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Pulse palpation is an important procedure that allows a physician to rapidly assess the status of a patient's cardiovascular system. This paper explores the possibility of using vibrotactile stimuli to render fine temporal profiles of pulse pressure waves. A lightweight wearable vibrotactile glove, called Hap-pulse, is designed to render fine pulse waves through vibrotactile stimuli on users’ fingertips. To preserve the fine features of original pulse waves, models are fitted from real pulse wave data (photoplethysmogram (PPG) pulse waveform database), using fourth-order polynomial functions. A square wave envelope mapping algorithm is proposed to produce vibration amplitudes of Linear Resonance Actuators (LRAs), which aims to render the detailed waveform of systolic and diastolic blood pressure states. Evaluation results suggest that Hap-pulse can render pulse waves with an average correlation coefficient 97.84%. To validate the distinguishability and fidelity of Hap-pulse's palpation rendering, a user study consisting of traditional Chinese medicine doctors and unskilled students is conducted. The correct recognition rate of identifying four typical pulse waves is 87.08% (doctors), 57.50% (untrained students) and 79.59% (trained students). These results indicate a novel application of rendering subtle pulse wave signals with vibrotactile gloves, which illustrates the potential of simulating patient palpation training in virtual or remote medical diagnosis. [ABSTRACT FROM AUTHOR]

The aim of this study was to identify changes in photoplethysmogram waveform with decreasing sampling frequency and quantization bit depth and suggest appropriate criteria for measurements. We performed down-sampling and re-quantization of phoplethysmogram measured at high resolution. Changes of waveform were quantitatively observed. Photoplethysmograms recorded at a 1-kHz sampling frequency and 16-bit quantization bit depth were converted to signals with sampling frequencies of 500-, 250-, 100-, 50-, 25-, and 10-Hz by means of down-sampling. We then performed re-quantization to convert the quantization bit depth into 16, 14, 12, 10, 8, and 6 bits for each down-sampled signal. Degradation of signal was quantified in terms of morphological change and feature-point deviation. Morphological analysis revealed that the correlation coefficient was decreased while normalized root mean square error was increased with decreasing sampling frequency and quantization bit depth. Feature-point analysis revealed that the mean absolute time error and amplitude error of feature points tended to increase with decreasing sampling frequency and quantization bit depth. Although there were differences according to pulse onset and systolic peak, sampling frequency ≥ 250-Hz and 16-bit quantization bits were required in order to have ≤ 1 ms of timing error and a normalized amplitude error ≤1%. In addition, sampling frequency ≥ 100-Hz and 12-bit quantization bits are recommended to have feature-point time errors and amplitude errors < 10 ms and a normalized amplitude error < 10%.

Photoplethysmogram (PPG) is one of the most widely measured biosignals alongside electrocardiogram (ECG). Due to the simplicity of measurement and the advent of wearable devices, there have been growing interest in using PPG for a variety of healthcare applications such as cardiac function estimation. However, unlike ECG, there are not many large databases available for clinically significant analyses of PPG. To overcome this issue, a Generative Adversarial Network-based model to generate PPG using ECG as input is proposed. The network was trained using a large open database of biosignals measured from surgical patients and was externally validated using an alternative database sourced from another hospital. The generated PPG was compared with the reference PPG using percent root mean square difference (PRD) and Pearson correlation coefficient to evaluate the morphological similarity. Additionally, heart rate measured from the reference ECG, reference PPG, and generated PPG, and compared through repeated measure analysis of variance to test for any significant differences. The mean PRD was 32± 10% and the mean correlation coefficient was 0.95± 0.05 in the test dataset. The HR from the three biosignals showed no significant difference with a $p$ -value of 0.473. When the optimized GAN model was tested on atrial fibrillation ECG from a third dataset, the mean correlation coefficient between the generated PPG heart rate and the ECG heart rate was 0.94± 0.15, with paired t-test resulting in $p$ -value of 0.64. The results indicate that the proposed method may provide a valuable alternative to augmenting biosignal databases that are abundant in one signal while lacking in another.

