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A review of machine learning techniques in photoplethysmography for the non-invasive cuff-less measurement of blood pressure
Hypertension or high blood pressure is a leading cause of death throughout the world and a critical factor for increasing the risk of serious diseases, including cardiovascular diseases such as stroke and heart failure. Blood pressure is a primary vital sign that must be monitored regularly for the early detection, prevention and treatment of cardiovascular diseases. Traditional blood pressure measurement techniques are either invasive or cuff-based, which are impractical, intermittent, and uncomfortable for patients. Over the past few decades, several indirect approaches using photoplethysmogram (PPG) have been investigated, namely, pulse transit time, pulse wave velocity, pulse arrival time and pulse wave analysis, in an effort to utilise PPG for estimating blood pressure. Recent advancements in signal processing techniques, including machine learning and artificial intelligence, have also opened up exciting new horizons for PPG-based cuff less and continuous monitoring of blood pressure. Such a device will have a significant and transformative impact in monitoring patients’ vital signs, especially those at risk of cardiovascular disease. This paper provides a comprehensive review for non-invasive cuff-less blood pressure estimation using the PPG approach along with their challenges and limitations
Estimating Blood Pressure from Photoplethysmogram Signal and Demographic Features using Machine Learning Techniques
Hypertension is a potentially unsafe health ailment, which can be indicated
directly from the Blood pressure (BP). Hypertension always leads to other
health complications. Continuous monitoring of BP is very important; however,
cuff-based BP measurements are discrete and uncomfortable to the user. To
address this need, a cuff-less, continuous and a non-invasive BP measurement
system is proposed using Photoplethysmogram (PPG) signal and demographic
features using machine learning (ML) algorithms. PPG signals were acquired from
219 subjects, which undergo pre-processing and feature extraction steps. Time,
frequency and time-frequency domain features were extracted from the PPG and
their derivative signals. Feature selection techniques were used to reduce the
computational complexity and to decrease the chance of over-fitting the ML
algorithms. The features were then used to train and evaluate ML algorithms.
The best regression models were selected for Systolic BP (SBP) and Diastolic BP
(DBP) estimation individually. Gaussian Process Regression (GPR) along with
ReliefF feature selection algorithm outperforms other algorithms in estimating
SBP and DBP with a root-mean-square error (RMSE) of 6.74 and 3.59 respectively.
This ML model can be implemented in hardware systems to continuously monitor BP
and avoid any critical health conditions due to sudden changes.Comment: Accepted for publication in Sensor, 14 Figures, 14 Table
Development of machine learning schemes for use in non-invasive and continuous patient health monitoring
Stephanie Baker developed machine learning schemes for the non-invasive and continuous measurement of blood pressure and respiratory rate from heart activity waveforms. She also constructed machine learning models for mortality risk assessment from vital sign variations. This research contributes several tools that offer significant advancements in patient monitoring and wearable healthcare
A Review of Deep Learning Methods for Photoplethysmography Data
Photoplethysmography (PPG) is a highly promising device due to its advantages
in portability, user-friendly operation, and non-invasive capabilities to
measure a wide range of physiological information. Recent advancements in deep
learning have demonstrated remarkable outcomes by leveraging PPG signals for
tasks related to personal health management and other multifaceted
applications. In this review, we systematically reviewed papers that applied
deep learning models to process PPG data between January 1st of 2017 and July
31st of 2023 from Google Scholar, PubMed and Dimensions. Each paper is analyzed
from three key perspectives: tasks, models, and data. We finally extracted 193
papers where different deep learning frameworks were used to process PPG
signals. Based on the tasks addressed in these papers, we categorized them into
two major groups: medical-related, and non-medical-related. The medical-related
tasks were further divided into seven subgroups, including blood pressure
analysis, cardiovascular monitoring and diagnosis, sleep health, mental health,
respiratory monitoring and analysis, blood glucose analysis, as well as others.
