KID-PPG: Knowledge Informed Deep Learning for Extracting Heart Rate from a Smartwatch

TL;DR

KID-PPG is proposed, a knowledge-informed deep learning model that integrates expert knowledge through adaptive linear filtering, deep probabilistic inference, and data augmentation that demonstrates a significant performance improvement in heart rate tracking through the incorporation of prior knowledge into deep learning models.

Abstract

Accurate extraction of heart rate from photoplethysmography (PPG) signals remains challenging due to motion artifacts and signal degradation. Although deep learning methods trained as a data-driven inference problem offer promising solutions, they often underutilize existing knowledge from the medical and signal processing community. In this paper, we address three shortcomings of deep learning models: motion artifact removal, degradation assessment, and physiologically plausible analysis of the PPG signal. We propose KID-PPG, a knowledge-informed deep learning model that integrates expert knowledge through adaptive linear filtering, deep probabilistic inference, and data augmentation. We evaluate KID-PPG on the PPGDalia dataset, achieving an average mean absolute error of 2.85 beats per minute, surpassing existing reproducible methods. Our results demonstrate a significant performance improvement in heart rate tracking through the incorporation of prior knowledge into deep learning models. This approach shows promise in enhancing various biomedical applications by incorporating existing expert knowledge in deep learning models.

Figures (10)

• Figure 1: Knowledge-Informed deep-learning (DL) for heart-rate extraction (KID-PPG) incorporates prior knowledge on motion artifacts (MA), unrecoverable blood volume pulse (BVP) samples, and BVP morphology into DL models through three mechanisms: linear filtering, probabilistic inference, and data augmentation. The input of KID-PPG consists of a PPG signal along with an accelerometer signal. The linear filter ($\hat{f}_{mix}$) separates the BVP from MA to produce a filtered PPG signal, which serves as an input to the DL model ($h$). The model uses probabilistic inference to assess the degradation of the PPG signal and uses data augmentation to better characterize the BVP morphology.
• Figure 2: Linear model for separating the motion artifacts (MA) from the blood volume pulse (BVP) components in the PPG signal. A linear two-layer convolutional network takes the three axis of the accelerometer signal as an input ($\textbf{x}_{acc}$ along with the PPG ($\textbf{x}_{ppg}$) to produce a filtered PPG signal ($\hat{\textbf{x}}_{bvp}$).
• Figure 3: Probabilistic HR inference example on a clean (left) vs MA degraded PPG sample (right). The top row shows the raw PPG data. The bottom row shows the FFT of the PPG. The example is taken from S6 of PPGDalia. The green circles indicate the true ECG HR for the two samples. The probability density functions of $\mathcal{N}(\mu_{hr}, \sigma_{hr}^2)$ are overlayed on the frequency representations of the corresponding PPG sample. The error classification probability, $p_{error}$ is also illustrated. In the right sample the non-probabilistic point estimate HR inference of DL model is represented as a black vertical line (Q-PPG).
• Figure 4: Inference after filtering the BVP component out of the PPG. Example taken from S6 of PPGDalia. (a) Inferences across the entire session from Q-PPG (red), probabilistic (blue) and guided probabilistic (orange). For the probabilistic models, the range of one standard deviation is also presented. (b) Sample example with initially clean PPG (grey) and synthetically degraded (black). (c) Frequency domain representation of the example sample and HR inferences from the three models. True HR is presented with a green circle. Both Q-PPG and probabilistic models estimate HR close to the ground truth, indicating potential learned shortcuts since there is no heart rate information in the signals. In contrast, the guided probabilistic model estimates a large standard deviation identifying the lack of relevant information.
• Figure 5: KID-PPG network architecture. $W^*$ indicates weight sharing between the convolution blocks for the $\hat{\textbf{x}}_{bvp_i}$ and $\hat{\textbf{x}}_{bvp_{i-1}}$ branches.