Respiratory ailments such as asthma, chronic obstructive pulmonary disease (COPD), pneumonia, and lung cancer are life-threatening. Respiration rate (RR) is a vital indicator of the wellness of a patient. Continuous monitoring of RR can provide early indication and thereby save lives. However, a real-time continuous RR monitoring facility is only available at the intensive care unit (ICU) due to the size and cost of the equipment. Recent researches have proposed Photoplethysmogram (PPG) and/ Electrocardiogram (ECG) signals for RR estimation however, the usage of ECG is limited due to the unavailability of it in wearable devices. Due to the advent of wearable smartwatches with built-in PPG sensors, it is now being considered for continuous monitoring of RR. This paper describes a novel approach for RR estimation using motion artifact correction and machine learning (ML) models with the PPG signal features. Feature selection algorithms were used to reduce computational complexity and the chance of overfitting. The best ML model and the best feature selection algorithm combination were fine-tuned to optimize its performance using hyperparameter optimization. Gaussian Process Regression (GPR) with Fit a Gaussian process regression model (Fitrgp) feature selection algorithm outperformed all other combinations and exhibits a root mean squared error (RMSE), mean absolute error (MAE), and two-standard deviation (2SD) of 2.63, 1.97, and 5.25 breaths per minute, respectively. Patients would be able to track RR at a lower cost and with less inconvenience if RR can be extracted efficiently and reliably from the PPG signal.

Continuous cuff-less blood pressure (BP) monitoring has become a research hotspot in recent years. Researches have studied the impact of pulse transit time (PTT) and ballistocardiogram (BCG) signals on BP. However, the accuracy of these methods are not high enough to put them into practice on a large scale. In this paper, we propose a new BP estimation model which combines features extracted from electrocardiogram (ECG), BCG and photoplethysmogram (PPG). We calculate several features containing amplitude, time and energy from these three signals and use stepwise regression to select key ones for this combination model. The combination model was examined in 20 young healthy subjects and it presents good results: a correlation coefficient (R) of 0.84 (systolic blood pressure, SBP) and 0.7 (diastolic blood pressure, DBP), a root-mean-squared error (RMSE) of 8.16 mmHg (SBP) and 6.63 mmHg (DBP), and a mean absolute error (MAE) of 6.84 mmHg (SBP) and 5.46 mmHg (SBP). Besides, The PTT-based BP estimation model and BCG-based estimation model are also established in this paper. The comparison of these three models shows that the PPG-ECG-BCG-based model has better performance.

Cardiac activity has numerous effects on human emotional conditions, such as the happy or amusement condition. One of the widely known methods used to determine the amusement condition is the electrocardiogram. However, photoplethysmogram is easier to use than electrocardiogram, despite not being widely explored. Therefore, this study examines the possibility of using a photoplethysmogram to assess the amusement condition. Data were collected from 6 respondents using 12 parameters extracted by using time and frequency domain analysis. The result showed several signal parameters can be used to estimate the amusement condition. [ABSTRACT FROM AUTHOR]

Aim: Atrial fibrillation (AF) detection is challenging because it is often asymptomatic and paroxysmal. We evaluated continuous photoplethysmogram (PPG) for signal quality and detection of AF. Methods: PPGs were recorded using a wrist-band device in 173 patients (76 AF, 97 sinus rhythm, SR) for 24 h. Simultaneously recorded 3-lead ambulatory ECG served as control. The recordings were split into 10-, 20-, 30-, and 60-min time-frames. The sensitivity, specificity, and F1-score of AF detection were evaluated for each time-frame. AF alarms were generated to simulate continuous AF monitoring. Sensitivities, specificities, and positive predictive values (PPVs) of the alarms were evaluated. User experiences of PPG and ECG recordings were assessed. The study was registered in the Clinical Trials database (NCT03507335). Results: The quality of PPG signal was better during night-time than in daytime (67.3 ± 22.4% vs. 30.5 ± 19.4%, p < 0.001). The 30-min time-frame yielded the highest F1-score (0.9536), identifying AF correctly in 72/76 AF patients (sensitivity 94.7%), only 3/97 SR patients receiving a false AF diagnosis (specificity 96.9%). The sensitivity and PPV of the simulated AF alarms were 78.2 and 97.2% at night, and 49.3 and 97.0% during the daytime. 82% of patients were willing to use the device at home. Conclusion: PPG wrist-band provided reliable AF identification both during daytime and night-time. The PPG data's quality was better at night. The positive user experience suggests that wearable PPG devices could be feasible for continuous rhythm monitoring. [ABSTRACT FROM AUTHOR]

