幅谱面积取决于心电图的幅度:评估不同的归一化方法。

IF 2.3 4区 医学 Q3 BIOPHYSICS
Luiz E V Silva, Hunter A Gaudio, Nicholas J Widmann, Rodrigo M Forti, Viveknarayanan Padmanabhan, Kumaran Senthil, Julia C Slovis, Constantine D Mavroudis, Yuxi Lin, Lingyun Shi, Wesley B Baker, Ryan W Morgan, Todd J Kilbaugh, Fuchiang Rich Tsui, Tiffany S Ko
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引用次数: 0

摘要

目的:心室颤动(VF)期间心电图(ECG)波形的振幅频谱区(AMSA)有望成为心肺复苏(CPR)期间除颤成功的预测指标。然而,AMSA 依赖于心电图波形的幅度,这引起了人们对不同环境下可重复性的担忧,因为这可能会带来幅度偏差。本研究旨在评估不同的 AMSA 归一化方法及其对消除偏差同时保留预测价值的影响:方法:在窒息相关 VF 心跳骤停和心肺复苏模型中记录 118 头仔猪(1-2 个月大)的心电图。初始子集(91/118)使用一台设备(设备 1)记录,其余仔猪使用第二台设备(设备 2)记录。对原始 AMSA 和三个 ECG 幅值指标进行估算,以评估设备之间与幅值相关的偏差。评估了五种 AMSA 归一化方法消除检测到的偏差和对除颤成功率进行分类的能力:主要结果:与设备 1 相比,设备 2 显示出明显较低的心电图幅度和原始 AMSA。基于心肺复苏的 AMSA 归一化方法减轻了设备相关偏差。以心肺复苏第 1 分钟的平均 AMSA(AMSA1m-cpr)归一化的原始 AMSA 在除颤成功与否的分类中表现出最佳灵敏度和特异性。虽然两种设备平衡灵敏度和特异性的最佳 AMSA1m-cpr 阈值是一致的,但两种设备的最佳原始 AMSA 阈值却各不相同。两种设备的 AMSA1m-cpr 接收者操作特征曲线下面积与原始 AMSA 没有显著差异(设备 1:0.74 vs. 0.88,P=0.14;设备 2:0.56 vs. 0.59,P=0.81):与原始 AMSA 不同,AMSA1m-cpr 在不同设备上显示出一致的结果,同时保持了对除颤成功的预测价值。这种一致性对 AMSA 的广泛应用以及未来制定除颤成功的最佳 AMSA 阈值指南具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Amplitude spectrum area is dependent on the electrocardiogram magnitude: evaluation of different normalization approaches.

Objective.Amplitude Spectrum Area (AMSA) of the electrocardiogram (ECG) waveform during ventricular fibrillation (VF) has shown promise as a predictor of defibrillation success during cardiopulmonary resuscitation (CPR). However, AMSA relies on the magnitude of the ECG waveform, raising concerns about reproducibility across different settings that may introduce magnitude bias. This study aimed to evaluate different AMSA normalization approaches and their impact on removing bias while preserving predictive value.Approach.ECG were recorded in 118 piglets (1-2 months old) during a model of asphyxia-associated VF cardiac arrest and CPR. An initial subset (91/118) was recorded using one device (Device 1), and the remaining piglets were recorded in the second device (Device 2). Raw AMSA and three ECG magnitude metrics were estimated to assess magnitude-related bias between devices. Five AMSA normalization approaches were assessed for their ability to remove detected bias and to classify defibrillation success.Main results.Device 2 showed significantly lower ECG magnitude and raw AMSA compared to Device 1. CPR-based AMSA normalization approaches mitigated device-associated bias. Raw AMSA normalized by the average AMSA in the 1st minute of CPR (AMSA1m-cpr) exhibited the best sensitivity and specificity for classification of successful and unsuccessful defibrillation. While the optimal AMSA1m-cprthresholds for balanced sensitivity and specificity were consistent across both devices, the optimal raw AMSA thresholds varied between the two devices. The area under the receiver operating characteristic curve for AMSA1m-cprdid not significantly differ from raw AMSA for both devices (Device 1: 0.74 vs. 0.88,P= 0.14; Device 2: 0.56 vs. 0.59,P= 0.81).Significance.Unlike raw AMSA, AMSA1m-cprdemonstrated consistent results across different devices while maintaining predictive value for defibrillation success. This consistency has important implications for the widespread use of AMSA and the development of future guidelines on optimal AMSA thresholds for successful defibrillation.

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来源期刊
Physiological measurement
Physiological measurement 生物-工程:生物医学
CiteScore
5.50
自引率
9.40%
发文量
124
审稿时长
3 months
期刊介绍: Physiological Measurement publishes papers about the quantitative assessment and visualization of physiological function in clinical research and practice, with an emphasis on the development of new methods of measurement and their validation. Papers are published on topics including: applied physiology in illness and health electrical bioimpedance, optical and acoustic measurement techniques advanced methods of time series and other data analysis biomedical and clinical engineering in-patient and ambulatory monitoring point-of-care technologies novel clinical measurements of cardiovascular, neurological, and musculoskeletal systems. measurements in molecular, cellular and organ physiology and electrophysiology physiological modeling and simulation novel biomedical sensors, instruments, devices and systems measurement standards and guidelines.
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