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Reviving the origins: acoustic biomarkers of heart failure with preserved ejection fraction.

作者信息

Tokodi Márton, Kovács Attila

机构信息

Division of Cardiovascular Diseases and Hypertension, Rutgers Robert Wood Johnson Medical School, 125 Paterson Street, New Brunswick, NJ 08091, USA.

Heart and Vascular Centre, Semmelweis University, 68 Városmajor Street, Budapest 1122, Hungary.

出版信息

Eur Heart J Digit Health. 2022 Dec 15;4(1):1-3. doi: 10.1093/ehjdh/ztac075. eCollection 2023 Jan.

DOI:10.1093/ehjdh/ztac075
PMID:36743872
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9890080/
Abstract
摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63a3/9890080/d82383490b3c/ztac075f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63a3/9890080/d82383490b3c/ztac075f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63a3/9890080/d82383490b3c/ztac075f1.jpg

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1
Reviving the origins: acoustic biomarkers of heart failure with preserved ejection fraction.追溯起源：射血分数保留的心力衰竭的声学生物标志物
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2
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Circ Heart Fail. 2016 Jan;9(1). doi: 10.1161/CIRCHEARTFAILURE.115.002551.
3
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本文引用的文献

1
Association between phonocardiography and echocardiography in heart failure patients with preserved ejection fraction.射血分数保留的心力衰竭患者心音图与超声心动图之间的关联。
Eur Heart J Digit Health. 2022 Nov 22;4(1):4-11. doi: 10.1093/ehjdh/ztac073. eCollection 2023 Jan.
2
A lightweight hybrid deep learning system for cardiac valvular disease classification.一种用于心脏瓣膜疾病分类的轻量级混合深度学习系统。
Sci Rep. 2022 Aug 22;12(1):14297. doi: 10.1038/s41598-022-18293-7.
3
A multi-scale and multi-domain heart sound feature-based machine learning model for ACC/AHA heart failure stage classification.
基于多尺度多域心音特征的机器学习模型用于 ACC/AHA 心力衰竭分期分类。
Physiol Meas. 2022 Jun 28;43(6). doi: 10.1088/1361-6579/ac6d40.
4
Utilizing Conversational Artificial Intelligence, Voice, and Phonocardiography Analytics in Heart Failure Care.利用会话式人工智能、语音和心音分析技术进行心力衰竭护理。
Heart Fail Clin. 2022 Apr;18(2):311-323. doi: 10.1016/j.hfc.2021.11.006. Epub 2022 Mar 4.
5
Gated recurrent unit-based heart sound analysis for heart failure screening.基于门控循环单元的心音分析用于心力衰竭筛查。
Biomed Eng Online. 2020 Jan 13;19(1):3. doi: 10.1186/s12938-020-0747-x.
6
Correlation with invasive left ventricular filling pressures and prognostic relevance of the echocardiographic diastolic parameters used in the 2016 ESC heart failure guidelines and in the 2016 ASE/EACVI recommendations: a systematic review in patients with heart failure with preserved ejection fraction.2016 ESC 心力衰竭指南和 2016 ASE/EACVI 建议中使用的超声心动图舒张参数与侵入性左心室充盈压的相关性及其对射血分数保留心力衰竭患者的预后意义：系统评价。
Eur J Heart Fail. 2018 Sep;20(9):1303-1311. doi: 10.1002/ejhf.1220. Epub 2018 Jun 7.
7
Beyond auscultation: acoustic cardiography in clinical practice.听诊之外：临床实践中的心音图检查
Int J Cardiol. 2014 Apr 1;172(3):548-60. doi: 10.1016/j.ijcard.2013.12.298. Epub 2014 Jan 10.
8
Noninvasive prediction of left ventricular systolic dysfunction in patients with clinically suspected heart failure using acoustic cardiography.使用心音图对临床疑似心力衰竭患者的左心室收缩功能障碍进行无创预测。
Congest Heart Fail. 2010 Nov-Dec;16(6):249-53. doi: 10.1111/j.1751-7133.2010.00191.x. Epub 2010 Oct 21.
9
Utility of a bedside acoustic cardiographic model to predict elevated left ventricular filling pressure.床边声学心动模型预测左心室充盈压升高的效用。
Emerg Med J. 2010 Sep;27(9):677-82. doi: 10.1136/emj.2009.080150. Epub 2010 Jun 1.
10
Bedside prediction of increased filling pressure using acoustic electrocardiography.使用心音心电图进行床边充盈压升高的预测。
Am J Emerg Med. 2009 May;27(4):397-408. doi: 10.1016/j.ajem.2008.03.014.