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The attempt of swallowing discrimination by heart rate variability using machine learning
Author(s) -
Egashira Yosuke,
Bando Shizuka,
Oiwa Kosuke,
Nozawa Akio
Publication year - 2017
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.22572
Subject(s) - swallowing , convolutional neural network , computer science , heart rate variability , physical medicine and rehabilitation , heart rate , artificial intelligence , medicine , machine learning , surgery , blood pressure
The number of patients with lifestyle‐related diseases has increased because of irregular dietary life, which is attributed in part to social issues. For maintaining a regular dietary life, managing the dietary history and information is needed. The current method of maintaining rhythm in regular dietary life is cumbersome and has not generally diffused. This paper focuses on swallowing, and experiments evaluate the detection method of swallowing by longitudinal data in heart rate variability with a device capable of measuring biological information. As a result, the detection model for the convolutional neural network has an average accuracy of 83.2%.