Deep generative model with domain adversarial training for predicting arterial blood pressure waveform from photoplethysmogram signal. (September 2021)

• Record Type: Journal Article
• Authors: Qin, Keke; Huang, Wu; Zhang, Tao
• Is Part Of: Biomedical signal processing and control. Volume 70(2021)
• Publication Date: 2021-09
• Subjects: Generative model -- Photoplethysmogram (PPG) -- Arterial blood pressure (ABP) -- Domain adversarial training -- Signal conversion; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102972
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Background and Motivations: Continuous blood pressure (BP) monitoring is of critical importance to health state tracking and disease prevention. However, current mainstream BP measurement approaches are cuff-based, which is inconvenient and limit its usage scenarios. Predicting arterial blood pressure (ABP) could provide richer information than isolated BP values. The individual differences among data may hinder training. Methods: A novel continuous, non-invasive and cuff-less approach is presented for generating ABP waveform using only raw photoplethysmogram (PPG) signal, from a signal conversion perspective, where a convolution-based deep autoencoder (DAE) model is developed. To overcome individual differences, Multi-domain adversarial training is merged with DAE (abbr. RDAE) to learn cross-domain features, and partial data is further used to calibrate (optional) the general model. Results: The mean absolute error (MAE) of uncalibrated RDAE reached 7.945, 4.114 and 3.834 mmHg in systolic BP (SBP), diastolic BP (DBP) and mean BP (MBP) prediction. After using 80 s data for calibration, the MAE of RDAE reduced to 5.424, 3.144 and 2.885 mmHg accordingly. Conclusion: Owning to the high-quality converted ABP segments, the resulting estimated BP is accurate. According to the BHS standard, RDAE achieved Grade C, Grade A and Grade A for SBP, DBP and MBP prediction, and the calibrated RDAE achieved Grade B, Grade A, Grade A accordingly. Significance: Both domain adversarial training and calibration improve the performance in varying degrees. RDAE is competitive to other mainstream regression-based deep learning methods, while with fewer model parameters, and to other representative machine learning methods, while no need of complicated feature engineering. Graphical abstract: Highlights: We try to use PPG signal to predict ABP waveform, instead of blood pressure value. Domain adversarial training is introduced to conquer individual differences. Different model configurations were presented by analyzing several key parameters. We experimentally confirmed the contribution of calibration procedure. We firstly illustrates the model in signal conversion through visualization skills.