Machine learning and deep learning for blood pressure prediction: A methodological review from multiple perspectives

Introduction

This repository contains the code for reproducing the results of the upcoming paper titled "Machine learning and deep learning for blood pressure prediction: A methodological review from multiple perspectives, and other useful resources.

The code was written using TensorFlow 2.4.0 and Python 3.8. All experiments were performed on an Ubuntu Server equipped with RTX 3080 TI GPU. If you have any questions, please contact: masterqkk@outlook.com.

Project Overview

This repository contains code for reproducing the results in the upcoming paper mentioned above and other useful resources.

Code

Specifically, we mainly analyzed the results of the trained ResNet model on MIMIC III dataset based on different splitting strategies.

The Dir 'models' contains the definition of model architecture used. If you want to use other model architecture for trainnig BP predictive model, please place the model architecture definition file under this Dir.

The Dir 'analyze' contains scripts for analyzing the experimental results, including the averaged evaluation results, significant test, etc.

The following table summarizes the function of several main python files.

	Script	Description
1	define_ResNet_1D.py	Definition of the ResNet model architecture for BP estimation
2	1download_mimic_iii_records.py	Script for downloading data from the MIMIC-III database
3	2prepare_MIMIC_dataset.py	Script for preprocessing (signal filtering, segmentation, abnormal exclusion) the data.
4	3split_and_save_dataset.py	Script for dividing the processed dataset into trainnig, validation and test test, and save it as tfrecord file.
5	4training_model_and_evaluation.py	Script for training a ResNet model for BP estimation and model evaluation.

Other resources

we list several representative surveys, research papers and the open-source implementations.

Run the experiments using command

Downloading data from the MIMIC-III database

The script 1download_mimic_iii_records.py can be used to download the records used for training BP estimation mdoel. The specific record names are provided in the file MIMIC-III_ppg_dataset_records.txt. The script can be called from the command line using the command

python3 1download_mimic_iii_records.py [-h] --input --output

positional arguments:
  --input       File containing the names of the records downloaded from the MIMIC-III DB
  --output      Folder for storing downloaded MIMIC-III records

Demo:
  python3 1download_mimic_iii_records.py --input ./MIMIC-III_ppg_dataset_records.txt  --output mimiciii
           

Noe that this requires a long time and the downloaded data is very large.

Preparing the PPG dataset

The script 2prepare_MIMIC_dataset.py is used to preprocess (signal filtering, segmentation, abnormal exclusion) the downlaoded data.

usage: 2prepare_MIMIC_dataset.py [-h] [--win_len WIN_LEN] [--win_overlap WIN_OVERLAP] [--maxsampsubject MAXSAMPSUBJECT]
                                [--maxsamp MAXSAMP] [--save_bp_data SAVE_BP_DATA]
                                datapath output

positional arguments:
  datapath              Path containing data records downloaded from the MIMIC-III database
  output                Target .h5 file

optional arguments:
  -h, --help            show this help message and exit
  --win_len WIN_LEN     PPG window length in seconds
  --win_overlap WIN_OVERLAP
                        ammount of overlap between adjacend windows in fractions of the window length (0...1)
  --maxsampsubject MAXSAMPSUBJECT
                        Maximum number of samples per subject
  --maxsamp MAXSAMP     Maximum total number os samples in the dataset
  --save_ppg_data SAVE_PPG_DATA
                        0: save BP data only; 1: save PPG and BP data

Demo: 
  python3 2prepare_MIMIC_dataset.py ./mimiciii ./processed
    
Note that the processed dataset ca not be uploaded to Github due to the limits of maximum allowed size. The processed data can be acquired at reasionable request via the email: masterqkk@outlook.com.

