Hypertension Assessment via ECG and PPG Signals: An Evaluation Using MIMIC Database

Recommendations for evaluating photoplethysmography-based algorithms for blood pressure assessment

Photoplethysmography (PPG) is a non-invasive optical technique that measures changes in blood volume in the microvascular tissue bed of the body. While it shows potential as a clinical tool for blood pressure (BP) assessment and hypertension management, several sources of error can affect its performance. One such source is the PPG-based algorithm, which can lead to measurement bias and inaccuracy. Here, we review seven widely used measures to assess PPG-based algorithm performance and recommend implementing standardized error evaluation steps in their development. This standardization can reduce bias and improve the reliability and accuracy of PPG-based BP estimation, leading to better health outcomes for patients managing hypertension.

A Systematic Approach Focused on Machine Learning Models for Exploring the Landscape of Physiological Measurement and Estimation Using Photoplethysmography (PPG)

A non-invasive optical technique known as photoplethysmography (PPG) can be used to provide various physiological measurements and estimations. PPG can be used to assess cardiovascular disease (CVD). Hypertension is a primary risk factor for CVD and a major health problem worldwide. PPG is popular because of its important applications in the evaluation of cardiac activity, variations in venous blood volume, blood oxygen saturation, blood pressure and heart rate variability, etc. In this study, we provide a comprehensive analysis of the extraction of various physiological parameters using PPG waveforms. In addition, we focused on the role of machine learning (ML) models used for the estimation of blood pressure and hypertension classification based on PPG waveforms to make future research and innovation recommendations. This study will be helpful for researchers, scientists, and medical practitioners working on PPG waveforms for monitoring, screening, and diagnosis, as a comparative study or reference.

Non-Invasive Heart Failure Evaluation Using Machine Learning Algorithms

Heart failure is a prevalent cardiovascular condition with significant health implications, necessitating effective diagnostic strategies for timely intervention. This study explores the potential of continuous monitoring of non-invasive signals, specifically integrating photoplethysmogram (PPG) and electrocardiogram (ECG), for enhancing early detection and diagnosis of heart failure. Leveraging a dataset from the MIMIC-III database, encompassing 682 heart failure patients and 954 controls, our approach focuses on continuous, non-invasive monitoring. Key features, including the QRS interval, RR interval, augmentation index, heart rate, systolic pressure, diastolic pressure, and peak-to-peak amplitude, were carefully selected for their clinical relevance and ability to capture cardiovascular dynamics. This feature selection not only highlighted important physiological indicators but also helped reduce computational complexity and the risk of overfitting in machine learning models. The use of these features in training machine learning algorithms led to a model with impressive accuracy (98%), sensitivity (97.60%), specificity (96.90%), and precision (97.20%). Our integrated approach, combining PPG and ECG signals, demonstrates superior performance compared to single-signal strategies, emphasizing its potential in early and precise heart failure diagnosis. The study also highlights the importance of continuous monitoring with wearable technology, suggesting a significant stride forward in non-invasive cardiovascular health assessment. The proposed approach holds promise for implementation in hardware systems to enable continuous monitoring, aiding in early detection and prevention of critical health conditions.

Continuous Atrial Fibrillation Monitoring From Photoplethysmography: Comparison Between Supervised Deep Learning and Heuristic Signal Processing

BACKGROUND

Continuous monitoring for atrial fibrillation (AF) using photoplethysmography (PPG) from smartwatches or other wearables is challenging due to periods of poor signal quality during motion or suboptimal wearing. As a result, many consumer wearables sample infrequently and only analyze when the user is at rest, which limits the ability to perform continuous monitoring or to quantify AF.

OBJECTIVES

This study aimed to compare 2 methods of continuous monitoring for AF in free-living patients: a well-validated signal processing (SP) heuristic and a convolutional deep neural network (DNN) trained on raw signal.

METHODS

We collected 4 weeks of continuous PPG and electrocardiography signals in 204 free-living patients. Both SP and DNN models were developed and validated both on holdout patients and an external validation set.

RESULTS

The results show that the SP model demonstrated receiver-operating characteristic area under the curve (AUC) of 0.972 (sensitivity 99.6%, specificity: 94.4%), which was similar to the DNN receiver-operating characteristic AUC of 0.973 (sensitivity 92.2, specificity: 95.5%); however, the DNN classified significantly more data (95% vs 62%), revealing its superior tolerance of tracings prone to motion artifact. Explainability analysis revealed that the DNN automatically suppresses motion artifacts, evaluates irregularity, and learns natural AF interbeat variability. The DNN performed better and analyzed more signal in the external validation cohort using a different population and PPG sensor (AUC, 0.994; 97% analyzed vs AUC, 0.989; 88% analyzed).

CONCLUSIONS

DNNs perform at least as well as SP models, classify more data, and thus may be better for continuous PPG monitoring.

Improved Hypertension Risk Assessment with Photoplethysmographic Recordings Combining Deep Learning and Calibration

Hypertension, a primary risk factor for various cardiovascular diseases, is a global health concern. Early identification and effective management of hypertensive individuals are vital for reducing associated health risks. This study explores the potential of deep learning (DL) techniques, specifically GoogLeNet, ResNet-18, and ResNet-50, for discriminating between normotensive (NTS) and hypertensive (HTS) individuals using photoplethysmographic (PPG) recordings. The research assesses the impact of calibration at different time intervals between measurements, considering intervals less than 1 h, 1-6 h, 6-24 h, and over 24 h. Results indicate that calibration is most effective when measurements are closely spaced, with an accuracy exceeding 90% in all the DL strategies tested. For calibration intervals below 1 h, ResNet-18 achieved the highest accuracy (93.32%), sensitivity (84.09%), specificity (97.30%), and F1-score (88.36%). As the time interval between calibration and test measurements increased, classification performance gradually declined. For intervals exceeding 6 h, accuracy dropped below 81% but with all models maintaining accuracy above 71% even for intervals above 24 h. This study provides valuable insights into the feasibility of using DL for hypertension risk assessment, particularly through PPG recordings. It demonstrates that closely spaced calibration measurements can lead to highly accurate classification, emphasizing the potential for real-time applications. These findings may pave the way for advanced, non-invasive, and continuous blood pressure monitoring methods that are both efficient and reliable.

An Open-Source Graphical User Interface-Embedded Automated Electrocardiogram Quality Assessment: A Balanced Class Representation Approach

The rise in cardiovascular diseases necessitates accurate electrocardiogram (ECG) diagnostics, making high-quality ECG recordings essential. Our CNN-LSTM model, embedded in an open-access GUI and trained on balanced datasets collected in clinical settings, excels in automating ECG quality assessment. When tested across three datasets featuring varying ratios of acceptable to unacceptable ECG signals, it achieved an F1 score ranging from 95.87% to 98.40%. Training the model on real noise sources significantly enhances its applicability in real-life scenarios, compared to simulations. Integrated into a user-friendly toolbox, the model offers practical utility in clinical environments. Furthermore, our study underscores the importance of balanced class representation during training and testing phases. We observed a notable F1 score change from 98.09% to 95.87% when the class ratio shifted from 85:15 to 50:50 in the same testing dataset with equal representation. This finding is crucial for future ECG quality assessment research, highlighting the impact of class distribution on the reliability of model training outcomes.

