Discrimination of the Cognitive Function of Community Subjects Using the Arterial Pulse Spectrum and Machine-Learning Analysis

Early identification of cognitive impairment would allow affected patients to receive care at earlier stage. Changes in the arterial stiffness have been identified as a prominent pathological feature of dementia. This study aimed to verify if applying machine-learning analysis to spectral indices of...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2022-01, Vol.22 (3), p.806
Main Authors: Hsiu, Hsin, Lin, Shun-Ku, Weng, Wan-Ling, Hung, Chaw-Mew, Chang, Che-Kai, Lee, Chia-Chien, Chen, Chao-Tsung
Format: Article
Language:English
Subjects:
Alzheimer Disease
Blood pressure
Coefficient of variation
Cognition
Cognitive ability
Cognitive Dysfunction - diagnosis
community subjects
Dementia
Discriminant analysis
Disease
Humans
Machine Learning
Mini-Mental State Examination
pulse
Questionnaires
Signal processing
Spectra
spectral analysis
Stiffness
Vascular Stiffness
Waveforms
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Summary:Early identification of cognitive impairment would allow affected patients to receive care at earlier stage. Changes in the arterial stiffness have been identified as a prominent pathological feature of dementia. This study aimed to verify if applying machine-learning analysis to spectral indices of the arterial pulse waveform can be used to discriminate different cognitive conditions of community subjects. 3-min Radial arterial blood pressure waveform (BPW) signals were measured noninvasively in 123 subjects. Eight machine-learning algorithms were used to evaluate the following 4 pulse indices for 10 harmonics (total 40 BPW spectral indices): amplitude proportion and its coefficient of variation; phase angle and its standard deviation. Significant differences were noted in the spectral pulse indices between Alzheimer's-disease patients and control subjects. Using them as training data (AUC = 70.32% by threefold cross-validation), a significant correlation ( = 0.36) was found between the prediction probability of the test data (comprising community subjects at two sites) and the Mini-Mental-State-Examination score. This finding illustrates possible physiological connection between arterial pulse transmission and cognitive function. The present findings from pulse-wave and machine-learning analyses may be useful for discriminating cognitive condition, and hence in the development of a user-friendly, noninvasive, and rapid method for the early screening of dementia.
ISSN:1424-8220
1424-8220
DOI:10.3390/s22030806