In this paper, we present a personalized deep learning approach to estimate blood pressure (BP) using the photoplethysmogram (PPG) signal. We propose a hybrid neural network architecture consisting of convolutional, recurrent, and fully connected layers that operates directly on the raw PPG time series and provides BP estimation every 5 seconds. To address the problem of limited personal PPG and BP data for individuals, we propose a transfer learning technique that personalizes specific layers of a network pre-trained with abundant data from other patients. We use the MIMIC III database which contains PPG and continuous BP data measured invasively via an arterial catheter to develop and analyze our approach. Our transfer learning technique, namely BP-CRNN-Transfer, achieves a mean absolute error (MAE) of 3.52 and 2.20 mmHg for SBP and DBP estimation, respectively, outperforming existing methods. Our approach satisfies both the BHS and AAMI blood pressure measurement standards for SBP and DBP. Moreover, our results demonstrate that as little as 50 data samples per person are required to train accurate personalized models. We carry out Bland-Altman and correlation analysis to compare our method to the invasive arterial catheter, which is the gold-standard BP measurement method. [ABSTRACT FROM AUTHOR]

Mental disorder is defined as psychological disorders that involve instabilities in brain function. This paper aims to provide an overview of photoplethysmogram (PPG) for detecting early symptoms of mental disorders encompassing physiology using different stimuli. This paper discusses mental disorder studies that have been highlighted in the previous literature. The contribution of the PPG is summarized through feature extraction and its accuracy classified using machine learning. Moreover, research challenges and recommendations in the field are discussed. In conclusion, there were significant changes against the early signs of mental disorders through emotional and stress levels from PPG signal morphology. [ABSTRACT FROM AUTHOR]

Background: It is believed that the intensity of oscillations in the photoplethysmographic waveform variability reflects the activity of vascular regulatory mechanisms. However, the relationship of such fluctuations with the state of health is poorly understood. Purpose: The aim of our study was to assess the possibility of using spectral indices that reflect the intensity of oscillations of the photoplethysmographic waveform variability at frequencies 0.04-0.4 Hz as markers of hypertension and coronary artery disease. We did not study women to exclude the influence of menopause and sex hormones on the results. Materials and Methods: We compared synchronous 10-minute records of finger photoplethysmogram and respiration at rest in 30 healthy males (48.8 ± 4.5 years; data presented as Mean ± SD) versus 30 patients with hypertension (aged 49.0 ± 4.3 years) versus 30 patients with stable coronary artery disease (49.2 ± 4.8 years). Percentages of high-frequency and low-frequency ranges in the total power of photoplethysmographic waveform variability spectrum (HF% and LF%), and LF/HF ratio were assessed. Results: HF% are subject to by 2- to 5-fold increase in hypertensive patients (p

The objective of this study was to investigate the efficacy of pulse transit time (PTT)-pulse wave analysis (PWA) fusion in cuff-less blood pressure (BP) trend tracking based on the wearable wrist ballistocardiogram (BCG). We constructed PTT and 36 candidate BCG PWA features based on the BCG and photoplethysmogram (PPG) signals acquired at the wrist. We performed a model-based analysis to select 6 candidate BCG PWA features most sensitive to BP. We aggregated the 6 BCG PWA features into novel predictors of diastolic, systolic, and pulse BP (DP, SP, and PP) orthogonal to PTT by covariance-maximizing dimensionality reduction. Then, we evaluated and compared the efficacy of BCG PTT and BCG PTT-PWA fusion in tracking the trend of DP, SP, and PP. Unique innovations of this study, generalizable to a range of physiological signals beyond the BCG, are (i) the systematic selection of PWA features based on model-based analysis and (ii) the development of PWA-based BP predictors orthogonal to PTT. Using the experimental wrist BCG and PPG signals collected from 23 human subjects, we demonstrated that (i) BCG PTT-PWA fusion may significantly outperform BCG PTT; (ii) PTT may play the primary role in tracking BP while BCG PWA features may still have an independent and complementary value (especially in tracking PP); and (iii) model-based selection of BCG PWA features can enhance the robustness of BP trend tracking based on BCG PTT-PWA fusion.

