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Journal ArticleDOI

Classification of Phonocardiogram Based on Multi-View Deep Network

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This article is published in Neural Processing Letters.The article was published on 2022-02-25. It has received 3 citations till now. The article focuses on the topics: Phonocardiogram & Computer science.

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Journal ArticleDOI

Automatic Detection and Classification of Cardiovascular Disorders Using Phonocardiogram and Convolutional Vision Transformers

TL;DR: Wang et al. as discussed by the authors developed a novel attention-based technique (CVT-Trans) on a convolutional vision transformer to recognize and categorize PCG signals into five classes.
Journal ArticleDOI

Time-Frequency Distributions of Heart Sound Signals: A Comparative Study using Convolutional Neural Networks

TL;DR: In this paper , the optimal use of single/combined Time Frequency Distributions (TFDs) for heart sound classification using deep learning was investigated and compared, and the results provided valuable insights for researchers and practitioners in the field of automatic diagnosis of heart sounds with deep learning.
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A Computer-Aided Heart Valve Disease Diagnosis System Based on Machine Learning

TL;DR: Wang et al. as mentioned in this paper proposed a computer-aided heart valve disease diagnosis system, including a heart sound acquisition module, a trained model for diagnosis, and software, which can diagnose four kinds of heart valve diseases.
References
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Proceedings ArticleDOI

Deep Residual Learning for Image Recognition

TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
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Squeeze-and-Excitation Networks

TL;DR: This work proposes a novel architectural unit, which is term the "Squeeze-and-Excitation" (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels and finds that SE blocks produce significant performance improvements for existing state-of-the-art deep architectures at minimal additional computational cost.
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Exact indexing of dynamic time warping

TL;DR: This work introduces a novel technique for the exact indexing of Dynamic time warping and proves its vast superiority over all competing approaches in the largest and most comprehensive set of time series indexing experiments ever undertaken.
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Res2Net: A New Multi-Scale Backbone Architecture

TL;DR: Res2Net as mentioned in this paper constructs hierarchical residual-like connections within one single residual block to represent multi-scale features at a granular level and increases the range of receptive fields for each network layer.
Proceedings ArticleDOI

Time series classification from scratch with deep neural networks: A strong baseline

TL;DR: In this article, the authors proposed a simple but strong baseline for time series classification from scratch with deep neural networks, which is pure end-to-end without any heavy preprocessing on the raw data or feature crafting.
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