Using Bayesian optimization and wavelet decomposition in GPU for arterial blood pressure estimation

Continuous monitoring of arterial blood pressure (ABP) of patients in hospital is currently carried out in an invasive way, which could represent a risk for them. In this paper, a noninvasive methodology to optimize ABP estimators using electrocardiogram and photoplethysmography signals is proposed. For this, the XGBoost machine learning model, optimized with Bayesian techniques, is executed in a Graphics Processing Unit, which drastically reduces execution time. The methodology is evaluated using the MIMIC-III Waveform Database. Systolic and diastolic pressures are estimated with mean absolute error values of 15.85 and 11.59 mmHg, respectively, similar to those of the state of the art. The main advantage of the proposed methodology with respect to others of the current state of the art is that it allows the optimization of the estimator model to be performed automatically and more efficiently at the computational level for the data available. Clinical Relevance— This approach has the advantage of using noninvasive methods to continuously monitor patient's arterial blood pressure, reducing the risk for patientsAgencia Gallega de Innovación | Ref. IN845D-2020/29Agencia Gallega de Innovación | Ref. IN607B-2021/1

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

Blood pressure estimation from photoplethysmogram and electrocardiogram signals using machine learning

Blood pressure measurement is a significant part of preventive healthcare and has been widely used in clinical risk and disease management. However, conventional measurement does not provide continuous monitoring and sometimes is inconvenient with a cuff. In addition to the traditional cuff-based blood pressure measurement methods, some researchers have developed various cuff-less and noninvasive blood pressure monitoring methods based on Pulse Transit Time (PTT). Some emerging methods have employed features of either photoplethysmogram (PPG) or electrocardiogram (ECG) signals, although no studies to our knowledge have employed the combined features from both PPG and ECG signals. Therefore this study aims to investigate the performance of a predictive, machine learning blood pressure monitoring system using both PPG and ECG signals. It validates that the employment of the combination of PPG and ECG signals has improved the accuracy of the blood pressure estimation, compared with previously reported results based on PPG signal only. © 2018 Institution of Engineering and Technology. All rights reserved

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)

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

Implementasi Pengukuran Dan Klasifikasi Tekanan Darah Berdasarkan Pulse Transit Time Menggunakan Metode Transformasi Wavelet Dan Support Vector Machines

Pengukuran tekanan darah melalui kuf atau manset lengan umumnya masih digunakan di bidang medis, padahal pengukuran melalui manset merupakan pengukuran dengan cara tidak langsung. Hal ini tentunya akan mempengaruhi hasil dari pengukuran tekanan darah yang selanjutnya akan digunakan sebagai patokan tekanan darah seseorang apakah ia hipertensi atau tidak. Sinyal ECG dan PPG merupakan gelombang sinyal elektrik yang didapatkan langsung dari tubuh yang berkaitan dengan aktivitas jantung. Terdapat selang waktu saat jantung memompa darah ke seluruh tubuh, hal ini disebut dengan Pulse Transit Time (PTT). Dengan melakukan penghitungan PTT dapat diketahui tekanan sistol dan diastol sehingga dapat digunakan sebagai metode pengukur tekanan darah. Pada rekaman sinyal ECG dan PPG, seringkali terganggu dengan derau sehingga harus dibersihkan terlebih dahulu. Oleh karena itu Tugas Akhir ini mengimplementasikan metode Transformasi Wavelet sebagai penghilang derau dan ekstraksi fitur dan diklasifikasi menjadi 4 kelas, yaitu normal, hipertensi, hipertensi stadium 1, dan hipertensi stadium 2 dengan menggunakan Support Vector Machines (SVM). Pada tugas akhir ini hasil yang didapat untuk menghilangkan derau menggunakan dekomposisi sinyalviii menggunakan DWT Daubechies 6 (db6) pada level 8 dan SVM one against all dengan kernel RBF dengan parameter c sebesar 20 dianggap efektif untuk mengambil fitur dan mengklasifikasi rekaman medis dari MIMIC II Database Physionet dan menghasilkan akurasi sebesar 91,67% ==================================================================================================== Measurement of blood pressure through the arm cuff commonly still used in the medical field, but through the cuff measurement is a measurement of an indirect way. This will certainly affect the results of blood pressure measurements will then be used as a benchmark a person's blood pressure or hypertension if he did not. ECG and PPG signal is an electrical signal waves obtained directly from the body that are associated with the activity of the heart. There is an interval of time when the heart pumps blood throughout the body, it is called the Pulse Transit Time (PTT). By calculating the PTT, diastolic and systolic pressure can be calculated so that it can be used as a method of measuring blood pressure. In the recording ECG and PPG signals, often disturbed by noise and should be cleaned first. Therefore, this final project implements Wavelet Transform method as noise removal and features extraction and then classified into four classes, who are normal, hypertension, stage 1 hypertension and stage 2 hypertension using Support Vector Machines (SVM). In this thesis the results obtained to remove noise using a decomposition signal using DWT Daubechies 6 at level 8 and SVM one against all using kernel RBF and parameter c of 20 is considered effective to take on the features and classifying medicalx records of MIMIC II Database Physionet and produce accuracy of 91.67

