MLP-BP: A novel framework for cuffless blood pressure measurement with PPG and ECG signals based on MLP-Mixer neural networks

• The multi-filter to multi-channel approach is proposed for preprocessing PPG and ECG signal. • The proposed method (MLP-BP) is a novel framework based on MLP-Mixer for estimating BP task. • gMLP-BP and MLPlstm-BP are both end-to-end pipelines without any handcrafted feature extraction. • The performance of three distinct input signal (PPG, ECG, or PPG & ECG) are investigated. • Experiments demonstrate that MLP-BP attains excellent results compared with SOTA methods. High blood pressure (BP) is a major source of death worldwide as it slows down the flow of blood and oxygen, and leads to various chronic diseases such as chest pain (angina), heart disease and heart failure. Therefore, regular measurement and monitoring of BP is an essential element of the home healthy settings, especially for the elderly. In this study, a novel deep neural network (called MLP-BP, which including gMLP-BP and MLPlstm-BP) adapted from MLP-Mixer is proposed to estimate BP from plethysmography (PPG) and electrocardiograph (ECG) signal. More precisely, a novel multi-filter to multi-channel (MFMC) is presented for preprocessing ECG and PPG signals, namely using various filters and filtering parameters to handle with ECG and PPG, and integrating the filtered bio-signals into multi-channel data. Then, the multi-channel data is fed into the proposed methods to estimate blood pressure directly. The up-to-date concept MLP-Mixer is employed in the proposed frameworks, which are both end-to-end pipelines without any handcrafted feature extraction operation. MLPlstm-BP (gMLP-BP) achieves the predicted diastolic BP with a mean absolute error (MAE) of 2.13 (2.47) mmHg, and with a standard deviation (SD) of 3.07 (3.52) mmHg; those of systolic BP is an MAE of 3.52 (4.18) mmHg, and a SD of 5.10 (5.87) mmHg on the MIMIC II dataset. In addition, the testing results are all meet the highest level of the Association for the Advancement of Medical Instrumentation (AAMI) and British Hypertension Society (BHS). Extensive experiments demonstrate that the proposed methods attain competitive results over state-of-the-art (SOTA) learning-based pipelines.