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A neural network for estimation of aortic pressure from the radial artery pressure pulse

A neural network is developed to estimate aortic pressure from the radial artery pressure pulse waveform. Invasively measured aortic and radial artery pressure in 51 adult subjects were used to train the network. Tests in a separate group of 21 subjects of similar age range showed a high correlation (r>0.93) between measured and estimated systolic, diastolic and pulse pressure, with mean absolute errors (%) of 2.5/spl plusmn/0.3, 3.5/spl plusmn/0.6, 4.8/spl plusmn/0.7 respectively. This method has potential applications in obtaining accurate estimates of central aortic pressure values from non-invasive radial artery pulse measurements. Such neural networks can be trained in specific subgroups (e.g. diabetics) to improve the estimation of central aortic pressure from the peripheral pulse.