Sphygmopalpation Using Tactile Robotic Fingers Reveals Fundamental Arterial Pulse Patterns

摘要

Sphygmopalpation at specific locations of human wrists has been used as a medical diagnostics technique in China since the Han Dynasty (202 BC - 220 AD) and it is now generally accepted that traditional Chinese medicine (TCM) doctors are able to decipher at least 28 fundamental pulse patterns among all patients using their fingertips. However, unlike collecting EEG (electroencephalography), ECG (electrocardiography), and EMG (electromyography) signals, there is no standardization on how the arterial pulse waves from the TCM sphygmopalpation methods should be digitalized and analyzed. We have developed a pulse sensing platform for studying and digitalizing arterial pulse patterns via a TCM approach. This platform consists of a robotic hand with three pressure-feedback-controlled robotic fingers (each with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4\times 6$ </tex-math></inline-formula> sensing pixel arrays) for pulse measurement and an artificial neural network (ANN) for pulse pattern recognition. Data analyses reveal that 3 types of consistent pulse patterns, i.e., “HUA” ( <fig orientation="portrait" position="float" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <graphic orientation="portrait" position="float" xlink:href="kong9-3144475.eps"/> </fig> ), “XI” ( <fig orientation="portrait" position="float" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <graphic orientation="portrait" position="float" xlink:href="kong10-3144475.eps"/> </fig> ), and “CHEN” ( <fig orientation="portrait" position="float" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <graphic orientation="portrait" position="float" xlink:href="kong11-3144475.eps"/> </fig> ) – key fundamental pulse patterns described by TCM doctors – could be identified in a selected group of subjects. The classification rates are 99.1% in the training process and 97.4% in testing result for these 3 basic pulse patterns. The results will lead to further development of a high-level artificial intelligence system incorporating knowledge from TCM – the robotics finger system could become a standard clinical equipment for digitalizing and visualizing human arterial pulses.