A novel CS-NET architecture based on the unification of CNN, SVM and super-resolution spectrogram to monitor and classify blood pressure using photoplethysmography

CONTEXT

Continuous blood pressure (BP) monitoring plays an important role while treating various cardiovascular diseases and hypertension. A high correlation between arterial blood pressure (ABP) and Photoplethysmogram (PPG) signal enables using a PPG signal to monitor and classify BP continuously. Control of BP in realtime is the basis for the prevention of hypertension.

PROPOSED APPROACH

This work proposes a CS-NET architecture by unifying CNN and SVM approaches to classify BP using PPG signals. The main objective of the CS-NET method is to establish an accurate and reliable algorithm for the ABP classification.

METHODOLOGY

ABP signals are labeled normal and abnormal using the hypertension criteria the American College of Cardiology (ACC)/American Heart Association (AHA) laid down. The proposed CS-NET model incorporates three critical steps in three successive stages. The first stage includes converting a preprocessed PPG signal into a time-frequency (TF) representation called a super-resolution spectrogram by superlet transform. The second stage uses a convolutional neural network (CNN) model with several hidden layers to extract morphological features from every PPG super-resolution spectrogram. The third stage uses a support vector machine (SVM) classifier to classify the PPG signal.

RESULTS

PPG signals are used to train and test the proposed model. The performance of the proposed CS-NET method is tested using MIMIC-II, MIMIC-III, and PPG-BP-figshare database in terms of accuracy and F1 score. Moreover, the CS-NET method achieves better results with high accuracy when compared with other benchmark approaches that require an electrocardiogram signal for reference.

CONCLUSIONS

The proposed model achieved an aggregate classification accuracy of 98.21% across a five-fold cross-validation technique, making it a reliable approach for BP classification in clinical settings and realtime monitoring.

Cuffless Hypertension Detection using Swarm Support Vector Machine Utilizing Photoplethysmogram and Electrocardiogram

Background

Hypertension is associated with severe complications, and its detection is important to provide early information about a hypertension event, which is essential to prevent further complications.

Objective

This study aimed to investigate a strategy for hypertension detection without a cuff using parameters of bioelectric signals, i.e., Electrocardiogram (ECG), Photoplethysmogram (PPG,) and an algorithm of Swarm-based Support Vector Machine (SSVM).

Material and Methods

This experimental study was conducted to develop a hypertension detection system. ECG and PPG bioelectrical records were collected from the Medical Information Mart for Intensive Care (MIMIC) from normal and hypertension participants and processed to find the parameters, used for the inputs of SSVM and comprised Pulse Arrival Time (PAT) and the characteristics of PPG signal derivatives. The SSVM was n Support Vector Machine (SVM) algorithm optimized using particle swarm optimization with Quantum Delta-potential-well (QDPSO). The SSVMs with different inputs were investigated to find the optimal detection performance.

Results

The proposed strategy was performed at 96% in terms of F1-score, accuracy, sensitivity, and specificity with better performance than the other methods tested and methods and also could develop a cuff-free hypertension monitoring system.

Conclusion

Hypertension using SSVM, ECG, and PPG parameters is acceptably performed. The hypertension detection had lower performance utilizing only PPG than both ECG and PPG.

Evaluation of the effect of photoplethysmograms on workers' exposure to methyl bromide using second derivative

Methyl bromide (MB) is worldwide the only effective fumigant heavily used for quarantine pre-shipment treatment and has a critical use exemption for soil fumigations due to its excellent permeability and insecticidal effect. However, MB should be replaced as it is an an ozone-depleting substance and also highly toxic to humans. Recently, MB has been shown to be hazardous even for asymptomatic workers, affecting their central and autonomic nervous systems. However, the effects of MB exposure on vascular health have not been explored. This study aimed to determine whether MB affects the arterial system of asymptomatic workers. We measured the second derivative of the photoplethysmogram (SDPTG) indices, which are indicators of vascular load and aging, and urinary bromide ion (Br-) concentrations in 44 fumigators (study group) and 20 inspectors (control group) before and after fumigation. In fumigators, the mean values of post-work SDPTG indices (b/a, c/a, d/a, e/a, and SDPTG aging index) and Br- levels were significantly changed compared to their pre-work values (p < 0.05), indicating a negative effect on their cardiovascular health. In contrast, SDPTG indices and Br- levels in inspectors did not show any differences before and after work. All SDPTG indices except c/a showed significant correlations with Br- levels in all individuals (p < 0.05). In conclusion, the Br- levels and SDPTG indices of fumigators varied after MB work, and they experienced negative effects on their health despite being asymptomatic.

A Novel Form Factor For PPG-based Blood Pressure Monitoring

Blood pressure (BP) is one of the four main vital signs in medicine and may be a useful signal for wellness tracking and for user-aware interfaces in human-computer interaction. The current standard for BP measurement uses cuff-based devices that block an artery temporarily to get a single, discrete measurement of BP. Recently, there have been significant efforts to measure correlates of BP continuously and non-invasively from relevant signals like photoplethysmography (PPG), which responds to volumetric changes in arteries due to blood pulsations. In this paper, we explore a novel setup with two points of instrumentation, one on the head and a second on the wrist, for recording PPG and estimating the pulse wave velocity, which is a major correlate of BP, along with other waveform-related features. We prospectively tested the device on 10 subjects who followed a protocol for the deliberate variation of BP while ground truth measurements were taken using a reference cuff-device. Generic absolute BP models, which use the collected data for leave-one-subject-out cross-validation, yielded an error of -0.14 ± 7.3 mmHg for systolic BP (SBP) and -0.21±6.7 mmHg for diastolic BP (DBP), which are within the regulatory limits of 5 ± 8 mmHg. Notably, the correlation between the predicted BPs and the ground truth BPs was higher for SBP (r = 0.74, p < 0.001) than for DBP (r = 0.34, p < 0.001). The results show that the proposed form factor can extract BP-related features that could be used for continuous, cuff-less BP monitoring.

Photoplethysmography Driven Hypertension Identification: A Pilot Study

To prevent and diagnose hypertension early, there has been a growing demand to identify its states that align with patients. This pilot study aims to research how a non-invasive method using photoplethysmographic (PPG) signals works together with deep learning algorithms. A portable PPG acquisition device (Max30101 photonic sensor) was utilized to (1) capture PPG signals and (2) wirelessly transmit data sets. In contrast to traditional feature engineering machine learning classification schemes, this study preprocessed raw data and applied a deep learning algorithm (LSTM-Attention) directly to extract deeper correlations between these raw datasets. The Long Short-Term Memory (LSTM) model underlying a gate mechanism and memory unit enables it to handle long sequence data more effectively, avoiding gradient disappearance and possessing the ability to solve long-term dependencies. To enhance the correlation between distant sampling points, an attention mechanism was introduced to capture more data change features than a separate LSTM model. A protocol with 15 healthy volunteers and 15 hypertension patients was implemented to obtain these datasets. The processed result demonstrates that the proposed model could present satisfactory performance (accuracy: 0.991; precision: 0.989; recall: 0.993; F1-score: 0.991). The model we proposed also demonstrated superior performance compared to related studies. The outcome indicates the proposed method could effectively diagnose and identify hypertension; thus, a paradigm to cost-effectively screen hypertension could rapidly be established using wearable smart devices.

