A Multi-Modal Unsupervised Machine Learning Approach for Biomedical Signal Processing in CPR

• URL: http://arxiv.org/abs/2411.11869v1
• Date: Sun, 03 Nov 2024 18:40:25 GMT
• Title: A Multi-Modal Unsupervised Machine Learning Approach for Biomedical Signal Processing in CPR
• Authors: Saidul Islam, Jamal Bentahar, Robin Cohen, Gaith Rjoub,
• Abstract summary: Real-time analysis of biomedical signals during CPR is essential for monitoring and decision-making. Traditional denoising methods, such as filters, struggle to adapt to the varying and complex noise patterns present in CPR signals. This paper introduces a novel unsupervised machine learning (ML) approach for denoising CPR signals using a multi-modality framework.
• Score: 12.81782890394599
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cardiopulmonary resuscitation (CPR) is a critical, life-saving intervention aimed at restoring blood circulation and breathing in individuals experiencing cardiac arrest or respiratory failure. Accurate and real-time analysis of biomedical signals during CPR is essential for monitoring and decision-making, from the pre-hospital stage to the intensive care unit (ICU). However, CPR signals are often corrupted by noise and artifacts, making precise interpretation challenging. Traditional denoising methods, such as filters, struggle to adapt to the varying and complex noise patterns present in CPR signals. Given the high-stakes nature of CPR, where rapid and accurate responses can determine survival, there is a pressing need for more robust and adaptive denoising techniques. In this context, an unsupervised machine learning (ML) methodology is particularly valuable, as it removes the dependence on labeled data, which can be scarce or impractical in emergency scenarios. This paper introduces a novel unsupervised ML approach for denoising CPR signals using a multi-modality framework, which leverages multiple signal sources to enhance the denoising process. The proposed approach not only improves noise reduction and signal fidelity but also preserves critical inter-signal correlations (0.9993) which is crucial for downstream tasks. Furthermore, it outperforms existing methods in an unsupervised context in terms of signal-to-noise ratio (SNR) and peak signal-to-noise ratio (PSNR), making it highly effective for real-time applications. The integration of multi-modality further enhances the system's adaptability to various biomedical signals beyond CPR, improving both automated CPR systems and clinical decision-making.

