A Comprehensive Survey on Heart Sound Analysis in the Deep Learning Era

• URL: http://arxiv.org/abs/2301.09362v2
• Date: Sat, 11 May 2024 16:37:53 GMT
• Title: A Comprehensive Survey on Heart Sound Analysis in the Deep Learning Era
• Authors: Zhao Ren, Yi Chang, Thanh Tam Nguyen, Yang Tan, Kun Qian, Björn W. Schuller,
• Abstract summary: Heart sound auscultation has been applied in clinical usage for early screening of cardiovascular diseases. Deep learning has outperformed classic machine learning in many research fields.
• Score: 54.53921568420471
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Heart sound auscultation has been applied in clinical usage for early screening of cardiovascular diseases. Due to the high demand for auscultation expertise, automatic auscultation can help with auxiliary diagnosis and reduce the burden of training professional clinicians. Nevertheless, there is a limit to classic machine learning's performance improvement in the era of big data. Deep learning has outperformed classic machine learning in many research fields, as it employs more complex model architectures with a stronger capability of extracting effective representations. Moreover, it has been successfully applied to heart sound analysis in the past years. As most review works about heart sound analysis were carried out before 2017, the present survey is the first to work on a comprehensive overview to summarise papers on heart sound analysis with deep learning published in 2017--2022. This work introduces both classic machine learning and deep learning for comparison, and further offer insights about the advances and future research directions in deep learning for heart sound analysis. Our repository is publicly available at \url{https://github.com/zhaoren91/awesome-heart-sound-analysis}.

Related papers

• Integrating Deep Learning in Cardiology: A Comprehensive Review of Atrial Fibrillation, Left Atrial Scar Segmentation, and the Frontiers of State-of-the-Art Techniques [0.5461938536945723]
  Atrial fibrillation (AFib) is the prominent cardiac arrhythmia in the world. This review delves into the recent progress in segmenting atrial scars using LGE-MRIs. It emphasizes the importance of precise scar measurement in the treatment and management of AFib.
  arXiv  Detail & Related papers  (2024-07-01T11:33:52Z)
• Heart Sound Segmentation Using Deep Learning Techniques [0.0]
  This paper presents a novel approach for heart sound segmentation and classification into S1 (LUB) and S2 (DUB) sounds. We employ FFT-based filtering, dynamic programming for event detection, and a Siamese network for robust classification. Our method demonstrates superior performance on the PASCAL heart sound dataset compared to existing approaches.
  arXiv  Detail & Related papers  (2024-06-09T05:30:05Z)
• Automatic Cardiac Pathology Recognition in Echocardiography Images Using Higher Order Dynamic Mode Decomposition and a Vision Transformer for Small Datasets [2.0286377328378737]
  Heart diseases are the main international cause of human defunction. According to the WHO, nearly 18 million people decease each year because of heart diseases. In this work, an automatic cardiac pathology recognition system based on a novel deep learning framework is proposed.
  arXiv  Detail & Related papers  (2024-04-30T14:16:45Z)
• A Survey of the Impact of Self-Supervised Pretraining for Diagnostic Tasks with Radiological Images [71.26717896083433]
  Self-supervised pretraining has been observed to be effective at improving feature representations for transfer learning. This review summarizes recent research into its usage in X-ray, computed tomography, magnetic resonance, and ultrasound imaging.
  arXiv  Detail & Related papers  (2023-09-05T19:45:09Z)
• Deep Feature Learning for Medical Acoustics [78.56998585396421]
  The purpose of this paper is to compare different learnables in medical acoustics tasks. A framework has been implemented to classify human respiratory sounds and heartbeats in two categories, i.e. healthy or affected by pathologies.
  arXiv  Detail & Related papers  (2022-08-05T10:39:37Z)
• The CirCor DigiScope Dataset: From Murmur Detection to Murmur Classification [5.879085008496386]
  A total of 5282 recordings have been collected from the four main auscultation locations of 1568 patients. For the first time, each cardiac murmur has been manually annotated by an expert annotator according to its timing, shape, pitch, grading and quality.
  arXiv  Detail & Related papers  (2021-08-02T12:30:40Z)
• Machine Learning Methods for Histopathological Image Analysis: A Review [62.14548392474976]
  Histopathological images (HIs) are the gold standard for evaluating some types of tumors for cancer diagnosis. One of the ways of accelerating such an analysis is to use computer-aided diagnosis (CAD) systems.
  arXiv  Detail & Related papers  (2021-02-07T19:12:32Z)
• Noise-Resilient Automatic Interpretation of Holter ECG Recordings [67.59562181136491]
  We present a three-stage process for analysing Holter recordings with robustness to noisy signal. First stage is a segmentation neural network (NN) with gradientdecoder architecture which detects positions of heartbeats. Second stage is a classification NN which will classify heartbeats as wide or narrow. Third stage is a boosting decision trees (GBDT) on top of NN features that incorporates patient-wise features.
  arXiv  Detail & Related papers  (2020-11-17T16:15:49Z)
• A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation [37.70077538403524]
  We argue the importance of heart sound segmentation as a prior step for heart sound classification. We then propose a robust classifier for abnormal heart sound detection. Our new classifier is also shown to be robust, stable and most importantly, explainable, with an accuracy of almost 100% on the widely used PhysioNet dataset.
  arXiv  Detail & Related papers  (2020-05-21T06:36:28Z)
• Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review [62.490310870300746]
  The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results on predictive healthcare tasks using ECG signals. This paper presents a systematic review of deep learning methods for ECG data from both modeling and application perspectives.
  arXiv  Detail & Related papers  (2019-12-28T02:44:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.