Splitting the data into training, validation and test set for model training, validation and evaluation

The script 3split_and_save_dataset.py is used to divide the dataset based on different splitting strategies. The splitted sets will be stored separately for training, validation and testset in .tfrecord files under the dir 'splits'.

usage: 3split_and_save_dataset.py [-h] [--ntrain NTRAIN] [--nval NVAL] [--ntest NTEST] [--divbysubj DIVBYSUBJ] input output

positional arguments:
  input                 Path to the .h5 file containing the dataset
  output                Target folder for the .tfrecord files

optional arguments:
  -h, --help            Show this help message and exit
  --ntrain NTRAIN       Number of samples in the training set (default: 9e5)
  --nval NVAL           Number of samples in the validation set (default: 3e5)
  --ntest NTEST         Number of samples in the test set (default: 3e5)
  --split_strategy      's'/'si'/'sir' denote sample level splitting strategy, 'r' denotes record level splitting strategy. for the detail meaning of these flags, please refer the file: 3split_and_save_dataset.py.
  --enlarge_ratio       The number of records used is 750 by default, and the actual number of records used in 750 * enlarge_ratio.
  --random_seed         Corresponding to different splits, which is used to control the randomness in data spliting (default: 0)

Demo: 
python3 3split_and_save_dataset.py  --split_strategy 's'  ./processed/MIMIC-III_ppg_dataset.h5  ./splits

python3 3split_and_save_dataset.py  --enlarge_ratio 3.0 --split_strategy 'r' --random_seed 0 ./processed/MIMIC-III_ppg_dataset.h5  ./splits

Training ResNet model for BP estimation

The script 4training_model_and_evaluation.py trains a ResNet model for BP estimation. After the experiment is finished, the model checkpoints are stored under the dir 'ckpts', the result files are stored under the dir 'results'.

usage: 4training_model_and_evaluation.py [-h] [--arch ARCH] [--lr LR] [--batch_size BATCH_SIZE] [--winlen WINLEN] [--epochs EPOCHS]
                                 [--gpuid GPUID]
                                 ExpName datadir resultsdir chkptdir

positional arguments:
  ExpName               unique name for the training
  datadir               folder containing the train, val and test subfolders containing tfrecord files
  resultsdir            Directory in which results are stored
  chkptdir              directory used for storing model checkpoints

optional arguments:
  -h, --help            show this help message and exit
  --arch ARCH           neural architecture used for training (alexnet (default), resnet, slapnicar, lstm)
  --lr LR               initial learning rate (default: 0.003)
  --batch_size BATCH_SIZE
                        batch size used for training (default: 32)
  --winlen WINLEN       length of the ppg windows in samples (default: 875)
  --epochs EPOCHS       maximum number of epochs for training (default: 60)
  --gpuid GPUID         GPU-ID used for training in a multi-GPU environment (default: None)
  --verbose             0/1: doesnot/does output log in stdandard I/O, 2: output log every epoch 

Demo: 
[for regular experiment]
python3 4training_model_and_evaluation.py --arch resnet --random_seed 0 --split_strategy 'r' exp_resnet ./splits ./results ./ckpts

[for experiment with enlarge_ratio !=1], 0.5 -> 64, 1.0 ->128, 2.0 ->256, 3.0 -> 384, 4.0 ->512, 
python3 4training_model_and_evaluation.py  --verbose 2 --arch resnet --random_seed 2 --split_strategy 'r' --enlarge_ratio 4.0 --batch_size 512 exp_resnet ./splits ./results ./ckpts

