基于神经网络的光体积描记图波形分析用于血管张力和动脉血压分类的研究。

Photoplethysmography waveform analysis for classification of vascular tone and arterial blood pressure: Study based on neural networks.

机构信息

Laboratorio de Bioingeniería, ICYTE-CONICET, Facultad de Ingeniería, Universidad Nacional de Mar del Plata, Buenos Aires, Argentina.

Departamento de Anestesiología, Hospital Privado de Comunidad, Mar del Plata, Buenos Aires, Argentina.

出版信息

Rev Esp Anestesiol Reanim (Engl Ed). 2023 Apr;70(4):209-217. doi: 10.1016/j.redare.2022.01.010. Epub 2023 Feb 27.

DOI:10.1016/j.redare.2022.01.010

PMID:36868265

Abstract

BACKGROUND

To test whether a Shallow Neural Network (S-NN) can detect and classify vascular tone dependent changes in arterial blood pressure (ABP) by advanced photopletysmographic (PPG) waveform analysis.

METHODS

PPG and invasive ABP signals were recorded in 26 patients undergoing scheduled general surgery. We studied the occurrence of episodes of hypertension (systolic arterial pressure (SAP) >140 mmHg), normotension and hypotension (SAP < 90 mmHg). Vascular tone according to PPG was classified in two ways: 1) By visual inspection of changes in PPG waveform amplitude and dichrotic notch position; where Classes I-II represent vasoconstriction (notch placed >50% of PPG amplitude in small amplitude waves), Class III normal vascular tone (notch placed between 20-50% of PPG amplitude in normal waves) and Classes IV-V-VI vasodilation (notch <20% of PPG amplitude in large waves). 2) By an automated analysis, using S-NN trained and validated system that combines seven PPG derived parameters.

RESULTS

The visual assessment was precise in detecting hypotension (sensitivity 91%, specificity 86% and accuracy 88%) and hypertension (sensitivity 93%, specificity 88% and accuracy 90%). Normotension presented as a visual Class III (III-III) (median and 1st-3rd quartiles), hypotension as a Class V (IV-VI) and hypertension as a Class II (I-III); all p < .0001. The automated S-NN performed well in classifying ABP conditions. The percentage of data with correct classification by S-ANN was 83% for normotension, 94% for hypotension, and 90% for hypertension.

CONCLUSIONS

Changes in ABP were correctly classified automatically by S-NN analysis of the PPG waveform contour.

摘要

背景

通过先进的光体积描记（PPG）波形分析，测试浅层神经网络（S-NN）是否可以检测和分类动脉血压（ABP）中依赖血管张力的变化。

方法

在 26 名接受择期普通外科手术的患者中记录 PPG 和侵入性 ABP 信号。我们研究了高血压（收缩压（SAP）>140mmHg）、正常血压和低血压（SAP<90mmHg）发作的情况。根据 PPG 分类血管张力有两种方法：1）通过 PPG 波形幅度和双折射陷波位置变化的视觉检查；其中 I-II 类代表血管收缩（在小幅度波中，陷波放置在 PPG 幅度的>50%处），III 类代表正常血管张力（在正常波中，陷波放置在 PPG 幅度的 20-50%之间），IV-V-VI 类代表血管舒张（在大波波中，陷波<PPG 幅度的 20%）。2）通过训练和验证系统的 S-NN 自动分析，该系统结合了七个 PPG 衍生参数。

结果

视觉评估在检测低血压（敏感性 91%，特异性 86%和准确性 88%）和高血压（敏感性 93%，特异性 88%和准确性 90%）方面非常准确。正常血压表现为视觉 III 类（III-III）（中位数和 1 至 3 四分位数），低血压表现为 V 类（IV-VI），高血压表现为 II 类（I-III）；所有 p<0.0001。自动 S-NN 在分类 ABP 条件方面表现良好。S-ANN 正确分类数据的百分比为正常血压 83%，低血压 94%，高血压 90%。