Novel Algorithm and Data Strategies to detect and Predict atrial fibrillation for post-stroke patients (NADSP)

项目摘要 房颤(房颤)是最常见的心律失常，全球有3350万人患有 流行率越来越高。房颤与严重的发病率和死亡率有关，包括20%的 中风，33%的住院与心律失常有关，患心脏病的风险增加两倍 死亡。为了减少与房颤相关的风险，如中风，能够在早期诊断房颤是很重要的 房颤无症状和阵发性时的轨迹，以启动有效的卒中 包括抗凝在内的预防干预。不幸的是，据估计有70万人 在美国可能有以前未知的房颤，并且在中风时新发现的房颤 在18%的房颤相关中风事件中。体积描记术(PPG)测量搏动的血液 音量会发生变化，高达71%的消费者可穿戴设备中都有这种功能。因为这是无与伦比的 可用性，基于PPG的房颤检测是实现低成本、长期和持续的理想选择 房颤监测的规模。然而，基于PPG的房颤检测在PPG时性能不佳 信号没有完美的信号质量仍然是充分发挥其潜力的关键障碍 一个规模化的房颤监测工具。拟议的研究旨在通过以下方式克服这一挑战 以下目标：1)设计、开发和验证一种新型深度神经网络(DNN)体系结构，该体系结构 将PPG信号质量评估与房颤检测相结合，即使对信号也能准确检测房颤 2)验证和测试所提出的DNN的进一步个性化 来自中风后患者的前瞻性数据将在门诊环境中收集；3)开发和 验证可解释的电子病历数据驱动的机器学习方法以识别患有高血压的患者 房颤的风险，基于PPG的房颤监测最有可能是有益的。

Project Summary Atrial fibrillation (AF) is the most common arrhythmia, affecting 33.5 million people globally with a growing prevalence. AF is associated with significant morbidity and mortality, including 20% of all strokes, 33% of hospitalizations related to cardiac arrhythmias, and a two-fold increase in risk of death. To reduce AF-associated risks such as stroke, it is important to be able to diagnose AF early in the AF trajectory when it is asymptomatic and paroxysmal in order to initiate effective stroke prevention interventions including anticoagulation. Unfortunately, it is estimated that 700,000 people in the USA may have previously unknown AF, and newly detected AF at the time of stroke was found among 18% of AF-associated stroke incidents. Plethysmography (PPG) measures pulsatile blood volume changes and is available in up to 71% of consumer wearables. Because of this unmatched availability, PPG-based AF detection is ideally poised to enable low-cost, long-term, and continuous AF monitoring at scale. However, modest performance of PPG-based AF detection when PPG signals do not have perfect signal quality remains a critical impediment to fully realize its potential as an AF-monitoring tool at scale. The proposed study aims to overcome this challenge by pursuing the following aims: 1) design, develop, and validate a novel deep neural network (DNN) architecture that integrate PPG signal quality assessment with AF detection to accurately detect AF even for signals with imperfect signal quality; 2) validate and test further personalization of the proposed DNN using prospective data from post stroke patients to be collected in ambulatory settings; 3) develop and validate interpretable EHR-data driven machine learning approaches to identify patients with elevated risk of AF for whom PPG-based AF monitoring can be most likely beneficial.