Palpation localization of radial artery based on 3-dimensional convolutional neural networks

Abstract

Palpation localization is essential for detecting physiological parameters of the radial artery for pulse diagnosis of Traditional Chinese Medicine (TCM). Detecting signal or applying pressure at the wrong location can seriously affect the measurement of pulse waves and result in misdiagnosis. In this paper, we propose an effective and high accuracy regression model using 3-dimensional convolution neural networks (CNN) processing near-infrared picture sequences to locate radial artery upon radius at the wrist. Comparing with early studies using 2-dimensional models, 3Dcnn introduces temporal features with the third dimension to leverage pulsation rhythms, and had achieved superior performance accuracy as 0.87 within 50 pixels at testing resolution of 1024 × 544. Model visualization shows that the additional dimension of the temporal convolution highlights dynamic changes within image sequences. This study presents the great potential of our constructed model to be applied in real wrist palpation location scenarios to bring the key convenience for pulse diagnosis.