Other useful resources

Representative surveys

	Paper	Publication	URL
1	Evaluation of the accuracy of cuffless blood pressure measurement devices: Challenges and proposals	Hypertension	https://doi.org/10.1161/HYPERTENSIONAHA.121.17747
2	Accuracy of cuff-measured blood pressure: systematic reviews and meta-analyses	Journal of the American College of Cardiology	https://www.jacc.org/doi/abs/10.1016/j.jacc.2017.05.064
3	Blood pressure measurement: clinic, home, ambulatory, and beyond	American Journal of Kidney Diseases	https://doi.org/10.1053/j.ajkd.2012.01.026
4	Cuffless single-site photoplethysmography for blood pressure monitoring	Journal of Clinical Medicine	https://pubmed.ncbi.nlm.nih.gov/32155976/
5	The Machine Learnings Leading the Cuffless PPG Blood Pressure Sensors Into the Next Stage	IEEE Sensors Journal	https://ieeexplore.ieee.org/abstract/document/9406011
6	A review of machine learning techniques in photoplethysmography for the non-invasive cuff-less measurement of blood pressure	Biomedical Signal Processing and Control	https://doi.org/10.1016/j.bspc.2020.101870
7	A review of machine learning in hypertension detection and blood pressure estimation based on clinical and physiological data	Biomedical Signal Processing and Control	https://doi.org/10.1016/j.bspc.2021.102813
8	Oscillometric blood pressure estimation: Past, Present, and Future	IEEE Reviews in Biomedical Engineering	https://ieeexplore.ieee.org/abstract/document/7109154
9	Cuffless blood pressure monitors: Principles, standards and approval for medical use	IEICE Transactions on Communications	https://doi.org/10.1587/transcom.2020HMI0002
10	Smartphones and video cameras: Future methods for blood pressure measurement	Frontiers in Digital Health	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8633391/
11	A survey: From shallow to deep machine learning approaches for blood pressure estimation using biosensors	Expert Systems with Applications	https://doi.org/10.1016/j.eswa.2022.116788

Open-source implementations

	Paper	URL
1	UNet-based model for reconstructing ABP waveform using PPG signal	https://github.com/nibtehaz/PPG2ABP
2	Seq2Seq model with attention mechanism for reconstructing ABP waveform using PPG signal	processed dataset: https://doi.org/10.5281/zenodo.4598938, code: https://github.com/AguirreNicolas/PPG2IABP
3	MLP-Mixer based model for predicting BP using PPG and ECG signals	data&code: https://github.com/ marshb/MLP-BP
4	Dendritic neural model for predicting BP using PPG and ECG signals	code: http://www.dnm.net.cn/index.html
5	Domain-adversarial model for predicting BP using bioimpedance sensors	code: https://github.com/stmilab/cufflessbp\_dann
6	Spectro-temporal neural network model for predicting BP using PPG signal	code: https://github.com/gasper321/bp-estimation-mimic3
7	Convolution-based model for predicting BP using PPG and rPPG signals	processed dataset: https://zenodo.org/record/5590603, code: https://github.com/Fabian-Sc85/non-invasive-bp-estimation-using-deep-learning
8	Deep RNN model for predicting long-term BP using PPG and ECG signals	code: https://github.com/psu1/DeepRNN
9	Two-stage hybrid model for predicting BP using PPG signal	code: https://github.com/jesmaelpoor/Blood-pressure-estimation--Deep-multistage-model
10	SVM model for predicting BP using PPG signal	code: https://github.com/thmedialab/DataDrivenBP
11	CycleGAN based model with federated learning for predicting ABP waveform using PPG signal	https://github.com/Brophy-E/T2TGAN
12	LSTM based model for predicting BP using PPG and ECG signals	code: https://github.com/ploymel/estimateBP
13	V-Net based model for predicting ABP waveform using PPG and ECG signals	https://github.com/brianhill11/ABPImputation
14	Random forest model with genetic algorithm for predicting BP using PPG and ECG signals	data&code: https://github.com/jeya-maria-jose/Cuff\_less_BP\_Prediction

