Why the study?
Does a novel multichannel wrist pulse system improve disease classification compared to conventional pulse systems in healthy subjects and diabetic patients?
Does a novel multichannel wrist pulse system improve disease classification compared to conventional pulse systems in healthy subjects and diabetic patients?
A novel multichannel wrist pulse system combining pressure and photoelectric sensors improves the objective acquisition of pulse information for disease classification.
Answers are grounded in this paper and cite their sources.
May support multichannel pulse sensing for diabetes classification; leaves open prospective validation before clinical adoption.
Wrist pulse has long been recognized as an important physiological symbol for judging the health status of patients. It is regarded as one of the four diagnosis techniques in traditional Chinese medicine and plays an important role in disease analysis. However, the pulse diagnosis skill is subjective and needs decades of experience to master. To make the pulse diagnosis more objective and easily understood by practitioners, many pulse-taking platforms were invented, but several disadvantages, including the simplicity and insufficiency, limit the applications. This paper proposes a novel wrist pulse system for the pulse diagnosis to overcome the shortcomings in previous pulse platforms. We integrated a pressure sensor with a photoelectric sensor to make a fusion sensor to feel pulse in different approaches and designed the multichannel sensor arrays structure to acquire more pulse information. Finally, pulse analysis algorithm and classification methods are introduced. Experiments on disease classification are carried out to test the system performance with multichannel and different sensor arrays. The results show that the novel system is not only able to distinguish between healthy pulse samples and subjects suffering from diabetes, but also good at obtaining more information than the conventional pulse system with single channel or simplex-type sensor.
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Wang et al. (2015) studied this question.
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