Aim: Atrial fibrillation (AF) detection is challenging because it is often asymptomatic and paroxysmal. We evaluated continuous photoplethysmogram (PPG) for signal quality and detection of AF.Methods: PPGs were recorded using a wrist-band device in 173 patients (76 AF, 97 sinus rhythm, SR) for 24 h. Simultaneously recorded 3-lead ambulatory ECG served as control. The recordings were split into 10-, 20-, 30-, and 60-min time-frames. The sensitivity, specificity, and F1-score of AF detection were evaluated for each time-frame. AF alarms were generated to simulate continuous AF monitoring. Sensitivities, specificities, and positive predictive values (PPVs) of the alarms were evaluated. User experiences of PPG and ECG recordings were assessed. The study was registered in the Clinical Trials database (NCT03507335).Results: The quality of PPG signal was better during night-time than in daytime (67.3 ± 22.4% vs. 30.5 ± 19.4%, p

A sudden cardiac event in patients with heart disease can lead to a heart attack in extreme cases. Therefore, prompt interventions for the particular heart situation and periodic monitoring are critical. This study focuses on a heart sound analysis method that can be monitored daily using multimodal signals acquired with wearable devices. The dual deterministic model-based heart sound analysis is designed in a parallel structure that uses two bio-signals (PCG and PPG signals) related to the heartbeat, enabling more accurate heart sound identification. The experimental results show promising performance of the proposed Model III (DDM-HSA with window and envelope filter), which had the highest performance, and S1 and S2 showed average accuracy (unit: %) of 95.39 (±2.14) and 92.55 (±3.74), respectively. The findings of this study are anticipated to provide improved technology to detect heart sounds and analyze cardiac activities using only bio-signals that can be measured using wearable devices in a mobile environment.

The early identification of microvascular changes in patients with Coronavirus Disease 2019 (COVID-19) may offer an important clinical opportunity. This study aimed to define a method, based on deep learning approaches, for the identification of COVID-19 patients from the analysis of the raw PPG signal, acquired with a pulse oximeter. To develop the method, we acquired the PPG signal of 93 COVID-19 patients and 90 healthy control subjects using a finger pulse oximeter. To select the good quality portions of the signal, we developed a template-matching method that excludes samples corrupted by noise or motion artefacts. These samples were subsequently used to develop a custom convolutional neural network model. The model accepts PPG signal segments as input and performs a binary classification between COVID-19 and control samples. The proposed model showed good performance in identifying COVID-19 patients, achieving 83.86% accuracy and 84.30% sensitivity (hold-out validation) on test data. The obtained results indicate that photoplethysmography may be a useful tool for microcirculation assessment and early recognition of SARS-CoV-2-induced microvascular changes. In addition, such a noninvasive and low-cost method is well suited for the development of a user-friendly system, potentially applicable even in resource-limited healthcare settings.