Related papers on BP estimation

	Paper	URL
1	Cuffless differential blood pressure estimation using smart phones
2	Combined deep {CNN-LSTM} network-based multitasking learning architecture for noninvasive continuous blood pressure estimation using difference in {ECG-PPG} features
3	Concatenated Convolutional Neural Network Model for Cuffless Blood Pressure Estimation Using Fuzzy Recurrence Properties of {PPG} Signals
4	Analysis of pulse arrival time as an indicator of blood pressure in a large surgical biosignal database: recommendations for developing ubiquitous blood pressure monitoring methods
5	Cuff-less high-accuracy calibration-free blood pressure estimation using pulse transit time
6	Non-invasive blood pressure estimation using phonocardiogram
7	Nonlinear cuffless blood pressure estimation of healthy subjects using pulse transit time and arrival time
8	Pervasive blood pressure monitoring using {P}hotoplethysmogram ({PPG}) sensor
9	Wireless medical sensor network for blood pressure monitoring based on machine learning for real-time data classification
10	Noninvasive classification of blood pressure based on photoplethysmography signals using bidirectional long short-term memory and time-frequency analysis
11	{PPG2ABP}: Translating photoplethysmogram ({PPG}) signals to arterial blood pressure ({ABP}) waveforms using fully convolutional neural networks
12	An estimation method of continuous non-invasive arterial blood pressure waveform using photoplethysmography: A {U-Net} architecture-based approach
13	Genetic Deep Convolutional Autoencoder Applied for Generative Continuous Arterial Blood Pressure via Photoplethysmography
14	Prediction of arterial blood pressure waveforms from photoplethysmogram signals via fully convolutional neural networks
15	Blood pressure morphology assessment from photoplethysmogram and demographic information using deep learning with attention mechanism
16	Continuous Blood Pressure Estimation Using Exclusively Photopletysmography by {LSTM}-Based Signal-to-Signal Translation
17	Aortic blood pressure estimation: A hybrid machine-learning and cross-relation approach
18	Offline and online learning techniques for personalized blood pressure prediction and health behavior recommendations
19	Dempster--{S}hafer Fusion Based on a Deep Boltzmann Machine for Blood Pressure Estimation
20	Continuous blood pressure measurement from one-channel electrocardiogram signal using deep-learning techniques
21	Towards accurate estimation of cuffless and continuous blood pressure using multi-order derivative and multivariate photoplethysmogram features
22	Cuffless blood pressure estimation from {PPG} signals and its derivatives using deep learning models
23	Blood pressure estimation from {PPG} signals using convolutional neural networks and {S}iamese network
24	Continuous blood pressure prediction using pulse features and {E}lman neural networks
25	Estimation and tracking of blood pressure using routinely acquired photoplethysmographic signals and deep neural networks
26	{PP-Net}: A deep learning framework for {PPG}-based blood pressure and heart rate estimation
27	A Personalised Blood Pressure Prediction System using {G}aussian Mixture Regression and Online Recurrent Extreme Learning Machine
28	Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques
29	Investigating the physiological mechanisms of the photoplethysmogram features for blood pressure estimation
30	Deep learning models for cuffless blood pressure monitoring from {PPG} signals using attention mechanism
31	End-to-end deep learning architecture for continuous blood pressure estimation using attention mechanism
32	A non-invasive continuous cuffless blood pressure estimation using dynamic recurrent neural networks
33	Investigation on the effect of {Womersley} number, {ECG} and {PPG} features for cuff less blood pressure estimation using machine learning
34	Estimation of the Blood Pressure Waveform using {E}lectrocardiography
35	Neural Recurrent Approches to Noninvasive Blood Pressure Estimation
36	{K-SVD}: An algorithm for designing overcomplete dictionaries for sparse representation
37	Blood pressure prediction via recurrent models with contextual layer
38	Double Channel Neural Non Invasive Blood Pressure Prediction
39	A novel dynamical approach in continuous cuffless blood pressure estimation based on {ECG} and {PPG} signals
40	Cuffless blood pressure estimation from electrocardiogram and photoplethysmogram using waveform based {ANN-LSTM} network
