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Background: To test whether a Shallow Neural Network (S-NN) can detect and classify vasculartone 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 accordingto 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 placedbetween 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 andvalidated system that combines seven PPG derived parameters.

Results: The visual assessment was precise in detecting hypotension (sensitivity 91%, specificity86% and accuracy 88%) and hypertension (sensitivity 93%, specificity 88% and accuracy 90%). Normotension presented as a visual Class III (III-III) (median and 1st-3rdquartiles), hypotensionas a Class V (IV-VI) and hypertension as a Class II (I-III); all p < 0.0001. The automated S-NNperformed well in classifying ABP conditions. The percentage of data with correct classificationby 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 PPGwaveform contour.