Wearable Continuous Blood Pressure Monitoring Devices Based on Pulse Wave Transit Time and Pulse Arrival Time: A Review

Continuous blood pressure (BP) monitoring is of great significance for the real-time monitoring and early prevention of cardiovascular diseases. Recently, wearable BP monitoring devices have made great progress in the development of daily BP monitoring because they adapt to long-term and high-comfort wear requirements. However, the research and development of wearable continuous BP monitoring devices still face great challenges such as obvious motion noise and slow dynamic response speeds. The pulse wave transit time method which is combined with photoplethysmography (PPG) waves and electrocardiogram (ECG) waves for continuous BP monitoring has received wide attention due to its advantages in terms of excellent dynamic response characteristics and high accuracy. Here, we review the recent state-of-art wearable continuous BP monitoring devices and related technology based on the pulse wave transit time; their measuring principles, design methods, preparation processes, and properties are analyzed in detail. In addition, the potential development directions and challenges of wearable continuous BP monitoring devices based on the pulse wave transit time method are discussed.

Blood pressure stratification using photoplethysmography and light gradient boosting machine

Introduction: Globally, hypertension (HT) is a substantial risk factor for cardiovascular disease and mortality; hence, rapid identification and treatment of HT is crucial. In this study, we tested the light gradient boosting machine (LightGBM) machine learning method for blood pressure stratification based on photoplethysmography (PPG), which is used in most wearable devices. Methods: We used 121 records of PPG and arterial blood pressure (ABP) signals from the Medical Information Mart for Intensive Care III public database. PPG, velocity plethysmography, and acceleration plethysmography were used to estimate blood pressure; the ABP signals were used to determine the blood pressure stratification categories. Seven feature sets were established and used to train the Optuna-tuned LightGBM model. Three trials compared normotension (NT) vs. prehypertension (PHT), NT vs. HT, and NT + PHT vs. HT. Results: The F1 scores for these three classification trials were 90.18%, 97.51%, and 92.77%, respectively. The results showed that combining multiple features from PPG and its derivative led to a more accurate classification of HT classes than using features from only the PPG signal. Discussion: The proposed method showed high accuracy in stratifying HT risks, providing a noninvasive, rapid, and robust method for the early detection of HT, with promising applications in the field of wearable cuffless blood pressure measurement.

Advancement in the Cuffless and Noninvasive Measurement of Blood Pressure: A Review of the Literature and Open Challenges

Hypertension is a chronic condition that is one of the prominent reasons behind cardiovascular disease, brain stroke, and organ failure. Left unnoticed and untreated, the deterioration in a health condition could even result in mortality. If it can be detected early, with proper treatment, undesirable outcomes can be avoided. Until now, the gold standard is the invasive way of measuring blood pressure (BP) using a catheter. Additionally, the cuff-based and noninvasive methods are too cumbersome or inconvenient for frequent measurement of BP. With the advancement of sensor technology, signal processing techniques, and machine learning algorithms, researchers are trying to find the perfect relationships between biomedical signals and changes in BP. This paper is a literature review of the studies conducted on the cuffless noninvasive measurement of BP using biomedical signals. Relevant articles were selected using specific criteria, then traditional techniques for BP measurement were discussed along with a motivation for cuffless measurement use of biomedical signals and machine learning algorithms. The review focused on the progression of different noninvasive cuffless techniques rather than comparing performance among different studies. The literature survey concluded that the use of deep learning proved to be the most accurate among all the cuffless measurement techniques. On the other side, this accuracy has several disadvantages, such as lack of interpretability, computationally extensive, standard validation protocol, and lack of collaboration with health professionals. Additionally, the continuing work by researchers is progressing with a potential solution for these challenges. Finally, future research directions have been provided to encounter the challenges.

Variations in the Second Derivative of a Photoplethysmogram with Age in Healthy Korean Adults

Second derivative of photoplethysmogram (SDPTG) indices correlate with aging and vascular health. The trend of SDPTG indices with age has not yet been studied in the Korean population. Various SDPTG indices were measured in 300 healthy Koreans (150 men and 150 women), aged 19−69 years, stratified into five age groups consisting of 60 people (30 men and 30 women) in each age group from their 20s to 60s. The values of the SDPTG indices clearly showed distinct variations with age in healthy Korean groups (p < 0.001 for all indices). b/a increased linearly with age (y = 0.0045x − 0.803), as did SDPTG aging index (SDPTG-AI) (y = 0.0162x − 1.1389). c/a decreased linearly with age (y = −0.0044x + 0.1017), as did d/a (y = −0.0062x + 0.034) and e/a (y = −0.001x + 0.2002). A significant sex difference was shown in b/a and e/a ratios and SDPTG-AI (p < 0.001 for all indices); women had a higher b/a ratio (−0.55 ± 0.14 versus −0.65 ± 0.13) and SDPTG-AI (−0.33 ± 0.3 versus −0.52 ± 0.33) and a lower e/a ratio (0.13 ± 0.06 versus 0.18 ± 0.07) than men. A linear regression model of diverse SDPTG indices was provided according to the age of healthy Koreans, which may be valuable in preventing diseases related to vascular conditions by estimating the degradation of arterial function.

Remote blood pressure monitoring with a wearable photoplethysmographic device in patients undergoing coronary angiography: the senbiosys substudy

AIMS

The purpose of this study is to evaluate the accuracy of the Senbiosys device in measuring blood pressure (BP) by photoplethysmography (PPG) in patients undergoing coronary angiography.

METHODS

This is a substudy within the Senbiosys trial, which is a prospective, single-arm, single-center study, evaluating the accuracy of BP estimation of the Senbiosys device compared to invasive BP. Patients referred for coronary angiography underwent invasive BP measurement and simultaneously wore the Senbiosys ring. SBP and DBP estimations measured by the Senbiosys device were compared with invasive measurements.

RESULTS

A total of 25 patients were included. Overall, 708 epochs with adequate PPG signal belonging to 17 patients were analyzed. A total of 84% of the SBP estimates and 99% of the DBP estimates have an absolute error of less than 10 mmHg compared with the invasive measurements. Mean difference was 2.3 ± 7.0 mmHg and 0.5 ± 3.5 mmHg for SBP and DBP, respectively.

CONCLUSION

The Senbiosys device is accurate enough to determine BP in a selected population undergoing coronary angiography.

[Diagnostic predictive value of liver biopsy for clinical aspects]

Background

The quest for improved diagnosis and treatment in home health care models has led to the development of wearable medical devices for remote vital signs monitoring. An accurate signal and a high diagnostic yield are critical for the cost-effectiveness of wearable health care monitoring systems and their widespread application in resource-constrained environments. Despite technological advances, the information acquired by these devices can be contaminated by motion artifacts (MA) leading to misdiagnosis or repeated procedures with increases in associated costs. This makes it necessary to develop methods to improve the quality of the signal acquired by these devices.

Objective

We aimed to present a novel method for electrocardiogram (ECG) signal denoising to reduce MA. We aimed to analyze the method’s performance and to compare its performance to that of existing approaches.

Methods

We present the novel Redundant denoising Independent Component Analysis method for ECG signal denoising based on the redundant and simultaneous acquisition of ECG signals and movement information, multichannel processing, and performance assessment considering the information contained in the signal waveform. The method is based on data including ECG signals from the patient’s chest and back, the acquisition of triaxial movement signals from inertial measurement units, a reference signal synthesized from an autoregressive model, and the separation of interest and noise sources through multichannel independent component analysis.

Results

The proposed method significantly reduced MA, showing better performance and introducing a smaller distortion in the interest signal compared with other methods. Finally, the performance of the proposed method was compared to that of wavelet shrinkage and wavelet independent component analysis through the assessment of signal-to-noise ratio, dynamic time warping, and a proposed index based on the signal waveform evaluation with an ensemble average ECG.

