Background. ⢠Current aortic systolic blood pressure (SBP) estimation ... (SP), diastolic (DP), mean (MP) and pulse pressure (PP) from radial artery (RA) features ...
DEPARTMENT OF BIOMEDICAL SCIENCES
Faculty of Medicine and Health Sciences
Application of neural networks for estimation of aortic systolic pressure from peripheral systolic and diastolic pressure 1
2,3
Hanguang Xiao , Ahmad Qasem
2
2
Mark Butlin , Alberto P Avolio .
1
Chongqing University of Technology, Banan District, Chongqing, China. 2 Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, Australia. 3 AtCor Medical, Sydney, Australia.
Background • •
Results
Current aortic systolic blood pressure (SBP) estimation methods require recording of a peripheral pressure waveform. This study investigates the possibility of aortic SBP estimation from peripheral SBP and diastolic blood pressure (DBP) using artificial neural networks (ANN) with (ANNSBP,DBP,HR) and without heart rate (ANNSBP,DBP) (HR).
Methods •
•
Ten-fold cross validation was applied to invasive, simultaneously recorded aortic and radial pressure during rest and nitroglycerin infusion (n=62 patients), drawn from a patient cohort previously reported [1]. The results of the ANN models were compared to an ANN model using additional waveform features (ANNwaveform), to an N-point moving average method (NPMA) and to an existing, validated generalized transfer function (GTF).
Figure 1: Illustration of the methods for estimating the central aortic pressure from the radial artery pressure waveform parameters including amplitude (A) and timing (T) features, and the end systolic period (ESP).
Figure 3: Estimated aortic SBP for all methods was on average