A STUDY OF MACHINE LEARNING APPLICATION IN BLOOD PRESSURE MEASUREMENT

Master'sMASTER OF ENGINEERIN

Cuffless bood pressure estimation

L'hypertension est une maladie qui affecte plus d'un milliard de personnes dans le monde. Il s'agit d'une des principales causes de décès; le suivi et la gestion de cette maladie sont donc cruciaux. La technologie de mesure de la pression artérielle la plus répandue, utilisant le brassard pressurisé, ne permet cependant pas un suivi en continu de la pression, ce qui limite l'étendue de son utilisation. Ces obstacles pourraient être surmontés par la mesure indirecte de la pression par l'entremise de l'électrocardiographie ou de la photopléthysmographie, qui se prêtent à la création d'appareils portables, confortables et peu coûteux. Ce travail de recherche, réalisé en collaboration avec le département d'ingénierie biomédicale de l'université de Lund, en Suède, porte principalement sur la base de données publique Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) Waveform Datasetde PhysioNet, largement utilisée dans la littérature portant sur le développement et la validation d'algorithmes d'estimation de la pression artérielle sans brassard pressurisé. Puisque ces données proviennent d'unités de soins intensifs et ont été recueillies dans des conditions non contrôlées, plusieurs chercheurs ont avancé que les modèles d'estimation de la pression artérielle se basant sur ces données ne sont pas valides pour la population générale. Pour la première fois dans la littérature, cette hypothèse est ici mise à l'épreuve en comparant les données de MIMIC à un ensemble de données de référence plus représentatif de la population générale et recueilli selon une procédure expérimentale bien définie. Des tests statistiques révèlent une différence significative entre les ensembles de données, ainsi qu'une réponse différente aux changements de pression artérielle, et ce, pour la majorité des caractéristiques extraites du photopléthysmogramme. De plus, les répercussions de ces différences sont démontrées à l'aide d'un test pratique d'estimation de la pression artérielle par apprentissage machine. En effet, un modèle entraîné sur l'un des ensembles de données perd en grande partie sa capacité prédictive lorsque validé sur l'autre ensemble, par rapport à sa performance en validation croisée sur l'ensemble d'entraînement. Ces résultats constituent les contributions principales de ce travail et ont été soumis sous forme d'article à la revue Physiological Measurement. Un volet additionnel de la recherche portant sur l'analyse du pouls par décomposition (pulse de composition analysis ou PDA) est présenté dans un deuxième temps. La PDA est une technique permettant de séparer l'onde du pouls en une composante excitative et ses réflexions, utilisée pour extraire des caractéristiques du signal dans le contexte de l'estimation de la pression artérielle. Les résultats obtenus démontrent que l'estimation de la position temporelle des réflexions à partir de points de référence de la dérivée seconde du signal donne d'aussi bons résultats que leur détermination par la méthode traditionnelle d'approximation successive, tout en étant beaucoup plus rapide. Une méthode récursive rapide de PDA est également étudiée, mais démontrée comme inadéquate dans un contexte de comparaison intersujet.Hypertension affects more than one billion people worldwide. As one of the leading causes of death, tracking and management of the condition is critical, but is impeded by the current cuff-based blood pressure monitoring technology. Continuous and more ubiquitous blood pressure monitoring may be achieved through simpler, cheaper and less invasive cuff-less devices, performing an indirect measure through electrocardiography or photoplethysmography. Produced in collaboration with the department of biomedical engineering of Lund Universityin Sweden, this work focuses on public data that has been widely used in the literature to develop and validate cuffless blood pressure estimation algorithms: The Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) Waveform Dataset from PhysioNet. Because it is sourced from intensive care units and collected in absence of controlled conditions, it has many times been hypothesized that blood pressure estimation models based on its data may not generalize to the normal population. This work tests that hypothesis for the first time by comparing the MIMIC dataset to another reference dataset more representative of the general population and obtained under controlled experimental conditions. Through statistical testing, a majority of photoplethysmogram based features extracted from MIMIC are shown to differ significantly from the reference dataset and to respond differently to blood pressure changes. In addition, the practical impact of those differences is tested through the training and cross validating of machine learning models on both datasets, demonstrating an acute loss of predictive powers of models facing data from outside the dataset used in the training phase. As the main contribution of this work, these findings have been submitted as a journal paper to Physiological Measurement. Additional original research is also presented in relation to pulse decomposition analysis (PDA), a technique used to separate the pulse wave from its reflections, in the context of blood pressure estimation. The results obtained through this work show that when using the timing of reflections as part of blood pressure predictors, estimating those timings from fiducial points in the second derivative works as well as using the traditional and computationally costly successive approximation PDA method, while being many times faster. An alternative fast recursive PDA algorithm is also presented and shown to perform inadequately in an inter-subject comparison context