41	Long-term blood pressure prediction with deep recurrent neural networks
42	Enhancement of blood pressure estimation method via machine learning
43	A new estimate technology of non-invasive continuous blood pressure measurement based on electrocardiograph
44	Prediction of blood pressure after induction of anesthesia using deep learning: a feasibility study
45	Sparse characterization of {PPG} based on {K-SVD} for beat-to-beat blood pressure prediction
46	Sparse representation of photoplethysmogram using {K-SVD} for cuffless estimation of arterial blood pressure
47	Predicting blood pressure from physiological index data using the {SVR} algorithm
48	Features extraction for cuffless blood pressure estimation by autoencoder from photoplethysmography
49	A new approach based on dynamical model of the {ECG} signal to blood pressure estimation
50	Cuffless and continuous blood pressure estimation from the heart sound signals
51	{PPG}-based blood pressure estimation using residual neural networks and spectrograms
52	Chest wearable apparatus for cuffless continuous blood pressure measurements based on {PPG} and {PCG} signals
53	Beat-to-beat ambulatory blood pressure estimation based on random forest
54	Estimation and Validation of Arterial Blood Pressure Using Photoplethysmogram Morphology Features in Conjunction With Pulse Arrival Time in Large Open Databases
55	Wearable piezoelectric-based system for continuous beat-to-beat blood pressure measurement
56	Beats-to-Beats Estimation of Blood Pressure During Supine Cycling Exercise Using a Probabilistic Nonparametric Metho
57	Multi-sensor fusion approach for cuff-less blood pressure measurement
58	Central Blood Pressure Estimation from Distal {PPG} Measurement using semiclassical signal analysis features
59	Wearable cuff-less blood pressure estimation at home via pulse transit time
60	Key Feature Selection and Model Analysis for Blood Pressure Estimation From Electrocardiogram, Ballistocardiogram and Photoplethysmogram
61	A Data-Driven Model with Feedback Calibration Embedded Blood Pressure Estimator Using Reflective Photoplethysmography
62	A novel continuous blood pressure estimation approach based on data mining techniques
63	A Shallow {U-Net} Architecture for Reliably Predicting Blood Pressure ({BP}) from Photoplethysmogram ({PPG}) and Electrocardiogram ({ECG}) Signals
64	Study of cuffless blood pressure estimation method based on multiple physiological parameters
65	Data-driven estimation of blood pressure using photoplethysmographic signals
66	Machine Learning Method for Continuous Noninvasive Blood Pressure Detection Based on Random Forest
67	Pulse transit time-pulse wave analysis fusion based on wearable wrist ballistocardiogram for cuff-less blood pressure trend tracking
68	{PCA}-based multi-wavelength photoplethysmography algorithm for cuffless blood pressure measurement on elderly subjects
69	Personalized Blood Pressure Estimation Using Photoplethysmography: A Transfer Learning Approach
70	Accurate Fiducial Point Detection Using {H}aar Wavelet for Beat-by-Beat Blood Pressure Estimation
71	Blood pressure estimation using photoplethysmogram signal and its morphological features
72	Cuffless blood pressure monitoring from an array of wrist bio-impedance sensors using subject-specific regression models: Proof of concept
73	Cuffless blood pressure estimation algorithms for continuous health-care monitoring
74	Cuffless blood pressure estimation methods: physiological model parameters versus machine-learned features
75	Conventional pulse transit times as markers of blood pressure changes in humans
76	Photoplethysmography Fast Upstroke Time Intervals Can Be Useful Features for Cuff-Less Measurement of Blood Pressure Changes in Humans
77	Enabling Wearable Pulse Transit Time-Based Blood Pressure Estimation for Medically Underserved Areas and Health Equity: Comprehensive Evaluation Study
78	Continuous {PPG}-based blood pressure monitoring using multi-linear regression
79	Multi-level information fusion for learning a blood pressure predictive model using sensor data
80	Cuffless blood pressure estimation based on photoplethysmography signal and its second derivative
81	A novel neural network model for blood pressure estimation using photoplethesmography without electrocardiogram
82	Learning-Based Model for Central Blood Pressure Estimation using Feature Extracted from {ECG} and {PPG} signals
83	Featureless Blood Pressure Estimation Based on Photoplethysmography Signal Using {CNN} and {BiLSTM} for {IoT} Devices