Conclusions

Our novel ECG denoising method is a contribution to converting wearable devices into medical monitoring tools that can be used to support the remote diagnosis and monitoring of cardiovascular diseases. A more accurate signal substantially improves the diagnostic yield of wearable devices. A better yield improves the devices’ cost-effectiveness and contributes to their widespread application.

Motion Artifact Reduction in Electrocardiogram Signals Through a Redundant Denoising Independent Component Analysis Method for Wearable Health Care Monitoring Systems: Algorithm Development and Validation

Background

The quest for improved diagnosis and treatment in home health care models has led to the development of wearable medical devices for remote vital signs monitoring. An accurate signal and a high diagnostic yield are critical for the cost-effectiveness of wearable health care monitoring systems and their widespread application in resource-constrained environments. Despite technological advances, the information acquired by these devices can be contaminated by motion artifacts (MA) leading to misdiagnosis or repeated procedures with increases in associated costs. This makes it necessary to develop methods to improve the quality of the signal acquired by these devices.

Objective

We aimed to present a novel method for electrocardiogram (ECG) signal denoising to reduce MA. We aimed to analyze the method’s performance and to compare its performance to that of existing approaches.

Methods

We present the novel Redundant denoising Independent Component Analysis method for ECG signal denoising based on the redundant and simultaneous acquisition of ECG signals and movement information, multichannel processing, and performance assessment considering the information contained in the signal waveform. The method is based on data including ECG signals from the patient’s chest and back, the acquisition of triaxial movement signals from inertial measurement units, a reference signal synthesized from an autoregressive model, and the separation of interest and noise sources through multichannel independent component analysis.

Results

The proposed method significantly reduced MA, showing better performance and introducing a smaller distortion in the interest signal compared with other methods. Finally, the performance of the proposed method was compared to that of wavelet shrinkage and wavelet independent component analysis through the assessment of signal-to-noise ratio, dynamic time warping, and a proposed index based on the signal waveform evaluation with an ensemble average ECG.

Conclusions

Our novel ECG denoising method is a contribution to converting wearable devices into medical monitoring tools that can be used to support the remote diagnosis and monitoring of cardiovascular diseases. A more accurate signal substantially improves the diagnostic yield of wearable devices. A better yield improves the devices’ cost-effectiveness and contributes to their widespread application.

Classification of Blood Pressure Levels Based on Photoplethysmogram and Electrocardiogram Signals with a Concatenated Convolutional Neural Network

Hypertension is a severe public health issue worldwide that significantly increases the risk of cardiac vascular disease, stroke, brain hemorrhage, and renal dysfunction. Early screening of blood pressure (BP) levels is essential to prevent the dangerous complication associated with hypertension as the leading cause of death. Recent studies have focused on employing photoplethysmograms (PPG) with machine learning to classify BP levels. However, several studies claimed that electrocardiograms (ECG) also strongly correlate with blood pressure. Therefore, we proposed a concatenated convolutional neural network which integrated the features extracted from PPG and ECG signals. This study used the MIMIC III dataset, which provided PPG, ECG, and arterial blood pressure (ABP) signals. A total of 14,298 signal segments were obtained from 221 patients, which were divided into 9150 signals of train data, 2288 signals of validation data, and 2860 signals of test data. In the training process, five-fold cross-validation was applied to select the best model with the highest classification performance. The proposed concatenated CNN architecture using PPG and ECG obtained the highest test accuracy of 94.56-95.15% with a 95% confidence interval in classifying BP levels into hypotension, normotension, prehypertension, hypertension stage 1, and hypertension stage 2. The result shows that the proposed method is a promising solution to categorize BP levels effectively, assisting medical personnel in making a clinical diagnosis.

Affective Computing Based on Morphological Features of Photoplethysmography for Patients with Hypertension

Negative and positive emotions are the risk and protective factors for the cause and prognosis of hypertension. This study aimed to use five photoplethysmography (PPG) waveform indices and affective computing (AC) to discriminate the emotional states in patients with hypertension. Forty-three patients with essential hypertension were measured for blood pressure and PPG signals under baseline and four emotional conditions (neutral, anger, happiness, and sadness), and the PPG signals were transformed into the mean standard deviation of five PPG waveform indices. A support vector machine was used as a classifier. The performance of the classifier was verified by using resubstitution and six-fold cross-validation (CV) methods. Feature selectors, including full search and genetic algorithm (GA), were used to select effective feature combinations. Traditional statistical analyses only differentiated between the emotional states and baseline, whereas AC achieved 100% accuracy in distinguishing between the emotional states and baseline by using the resubstitution method. AC showed high accuracy rates when used with 10 waveform features in distinguishing the records into two, three, and four classes by applying a six-fold CV. The GA feature selector further boosted the accuracy to 78.97%, 74.22%, and 67.35% in two-, three-, and four-class differentiation, respectively. The proposed AC achieved high accuracy in categorizing PPG records into distinct emotional states with features extracted from only five waveform indices. The results demonstrated the effectiveness of the five indices and the proposed AC in patients with hypertension.

Blood Pressure Measurement: From Cuff-Based to Contactless Monitoring

Blood pressure (BP) determines whether a person has hypertension and offers implications as to whether he or she could be affected by cardiovascular disease. Cuff-based sphygmomanometers have traditionally provided both accuracy and reliability, but they require bulky equipment and relevant skills to obtain precise measurements. BP measurement from photoplethysmography (PPG) signals has become a promising alternative for convenient and unobtrusive BP monitoring. Moreover, the recent developments in remote photoplethysmography (rPPG) algorithms have enabled new innovations for contactless BP measurement. This paper illustrates the evolution of BP measurement techniques from the biophysical theory, through the development of contact-based BP measurement from PPG signals, and to the modern innovations of contactless BP measurement from rPPG signals. We consolidate knowledge from a diverse background of academic research to highlight the importance of multi-feature analysis for improving measurement accuracy. We conclude with the ongoing challenges, opportunities, and possible future directions in this emerging field of research.

Advances in Photoplethysmography for Personalized Cardiovascular Monitoring

Photoplethysmography (PPG) is garnering substantial interest due to low cost, noninvasiveness, and its potential for diagnosing cardiovascular diseases, such as cardiomyopathy, heart failure, and arrhythmia. The signals obtained through PPG can yield information based on simple analyses, such as heart rate. In contrast, when accompanied by the complex analysis of sophisticated signals, valuable information, such as blood pressure, sympathetic nervous system activity, and heart rate variability, can be obtained. For a complex analysis, a better understanding of the sources of noise, which create limitations in the application of PPG, is needed to get reliable information to assess cardiovascular health. Therefore, this Special Issue handles literature about noises and how they affect the waveform of the PPG caused by individual variations (e.g., skin tone, obesity, age, and gender), physiology (e.g., respiration, venous pulsation, body site of measurement, and body temperature), and external factors (e.g., motion artifact, ambient light, and applied pressure to the skin). It also covers the issues that still need to be considered in each situation.

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.

Research on Digital Technology Use in Cardiology: Bibliometric Analysis

Background

Digital technology uses in cardiology have become a popular research focus in recent years. However, there has been no published bibliometric report that analyzed the corresponding academic literature in order to derive key publishing trends and characteristics of this scientific area.

Objective

We used a bibliometric approach to identify and analyze the academic literature on digital technology uses in cardiology, and to unveil popular research topics, key authors, institutions, countries, and journals. We further captured the cardiovascular conditions and diagnostic tools most commonly investigated within this field.

