AI-driven system for non-contact continuous nocturnal blood pressure monitoring using fiber optic ballistocardiography

Figures

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  Fig. 1. The concept and system overview of non-contact continuous nocturnal blood pressure (BP) monitor. a A typical use scenario of our system, where the fiber optic sensing mat is placed beneath the user’s pillow. b The system prototype deployed in the real world. c Block diagrams of the system. The data preparation stage preprocesses ballistocardiogram signals for posture recognition and fiducial point delineation. The feature extraction stage extracts meaningful features for training a base model. The base model will be directly deployed in the edge device, while a Random Forest model is trained as the personalized regressor to infer the BP in the edge. The customized user interface enables WiFi connectivity for live vital sign monitoring. d System’s circuit and block diagram. The fiber optic sensing capability primarily builds upon a laser emitter, a section of multimode fiber, and a photodiode receiver. e Sample waveforms from time-synchronized electrocardiogram (ECG), ballistocardiogram (BCG), and finger cuff-based pulse sensors. BPF is a bandpass filter, Vref is reference voltage, VBUS is USB power voltage, and a.u. is arbitrary units.

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  Fig. 2. Flexible fiber optic BCG sensor design and the extraction of hand-crafted fiducial features. a Exploded schematic illustration of the constituent layers: a bare multimode optical fiber core, a grid-shape nylon deformer, two polyethylene terephthalate (PET) plastic films as core cover, two silicon elastomers encapsulation layers, and a fitting polyester outer covering. b schematic of the microbend fiber sensor. c Sensor output as a function of weight. d Sensor sensitivity as a function of weight. e Illustration of fiducial points labeling. f Sample illustration of five types of fiducial features: extremum—A(J), displacement—∣A(J) − A(K)∣, time interval—T(H, L), time ratio—T1/T, and area under the curve—AUC(J, K). The periodic contraction (systole) and relaxation (diastole) of the heart can be illustrated into five major stages: (1) atrial systole (AS), (2) isometric ventricular contraction (IC), (3) ventricular ejection (VE), (4) isometric ventricular relaxation (IR), and (5) ventricular filling (VF).

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  Fig. 3. Performance of the clinical study in the daytime dataset. a, b The correlation and Bland–Altman plots for the open-source dataset, where a is for systolic blood pressure (SBP) results, and b is for diastolic blood pressure (DBP) results. c, d The correlation and Bland–Altman plots for the self-collected daytime dataset, where c is for SBP results, and b is for DBP results. e, f Mean absolute error (MAE) for different blood pressure levels before and after personalization, where e is for SBP results, and f is for DBP results. The blue line indicates the number of samples, while the box plots indicate absolute errors. Error bars represent the range of data within 1.5 times the interquartile range (IQR) from the first quartile (Q1) to the third quartile (Q3). g, h The distribution of MAE of all subjects at different BP change intervals, where g is for SBP results, and h is for DBP results. The red line indicates MAE = 7 mmHg. SD is the standard deviation of mean error.

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  Fig. 4. Performance of the clinical study for continuous nocturnal blood pressure monitoring. a Confusion matrix of sleep posture prediction during the day, grouped by four different sleep postures. b, c Receiver operating characteristic curves for four postures classification (b), and binary posture classification during the night (c). d, e The correlation and Bland–Altman plots for the self-collected nocturnal dataset, where d is for systolic blood pressure (SBP) results, and e is for diastolic blood pressure (DBP) results. f, g Mean absolute error (MAE) for different blood pressure levels before and after personalization, where f is for SBP results, and g is for DBP results. The blue line indicates the number of samples, while the box plots indicate absolute errors. Error bars represent the range of data within 1.5 times the interquartile range (IQR) from the first quartile (Q1) to the third quartile (Q3). h, i The distribution of MAE of all subjects at different BP change intervals, where h is for SBP results, and i is for DBP results. The red line indicates MAE = 7 mmHg. SD is the standard deviation of mean error.

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  Fig. 5. Interpretation of artificial intelligence model using T-SNE and SHAP analysis. a, b Scatter plot of extracted features after dimension reduction using t-distributed stochastic neighborhood embedding (T-SNE) techniques, where a uses daytime data, and b uses nocturnal data. c, d Feature important ranking using Shapley additive exPlanations (SHAP) analysis, where c is for SBP and d is for DBP.