84	Blood pressure estimation using photoplethysmography only: comparison between different machine learning approaches
85	Non-invasive blood pressure estimation from {ECG} using machine learning techniques
86	Predicting increased blood pressure using machine learning
87	Developing personalized models of blood pressure estimation from wearable sensors data using minimally-trained domain adversarial neural networks
88	Continuous blood pressure estimation through optimized echo state networks
89	Continuous blood pressure estimation from {PPG} signal
90	Cuffless blood pressure estimation based on haemodynamic principles: progress towards mobile healthcare
91	Estimating blood pressure trends and the nocturnal dip from photoplethysmography
92	{ECG}-Based Blood Pressure Estimation Using {M}echano-{E}lectric Coupling Concept
93	Blood pressure estimation from appropriate and inappropriate {PPG} signals using A whole-based method
94	Noninvasive cuffless blood pressure estimation using pulse transit time, {W}omersley number, and photoplethysmogram intensity ratio
95	{iPhone} {A}pp compared with standard blood pressure measurement--The {iPARR} trial
96	Pulse transit time estimation of aortic pulse wave velocity and blood pressure using machine learning and simulated training data
97	Cuffless Blood Pressure Measurement Using Linear and Nonlinear Optimized Feature Selection
98	Photoplethysmogram intensity ratio: A potential indicator for improving the accuracy of {PTT}-based cuffless blood pressure estimation
99	{PPG}-based systolic blood pressure estimation method using {PLS} and level-crossing feature
100	Cuffless blood pressure estimation using only a smartphone
101	Highly wearable cuff-less blood pressure and heart rate monitoring with single-arm electrocardiogram and photoplethysmogram signals
102	Intelligent Bio-Impedance System for Personalized Continuous Blood Pressure Measurement
103	Health data driven on continuous blood pressure prediction based on gradient boosting decision tree algorithm
104	Cuff-less blood pressure measurement using supplementary {ECG} and {PPG} features extracted through wavelet transformation
105	Feasibility study for the non-invasive blood pressure estimation based on {PPG} morphology: Normotensive subject study
106	An Unobtrusive and Calibration-free Blood pressure estimation Method using photoplethysmography and Biometrics
107	{InstaBP}: Cuff-less blood pressure monitoring on smartphone using single {PPG} sensor
108	Blood pressure estimation using on-body continuous wave radar and photoplethysmogram in various posture and exercise conditions
109	Discussion of Cuffless Blood Pressure Prediction Using Plethysmograph Based on a Longitudinal Experiment: Is the Individual Model Necessary?
110	A highly sensitive pressure-sensing array for blood pressure estimation assisted by machine-learning techniques
111	A new wearable device for blood pressure estimation using photoplethysmogram
112	A non-invasive continuous blood pressure estimation approach based on machine learning
113	Estimating blood pressure from the photoplethysmogram signal and demographic features using machine learning techniques
114	Real-time cuffless continuous blood pressure estimation using deep learning model
115	Assessment of deep learning based blood pressure prediction from {PPG} and {rPPG} signals
116	Assessment of Non-Invasive Blood Pressure Prediction from {PPG} and {rPPG} Signals Using Deep Learning
117	Cuffless blood pressure estimation using single channel photoplethysmography: A two-step method
118	Noninvasive Cuffless Blood Pressure Estimation With Dendritic Neural Regression
119	Improved {PPG}-based estimation of the blood pressure using latent space features
120	Blood pressure estimation from photoplethysmogram using latent parameters}
121	Non-invasive continuous blood pressure measurement based on mean impact value method, {BP} neural network, and genetic algorithm
122	Cuffless continuous blood pressure estimation from pulse morphology of photoplethysmograms
123	A deep learning approach to predict blood pressure from {PPG} signals
124	An empirical study on predicting blood pressure using classification and regression trees
125	Continuous Blood Pressure Estimation From Electrocardiogram and Photoplethysmogram During Arrhythmias
126	Calibration-Free Cuffless Blood Pressure Estimation Based on a Population With a Diverse Range of Age and Blood Pressure