Methods

The Web of Science electronic database was queried to identify relevant papers on digital technology uses in cardiology. Publication and citation data were acquired directly from the database. Complete bibliographic data were exported to VOSviewer, a dedicated bibliometric software package, and related to the semantic content of titles, abstracts, and keywords. A term map was constructed for findings visualization.

Results

The analysis was based on data from 12,529 papers. Of the top 5 most productive institutions, 4 were based in the United States. The United States was the most productive country (4224/12,529, 33.7%), followed by United Kingdom (1136/12,529, 9.1%), Germany (1067/12,529, 8.5%), China (682/12,529, 5.4%), and Italy (622/12,529, 5.0%). Cardiovascular diseases that had been frequently investigated included hypertension (152/12,529, 1.2%), atrial fibrillation (122/12,529, 1.0%), atherosclerosis (116/12,529, 0.9%), heart failure (106/12,529, 0.8%), and arterial stiffness (80/12,529, 0.6%). Recurring modalities were electrocardiography (170/12,529, 1.4%), angiography (127/12,529, 1.0%), echocardiography (127/12,529, 1.0%), digital subtraction angiography (111/12,529, 0.9%), and photoplethysmography (80/12,529, 0.6%). For a literature subset on smartphone apps and wearable devices, the Journal of Medical Internet Research (20/632, 3.2%) and other JMIR portfolio journals (51/632, 8.0%) were the major publishing venues.

Conclusions

Digital technology uses in cardiology target physicians, patients, and the general public. Their functions range from assisting diagnosis, recording cardiovascular parameters, and patient education, to teaching laypersons about cardiopulmonary resuscitation. This field already has had a great impact in health care, and we anticipate continued growth.

The Relevance of Calibration in Machine Learning-Based Hypertension Risk Assessment Combining Photoplethysmography and Electrocardiography

The detection of hypertension (HT) is of great importance for the early diagnosis of cardiovascular diseases (CVDs), as subjects with high blood pressure (BP) are asymptomatic until advanced stages of the disease. The present study proposes a classification model to discriminate between normotensive (NTS) and hypertensive (HTS) subjects employing electrocardiographic (ECG) and photoplethysmographic (PPG) recordings as an alternative to traditional cuff-based methods. A total of 913 ECG, PPG and BP recordings from 69 subjects were analyzed. Then, signal preprocessing, fiducial points extraction and feature selection were performed, providing 17 discriminatory features, such as pulse arrival and transit times, that fed machine-learning-based classifiers. The main innovation proposed in this research uncovers the relevance of previous calibration to obtain accurate HT risk assessment. This aspect has been assessed using both close and distant time test measurements with respect to calibration. The k-nearest neighbors-classifier provided the best outcomes with an accuracy for new subjects before calibration of 51.48%. The inclusion of just one calibration measurement into the model improved classification accuracy by 30%, reaching gradually more than 96% with more than six calibration measurements. Accuracy decreased with distance to calibration, but remained outstanding even days after calibration. Thus, the use of PPG and ECG recordings combined with previous subject calibration can significantly improve discrimination between NTS and HTS individuals. This strategy could be implemented in wearable devices for HT risk assessment as well as to prevent CVDs.

Analysis and risk estimation system for heart attack using EDENN algorithm

Heart related diseases are very common in the present scenario. In the past two decades the number of heart patients have increased to a large extent. Due to this abrupt rise in the number of patients, the death count has also increased. Thus, an efficient and accurate system must be developed for the diagnosis of heart related diseases, as the present methods available are not accurate enough and are insufficient for the Heart Attack (HA) and its Risk Analysis (RA). This paper propounds a system for HA risk estimation by the use of an Enhanced Deep Elman Neural Network (EDENN). In this system a Photoplethysmography (PPG) signal is inputted and pre-processed for noise removal. Further, Signal Decomposition (SD) is done, and the vital signs are estimated like Blood Pressure (BP), Respiratory Rate (RR) and Cardiac Autonomic Nervous System (CANS). For the BP estimation, Modified Maximum Amplitude Algorithm (MMAA) method is used and for the decomposed signal processing the Improved Incremental Merge Segmentation (IIMS) is used. As for features, Variation of amplitude, frequency and intensity are calculated and merged.

Automated Detection of Hypertension Using Continuous Wavelet Transform and a Deep Neural Network with Ballistocardiography Signals

Managing hypertension (HPT) remains a significant challenge for humanity. Despite advancements in blood pressure (BP)-measuring systems and the accessibility of effective and safe anti-hypertensive medicines, HPT is a major public health concern. Headaches, dizziness and fainting are common symptoms of HPT. In HPT patients, normalcy may be observed at one instant and abnormality may prevail during a long duration of 24 h ambulatory BP. This may cause difficulty in identifying patients with HPT, and hence there is a possibility that individuals may be untreated or administered insufficiently. Most importantly, uncontrolled HPT can lead to severe complications (stroke, heart attack, kidney disease, and heart failure), mainly ignoring the signs in nascent stages. HPT in the beginning stages may not present distinct symptoms and may be difficult to diagnose from standard physiological signals. Hence, ballistocardiography (BCG) signal was used in this study to detect HPT automatically. The processed signals from BCG were converted into scalogram images using a continuous wavelet transform (CWT) and were then fed into a 2-D convolutional neural network model (2D-CNN). The model was trained to learn and recognize BCG patterns of healthy controls (HC) and HPT classes. Our proposed model obtained a high classification accuracy of 86.14% with a ten-fold cross-validation (CV) strategy. Hence, this is the first use of a 2D-CNN model (deep-learning algorithm) to detect HPT employing BCG signals.

A supervised machine learning semantic segmentation approach for detecting artifacts in plethysmography signals from wearables

Objective. Wearable devices equipped with plethysmography (PPG) sensors provided a low-cost, long-term solution to early diagnosis and continuous screening of heart conditions. However PPG signals collected from such devices often suffer from corruption caused by artifacts. The objective of this study is to develop an effective supervised algorithm to locate the regions of artifacts within PPG signals.Approach. We treat artifact detection as a 1D segmentation problem. We solve it via a novel combination of an active-contour-based loss and an adapted U-Net architecture. The proposed algorithm was trained on the PPG DaLiA training set, and further evaluated on the PPG DaLiA testing set, WESAD dataset and TROIKA dataset.Main results. We evaluated with the DICE score, a well-established metric for segmentation accuracy evaluation in the field of computer vision. The proposed method outperforms baseline methods on all three datasets by a large margin (≈7 percentage points above the next best method). On the PPG DaLiA testing set, WESAD dataset and TROIKA dataset, the proposed method achieved 0.8734 ± 0.0018, 0.9114 ± 0.0033 and 0.8050 ± 0.0116 respectively. The next best method only achieved 0.8068 ± 0.0014, 0.8446 ± 0.0013 and 0.7247 ± 0.0050.Significance. The proposed method is able to pinpoint exact locations of artifacts with high precision; in the past, we had only a binary classification of whether a PPG signal has good or poor quality. This more nuanced information will be critical to further inform the design of algorithms to detect cardiac arrhythmia.

