40 research outputs found
Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach
Ischemic heart disease is the highest cause of mortality globally each year.
This not only puts a massive strain on the lives of those affected but also on
the public healthcare systems. To understand the dynamics of the healthy and
unhealthy heart doctors commonly use electrocardiogram (ECG) and blood pressure
(BP) readings. These methods are often quite invasive, in particular when
continuous arterial blood pressure (ABP) readings are taken and not to mention
very costly. Using machine learning methods we seek to develop a framework that
is capable of inferring ABP from a single optical photoplethysmogram (PPG)
sensor alone. We train our framework across distributed models and data sources
to mimic a large-scale distributed collaborative learning experiment that could
be implemented across low-cost wearables. Our time series-to-time series
generative adversarial network (T2TGAN) is capable of high-quality continuous
ABP generation from a PPG signal with a mean error of 2.54 mmHg and a standard
deviation of 23.7 mmHg when estimating mean arterial pressure on a previously
unseen, noisy, independent dataset. To our knowledge, this framework is the
first example of a GAN capable of continuous ABP generation from an input PPG
signal that also uses a federated learning methodology
Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning
Exploiting photoplethysmography signals (PPG) for non-invasive blood pressure (BP) measurement is interesting for various reasons. First, PPG can easily be measured using fingerclip sensors. Second, camera based approaches allow to derive remote PPG (rPPG) signals similar to PPG and therefore provide the opportunity for non-invasive measurements of BP. Various methods relying on machine learning techniques have recently been published. Performances are often reported as the mean average error (MAE) on the data which is problematic. This work aims to analyze the PPG- and rPPG based BP prediction error with respect to the underlying data distribution. First, we train established neural network (NN) architectures and derive an appropriate parameterization of input segments drawn from continuous PPG signals. Second, we use this parameterization to train NNs with a larger PPG dataset and carry out a systematic evaluation of the predicted blood pressure. The analysis revealed a strong systematic increase of the prediction error towards less frequent BP values across NN architectures. Moreover, we tested different train/test set split configurations which underpin the importance of a careful subject-aware dataset assignment to prevent overly optimistic results. Third, we use transfer learning to train the NNs for rPPG based BP prediction. The resulting performances are similar to the PPG-only case. Finally, we apply different personalization techniques and retrain our NNs with subject-specific data for both the PPG-only and rPPG case. Whilst the particular technique is less important, personalization reduces the prediction errors significantly
A Novel Clustering-Based Algorithm for Continuous and Non-invasive Cuff-Less Blood Pressure Estimation
Extensive research has been performed on continuous, non-invasive, cuffless
blood pressure (BP) measurement using artificial intelligence algorithms. This
approach involves extracting certain features from physiological signals like
ECG, PPG, ICG, BCG, etc. as independent variables and extracting features from
Arterial Blood Pressure (ABP) signals as dependent variables, and then using
machine learning algorithms to develop a blood pressure estimation model based
on these data. The greatest challenge of this field is the insufficient
accuracy of estimation models. This paper proposes a novel blood pressure
estimation method with a clustering step for accuracy improvement. The proposed
method involves extracting Pulse Transit Time (PTT), PPG Intensity Ratio (PIR),
and Heart Rate (HR) features from Electrocardiogram (ECG) and
Photoplethysmogram (PPG) signals as the inputs of clustering and regression,
extracting Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP)
features from ABP signals as dependent variables, and finally developing
regression models by applying Gradient Boosting Regression (GBR), Random Forest
Regression (RFR), and Multilayer Perceptron Regression (MLP) on each cluster.
