Machine-Learning Classification of Pulse Waveform Quality

Abstract

Pulse measurements made using wearable devices can aid the monitoring of human physiological condition. Accurate estimation of waveforms is often difficult for nonexperts; motion artifacts may occur during tonometry measurements when the skin-sensor contact pressure is insufficient. An alternative approach is to extract only high-quality pulses for use in index calculations. The present study aimed to determine the effectiveness of using machine-learning analysis in discriminating between high-quality and low-quality pulse waveforms induced by applying different contact pressures. Radial blood pressure waveform (BPW) signals were measured noninvasively in healthy young subjects using a strain-gauge transducer. One-minute-long trains of pulse data were measured when applying the appropriate contact pressure (67.80 ± 1.55 mmHg) and a higher contact pressure (151.80 ± 3.19 mmHg). Eight machine-learning algorithms were employed to evaluate the following 40 harmonic pulse indices: amplitude proportions and their coefficients of variation and phase angles and their standard deviations. Significant differences were noted in BPW indices between applying appropriate and higher skin-surface contact pressures. The present appropriate contact pressure could not only provide a suitable holding force for the wearable device but also helped to maintain the physiological stability of the underlying tissues. Machine-learning analysis provides an effective method for distinguishing between the high-quality and low-quality pulses with excellent discrimination performance (leave-one-subject-out test: random-forest AUC = 0.96). This approach will aid the development of an automatic screening method for waveform quality and thereby improve the noninvasive acquisition reliability. Other possible interfering factors in practical applications can also be systematically studied using a similar procedure.

Keywords: contacting pressure; machine learning; pulse; spectral analysis; waveform quality.

Figure 1

Typical measured pulse waveforms for (a) Data A (high quality) and (b) Data B (low quality).

Figure 2

Flowchart of information processing in the study. Forty indices (C_n, P_n, CV_n, and P_n_SD; n = 1–10) were calculated, which were used as the features for the information processing. Threefold cross-validation was used to evaluate the model performance, and then leave-one-subject-out analysis was used to test the model accuracy.

Figure 3

Comparisons of harmonic indices between applying the appropriate and higher contact pressures: C_n, CV_n, P_n, and P_n_SD. Data are mean and standard-deviation values. C₆–C₁₀ values have been multiplied by 10 to make the differences clearer. “*” indicates p < 0.05; “+” indicates 0.05 < p < 0.1. All C_n and CV₁–CV₉ were significantly larger when applying higher-than-appropriate contact pressure. Regarding phase-angle indices, there were significant differences in P₂, P₄, and P₆–P₁₀. Regarding P_n_SD indices, P₂_SD–P₁₀_SD were either significantly (p < 0.05) or marginally (0.05 < p < 0.1) larger when applying a higher-than-appropriate contact pressure.

Figure 3

Comparisons of harmonic indices between applying the appropriate and higher contact pressures: C_n, CV_n, P_n, and P_n_SD. Data are mean and standard-deviation values. C₆–C₁₀ values have been multiplied by 10 to make the differences clearer. “*” indicates p < 0.05; “+” indicates 0.05 < p < 0.1. All C_n and CV₁–CV₉ were significantly larger when applying higher-than-appropriate contact pressure. Regarding phase-angle indices, there were significant differences in P₂, P₄, and P₆–P₁₀. Regarding P_n_SD indices, P₂_SD–P₁₀_SD were either significantly (p < 0.05) or marginally (0.05 < p < 0.1) larger when applying a higher-than-appropriate contact pressure.

Figure 4

Comparisons of harmonic indices between Data A (high quality) and Data B (low quality): C_n, CV_n, P_n, and P_n_SD. Data are mean and standard-deviation values. C₆–C₁₀ values have been multiplied by 10 to make the differences clearer. “*” indicates p < 0.05. “+” indicates 0.05 < p < 0.1. C_n, CV_n, and P_n_SD were all significantly larger for Data B than for Data A. Regarding phase-angle indices, there were significant differences in P₂, P₅, and P₇–P₁₀.