Assessing hemodynamics from the photoplethysmogram to gain insights into vascular age: A review from VascAgeNet

The photoplethysmogram (PPG) signal is widely measured by clinical and consumer devices, and it is emerging as a potential tool for assessing vascular age. The shape and timing of the PPG pulse wave are both influenced by normal vascular aging, changes in arterial stiffness and blood pressure, and atherosclerosis. This review summarizes research into assessing vascular age from the PPG. Three categories of approaches are described: 1) those which use a single PPG signal (based on pulse wave analysis), 2) those which use multiple PPG signals (such as pulse transit time measurement), and 3) those which use PPG and other signals (such as pulse arrival time measurement). Evidence is then presented on the performance, repeatability and reproducibility, and clinical utility of PPG-derived parameters of vascular age. Finally, the review outlines key directions for future research to realize the full potential of photoplethysmography for assessing vascular age.

Pulse arrival time as a surrogate of blood pressure

Various models have been proposed for the estimation of blood pressure (BP) from pulse transit time (PTT). PTT is defined as the time delay of the pressure wave, produced by left ventricular contraction, measured between a proximal and a distal site along the arterial tree. Most researchers, when they measure the time difference between the peak of the R-wave in the electrocardiogram signal (corresponding to left ventricular depolarisation) and a fiducial point in the photoplethysmogram waveform (as measured by a pulse oximeter attached to the fingertip), describe this erroneously as the PTT. In fact, this is the pulse arrival time (PAT), which includes not only PTT, but also the time delay between the electrical depolarisation of the heart's left ventricle and the opening of the aortic valve, known as pre-ejection period (PEP). PEP has been suggested to present a significant limitation to BP estimation using PAT. This work investigates the impact of PEP on PAT, leading to a discussion on the best models for BP estimation using PAT or PTT. We conducted a clinical study involving 30 healthy volunteers (53.3% female, 30.9 ± 9.35 years old, with a body mass index of 22.7 ± 3.2 kg/m[Formula: see text]). Each session lasted on average 27.9 ± 0.6 min and BP was varied by an infusion of phenylephrine (a medication that causes venous and arterial vasoconstriction). We introduced new processing steps for the analysis of PAT and PEP signals. Various population-based models (Poon, Gesche and Fung) and a posteriori models (inverse linear, inverse squared and logarithm) for estimation of BP from PTT or PAT were evaluated. Across the cohort, PEP was found to increase by 5.5 ms ± 4.5 ms from its baseline value. Variations in PTT were significantly larger in amplitude, - 16.8 ms ± 7.5 ms. We suggest, therefore, that for infusions of phenylephrine, the contribution of PEP on PAT can be neglected. All population-based models produced large BP estimation errors, suggesting that they are insufficient for modelling the complex pathways relating changes in PTT or PAT to changes in BP. Although PAT is inversely correlated with systolic blood pressure (SBP), the gradient of this relationship varies significantly from individual to individual, from - 2946 to - 470.64 mmHg/s in our dataset. For the a posteriori inverse squared model, the root mean squared errors (RMSE) for systolic and diastolic blood pressure (DBP) estimation from PAT were 5.49 mmHg and 3.82 mmHg, respectively. The RMSEs for SBP and DBP estimation by PTT were 4.51 mmHg and 3.53 mmHg, respectively. These models take into account individual calibration curves required for accurate blood pressure estimation. The best performing population-based model (Poon) reported error values around double that of the a posteriori inverse squared model, and so the use of population-based models is not justified.

Improving the Accuracy in Classification of Blood Pressure from Photoplethysmography Using Continuous Wavelet Transform and Deep Learning

Background

Continuous wavelet transform (CWT) based scalogram can be used for photoplethysmography (PPG) signal transformation to classify blood pressure (BP) with deep learning. We aimed to investigate the determinants that can improve the accuracy of BP classification based on PPG and deep learning and establish a better algorithm for the prediction.

Methods

The dataset from PhysioNet was accessed to extract raw PPG signals for testing and its corresponding BPs as category labels. The BP category of normal or abnormal followed the criteria of the 2017 American College of Cardiology/American Heart Association (ACC/AHA) Hypertension Guidelines. The PPG signals were transformed into 224 ∗ 224 ∗ 3-pixel scalogram via different CWTs and segment units. All of them are fed into different convolutional neural networks (CNN) for training and validation. The receiver-operating characteristic and loss and accuracy curves were used to evaluate and compare the performance of different methods.

Results

Both wavelet type and segment length could affect the accuracy, and Cgau1 wavelet and segment-300 revealed the best performance (accuracy 90%) without obvious overfitting. This method performed better than previously reported MATLAB Morse wavelet transformed scalogram on both of our proposed CNN and CNN-GoogLeNet.

Conclusions

We have established a new algorithm with high accuracy to predict BP classification from PPG via matching of CWT type and segment length, which is a promising solution for rapid prediction of BP classification from real-time processing of PPG signal on a wearable device.

Hypertension assessment based on feature extraction using a photoplethysmography signal and its derivatives

Objective.Long-term abnormal blood pressure (BP) can lead to various cardiovascular diseases; therefore, it is significant to assess BP status as a preventative measure. In this study, a feature-extraction-based approach is proposed and performed on an open clinical trial dataset.Approach.Firstly, a complete ensemble of empirical mode decomposition with an adaptive noise algorithm and wavelet threshold analysis is applied to eliminate the noise interference from an original photoplethysmography (PPG) signal compared to other signal filters. Considering the strong connection between hypertension and diabetes, an analysis of variance test with a 95% confidence interval is firstly carried out to select these leading extracted morphological features, which are uniquely related to hypertension, from the PPG signal and its derivatives. Subsequently a variety of classification models are evaluated at different BP levels and their performances are compared.Main results and Significance.The test results demonstrate that the support vector machine classification model achieves a greater performance compared to other explored models in this paper, with accuracy of 78%, 87% and 88% for cases including normal versus prehypertension subjects, normotension versus hypertension subjects and non-hypertension versus hypertension subjects, respectively, which further illustrates the great potential of the proposed method in hypertension assessment.

Learning and non-learning algorithms for cuffless blood pressure measurement: a review

The machine learning approach has gained a significant attention in the healthcare sector because of the prospect of developing new techniques for medical devices and handling the critical database of chronic diseases. The learning approach has potential to analyze complex medical data, disease diagnosis, and patient monitoring system, and to monitor e-health record. Non-invasive cuffless blood pressure (CLBP) measurement secured a significant position in the patient monitoring system. From a few recent decades, the importance of cuffless technology has been perceived towards continuous monitoring of blood pressure (BP) and supplementary efforts have been made towards its continuous monitoring. However, the optimal method that measures BP unambiguously and continuously has not yet emerged along with issues like calibration time, accuracy and long-term estimation of BP with miniaturizing hardware. The present study provides an insight into several learning algorithms along with their feature selection models. Various challenges and future improvements towards the current state of machine learning in healthcare industries are discussed in the present review. The bottom line of this study is to provide a comprehensive perspective of the machine learning approach of CLBP for the generation of highly precise predictive models for continuous BP measurement.

Automated Detection of Hypertension Using Physiological Signals: A Review

Arterial hypertension (HT) is a chronic condition of elevated blood pressure (BP), which may cause increased incidence of cardiovascular disease, stroke, kidney failure and mortality. If the HT is diagnosed early, effective treatment can control the BP and avert adverse outcomes. Physiological signals like electrocardiography (ECG), photoplethysmography (PPG), heart rate variability (HRV), and ballistocardiography (BCG) can be used to monitor health status but are not directly correlated with BP measurements. The manual detection of HT using these physiological signals is time consuming and prone to human errors. Hence, many computer-aided diagnosis systems have been developed. This paper is a systematic review of studies conducted on the automated detection of HT using ECG, HRV, PPG and BCG signals. In this review, we have identified 23 studies out of 250 screened papers, which fulfilled our eligibility criteria. Details of the study methods, physiological signal studied, database used, various nonlinear techniques employed, feature extraction, and diagnostic performance parameters are discussed. The machine learning and deep learning based methods based on ECG and HRV signals have yielded the best performance and can be used for the development of computer-aided diagnosis of HT. This work provides insights that may be useful for the development of wearable for continuous cuffless remote monitoring of BP based on ECG and HRV signals.

