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从体表心电图提取呼吸信号的三种方法的发展:综述

Development of three methods for extracting respiration from the surface ECG: a review.

作者信息

Helfenbein Eric, Firoozabadi Reza, Chien Simon, Carlson Eric, Babaeizadeh Saeed

机构信息

Advanced Algorithm Research Center, Philips Healthcare, Andover, MA, USA.

Advanced Algorithm Research Center, Philips Healthcare, Andover, MA, USA.

出版信息

J Electrocardiol. 2014 Nov-Dec;47(6):819-25. doi: 10.1016/j.jelectrocard.2014.07.020. Epub 2014 Aug 4.

Abstract

BACKGROUND

Respiration rate (RR) is a critical vital sign that can be monitored to detect acute changes in patient condition (e.g., apnea) and potentially provide an early warning of impending life-threatening deterioration. Monitoring respiration signals is also critical for detecting sleep disordered breathing such as sleep apnea. Additionally, analyzing a respiration signal can enhance the quality of medical images by gating image acquisition based on the same phase of the patient's respiratory cycle. Although many methods exist for measuring respiration, in this review we focus on three ECG-derived respiration techniques we developed to obtain respiration from an ECG signal.

METHODS

The first step in all three techniques is to analyze the ECG to detect beat locations and classify them. 1) The EDR method is based on analyzing the heart axis shift due to respiration. In our method, one respiration waveform value is calculated for each normal QRS complex by measuring the peak to QRS trough amplitude. Compared to other similar EDR techniques, this method does not need removal of baseline wander from the ECG signal. 2) The RSA method uses instantaneous heart rate variability to derive a respiratory signal. It is based on the observed respiratory sinus arrhythmia governed by baroreflex sensitivity. 3) Our EMGDR method for computing a respiratory waveform uses measurement of electromyogram (EMG) activity created by respiratory effort of the intercostal muscles and diaphragm. The ECG signal is high-pass filtered and processed to reduce ECG components and accentuate the EMG signal before applying RMS and smoothing.

RESULTS

Over the last five years, we have performed six studies using the above methods: 1) In 1907 sleep lab patients with >1.5M 30-second epochs, EDR achieved an apnea detection accuracy of 79%. 2) In 24 adult polysomnograms, use of EDR and chest belts for RR computation was compared to airflow RR; mean RR error was EDR: 1.8±2.7 and belts: 0.8±2.1. 3) During cardiac MRI, a comparison of EMGDR breath locations to the reference abdominal belt signal yielded sensitivity/PPV of 94/95%. 4) Another comparison study for breath detection during MRI yielded sensitivity/PPV pairs of EDR: 99/97, RSA: 79/78, and EMGDR: 89/86%. 5) We tested EMGDR performance in the presence of simulated respiratory disease using CPAP to produce PEEP. For 10 patients, no false breath waveforms were generated with mild PEEP, but they appeared in 2 subjects at high PEEP. 6) A patient monitoring study compared RR computation from EDR to impedance-derived RR, and showed that EDR provides a near equivalent RR measurement with reduced hardware circuitry requirements.

摘要

背景

呼吸频率(RR)是一项关键的生命体征,可通过监测来检测患者病情的急性变化(如呼吸暂停),并有可能对即将发生的危及生命的病情恶化发出早期预警。监测呼吸信号对于检测睡眠呼吸障碍(如睡眠呼吸暂停)也至关重要。此外,通过基于患者呼吸周期的同一阶段对图像采集进行门控,分析呼吸信号可以提高医学图像的质量。尽管存在许多测量呼吸的方法,但在本综述中,我们重点介绍我们开发的三种从心电图信号中获取呼吸的基于心电图的呼吸技术。

方法

所有这三种技术的第一步都是分析心电图以检测心跳位置并对其进行分类。1)EDR方法基于分析由于呼吸引起的心脏轴偏移。在我们的方法中,通过测量从峰值到QRS波谷的幅度,为每个正常QRS复合波计算一个呼吸波形值。与其他类似的EDR技术相比,该方法无需从心电图信号中去除基线漂移。2)RSA方法利用瞬时心率变异性来推导呼吸信号。它基于由压力反射敏感性控制的观察到的呼吸性窦性心律不齐。3)我们用于计算呼吸波形的EMGDR方法利用对肋间肌和膈肌呼吸努力产生的肌电图(EMG)活动的测量。在应用均方根(RMS)和平滑之前,对心电图信号进行高通滤波和处理,以减少心电图成分并突出EMG信号。

结果

在过去五年中,我们使用上述方法进行了六项研究:1)在1907名睡眠实验室患者中,有超过150万个30秒的时段,EDR的呼吸暂停检测准确率达到79%。2)在24份成人多导睡眠图中,将EDR和胸带用于RR计算与气流RR进行了比较;RR的平均误差为:EDR为1.8±2.7,胸带为0.8±2.1。3)在心脏磁共振成像(MRI)期间,将EMGDR的呼吸位置与参考腹带信号进行比较,灵敏度/阳性预测值为94/95%。4)另一项MRI期间呼吸检测的比较研究得出的灵敏度/阳性预测值对为:EDR为99/97,RSA为79/78,EMGDR为89/86。5)我们使用持续气道正压通气(CPAP)产生呼气末正压(PEEP),在存在模拟呼吸系统疾病的情况下测试了EMGDR的性能。对于10名患者,轻度PEEP时未产生假呼吸波形,但在2名患者中,高PEEP时出现了假呼吸波形。6)一项患者监测研究将EDR计算的RR与阻抗衍生的RR进行了比较,结果表明EDR提供了近乎等效的RR测量,同时降低了硬件电路要求。

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