Related papers

• NMCSE: Noise-Robust Multi-Modal Coupling Signal Estimation Method via Optimal Transport for Cardiovascular Disease Detection [7.255170888607717]
  We propose Noise-Robust Multi-Modal Coupling Signal Estimation (NMCSE), which reformulates the problem as distribution matching via optimal transport theory.<n>Our approach achieves 97.38% accuracy and 0.98 AUC in CVD detection, outperforming state-of-the-art methods and demonstrating robust performance for real-world clinical applications.
  arXiv  Detail & Related papers  (2025-05-14T18:25:43Z)
• Structure-Accurate Medical Image Translation based on Dynamic Frequency Balance and Knowledge Guidance [60.33892654669606]
  Diffusion model is a powerful strategy to synthesize the required medical images. Existing approaches still suffer from the problem of anatomical structure distortion due to the overfitting of high-frequency information. We propose a novel method based on dynamic frequency balance and knowledge guidance.
  arXiv  Detail & Related papers  (2025-04-13T05:48:13Z)
• SQUWA: Signal Quality Aware DNN Architecture for Enhanced Accuracy in Atrial Fibrillation Detection from Noisy PPG Signals [37.788535094404644]
  Atrial fibrillation (AF) significantly increases the risk of stroke, heart disease, and mortality. Photoplethysmography ( PPG) signals are susceptible to corruption from motion artifacts and other factors often encountered in ambulatory settings. We propose a novel deep learning model, designed to learn how to retain accurate predictions from partially corrupted PPG.
  arXiv  Detail & Related papers  (2024-04-15T01:07:08Z)
• Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training [75.40980802817349]
  Deep learning models have shown promise for automatically segmenting MS lesions, but the scarcity of accurately annotated data hinders progress in this area. We introduce a Decoupled Hard Label Correction (DHLC) strategy that considers the imbalanced distribution and fuzzy boundaries of MS lesions. We also introduce a Centrally Enhanced Label Correction (CELC) strategy, which leverages the aggregated central model as a correction teacher for all sites.
  arXiv  Detail & Related papers  (2023-08-31T00:36:10Z)
• Brain Imaging-to-Graph Generation using Adversarial Hierarchical Diffusion Models for MCI Causality Analysis [44.45598796591008]
  Brain imaging-to-graph generation (BIGG) framework is proposed to map functional magnetic resonance imaging (fMRI) into effective connectivity for mild cognitive impairment analysis. The hierarchical transformers in the generator are designed to estimate the noise at multiple scales. Evaluations of the ADNI dataset demonstrate the feasibility and efficacy of the proposed model.
  arXiv  Detail & Related papers  (2023-05-18T06:54:56Z)
• Machine Learning-based Signal Quality Assessment for Cardiac Volume Monitoring in Electrical Impedance Tomography [0.8541111605978491]
  In clinical applications, a cardiac volume signal is often of low quality, mainly because of the patient's deliberate movements or inevitable motions during clinical interventions. This study aims to develop a signal quality indexing method that assesses the influence of motion artifacts on transient cardiac volume signals. The proposed method can be utilized to provide immediate warnings so that clinicians can minimize confusion regarding patients' conditions.
  arXiv  Detail & Related papers  (2023-01-04T07:13:21Z)
• A Causal Intervention Scheme for Semantic Segmentation of Quasi-periodic Cardiovascular Signals [7.182731690965173]
  We propose contrastive causal intervention (CCI) to form a novel training paradigm under a frame-level contrastive framework. The intervention can eliminate the implicit statistical bias brought by the single attribute and lead to more objective representations.
  arXiv  Detail & Related papers  (2022-09-19T13:54:51Z)
• Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation [67.19443246236048]
  Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases. Some small-sized airway branches (e.g., bronchus and terminaloles) significantly aggravate the difficulty of automatic segmentation. This paper presents an efficient method for airway segmentation, comprising a novel fuzzy attention neural network and a comprehensive loss function.
  arXiv  Detail & Related papers  (2022-09-05T16:38:13Z)
• Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification [74.84221280249876]
  An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists. We propose a novel Discrete Wavelet Transform (DWT)-based method for the efficient identification and encoding of visual information.
  arXiv  Detail & Related papers  (2022-05-08T15:29:54Z)
• A Novel Non-Invasive Estimation of Respiration Rate from Photoplethysmograph Signal Using Machine Learning Model [0.0]
  Respiration rate (RR) is a vital indicator of the wellness of a patient. Real-time continuous RR monitoring facility is only available at the intensive care unit (ICU) Recent researches have proposed Photoplethysmogram (ECG) and/ Electrocardiogram (ECG) signals for RR estimation. This paper describes a novel approach to RR estimation using machine learning (ML) models with the PPG signal features.
  arXiv  Detail & Related papers  (2021-02-18T17:08:50Z)
• Simultaneous Denoising and Dereverberation Using Deep Embedding Features [64.58693911070228]
  We propose a joint training method for simultaneous speech denoising and dereverberation using deep embedding features. At the denoising stage, the DC network is leveraged to extract noise-free deep embedding features. At the dereverberation stage, instead of using the unsupervised K-means clustering algorithm, another neural network is utilized to estimate the anechoic speech.
  arXiv  Detail & Related papers  (2020-04-06T06:34:01Z)
• 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)
• ADRN: Attention-based Deep Residual Network for Hyperspectral Image Denoising [52.01041506447195]
  We propose an attention-based deep residual network to learn a mapping from noisy HSI to the clean one. Experimental results demonstrate that our proposed ADRN scheme outperforms the state-of-the-art methods both in quantitative and visual evaluations.
  arXiv  Detail & Related papers  (2020-03-04T08:36:27Z)

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.