Sources of Inaccuracy in Photoplethysmography for Continuous Cardiovascular Monitoring

Photoplethysmography (PPG) is a low-cost, noninvasive optical technique that uses change in light transmission with changes in blood volume within tissue to provide information for cardiovascular health and fitness. As remote health and wearable medical devices become more prevalent, PPG devices are being developed as part of wearable systems to monitor parameters such as heart rate (HR) that do not require complex analysis of the PPG waveform. However, complex analyses of the PPG waveform yield valuable clinical information, such as: blood pressure, respiratory information, sympathetic nervous system activity, and heart rate variability. Systems aiming to derive such complex parameters do not always account for realistic sources of noise, as testing is performed within controlled parameter spaces. A wearable monitoring tool to be used beyond fitness and heart rate must account for noise sources originating from individual patient variations (e.g., skin tone, obesity, age, and gender), physiology (e.g., respiration, venous pulsation, body site of measurement, and body temperature), and external perturbations of the device itself (e.g., motion artifact, ambient light, and applied pressure to the skin). Here, we present a comprehensive review of the literature that aims to summarize these noise sources for future PPG device development for use in health monitoring.

Using CNN and HHT to Predict Blood Pressure Level Based on Photoplethysmography and Its Derivatives

According to the WTO, there were 1.13 billion hypertension patients worldwide in 2015. The WTO encouraged people to check the blood pressure regularly because a large amount of patients do not have any symptoms. However, traditional cuff measurement results are not enough to represent the patient′s blood pressure status over a period of time. Therefore, there is an urgent need for portable, easy to operate, continuous measurement, and low-cost blood pressure measuring devices. In this paper, we adopted the convolutional neural network (CNN), based on the Hilbert–Huang Transform (HHT) method, to predict blood pressure (BP) risk level using photoplethysmography (PPG). Considering that the PPG′s first and second derivative signals are related to atherosclerosis and vascular elasticity, we created a dataset called PPG+; the images of PPG+ carry information on PPG and its derivatives. We built three classification experiments by collecting 582 data records (the length of each record is 10 s) from the Medical Information Mart for Intensive Care (MIMIC) database: NT (normotension) vs. HT (hypertension), NT vs. PHT (prehypertension), and (NT + PHT) vs. HT; the F1 scores of the PPG + experiments using AlexNet were 98.90%, 85.80%, and 93.54%, respectively. We found that, first, the dataset established by the HHT method performed well in the BP grade prediction experiment. Second, because the Hilbert spectra of the PPG are simple and periodic, AlexNet, which has only 8 layers, got better results. More layers instead increased the cost and difficulty of training.

Expert Hypertension Detection System Featuring Pulse Plethysmograph Signals and Hybrid Feature Selection and Reduction Scheme

Hypertension is an antecedent to cardiac disorders. According to the World Health Organization (WHO), the number of people affected with hypertension will reach around 1.56 billion by 2025. Early detection of hypertension is imperative to prevent the complications caused by cardiac abnormalities. Hypertension usually possesses no apparent detectable symptoms; hence, the control rate is significantly low. Computer-aided diagnosis based on machine learning and signal analysis has recently been applied to identify biomarkers for the accurate prediction of hypertension. This research proposes a new expert hypertension detection system (EHDS) from pulse plethysmograph (PuPG) signals for the categorization of normal and hypertension. The PuPG signal data set, including rich information of cardiac activity, was acquired from healthy and hypertensive subjects. The raw PuPG signals were preprocessed through empirical mode decomposition (EMD) by decomposing a signal into its constituent components. A combination of multi-domain features was extracted from the preprocessed PuPG signal. The features exhibiting high discriminative characteristics were selected and reduced through a proposed hybrid feature selection and reduction (HFSR) scheme. Selected features were subjected to various classification methods in a comparative fashion in which the best performance of 99.4% accuracy, 99.6% sensitivity, and 99.2% specificity was achieved through weighted k-nearest neighbor (KNN-W). The performance of the proposed EHDS was thoroughly assessed by tenfold cross-validation. The proposed EHDS achieved better detection performance in comparison to other electrocardiogram (ECG) and photoplethysmograph (PPG)-based methods.

AI-Based Methods and Technologies to Develop Wearable Devices for Prosthetics and Predictions of Degenerative Diseases

Neurodegenerative diseases, mainly amyotrophic lateral sclerosis, Parkinson, Alzheimer, and rarer diseases, have gained the attention of healthcare service providers due to their impact on the economy of countries where healthcare is a public service. These diseases increase with aging and affect the neuromotor cells and cognitive areas in the brain, causing serious disabilities in people affected by them.Early prediction of these syndromes is the first strategy to be implemented, then the developing of prostheses that rehabilitate motion and the primary cognitive functions. Prostheses could recover some important disabilities such as motion and aphasia, reduce the cost of assistance and increase the life quality of people affected by neurodegenerative diseases.Due to recent advances in the field of artificial intelligence (AI) (deep learning, brain-inspired computational paradigms, nonlinear predictions, neuro-fuzzy modeling), the early prediction of neurodegenerative diseases is possible using state-of-the-art computational technologies. The latest generation of artificial neural networks (ANNs) exploits capabilities such as online learning, fast training, high level knowledge representation, online evolution, learning by data and inferring rules.Wearable electronics is also developing rapidly and represents an important enabling technology to deploy physical and practical (noninvasive) devices using AI-based models for early prediction of neurodegenerative diseases and of intelligent prostheses.Here we describe how to apply advanced brain-inspired methods for inference and prediction, the evolving fuzzy neural network (EFuNN) paradigm and the spiking neural network (SNN) paradigm, and the system requirements to develop a wearable electronic prosthesis for functional rehabilitation.

Assessment of Hypertension Using Clinical Electrocardiogram Features: A First-Ever Review

Hypertension affects an estimated 1.4 billion people and is a major cause of morbidity and mortality worldwide. Early diagnosis and intervention can potentially decrease cardiovascular events later in life. However, blood pressure (BP) measurements take time and require training for health care professionals. The measurements are also inconvenient for patients to access, numerous daily variables affect BP values, and only a few BP readings can be collected per session. This leads to an unmet need for an accurate, 24-h continuous, and portable BP measurement system. Electrocardiograms (ECGs) have been considered as an alternative way to measure BP and may meet this need. This review summarizes the literature published from January 1, 2010, to January 1, 2020, on the use of only ECG wave morphology to monitor BP or identify hypertension. From 35 articles analyzed (9 of those with no listed comorbidities and confounders), the P wave, QTc intervals and TpTe intervals may be promising for this purpose. Unfortunately, with the limited number of articles and the variety of participant populations, we are unable to make conclusions about the effectiveness of ECG-only BP monitoring. We provide 13 recommendations for future ECG-only BP monitoring studies and highlight the limited findings in pregnant and pediatric populations. With the advent of convenient and portable ECG signal recording in smart devices and wearables such as watches, understanding how to apply ECG-only findings to identify hypertension early is crucial to improving health outcomes worldwide.

