledge the partial support of the NSF, USA Grant No. 1747751, 1935076, and 1951971.  ... 
doi:10.3390/s22041521 pmid:35214424 pmcid:PMC8875944 fatcat:ubb3zmzv5zbazpfgk5ykwur3fq

Deep Neural Networks for COVID-19 Detection and Diagnosis using Images and Acoustic-based Techniques: A Recent Review [article]

Walid Hariri, Ali Narin
2021 arXiv   pre-print
Next, deep features are extracted using multiple types of deep models (pre-trained models, generative models, generic neural networks, etc.).  ...  For this reason, it has become an area of interest to develop early diagnosis and detection methods to ensure a rapid treatment process and prevent the virus from spreading.  ...  Acknowledgements The authors would like to thank the 'Agence Nationale de Valorisation des Résultats de la Recherche et du Développement Technologique (DGRSDT), Algérie'.  ... 
arXiv:2012.07655v4 fatcat:ivmhcw4kmfbgzouxaffdh3oa6i
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