Intervals of low-quality photoplethysmogram (PPG) signals might lead to significant inaccuracies in estimation of pulse arrival time (PAT) during polysomnography (PSG) studies. While PSG is considered to be a “gold standard” test for diagnosing obstructive sleep apnea (OSA), it also enables tracking apnea-related nocturnal blood pressure fluctuations correlated with PAT. Since the electrocardiogram (ECG) is recorded synchronously with the PPG during PSG, it makes sense to use the ECG signal for PPG signal-quality assessment. (1) Objective: to develop a PPG signal-quality assessment algorithm for robust PAT estimation, and investigate the influence of signal quality on PAT during various sleep stages and events such as OSA. (2) Approach: the proposed algorithm uses R and T waves from the ECG to determine approximate locations of PPG pulse onsets. The MESA database of 2055 PSG recordings was used for this study. (3) Results: the proportions of high-quality PPG were significantly lower in apnea-related oxygen desaturation (matched-pairs rc = 0.88 and rc = 0.97, compared to OSA and hypopnea, respectively, when p < 0.001) and arousal (rc = 0.93 and rc = 0.98, when p < 0.001) than in apnea events. The significantly large effect size of interquartile ranges of PAT distributions was between low- and high-quality PPG (p < 0.001, rc = 0.98), and regular and irregular pulse waves (p < 0.001, rc = 0.74), whereas a lower quality of the PPG signal was found to be associated with a higher interquartile range of PAT across all subjects. Suggested PPG signal quality-based PAT evaluation reduced deviations (e.g., rc = 0.97, rc = 0.97, rc = 0.99 in hypopnea, oxygen desaturation, and arousal stages, respectively, when p < 0.001) and allowed obtaining statistically larger differences between different sleep stages and events. (4) Significance: the implemented algorithm has the potential to increase the robustness of PAT estimation in PSG studies related to nocturnal blood pressure monitoring.

Heart Rate Variability (HRV) is a biomarker that can be obtained non-invasively from the electrocardiogram (ECG) or the photoplethysmogram (PPG) fiducial points. However, the accuracy of HRV can be compromised by the presence of artifacts. In the herein presented work, a Simulink® model with a deep learning component was studied for overly noisy PPG signals. A subset with these noisy signals was selected for this study, with the purpose of testing a real-time machine learning based HRV estimation system in substandard artifact-ridden signals. Home-based and wearable HRV systems are prone to dealing with higher contaminated signals, given the less controlled environment where the acquisitions take place, namely daily activity movements. This was the motivation behind this work. The results for overly noisy signals show that the real-time PPG-based HRV estimation system produced RMSE and Pearson correlation coefficient mean and standard deviation of 0.178 ± 0.138 s and 0.401 ± 0.255, respectively. This RMSE value is roughly one order of magnitude above the closest comparative results for which the real-time system was also used.

Objective. Assessing signal quality is crucial for biomedical signal processing, yet a precise mathematical model for defining signal quality is often lacking, posing challenges for experts in labeling signal qualities. The situation is even worse in the free living environment. Approach. We propose to model a PPG signal by the adaptive non-harmonic model (ANHM) and apply a decomposition algorithm to explore its structure, based on which we advocate a reconsideration of the concept of signal quality. Main results. We demonstrate the necessity of this reconsideration and highlight the relationship between signal quality and signal decomposition with examples recorded from the free living environment. We also demonstrate that relying on mean and instantaneous heart rates derived from PPG signals labeled as high quality by experts without proper reconsideration might be problematic. Significance. A new method, distinct from visually inspecting the raw PPG signal to assess its quality, is needed. Our proposed ANHM model, combined with advanced signal processing tools, shows potential for establishing a systematic signal decomposition based signal quality assessment model., (© 2024 Institute of Physics and Engineering in Medicine.)

Comprehending the regulatory mechanisms influencing blood pressure control is pivotal for continuous monitoring of this parameter. Implementing a personalized machine learning model, utilizing data-driven features, presents an opportunity to facilitate tracking blood pressure fluctuations in various conditions. In this work, data-driven photoplethysmograph features extracted from the brachial and digital arteries of 28 healthy subjects were used to feed a random forest classifier in an attempt to develop a system capable of tracking blood pressure. We evaluated the behavior of this latter classifier according to the different sizes of the training set and degrees of personalization used. Aggregated accuracy, precision, recall, and F 1-score were equal to 95.1%, 95.2%, 95%, and 95.4% when 30% of a target subject's pulse waveforms were combined with five randomly selected source subjects available in the dataset. Experimental findings illustrated that incorporating a pre-training stage with data from different subjects made it viable to discern morphological distinctions in beat-to-beat pulse waveforms under conditions of cognitive or physical workload.