The non-medical-related tasks were divided into four subgroups, which encompass
signal processing, biometric identification, electrocardiogram reconstruction,
and human activity recognition. In conclusion, significant progress has been
made in the field of using deep learning methods to process PPG data recently.
This allows for a more thorough exploration and utilization of the information
contained in PPG signals. However, challenges remain, such as limited quantity
and quality of publicly available databases, a lack of effective validation in
real-world scenarios, and concerns about the interpretability, scalability, and
complexity of deep learning models. Moreover, there are still emerging research
areas that require further investigation
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
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)
Cuffless Blood Pressure in clinical practice: challenges, opportunities and current limits.
Background: Cuffless blood pressure measurement technologies have attracted significant attention for their potential to transform cardiovascular monitoring.Methods: This updated narrative review thoroughly examines the challenges, opportunities, and limitations associated with the implementation of cuffless blood pressure monitoring systems.Results: Diverse technologies, including photoplethysmography, tonometry, and ECG analysis, enable cuffless blood pressure measurement and are integrated into devices like smartphones and smartwatches. Signal processing emerges as a critical aspect, dictating the accuracy and reliability of readings. Despite its potential, the integration of cuffless technologies into clinical practice faces obstacles, including the need to address concerns related to accuracy, calibration, and standardization across diverse devices and patient populations. The development of robust algorithms to mitigate artifacts and environmental disturbances is essential for extracting clear physiological signals. Based on extensive research, this review emphasizes the necessity for standardized protocols, validation studies, and regulatory frameworks to ensure the reliability and safety of cuffless blood pressure monitoring devices and their implementation in mainstream medical practice. Interdisciplinary collaborations between engineers, clinicians, and regulatory bodies are crucial to address technical, clinical, and regulatory complexities during implementation. In conclusion, while cuffless blood pressure monitoring holds immense potential to transform cardiovascular care. The resolution of existing challenges and the establishment of rigorous standards are imperative for its seamless incorporation into routine clinical practice.Conclusion: The emergence of these new technologies shifts the paradigm of cardiovascular health management, presenting a new possibility for non-invasive continuous and dynamic monitoring. The concept of cuffless blood pressure measurement is viable and more finely tuned devices are expected to enter the market, which could redefine our understanding of blood pressure and hypertension
Towards Non-Invasive and Continuous Blood Pressure Monitoring in Neonatal Intensive Care Using Artificial Intelligence: A Narrative Review
Preterm birth is a live birth that occurs before 37 completed weeks of pregnancy. Approximately 11% of babies are born preterm annually worldwide. Blood pressure (BP) monitoring is essential for managing the haemodynamic stability of preterm infants and impacts outcomes. However, current methods have many limitations associated, including invasive measurement, inaccuracies, and infection risk. In this narrative review, we find that artificial intelligence (AI) is a promising tool for the continuous measurement of BP in a neonatal cohort, based on data obtained from non-invasive sensors. Our findings highlight key sensing technologies, AI techniques, and model assessment metrics for BP sensing in the neonatal cohort. Moreover, our findings show that non-invasive BP monitoring leveraging AI has shown promise in adult cohorts but has not been broadly explored for neonatal cohorts. We conclude that there is a significant research opportunity in developing an innovative approach to provide a non-invasive alternative to existing continuous BP monitoring methods, which has the potential to improve outcomes for premature babies
Methodological Role of Mathematics to Estimate Human Blood Pressure Through Biosensors
This paper presents a non-invasive technique and cuff less method for blood pressure measurement with a hardware prototype implementation. The sophisticated feature called pulse transit time (PTT) is extracted and investigated with a development of a smart system which consists of ECG, PPG sensor to estimate the systolic and diastolic blood pressure with support of advanced signal processing methodologies. The proposed method experiments have been carried out in hospital environment and tested with real time patients to validate the proposed method. The maximum error percentage of the proposed system has been shown to be 5.3% of systolic blood pressure (mmHg) and 4.7% of diastolic blood pressure (mmHg). This system also allows the monitoring of patient hypertension and overcome the limitation of cuff-based hospitalized measurement system
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