CarDS-Plus ECG Platform: Development and Feasibility Evaluation of a Multiplatform Artificial Intelligence Toolkit for Portable and Wearable Device Electrocardiograms

• URL: http://arxiv.org/abs/2310.07000v1
• Date: Tue, 10 Oct 2023 20:33:48 GMT
• Title: CarDS-Plus ECG Platform: Development and Feasibility Evaluation of a Multiplatform Artificial Intelligence Toolkit for Portable and Wearable Device Electrocardiograms
• Authors: Sumukh Vasisht Shankar, Evangelos K Oikonomou, Rohan Khera
• Abstract summary: Single-lead electrocardiogram (ECG) recordings have emerged as a crucial source of information for monitoring cardiovascular health. There has been significant advances in artificial intelligence capable of interpreting these 1-lead ECGs. This design study describes the development of an innovative multiplatform system aimed at the rapid deployment of AI-based ECG solutions.
• Score: 0.3069335774032178
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In the rapidly evolving landscape of modern healthcare, the integration of wearable & portable technology provides a unique opportunity for personalized health monitoring in the community. Devices like the Apple Watch, FitBit, and AliveCor KardiaMobile have revolutionized the acquisition and processing of intricate health data streams. Amidst the variety of data collected by these gadgets, single-lead electrocardiogram (ECG) recordings have emerged as a crucial source of information for monitoring cardiovascular health. There has been significant advances in artificial intelligence capable of interpreting these 1-lead ECGs, facilitating clinical diagnosis as well as the detection of rare cardiac disorders. This design study describes the development of an innovative multiplatform system aimed at the rapid deployment of AI-based ECG solutions for clinical investigation & care delivery. The study examines design considerations, aligning them with specific applications, develops data flows to maximize efficiency for research & clinical use. This process encompasses the reception of single-lead ECGs from diverse wearable devices, channeling this data into a centralized data lake & facilitating real-time inference through AI models for ECG interpretation. An evaluation of the platform demonstrates a mean duration from acquisition to reporting of results of 33.0 to 35.7 seconds, after a standard 30 second acquisition. There were no substantial differences in acquisition to reporting across two commercially available devices (Apple Watch and KardiaMobile). These results demonstrate the succcessful translation of design principles into a fully integrated & efficient strategy for leveraging 1-lead ECGs across platforms & interpretation by AI-ECG algorithms. Such a platform is critical to translating AI discoveries for wearable and portable ECG devices to clinical impact through rapid deployment.

Related papers

• Self-supervised inter-intra period-aware ECG representation learning for detecting atrial fibrillation [41.82319894067087]
  We propose an inter-intra period-aware ECG representation learning approach. Considering ECGs of atrial fibrillation patients exhibit the irregularity in RR intervals and the absence of P-waves, we develop specific pre-training tasks for interperiod and intraperiod representations. Our approach demonstrates remarkable AUC performances on the BTCH dataset, textiti.e., 0.953/0.996 for paroxysmal/persistent atrial fibrillation detection.
  arXiv  Detail & Related papers  (2024-10-08T10:03:52Z)
• The Rlign Algorithm for Enhanced Electrocardiogram Analysis through R-Peak Alignment for Explainable Classification and Clustering [34.88496713576635]
  We aim to reintroduce shallow learning techniques, including support vector machines and principal components analysis, into ECG signal processing. To this end, we developed and evaluated a transformation that effectively restructures ECG signals into a fully structured format. Our approach demonstrates a significant advantage for shallow machine learning methods over CNNs, especially when dealing with limited training data.
  arXiv  Detail & Related papers  (2024-07-22T11:34:47Z)
• 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)
• Large-scale Training of Foundation Models for Wearable Biosignals [1.8291790356553643]
  Tracking biosignals is crucial for monitoring wellness and preempting the development of severe medical conditions. Despite wearable and existing digital biomarkers, the absence of data with labels hinders the development of new biomarkers. We train foundation models for two common biosignals: photo movement and electrocardiogram.
  arXiv  Detail & Related papers  (2023-12-08T23:44:34Z)
• 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)
• A Revolution of Personalized Healthcare: Enabling Human Digital Twin with Mobile AIGC [54.74071593520785]
  Mobile AIGC can be a key enabling technology for an emerging application, called human digital twin (HDT) HDT empowered by the mobile AIGC is expected to revolutionize the personalized healthcare by generating rare disease data, modeling high-fidelity digital twin, building versatile testbeds, and providing 24/7 customized medical services.
  arXiv  Detail & Related papers  (2023-07-22T15:59:03Z)
• Enhancing ECG Analysis of Implantable Cardiac Monitor Data: An Efficient Pipeline for Multi-Label Classification [0.0]
  Implantable Cardiac Monitor (ICM) devices are demonstrating as of today, the fastest-growing market for implantable cardiac devices. ICMs constantly monitor and record a patient's heart rhythm and when triggered - send it to a secure server where health care professionals can review it. This study presents the challenges and solutions in automatically analyzing ICM data and introduces a method for its classification that outperforms existing methods on such data.
  arXiv  Detail & Related papers  (2023-07-12T12:49:18Z)
• ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning [20.465733855762835]
  Electrocardiogram (ECG) monitoring is one of the most powerful technique of cardiovascular disease (CVD) early identification. Classic rule-based algorithms are now completely outperformed by deep learning based methods. We propose a multi-resolution model that can sustain high-resolution low-level semantic information throughout.
  arXiv  Detail & Related papers  (2023-04-10T15:19:00Z)
• 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 [19.706400613998703]
  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.
  arXiv  Detail & Related papers  (2022-07-11T19:08:46Z)
• 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)
• CardioLearn: A Cloud Deep Learning Service for Cardiac Disease Detection from Electrocardiogram [5.697086783381729]
  We demonstrate our work on building, training, and serving such out-of-the-box cloud deep learning service for cardiac disease detection from ECG named CardioLearn. As a practical example, we also design a portable smart hardware device along with an interactive mobile program, which can collect ECG and detect potential cardiac diseases anytime and anywhere.
  arXiv  Detail & Related papers  (2020-07-04T18:48:24Z)

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.