Declining Trends of Heart Rate Variability According to Aging in Healthy Asian Adults

Heart rate variability (HRV) indices correlate with aging and are related to the autonomic nervous system. However, the trend of HRV with age has not been explored for the Asian population. Therefore, we proposed a linear regression model of HRV indices that decreased with aging in healthy Asian adults. HRV parameters [High frequency (HF), Low frequency (LF), Very low frequency (VLF), Total power (TP), HRV triangular index (HRV-index), Standard deviation of the normal-to-normal interval (SDNN), and Proportion of normal-to-normal intervals greater than 50 ms (pNN50)] were measured in a total of 300 healthy participants (150 men and 150 women) aged 19-69 years stratified into five age groups: 19-29, 30-39, 40-49, 50-59, and 60-69 years comprising 60 people each in Seoul, South Korea. A simple regression analysis was performed to reveal the linear declining trend of HRV indices with age. Independent t-tests were conducted to investigate the gender differences in HRV values depending on each age group. The values of all HRV indices showed a decreasing trend with age in healthy Korean adults, as observed in the Western population (P < 0.001 for all indices); HF (Y = -0.039x + 6.833, R 2 = 0.287), LF (Y = -0.047x + 7.197, R 2 = 0.414), VLF (Y = -0.025x + 6.861, R 2 = 0.177), TP (Y = -0.034x + 8.082, R 2 = 0.352), HRV-index (Y = -0.125x + 15.628, R 2 = 0.298), SDNN (Y = -0.502x + 53.907, R 2 = 0.343), and pNN50 (Y = -0.650x + 53.852, R 2 = 0.345) all decreased with age. There was no significant gender difference in any HRV parameter. A linear regression model of various HRV indices has been presented considering the age of healthy Asians, which may be useful to prevent diseases related to the autonomic nervous system by estimating or tracking autonomic functional degeneration in the Asian population.

Seeking predictors for paroxysmal atrial fibrillation in stroke with an online clinical database

OBJECTIVE:

A considerable fraction of ischemic stroke cases remain cryptogenic and there is increasing data suggesting the role of missed paroxysmal atrial fibrillations (pAF) in at least a number of these cases. Since electrophysiological identification of pAFs can be challenging, there has been an accumulation of proposed predictors and biomarkers for pAFs. The predictive values of these is varying and sometimes conflicting among studies. Therefore, we aimed to verify a fraction of previously reported parameters for pAF detection by investigating an independent clinical sample.

METHODS:

Using a publicly available data downloaded from the MIMIC-3 intensive care unit database, we tested the predictive role of particular risk factors and biomarkers for pAF detection after ischemic stroke in 124 patients with ischemic stroke admitted within 24 hours of stroke onset.

RESULTS:

Our evaluation revealed a strong association of older age in women, as well as admission National Institutes of Health Stroke Scale (NIHSS) and discharge modified Rankin Scores (mRS) in both sexes for pAFs, in patients that were in sinus rhythm on admission. We also detected a trend for lower gender-adjusted hemoglobin in patients with pAF, although the difference was insignificant. On the other hand, we did not find any significant association of pAF detection with some other previously reported biomarkers: serum magnesium level, leukocyte count, neutrophil/lymphocyte ratio or left atrial dilatation.

CONCLUSION:

Even though our analysis did not reveal a strong and specific biomarker to predict pAFs after stroke, it identified key risk factors. It may be necessary to consider the possibility of pAFs and perform rigorous evaluation to prevent further events of embolic stroke in female patients older than 75 years, with more severe neurological deficits on admission, higher disability on discharge and also with relatively lower hemoglobin level. This first study from Turkey using clinical data from the MIMIC-3 database also demonstrates the value of publicized clinical data for confirmatory studies on various medical fields across the World.

Blood Pressure Prediction by a Smartphone Sensor using Fully Convolutional Networks

Heart disease and stroke are the leading causes of death worldwide. High blood pressure greatly increases the risk of heart disease and stroke. Therefore, it is important to control blood pressure (BP) through regular BP monitoring; as such, it is necessary to develop a method to accurately and conveniently predict BP in a variety of settings. In this paper, we propose a method for predicting BP without feature extraction using fully convolutional neural networks (CNNs). We measured single multi-wave photoplethysmography (PPG) signals using a smartphone. To find an effective wavelength of PPG signals for the generation of accurate BP measurements, we investigated the BP prediction performance by changing the combinations of the input PPG signals. Our CNN-based BP predictor yielded the best performance metrics when a green PPG time signal was used in combination with an instantaneous frequency signal. This combination had an overall mean absolute error (MAE) of 5.28 and 4.92 mmHg for systolic and diastolic BP, respectively. Thus, our CNN-based approach achieved comparable results to other approaches that use a single PPG signal.

Estimating Blood Pressure from the 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 noninvasive BP measurement system is proposed using the photoplethysmograph (PPG) signal and demographic features using machine learning (ML) algorithms. PPG signals were acquired from 219 subjects, which undergo preprocessing 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 the 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.

Multimodal Photoplethysmography-Based Approaches for Improved Detection of Hypertension

Elevated blood pressure (BP) is a major cause of death, yet hypertension commonly goes undetected. Owing to its nature, it is typically asymptomatic until later in its progression when the vessel or organ structure has already been compromised. Therefore, noninvasive and continuous BP measurement methods are needed to ensure appropriate diagnosis and early management before hypertension leads to irreversible complications. Photoplethysmography (PPG) is a noninvasive technology with waveform morphologies similar to that of arterial BP waveforms, therefore attracting interest regarding its usability in BP estimation. In recent years, wearable devices incorporating PPG sensors have been proposed to improve the early diagnosis and management of hypertension. Additionally, the need for improved accuracy and convenience has led to the development of devices that incorporate multiple different biosignals with PPG. Through the addition of modalities such as an electrocardiogram, a final measure of the pulse wave velocity is derived, which has been proved to be inversely correlated to BP and to yield accurate estimations. This paper reviews and summarizes recent studies within the period 2010–2019 that combined PPG with other biosignals and offers perspectives on the strengths and weaknesses of current developments to guide future advancements in BP measurement. Our literature review reveals promising measurement accuracies and we comment on the effective combinations of modalities and success of this technology.

A Comparative Study of Brachial-Ankle Pulse Wave Velocity and Heart-Finger Pulse Wave Velocity in Korean Adults

Arterial stiffness is considered an index of vascular aging. The brachial–ankle pulse wave velocity (baPWV) method is widely used because of its proven effectiveness; and the pulse wave velocity measurement method using both electrocardiogram (ECG) and photoplethysmogram (PPG) is actively being studied due to the convenience of measurement and the possibility of miniaturization. The aim of this study was to evaluate and compare the effects of age and gender in Korean adults using both the baPWV method and the PWV method with ECG and finger PPG (heart–finger PWV). The measurements have been carried out for 185 healthy subjects of Korean adults, and the results showed that the baPWV was highly correlated with age in both genders (r = 0.94 for both males and females). However, the correlation values in heart–finger PWV measurement were significantly lower than those of baPWV (r = 0.37 for males and r = 0.71 for females). Although the heart–finger PWV method is suitable for mobile applications because it can be easily miniaturized while maintaining its signal quality, these results show that the heart–finger PWV method is not as effective as baPWV at evaluating the arterial stiffness.