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在无线体域网中通过衍生12导联心电图对房颤进行高效的现场确认测试。

Efficient on-site confirmatory testing for atrial fibrillation with derived 12-lead ECG in a wireless body area network.

作者信息

Koya Aneesh M, Deepthi P P

机构信息

National Institute of Technology Calicut, Calicut, Kerala India.

出版信息

J Ambient Intell Humaniz Comput. 2023;14(6):6797-6815. doi: 10.1007/s12652-021-03543-9. Epub 2021 Nov 26.

DOI:10.1007/s12652-021-03543-9
PMID:34849174
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8619662/
Abstract

Smartphones that can support and assist the screening of various cardiovascular diseases are gaining popularity in recent years. The timely detection, diagnosis, and treatment of atrial fibrillation (AF) are critical, especially for those who are at risk of stroke. AF detection via screening with wearable devices should always be confirmed by a standard 12-lead electrocardiogram (ECG). However, the inability to perform on-site AF confirmatory testing results in increased patient anxiety, followed by unnecessary diagnostic procedures and treatments. Also, the delay in confirmation procedure may conclude the condition as non-AF while it was indeed present at the time of screening. To overcome these challenges, we propose an efficient on-site confirmatory testing for AF with 12-lead ECG derived from the reduced lead set (RLS) in a wireless body area network (WBAN) environment. The reduction in the number of leads enhances the comfort level of patients as well as minimizes the hurdles associated with continuous telemonitoring applications such as data transmission, storage, and bandwidth of the overall system. The proposed method is characterized by segment-wise regression and a lead selection algorithm, facilitating improved P-wave reconstruction. Further, an efficient AF detection algorithm is proposed by incorporating a novel three-level P-wave evidence score with an RR irregularity evidence score. The proposed on-site AF confirmation test reduces false positives and false negatives by 88% and 53% respectively, compared to single lead screening. In addition, the proposed lead derivation method improves accuracy, -score, and Matthews correlation coefficient (MCC) for the on-site AF detection compared to existing related methods.

摘要

近年来,能够支持和辅助筛查各种心血管疾病的智能手机越来越受欢迎。心房颤动(AF)的及时检测、诊断和治疗至关重要,尤其是对于有中风风险的患者。通过可穿戴设备进行筛查检测到的房颤,应始终通过标准12导联心电图(ECG)进行确认。然而,无法进行现场房颤确认测试会导致患者焦虑加剧,进而引发不必要的诊断程序和治疗。此外,确认程序的延迟可能会在筛查时确实存在房颤的情况下将病情判定为非房颤。为了克服这些挑战,我们提出了一种在无线体域网(WBAN)环境中基于简化导联集(RLS)进行12导联心电图的高效现场房颤确认测试。导联数量的减少提高了患者的舒适度,并最大限度地减少了与连续远程监测应用相关的障碍,如数据传输、存储和整个系统的带宽。该方法的特点是分段回归和导联选择算法,有助于改进P波重建。此外,通过结合新颖的三级P波证据评分和RR不规则证据评分,提出了一种高效的房颤检测算法。与单导联筛查相比,所提出的现场房颤确认测试分别将假阳性和假阴性降低了88%和53%。此外,与现有相关方法相比,所提出的导联推导方法提高了现场房颤检测的准确性、-评分和马修斯相关系数(MCC)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ec05/8619662/0c43ce234548/12652_2021_3543_Fig13_HTML.jpg
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Accurate detection of atrial fibrillation from 12-lead ECG using deep neural network.
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