127	Intermittent blood pressure prediction via multiscale entropy and ensemble artificial neural networks
128	{SVR} ensemble-based continuous blood pressure prediction using multi-channel photoplethysmogram
129	An integrated blood pressure measurement system for suppression of motion artifacts
130	Cuffless blood pressure estimation based on data-oriented continuous health monitoring system
131	Towards a portable-noninvasive blood pressure monitoring system utilizing the photoplethysmogram signal
132	Feature exploration for knowledge-guided and data-driven approach based cuffless blood pressure measurement
133	A noninvasive time-frequency-based approach to estimate cuffless arterial blood pressure
134	Smart phone based blood pressure indicator
135	Cuffless blood pressure measurement using a smartphone-case based {ECG} monitor with photoplethysmography in hypertensive patients
136	A Continuous Cuffless Blood Pressure Estimation Using Tree-Based Pipeline Optimization Tool
137	Estimation of Continuous Blood Pressure from {PPG} via a Federated Learning Approach
138	Novel Blood Pressure Waveform Reconstruction from Photoplethysmography using Cycle Generative Adversarial Networks
139	Toward ubiquitous blood pressure monitoring via pulse transit time: theory and practice
140	A review of methods for non-invasive and continuous blood pressure monitoring: Pulse transit time method is promising?
141	Noninvasive and nonocclusive blood pressure estimation via a chest sensor
142	PPG sensor contact pressure should be taken into account for cuff-less blood pressure measurement
143	A revised point-to-point calibration approach with adaptive errors correction to weaken initial sensitivity of cuff-less blood pressure estimation
144	A linear regression model with dynamic pulse transit time features for noninvasive blood pressure prediction
145	Accelerometric Method for Cuffless Continuous Blood Pressure Measurement
146	Pulse transit time based continuous cuffless blood pressure estimation: A new extension and a comprehensive evaluation
147	Cuff-less continuous measurement of blood pressure using wrist and fingertip photo-plethysmograms: Evaluation and feature analysis
148	A novel approach to estimate blood pressure of blood loss continuously based on stacked auto-encoder neural networks
149	Cuff-less continuous blood pressure measurement based on multiple types of information fusion
150	Optical blood pressure estimation with photoplethysmography and {FFT}-based neural networks
151	Continuous blood pressure estimation based on two-domain fusion model
152	A hybrid model for blood pressure prediction from a {PPG} signal based on {MIV} and {GA-BP} neural network
153	A novel frequency domain method for estimating blood pressure from photoplethysmogram
154	{MLP-BP}: A novel framework for cuffless blood pressure measurement with {PPG} and {ECG} signals based on {MLP-Mixer} neural networks
155	Generalized deep neural network model for cuffless blood pressure estimation with photoplethysmogram signal only
156	Energy-efficient Blood Pressure Monitoring based on Single-site Photoplethysmogram on Wearable Devices
157	A clinical set-up for noninvasive blood pressure monitoring using two photoplethysmograms and based on convolutional neural networks
158	Cuffless blood pressure estimation based on composite neural network and graphics information
159	Blood Pressure Prediction by a Smartphone Sensor using Fully Convolutional Networks
159	End-to-end blood pressure prediction via fully convolutional networks
160	Cuffless and continuous blood pressure estimation from ppg signals using recurrent neural networks
161	Continuous systolic and diastolic blood pressure estimation utilizing long short-term memory network
162	Blood pressure prediction with multi-cue based {RBF} and {LSTM} model
163	Prediction of blood pressure variability using deep neural networks
164	A Novel Machine Learning-Based Systolic Blood Pressure Predicting Model
165	Beat-to-beat continuous blood pressure estimation using bidirectional long short-term memory network
166	Cuffless deep learning-based blood pressure estimation for smart wristwatches
167	Novel Data Augmentation Employing Multivariate {G}aussian Distribution for Neural Network-Based Blood Pressure Estimation
168	A multi-type features fusion neural network for blood pressure prediction based on photoplethysmography
169	A hybrid neural network for continuous and non-invasive estimation of blood pressure from raw electrocardiogram and photoplethysmogram waveforms
