Towards Personalized Healthcare in Cardiac Population: The Development of a Wearable ECG Monitoring System, an ECG Lossy Compression Schema, and a ResNet-Based AF Detector

• URL: http://arxiv.org/abs/2207.05138v1
• Date: Mon, 11 Jul 2022 19:08:46 GMT
• Title: Towards Personalized Healthcare in Cardiac Population: The Development of a Wearable ECG Monitoring System, an ECG Lossy Compression Schema, and a ResNet-Based AF Detector
• Authors: Wei-Ying Yi, Peng-Fei Liu, Sheung-Lai Lo, Ya-Fen Chan, Yu Zhou, Yee Leung, Kam-Sang Woo, Alex Pui-Wai Lee, Jia-Min Chen and Kwong-Sak Leung
• Abstract summary: The atrial fibrillation (AF) is usually diagnosed using electrocardiography (ECG) which is a risk-free, non-intrusive, and cost-efficient tool. In this manuscript, the design and implementation of a personalized healthcare system embodying a wearable ECG device, a mobile application, and a back-end server are presented.
• Score: 19.706400613998703
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cardiovascular diseases (CVDs) are the number one cause of death worldwide. While there is growing evidence that the atrial fibrillation (AF) has strong associations with various CVDs, this heart arrhythmia is usually diagnosed using electrocardiography (ECG) which is a risk-free, non-intrusive, and cost-efficient tool. Continuously and remotely monitoring the subjects' ECG information unlocks the potentials of prompt pre-diagnosis and timely pre-treatment of AF before the development of any life-threatening conditions/diseases. Ultimately, the CVDs associated mortality could be reduced. In this manuscript, the design and implementation of a personalized healthcare system embodying a wearable ECG device, a mobile application, and a back-end server are presented. This system continuously monitors the users' ECG information to provide personalized health warnings/feedbacks. The users are able to communicate with their paired health advisors through this system for remote diagnoses, interventions, etc. The implemented wearable ECG devices have been evaluated and showed excellent intra-consistency (CVRMS=5.5%), acceptable inter-consistency (CVRMS=12.1%), and negligible RR-interval errors (ARE<1.4%). To boost the battery life of the wearable devices, a lossy compression schema utilizing the quasi-periodic feature of ECG signals to achieve compression was proposed. Compared to the recognized schemata, it outperformed the others in terms of compression efficiency and distortion, and achieved at least 2x of CR at a certain PRD or RMSE for ECG signals from the MIT-BIH database. To enable automated AF diagnosis/screening in the proposed system, a ResNet-based AF detector was developed. For the ECG records from the 2017 PhysioNet CinC challenge, this AF detector obtained an average testing F1=85.10% and a best testing F1=87.31%, outperforming the state-of-the-art.

