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

Autor: Echeverría NI; Laboratorio de Bioingeniería, ICYTE-CONICET, Facultad de Ingeniería, Universidad Nacional de Mar del Plata, Buenos Aires, Argentina., Scandurra AG; Laboratorio de Bioingeniería, ICYTE-CONICET, Facultad de Ingeniería, Universidad Nacional de Mar del Plata, Buenos Aires, Argentina., Acosta CM; Departamento de Anestesiología, Hospital Privado de Comunidad, Mar del Plata, Buenos Aires, Argentina., Meschino GJ; Laboratorio de Bioingeniería, ICYTE-CONICET, Facultad de Ingeniería, Universidad Nacional de Mar del Plata, Buenos Aires, Argentina., Suarez Sipmann F; Laboratorio Hedenstierna, Departamento de Ciencias quirúrgicas, Universidad de Uppsala, Uppsala, Sweden; Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Madrid, Spain; Departamento de Cuidados Críticos, Hospital Universitario de La Princesa, Madrid, Spain., Tusman G; Departamento de Anestesiología, Hospital Privado de Comunidad, Mar del Plata, Buenos Aires, Argentina. Electronic address: gtusman@hotmail.com.
Jazyk: angličtina
Zdroj: Revista espanola de anestesiologia y reanimacion [Rev Esp Anestesiol Reanim (Engl Ed)] 2023 Apr; Vol. 70 (4), pp. 209-217. Date of Electronic Publication: 2023 Feb 27.
DOI: 10.1016/j.redare.2022.01.010
Abstrakt: 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.
(Copyright © 2022 Sociedad Española de Anestesiología, Reanimación y Terapéutica del Dolor. Publicado por Elsevier España, S.L.U. All rights reserved.)
Databáze: MEDLINE
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