170	A multistage deep neural network model for blood pressure estimation using photoplethysmogram signals
171	Non-invasive cuff-less blood pressure estimation using a hybrid deep learning model
172	Photoplethysmography-Based Blood Pressure Estimation Using Deep Learning
173	{HYPE}: Predicting Blood Pressure from Photoplethysmograms in a Hypertensive Population
174	Personalized effect of health behavior on blood pressure: Machine learning based prediction and recommendation
175	Using Wearables and Machine Learning to Enable Personalized Lifestyle Recommendations to Improve Blood Pressure
176	Homecare-oriented intelligent long-term monitoring of blood pressure using electrocardiogram signals
177	Smartphones and Video Cameras: Future Methods for Blood Pressure Measurement
178	Non-contact method of blood pressure estimation using only facial video
179	Remote estimation of pulse wave features related to arterial stiffness and blood pressure using a camera
180	Techniques for estimating blood pressure variation using video images
181	Blood pressure estimation using video plethysmography
182	Using imaging Photoplethysmography ({iPPG}) Signal for Blood Pressure Estimation
183	Smartphone-based blood pressure measurement using transdermal optical imaging technology
184	Remote {PPG} based vital sign measurement using adaptive facial regions
185	Robust blood pressure estimation using an {RGB} camera
186	The noninvasive blood pressure measurement based on facial images processing
187	Introducing contactless blood pressure assessment using a high speed video camera
188	Multi-point near-field {RF} sensing of blood pressures and heartbeat dynamics
189	Blood Pressure States Transition Inference Based on Multi-State {M}arkov Model
190	Non-contact heart rate and blood pressure estimations from video analysis and machine learning modelling applied to food sensory responses: A case study for chocolate
191	A blood pressure prediction method based on imaging photoplethysmography in combination with machine learning
192	Deep generative model with domain adversarial training for predicting arterial blood pressure waveform from photoplethysmogram signal
193	A Continuous Blood Pressure Estimation Method Using Photoplethysmography by {GRNN}-Based Model
194	Estimation of arterial blood pressure waveform from photoplethysmogram signal using linear transfer function approach
195	Imputation of the continuous arterial line blood pressure waveform from non-invasive measurements using deep learning
196	Oscillometric blood pressure estimation based on deep learning
197	Deep belief networks ensemble for blood pressure estimation
198	Combining bootstrap aggregation with support vector regression for small blood pressure measurement
199	Statistical approaches based on deep learning regression for verification of normality of blood pressure estimates
200	Uncertainty in blood pressure measurement estimated using ensemble-based recursive methodology
201	Ensemble methodology for confidence interval in oscillometric blood pressure measurements
202	{GMM-HMM}-based blood pressure estimation using time-domain features
203	Feature-based neural network approach for oscillometric blood pressure estimation
204	Electrocardiogram-assisted blood pressure estimation
205	Blood pressure estimation from time-domain features of oscillometric waveforms using long short-term memory recurrent neural networks
206	Blood pressure estimation from beat-by-beat time-domain features of oscillometric waveforms using deep-neural-network classification models
207	Deep {B}oltzmann regression with mimic features for oscillometric blood pressure estimation
208	Blood pressure estimation using time domain features of auscultatory waveforms and {GMM-HMM} classification approach
209	A Novel Automated Blood Pressure Estimation Algorithm Using Sequences of {K}orotkoff Sounds
210	A novel deep learning based automatic auscultatory method to measure blood pressure
211	Blood pressure estimation from photoplethysmogram using a spectro-temporal deep neural network
212	An Adaptive Weight Learning-Based Multitask Deep Network for Continuous Blood Pressure Estimation Using {E}lectrocardiogram Signals
213	Attention Mechanism-Based Convolutional Long Short-Term Memory Neural Networks to Electrocardiogram-Based Blood Pressure Estimation
214	Photoplethysmogram-based blood pressure evaluation using Kalman filtering and neural networks

This repository is continuously updating ......