The method was implemented using the MIMICII dataset with the silhouette
criterion used to determine the optimal number of clusters. The results showed
that because of the inconsistency, high dispersion, and multi-trend behavior of
the extracted features vectors, the accuracy can be significantly improved by
running a clustering algorithm and then developing a regression model on each
cluster, and finally weighted averaging of the results based on the error of
each cluster. When implemented with 5 clusters and GBR, this approach yielded
an MAE of 2.56 for SBP estimates and 2.23 for DBP estimates, which were
significantly better than the best results without clustering (DBP: 6.27, SBP:
6.36)
Beat-to-beat blood pressure estimation by photoplethysmography and its interpretation
Blood pressure (BP) is among the most important vital signals. Estimation of absolute BP solely using photoplethysmography (PPG) has gained immense attention over the last years. Available works differ in terms of used features as well as classifiers and bear large differences in their results. This work aims to provide a machine learning method for absolute BP estimation, its interpretation using computational methods and its critical appraisal in face of the current literature. We used data from three different sources including 273 subjects and 259,986 single beats. We extracted multiple features from PPG signals and its derivatives. BP was estimated by xgboost regression. For interpretation we used Shapley additive values (SHAP). Absolute systolic BP estimation using a strict separation of subjects yielded a mean absolute error of 9.456mmHg and correlation of 0.730. The results markedly improve if data separation is changed (MAE: 6.366mmHg, r: 0.874). Interpretation by means of SHAP revealed four features from PPG, its derivation and its decomposition to be most relevant. The presented approach depicts a general way to interpret multivariate prediction algorithms and reveals certain features to be valuable for absolute BP estimation. Our work underlines the considerable impact of data selection and of training/testing separation, which must be considered in detail when algorithms are to be compared. In order to make our work traceable, we have made all methods available to the public
PPG2ABP: Translating Photoplethysmogram (PPG) Signals to Arterial Blood Pressure (ABP) Waveforms using Fully Convolutional Neural Networks
Cardiovascular diseases are one of the most severe causes of mortality,
taking a heavy toll of lives annually throughout the world. The continuous
monitoring of blood pressure seems to be the most viable option, but this
demands an invasive process, bringing about several layers of complexities.
This motivates us to develop a method to predict the continuous arterial blood
pressure (ABP) waveform through a non-invasive approach using
photoplethysmogram (PPG) signals. In addition we explore the advantage of deep
learning as it would free us from sticking to ideally shaped PPG signals only,
by making handcrafted feature computation irrelevant, which is a shortcoming of
the existing approaches. Thus, we present, PPG2ABP, a deep learning based
method, that manages to predict the continuous ABP waveform from the input PPG
signal, with a mean absolute error of 4.604 mmHg, preserving the shape,
magnitude and phase in unison. However, the more astounding success of PPG2ABP
turns out to be that the computed values of DBP, MAP and SBP from the predicted
ABP waveform outperforms the existing works under several metrics, despite that
PPG2ABP is not explicitly trained to do so
Schr\"odinger Spectrum based Continuous Cuff-less Blood Pressure Estimation using Clinically Relevant Features from PPG Signal and its Second Derivative
The presented study aims to estimate blood pressure (BP) using
photoplethysmogram (PPG) signals while employing multiple machine learning
models. The study proposes a novel algorithm for signal reconstruction, which
utilizes the semi-classical signal analysis (SCSA) technique. The proposed
algorithm optimises the semi-classical constant and eliminates the trade-off
between complexity and accuracy in reconstruction. The reconstructed signals'
spectral features are extracted and incorporated with clinically relevant PPG
and its second derivative's (SDPPG) morphological features. The developed
method was assessed using a publicly available virtual in-silico dataset with
more than 4000 subjects, and the Multi-Parameter Intelligent Monitoring in
Intensive Care Units dataset. Results showed that the method attained a mean
absolute error of 5.37 and 2.96 mmHg for systolic and diastolic BP,
respectively, using the CatBoost supervisory algorithm. This approach met the
standards set by the Advancement of Medical Instrumentation, and achieved Grade
A for all BP categories in the British Hypertension Society protocol. The
