Heart Murmur and Abnormal PCG Detection via Wavelet Scattering Transform & a 1D-CNN

• URL: http://arxiv.org/abs/2303.11423v2
• Date: Fri, 24 May 2024 16:31:43 GMT
• Title: Heart Murmur and Abnormal PCG Detection via Wavelet Scattering Transform & a 1D-CNN
• Authors: Ahmed Patwa, Muhammad Mahboob Ur Rahman, Tareq Y. Al-Naffouri,
• Abstract summary: This work does automatic and accurate heart murmur detection from phonocardiogram (PCG) recordings. Two public PCG datasets (CirCor Digiscope 2022 dataset and PCG 2016 dataset) are utilized to train and test three custom neural networks (NNs)
• Score: 10.744998586806474
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Heart murmurs provide valuable information about mechanical activity of the heart, which aids in diagnosis of various heart valve diseases. This work does automatic and accurate heart murmur detection from phonocardiogram (PCG) recordings. Two public PCG datasets (CirCor Digiscope 2022 dataset and PCG 2016 dataset) from Physionet online database are utilized to train and test three custom neural networks (NN): a 1D convolutional neural network (CNN), a long short-term memory (LSTM) recurrent neural network (RNN), and a convolutional RNN (C-RNN). We first do pre-processing which includes the following key steps: denoising, segmentation, re-labeling of noise-only segments, data normalization, and time-frequency analysis of the PCG segments using wavelet scattering transform. We then conduct four experiments, first three (E1-E3) using PCG 2022 dataset, and fourth (E4) using PCG 2016 dataset. It turns out that our custom 1D-CNN outperforms other two NNs (LSTM-RNN and C-RNN). Further, our 1D-CNN model outperforms the related work in terms of accuracy, weighted accuracy, F1-score and AUROC, for experiment E3 (that utilizes the cleaned and re-labeled PCG 2022 dataset). As for experiment E1 (that utilizes the original PCG 2022 dataset), our model performs quite close to the related work in terms of weighted accuracy and F1-score.

Related papers

• Prototyping an End-to-End Multi-Modal Tiny-CNN for Cardiovascular Sensor Patches [35.899401205042615]
  We propose a convolutional neural network with early fusion of data to solve a binary classification problem.<n>Our approach reduces memory footprint and compute cost by three orders of magnitude compared to the state-of-the-art.
  arXiv  Detail & Related papers  (2025-10-21T14:23:20Z)
• Brain Tumor Classification on MRI in Light of Molecular Markers [61.77272414423481]
  Co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas. This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection.
  arXiv  Detail & Related papers  (2024-09-29T07:04:26Z)
• Convolutional Neural Networks for the classification of glitches in gravitational-wave data streams [52.77024349608834]
  We classify transient noise signals (i.e.glitches) and gravitational waves in data from the Advanced LIGO detectors. We use models with a supervised learning approach, both trained from scratch using the Gravity Spy dataset. We also explore a self-supervised approach, pre-training models with automatically generated pseudo-labels.
  arXiv  Detail & Related papers  (2023-03-24T11:12:37Z)
• Searching for Effective Neural Network Architectures for Heart Murmur Detection from Phonocardiogram [5.183688633606942]
  The George B. Moody PhysioNet Challenge 2022 raised problems of heart murmur detection and related abnormal cardiac function identification from phonocardiograms (PCGs) This work describes the novel approaches developed by our team, Revenger, to solve these problems.
  arXiv  Detail & Related papers  (2023-03-06T09:31:42Z)
• Generalizing electrocardiogram delineation: training convolutional neural networks with synthetic data augmentation [63.51064808536065]
  Existing databases for ECG delineation are small, being insufficient in size and in the array of pathological conditions they represent. This article delves has two main contributions. First, a pseudo-synthetic data generation algorithm was developed, based in probabilistically composing ECG traces given "pools" of fundamental segments, as cropped from the original databases, and a set of rules for their arrangement into coherent synthetic traces. Second, two novel segmentation-based loss functions have been developed, which attempt at enforcing the prediction of an exact number of independent structures and at producing closer segmentation boundaries by focusing on a reduced number of samples.
  arXiv  Detail & Related papers  (2021-11-25T10:11:41Z)
• Robust Peak Detection for Holter ECGs by Self-Organized Operational Neural Networks [12.773050144952593]
  Deep convolutional neural networks (CNNs) have achieved state-of-the-art performance levels in Holter monitors. In this study, we propose 1-D Self-Organized ONNs (Self-ONNs) with generative neurons. Results demonstrate that the proposed solution achieves a 99.10% F1-score, 99.79% sensitivity, and 98.42% positive predictivity in the CPSC dataset.
  arXiv  Detail & Related papers  (2021-09-30T19:45:06Z)
• Deep Learning Based Classification of Unsegmented Phonocardiogram Spectrograms Leveraging Transfer Learning [0.0]
  Heart murmurs are the most common abnormalities detected during the auscultation process. The two widely used publicly available phonocardiogram (PCG) datasets are from PhysioNet/CinC and PASCAL (2011) We propose a novel, less complex and relatively light custom CNN model for the classification of PhysioNet, combined and PASCAL datasets.
  arXiv  Detail & Related papers  (2020-12-15T16:32:29Z)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)
• Spherical coordinates transformation pre-processing in Deep Convolution Neural Networks for brain tumor segmentation in MRI [0.0]
  Deep Convolutional Neural Networks (DCNN) have recently shown very promising results. DCNN models need large annotated datasets to achieve good performance. In this work, a 3D Spherical coordinates transform has been hypothesized to improve DCNN models' accuracy.
  arXiv  Detail & Related papers  (2020-08-17T05:11:05Z)
• CovidDeep: SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors and Efficient Neural Networks [51.589769497681175]
  The novel coronavirus (SARS-CoV-2) has led to a pandemic. The current testing regime based on Reverse Transcription-Polymerase Chain Reaction for SARS-CoV-2 has been unable to keep up with testing demands. We propose a framework called CovidDeep that combines efficient DNNs with commercially available WMSs for pervasive testing of the virus.
  arXiv  Detail & Related papers  (2020-07-20T21:47:28Z)
• DENS-ECG: A Deep Learning Approach for ECG Signal Delineation [15.648061765081264]
  This paper proposes a deep learning model for real-time segmentation of heartbeats. The proposed algorithm, named as the DENS-ECG algorithm, combines convolutional neural network (CNN) and long short-term memory (LSTM) model.
  arXiv  Detail & Related papers  (2020-05-18T13:13:41Z)
• Assessing Graph-based Deep Learning Models for Predicting Flash Point [52.931492216239995]
  Graph-based deep learning (GBDL) models were implemented in predicting flash point for the first time. Average R2 and Mean Absolute Error (MAE) scores of MPNN are, respectively, 2.3% lower and 2.0 K higher than previous comparable studies.
  arXiv  Detail & Related papers  (2020-02-26T06:10:12Z)

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.