The main objective of this research is based on finding out some assertive and robust Photoplethysmogram's PPG & Electrocardiogram's ECG blood pressure-related parameters by the implementation of a novel method with innovations in signal processing and analysis. The biomedical ECG and PPG signals are recorded using a mobile monitor CardioQVark. To increase the cuffless blood pressure measurement accuracy, a technique that involves not only the ECG and PPG joint parameters extraction but also some individual PPG's morphology features, is proposed in this work. Firstly, the biomedical ECG and PPG signals are time-frequency filtered. Secondly, some novel parameters from the morphology of photoplethysmogram signal, which may be correlated with blood pressure, are considered in addition to the pulse transit time. Additionally, a neural network is built to determine the relationship between the estimated and reference blood pressure. Finally, the correlation coefficient and regression line are obtained to evaluate the feasibility. [ABSTRACT FROM AUTHOR]

The proliferating and excessive use of internet games has caused various comorbid diseases, such as game addiction, which is now a major social problem. Recently, the American Psychiatry Association classified "Internet gaming disorder (IGD)" as an addiction/mental disorder. Although many studies have been conducted on the diagnosis, treatment, and prevention of IGD, screening studies for IGD are still scarce. In this study, we classified gamers using multiple physiological signals to contribute to the treatment and prevention of IGD. Participating gamers were divided into three groups based on Young's Internet Addiction Test score and average game time as follows: Group A, those who rarely play games; Group B, those who enjoy and play games regularly; and Group C, those classified as having IGD. In our game-related cue-based experiment, we obtained self-reported craving scores and multiple physiological data such as electrooculogram (EOG), photoplethysmogram (PPG), and electroencephalogram (EEG) from the users while they watched neutral (natural scenery) or stimulating (gameplay) videos. By analysis of covariance (ANCOVA), 13 physiological features (vertical saccadic movement from EOG, standard deviation of N-N intervals, and PNN50 from PPG, and many EEG spectral power indicators) were determined to be significant to classify the three groups. The classification was performed using a 2-layers feedforward neural network. The fusion of three physiological signals showed the best result compared to other cases (combination of EOG and PPG or EEG only). The accuracy was 0.90 and F-1 scores were 0.93 (Group A), 0.89 (Group B), and 0.88 (Group C). However, the subjective self-reported scores did not show a significant difference among the three groups by ANCOVA analysis. The results indicate that the fusion of physiological signals can be an effective method to objectively classify gamers. [ABSTRACT FROM AUTHOR]

The proliferating and excessive use of internet games has caused various comorbid diseases, such as game addiction, which is now a major social problem. Recently, the American Psychiatry Association classified “Internet gaming disorder (IGD)” as an addiction/mental disorder. Although many studies have been conducted on the diagnosis, treatment, and prevention of IGD, screening studies for IGD are still scarce. In this study, we classified gamers using multiple physiological signals to contribute to the treatment and prevention of IGD. Participating gamers were divided into three groups based on Young’s Internet Addiction Test score and average game time as follows: Group A, those who rarely play games; Group B, those who enjoy and play games regularly; and Group C, those classified as having IGD. In our game-related cue-based experiment, we obtained self-reported craving scores and multiple physiological data such as electrooculogram (EOG), photoplethysmogram (PPG), and electroencephalogram (EEG) from the users while they watched neutral (natural scenery) or stimulating (gameplay) videos. By analysis of covariance (ANCOVA), 13 physiological features (vertical saccadic movement from EOG, standard deviation of N-N intervals, and PNN50 from PPG, and many EEG spectral power indicators) were determined to be significant to classify the three groups. The classification was performed using a 2-layers feedforward neural network. The fusion of three physiological signals showed the best result compared to other cases (combination of EOG and PPG or EEG only). The accuracy was 0.90 and F-1 scores were 0.93 (Group A), 0.89 (Group B), and 0.88 (Group C). However, the subjective self-reported scores did not show a significant difference among the three groups by ANCOVA analysis. The results indicate that the fusion of physiological signals can be an effective method to objectively classify gamers.