proposed framework performs well even when applied to a combined database of
the MIMIC-III and the Queensland dataset. This study also evaluates the
proposed method's performance in a non-clinical setting with noisy and deformed
PPG signals, to validate the efficacy of the SCSA method. The noise stress
tests showed that the algorithm maintained its key feature detection, signal
reconstruction capability, and estimation accuracy up to a 10 dB SNR ratio. It
is believed that the proposed cuff-less BP estimation technique has the
potential to perform well on resource-constrained settings due to its
straightforward implementation approach.Comment: 16 pages, 8 figures, 8 tables, submitted to Biomedical Signal
Processing and Control, Elsevie
A Comparison of Deep Learning Techniques for Arterial Blood Pressure Prediction
Continuous vital signal monitoring is becoming more relevant in preventing diseases that afflict a large part of the worldâs population; for this reason, healthcare equipment should be easy to wear and simple to use. Non-intrusive and non-invasive detection methods are a basic requirement for wearable medical devices, especially when these are used in sports applications or by the elderly for self-monitoring. Arterial blood pressure (ABP) is an essential physiological parameter for health monitoring. Most blood pressure measurement devices determine the systolic and diastolic arterial blood pressure through the inflation and the deflation of a cuff. This technique is uncomfortable for the user and may result in anxiety, and consequently affect the blood pressure and its measurement. The purpose of this paper is the continuous measurement of the ABP through a cuffless, non-intrusive approach. The approach of this paper is based on deep learning techniques where several neural networks are used to infer ABP, starting from photoplethysmogram (PPG) and electrocardiogram (ECG) signals. The ABP was predicted first by utilizing only PPG and then by using both PPG and ECG. Convolutional neural networks (ResNet and WaveNet) and recurrent neural networks (LSTM) were compared and analyzed for the regression task. Results show that the use of the ECG has resulted in improved performance for every proposed configuration. The best performing configuration was obtained with a ResNet followed by three LSTM layers: this led to a mean absolute error (MAE) of 4.118Â mmHg on and 2.228Â mmHg on systolic and diastolic blood pressures, respectively. The results comply with the American National Standards of the Association for the Advancement of Medical Instrumentation. ECG, PPG, and ABP measurements were extracted from the MIMIC database, which contains clinical signal data reflecting real measurements. The results were validated on a custom dataset created at Neuronica Lab, Politecnico di Torino
A Shallow U-Net Architecture for Reliably Predicting Blood Pressure (BP) from Photoplethysmogram (PPG) and Electrocardiogram (ECG) Signals
Cardiovascular diseases are the most common causes of death around the world. To detect and treat heart-related diseases, continuous blood pressure (BP) monitoring along with many other parameters are required. Several invasive and non-invasive methods have been developed for this purpose. Most existing methods used in hospitals for continuous monitoring of BP are invasive. On the contrary, cuff-based BP monitoring methods, which can predict systolic blood pressure (SBP) and diastolic blood pressure (DBP), cannot be used for continuous monitoring. Several studies attempted to predict BP from non-invasively collectible signals such as photoplethysmograms (PPG) and electrocardiograms (ECG), which can be used for continuous monitoring. In this study, we explored the applicability of autoencoders in predicting BP from PPG and ECG signals. The investigation was carried out on 12,000 instances of 942 patients of the MIMIC-II dataset, and it was found that a very shallow, one-dimensional autoencoder can extract the relevant features to predict the SBP and DBP with state-of-the-art performance on a very large dataset. An independent test set from a portion of the MIMIC-II dataset provided a mean absolute error (MAE) of 2.333 and 0.713 for SBP and DBP, respectively. On an external dataset of 40 subjects, the model trained on the MIMIC-II dataset provided an MAE of 2.728 and 1.166 for SBP and DBP, respectively. For both the cases, the results met British Hypertension Society (BHS) Grade A and surpassed the studies from the current literature. 2022 by the authors. Licensee MDPI, Basel, Switzerland.Funding: This work was supported in part by the Qatar National Research Fund under Grant NPRP12S-0227-190164 and in part by the International Research Collaboration Co-Fund (IRCC) through Qatar University under Grant IRCC-2021-001. The statements made herein are solely the responsibility of the authors.Scopu