Related papers

• 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)
• EB-GAME: A Game-Changer in ECG Heartbeat Anomaly Detection [7.574088346030774]
  This paper focuses on detecting abnormal signals in electrocardi-ograms (ECGs) using only labels from normal signals as training data. In-spired by self-supervised vision transformers, which learn by dividing images into patches, and masked auto-encoders, we introduce the ECG Heartbeat Anomaly Detection model, EB-GAME.
  arXiv  Detail & Related papers  (2024-04-08T13:01:59Z)
• MEIT: Multi-Modal Electrocardiogram Instruction Tuning on Large Language Models for Report Generation [41.324530807795256]
  Electrocardiogram (ECG) is the primary non-invasive diagnostic tool for monitoring cardiac conditions. Recent studies have concentrated on classifying cardiac conditions using ECG data but have overlooked ECG report generation. We propose the Multimodal ECG Instruction Tuning (MEIT) framework, the first attempt to tackle ECG report generation with LLMs and multimodal instructions.
  arXiv  Detail & Related papers  (2024-03-07T23:20:56Z)
• Improving Diffusion Models for ECG Imputation with an Augmented Template Prior [43.6099225257178]
  noisy and poor-quality recordings are a major issue for signals collected using mobile health systems. Recent studies have explored the imputation of missing values in ECG with probabilistic time-series models. We present a template-guided denoising diffusion probabilistic model (DDPM), PulseDiff, which is conditioned on an informative prior for a range of health conditions.
  arXiv  Detail & Related papers  (2023-10-24T11:34:15Z)
• Digital twinning of cardiac electrophysiology models from the surface ECG: a geodesic backpropagation approach [39.36827689390718]
  We introduce a novel method, Geodesic-BP, to solve the inverse eikonal problem. We show that Geodesic-BP can reconstruct a simulated cardiac activation with high accuracy in a synthetic test case. Given the future shift towards personalized medicine, Geodesic-BP has the potential to help in future functionalizations of cardiac models.
  arXiv  Detail & Related papers  (2023-08-16T14:57:12Z)
• Multi-scale Cross-restoration Framework for Electrocardiogram Anomaly Detection [33.48389041651675]
  Electrocardiogram (ECG) is a widely used diagnostic tool for detecting heart conditions. Rare cardiac diseases may be underdiagnosed using traditional ECG analysis, considering that no training dataset can exhaust all possible cardiac disorders. This paper proposes using anomaly detection to identify any unhealthy status, with normal ECGs solely for training.
  arXiv  Detail & Related papers  (2023-08-03T09:16:57Z)
• PulseNet: Deep Learning ECG-signal classification using random augmentation policy and continous wavelet transform for canines [46.09869227806991]
  evaluating canine electrocardiograms (ECG) require skilled veterinarians. Current availability of veterinary cardiologists for ECG interpretation and diagnostic support is limited. We implement a deep convolutional neural network (CNN) approach for classifying canine electrocardiogram sequences as either normal or abnormal.
  arXiv  Detail & Related papers  (2023-05-17T09:06:39Z)
• Performer: A Novel PPG to ECG Reconstruction Transformer For a Digital Biomarker of Cardiovascular Disease Detection [0.0]
  Cardiovascular diseases (CVDs) have become the top one cause of death; three-quarters of these deaths occur in lower-income communities. Electrocardiography (ECG) is infeasible for continuous cardiac monitoring due to its requirement for user participation. Photoplethysmography is easy to collect, but the limited accuracy constrains its clinical usage.
  arXiv  Detail & Related papers  (2022-04-25T17:10:13Z)
• Blind ECG Restoration by Operational Cycle-GANs [15.264145425539128]
  Continuous long-term monitoring of electrocardiography signals is crucial for the early detection of cardiac abnormalities such as arrhythmia. Non-clinical ECG recordings often suffer from severe artifacts such as baseline wander, signal cuts, motion artifacts, variations on QRS amplitude, noise, and other interferences. We propose a novel approach for blind ECG restoration using cycle-consistent generative adversarial networks (Cycle-GANs)
  arXiv  Detail & Related papers  (2022-01-29T19:47:17Z)
• ECG Signal Super-resolution by Considering Reconstruction and Cardiac Arrhythmias Classification Loss [0.0]
  We propose a deep-learning-based ECG signal super-resolution framework (termed ESRNet) to recover compressed ECG signals. Experimental results show that the proposed ESRNet framework can well reconstruct ECG signals from the 10-times compressed ones.
  arXiv  Detail & Related papers  (2020-12-07T15:43:50Z)
• CardioGAN: Attentive Generative Adversarial Network with Dual Discriminators for Synthesis of ECG from PPG [25.305949034527202]
  Electrocardiogram (ECG) is the electrical measurement of cardiac activity. Photoplethysmogram ( PPG) is the optical measurement of changes in blood circulation. We propose CardioGAN, an adversarial model which takes PPG as input and generates ECG as output.
  arXiv  Detail & Related papers  (2020-09-30T20:49:30Z)

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.