Respiratory ailments are a very serious health issue and can be life-threatening, especially for patients with COVID. Respiration rate (RR) is a very important vital health indicator for patients. Any abnormality in this metric indicates a deterioration in health. Hence, continuous monitoring of RR can act as an early indicator. Despite that, RR monitoring equipment is generally provided only to intensive care unit (ICU) patients. Recent studies have established the feasibility of using photoplethysmogram (PPG) signals to estimate RR. This paper proposes a deep-learning-based end-to-end solution for estimating RR directly from the PPG signal. The system was evaluated on two popular public datasets: VORTAL and BIDMC. A lightweight model, ConvMixer, outperformed all of the other deep neural networks. The model provided a root mean squared error (RMSE), mean absolute error (MAE), and correlation coefficient (R) of 1.75 breaths per minute (bpm), 1.27 bpm, and 0.92, respectively, for VORTAL, while these metrics were 1.20 bpm, 0.77 bpm, and 0.92, respectively, for BIDMC. The authors also showed how fine-tuning a small subset could increase the performance of the model in the case of an out-of-distribution dataset. In the fine-tuning experiments, the models produced an average R of 0.81. Hence, this lightweight model can be deployed to mobile devices for real-time monitoring of patients.

The main objective of this research is based on finding out some assertive and robust Photoplethysmogram’s PPG & Electrocardiogram’s ECG blood pressure-related parameters by the implementation of a novel method with innovations in signal processing and analysis. The biomedical ECG and PPG signals are recorded using a mobile monitor CardioQVark. To increase the cuffless blood pressure measurement accuracy, a technique that involves not only the ECG and PPG joint parameters extraction but also some individual PPG’s morphology features, is proposed in this work. Firstly, the biomedical ECG and PPG signals are time–frequency filtered. Secondly, some novel parameters from the morphology of photoplethysmogram signal, which may be correlated with blood pressure, are considered in addition to the pulse transit time. Additionally, a neural network is built to determine the relationship between the estimated and reference blood pressure. Finally, the correlation coefficient and regression line are obtained to evaluate the feasibility.

Obstructive sleep apnea (OSA), a very common sleep disorder remains as an underdiagnosed root cause for several cardiovascular and cerebrovascular diseases. In this paper, we propose an efficient and accurate system that utilizes a single sensor for effective screening of OSA using machine learning algorithms. The automatic screening system involves a photoplethysmogram (PPG) signal, a novel algorithm to detect and remove the corrupted part of the signal, a feature extraction module to extract several features from the PPG waveform and a classifier module which helps in screening for OSA. The elemental idea behind this work is that there is a characteristic relationship between the shape of the PPG waveform and the oxygen desaturation in the apnea patients. The method as described was tested on 285 subjects, inclusive of both normal and apnea patients, and the results were obtained after 10-fold-cross validation of the different machine learning techniques viz., univariate regression, multivariate regression, support vector machine and random forest. The best results in screening OSA were obtained from random forest algorithm with the highest performance (Acc: 98.0%, Sen: 98.6%, Spec: 99.3%) for all the combined features. The proposed work is an effective system for automatic screening of OSA from a single PPG sensor, thereby reducing the need for a very expensive and overnight polysomnography sleep study. [ABSTRACT FROM AUTHOR]

