A Method for Detecting Murmurous Heart Sounds based on Self-similar Properties

• URL: http://arxiv.org/abs/2306.05283v1
• Date: Sun, 28 May 2023 22:21:31 GMT
• Title: A Method for Detecting Murmurous Heart Sounds based on Self-similar Properties
• Authors: Dixon Vimalajeewa, Chihoon Lee, Brani Vidakovic
• Abstract summary: A heart murmur is an atypical sound produced by the flow of blood through the heart. Current methods for identifying murmurous heart sounds do not fully utilize the insights that can be gained by exploring intrinsic properties of heart sound signals. This study proposes a new discriminatory set of multiscale features based on the self-similarity and complexity properties of heart sounds.
• Score: 0.03222802562733786
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A heart murmur is an atypical sound produced by the flow of blood through the heart. It can be a sign of a serious heart condition, so detecting heart murmurs is critical for identifying and managing cardiovascular diseases. However, current methods for identifying murmurous heart sounds do not fully utilize the valuable insights that can be gained by exploring intrinsic properties of heart sound signals. To address this issue, this study proposes a new discriminatory set of multiscale features based on the self-similarity and complexity properties of heart sounds, as derived in the wavelet domain. Self-similarity is characterized by assessing fractal behaviors, while complexity is explored by calculating wavelet entropy. We evaluated the diagnostic performance of these proposed features for detecting murmurs using a set of standard classifiers. When applied to a publicly available heart sound dataset, our proposed wavelet-based multiscale features achieved comparable performance to existing methods with fewer features. This suggests that self-similarity and complexity properties in heart sounds could be potential biomarkers for improving the accuracy of murmur detection.

Related papers

• Deciphering Heartbeat Signatures: A Vision Transformer Approach to Explainable Atrial Fibrillation Detection from ECG Signals [4.056982620027252]
  We develop a vision transformer approach to identify atrial fibrillation based on single-lead ECG data. A residual network (ResNet) approach is also developed for comparison with the vision transformer approach.
  arXiv  Detail & Related papers  (2024-02-12T11:04:08Z)
• Three-dimensional micro-structurally informed in silico myocardium -- towards virtual imaging trials in cardiac diffusion weighted MRI [58.484353709077034]
  We propose a novel method to generate a realistic numerical phantom of myocardial microstructure. In-silico tissue models enable evaluating quantitative models of magnetic resonance imaging.
  arXiv  Detail & Related papers  (2022-08-22T22:01:44Z)
• An Algorithm for the Labeling and Interactive Visualization of the Cerebrovascular System of Ischemic Strokes [59.116811751334225]
  VirtualDSA++ is an algorithm designed to segment and label the cerebrovascular tree on CTA scans. We extend the labeling mechanism for the cerebral arteries to identify occluded vessels. We present the generic concept of iterative systematic search for pathways on all nodes of said model, which enables new interactive features.
  arXiv  Detail & Related papers  (2022-04-26T14:20:26Z)
• Machine Learning-based Efficient Ventricular Tachycardia Detection Model of ECG Signal [0.0]
  In primary diagnosis and analysis of heart defects, an ECG signal plays a significant role. This paper presents a model for the prediction of ventricular tachycardia arrhythmia using noise filtering, a unique set of ECG features, and a machine learning-based classifier model.
  arXiv  Detail & Related papers  (2021-12-24T05:56:09Z)
• Segmentation-free Heart Pathology Detection Using Deep Learning [12.065014651638943]
  We propose a novel segmentation-free heart sound classification method. Specifically, we apply discrete wavelet transform to denoise the signal, followed by feature extraction and feature reduction. Support Vector Machines and Deep Neural Networks are utilised for classification.
  arXiv  Detail & Related papers  (2021-08-09T16:09:30Z)
• A Visual Domain Transfer Learning Approach for Heartbeat Sound Classification [0.0]
  Heart disease is the most common reason for human mortality that causes almost one-third of deaths throughout the world. Detecting the disease early increases the chances of survival of the patient and there are several ways a sign of heart disease can be detected early. 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 categorize between heart sounds.
  arXiv  Detail & Related papers  (2021-07-28T09:41:38Z)
• 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)
• Respiratory Sound Classification Using Long-Short Term Memory [62.997667081978825]
  This paper examines the difficulties that exist when attempting to perform sound classification as it relates to respiratory disease classification. An examination on the use of deep learning and long short-term memory networks is performed in order to identify how such a task can be implemented.
  arXiv  Detail & Related papers  (2020-08-06T23:11:57Z)
• 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)
• Heart Sound Segmentation using Bidirectional LSTMs with Attention [37.62160903348547]
  We propose a novel framework for the segmentation of phonocardiogram (PCG) signals into heart states. We exploit recent advancements in attention based learning to segment the PCG signal. The proposed method attains state-of-the-art performance on multiple benchmarks including both human and animal heart recordings.
  arXiv  Detail & Related papers  (2020-04-02T02:09:11Z)

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.