TY -的盟Santala Onni E盟——Lipponen Jukka盟——Jantti,海伦娜AU - Rissanen图T非盟- Tarvainen,米卡P AU - Laitinen,汤米•P AU - Laitinen Tiina M盟——Castren Maaret盟——Valiaho Eemu-Samuli盟——Rantula Olli AU - Naukkarinen Noora年代盟——Hartikainen Juha E K盟,哈洛宁Jari盟——Martikainen Tero J PY - 2022 DA - 2022/6/21 TI -连续mHealth补丁监测算法检测心房纤颤的:可行性和诊断准确性研究乔- JMIR有氧运动SP - e31230六世- 6 - 1 KW -心房纤维性颤动KW -心率变异性KW - HRV KW -算法KW -中风KW -移动健康KW - mHealth KW - Awario分析服务,筛选KW -风险KW -中风危险KW -心率KW -可行性千瓦可靠性KW -人工智能KW -移动补丁KW -可穿戴KW -心律失常KW -筛选AB -背景:心房颤动(AF)的检测是一个主要的临床挑战，因为AF通常是阵发性和无症状的。新型移动医疗(mHealth)技术可以为房颤筛查提供经济可靠的解决方案。然而，许多这些技术还没有得到临床验证。目的:本研究的目的是评估人工智能(AI)心律失常分析用于检测房颤的可行性和可靠性。方法:从急诊科招募有房颤(N= 79,44%)或窦性心律(N= 99,56%)的患者(N=178)。使用mHealth贴片设备记录单导联、24小时基于心电图的心率变异性(HRV)测量，并使用新型AI心律失常分析软件进行分析。同时登记的3导联心电图(Holter)作为最终节律诊断的金标准。结果:在单导联mHealth补丁产生的HRV数据中，81.5%(3099/3802小时)是可解释的，基于主题的可解释HRV数据的中位数为99%(第25百分位=77%，第75百分位=100%)。 The AI arrhythmia detection algorithm detected AF correctly in all patients in the AF group and suggested the presence of AF in 5 patients in the control group, resulting in a subject-based AF detection accuracy of 97.2%, a sensitivity of 100%, and a specificity of 94.9%. The time-based AF detection accuracy, sensitivity, and specificity of the AI arrhythmia detection algorithm were 98.7%, 99.6%, and 98.0%, respectively. Conclusions: The 24-hour HRV monitoring by the mHealth patch device enabled accurate automatic AF detection. Thus, the wearable mHealth patch device with AI arrhythmia analysis is a novel method for AF screening. Trial Registration: ClinicalTrials.gov NCT03507335; https://clinicaltrials.gov/ct2/show/NCT03507335 SN - 2561-1011 UR - https://cardio.www.mybigtv.com/2022/1/e31230 UR - https://doi.org/10.2196/31230 UR - http://www.ncbi.nlm.nih.gov/pubmed/35727618 DO - 10.2196/31230 ID - info:doi/10.2196/31230 ER -