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

Abstract

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 (R² = 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.

Keywords: Mini-Mental State Examination; community subjects; dementia; machine learning; pulse; spectral analysis.

Figure 1

Typical measured pulse waveforms. (a) AD patient; (b) Control; (c) Community Site 1; (d) Community Site 2; (e) Young.

Figure 2

Procedure for information processing.

Figure 3

Comparisons of BPW harmonic indices of AD patients, control, community (Sites A and B), and young subjects: (a) C_n, (b) CV_n, (c) P_n, and (d) P_n_SD. Data are mean and standard-deviation values. C₆–C₁₀ values have been multiplied by 10 to make the differences clearer. p values are listed in Table 3.

Figure 4

MLP analysis results for comparisons of BPW indices between AD patients and Group Control. Training and validation accuracy plots, AUC, and contradiction matrix are presented for the threefold cross-validation. The mean accuracy, sensitivity, specificity, and AUC were 70.32%, 0.68, 0.72, and 0.70, respectively. “1” indicates AD patients and “0” indicates Control. (a) 1st part; (b) 2nd part; (c) 3rd part of the threefold cross-validation.

Figure 5

Correlation between the prediction probability and MMSE score. Group AD and Control were used as training data. Community subjects at Sites A and B, and Group Young were used as test subjects. (a), There was a significant negative correlation for the testing community subjects (R² = 0.36, p < 0.05 by F-test). (b), When the young group was excluded, there was still a significant negative correlation (R² = 0.31, p < 0.05 by F-test).