Photoplethysmography (PPG) is a popular diagnostic tool for the assessment of various cardiovascular functions. Under continuous ambulatory measurements, PPG data get corrupted due to motion artifact (MA). Thus, on-board pulse signal quality assessment (SQA) before transmission can save the battery life of the wearable device for portable health monitoring applications. This article describes an SQA guided compression (SQAGC) of PPG data using a modified gain-shaped vector quantization (GSVQ) technique. The SQA was performed using kurtosis and autocorrelation to generate a binary classification rule to detect good quality pulses. Only these were considered for further compression. A notable contribution is reconstruction error minimization using the extracted features from the residual signal using a deep autoencoder (DAE), achieving a low percentage root-mean-squared difference (PRD). The SQAGC technique was evaluated using public databases like MIMIC-II, BIDMC, and PRRB as well as with real volunteers’ PPG collected in the laboratory environment. The SQA achieved an accuracy of 96.5% to identify good quality PPG segments out of expert annotated 9200 beats. The compression factor (and PRD) with 400 min duration data from Physionet MIMIC-II, BIDMC, PRRB, and volunteers’ data were 15.8 (and 0.31), 15.7 (and 0.21), 17.8 (and 0.33), and 18.2 (and 0.59), respectively, using 12-bit resolution and 125 Hz sampling. A real-time on-device implementation using quad-core ARM Cortex-A53, 1.2 GHz, supported by 1 GB RAM, achieved a latency of 546 ms with 327 kB of memory engagement for a 3 s PPG window. The compression ratio (CR) achieved comparable results, while PRD outperforms the published results using MIMIC-II data set. [ABSTRACT FROM AUTHOR]

This article presents a review of signals used for measuring physiology and activity during sleep and techniques for extracting information from these signals. We examine both clinical needs and biomedical signal processing approaches across a range of sensor types. Issues with recording and analysing the signals are discussed, together with their applicability to various clinical disorders. Both univariate and data fusion (exploiting the diverse characteristics of the primary recorded signals) approaches are discussed, together with a comparison of automated methods for analysing sleep.

To date, the applications of photoplethysmograms in the estimation of cuff-less blood pressure, arteriosclerosis, and so on have been studied. Photoplethysmogram waveform features have often been used to estimate the target volumes. For these estimations, it is necessary to acquire photoplethysmogram waveforms and changes in their derivative waveform details, for which the photoplethysmograms measured at comparatively high sampling rates have been used. The performance of smartphone cameras and wearable photoplethysmogram sensors has improved; with regard to mobile health technology, photoplethysmograms measured at lower sampling rates offer considerable advantages. These include lower computational resources, compression of accumulated data, and lower sensor power consumption. However, compared to photoplethysmograms measured at a high sampling rate, photoplethysmogram measurement at a low sampling rate will result in waveform signal degradation. This paper investigates the possibility of using photoplethysmograms measured at a low sampling rate. To this end, we statistically compared photoplethysmogram waveform features obtained from 63 male subjects free of circulatory diseases, at a sampling rate of 240 Hz, with waveform features obtained at low sampling rates (120, 60, 30, 20, and 10 Hz) through downsampling, and evaluated possible commercial use.

We present a beat-to-beat heart rate tracking algorithm that is designed especially to handle the nonstationary motion artifacts often encountered using photoplethysmographic (PPG) signals acquired from smartwatches or a forehead-worn device, during intense exercise. To date, many algorithms have been based on tracking heart rates during intense exercise using an 8-second average of heart rates, which does not accurately capture the large variation in instantaneous heart rates during exercise. In this paper, we propose a novel technique that can accurately estimate heart rates from wearable PPG signals with subjects running on a treadmill and making other sudden movements. The proposed algorithm includes three parts: 1) time-frequency spectrum estimation of PPG and accelerometer signals, 2) motion artifact removal by subtraction of the time-frequency spectra of the accelerometer signals from the PPG signals, and 3) postprocessing to further reject motion artifact-affected heart rates followed by interpolation of removed heart beats using a cubic spline approach. The proposed approach was compared to one of the recent and most accurate algorithms. The results of the proposed and compared algorithms were evaluated with two datasets (IEEE Signal Processing Cup (N=12) and our own dataset (N=10)) obtained from a smartwatch and a forehead PPG sensor with subjects running on a treadmill. The reference heart rates were obtained from a chest-worn ECG. Our method, using a 12 second windowed segment, resulted in an average absolute error of only 2.94 beats per minute and an average relative error of 2.42 beats per minute, which are a 71% and 94% improvement, respectively, over the compared algorithm.

Cuffless blood pressure monitoring is critical for the prevention and early diagnosis of a wide variety of cardiovascular diseases. However, cuffless blood pressure estimation devices suitable for home healthcare applications still suffer from numerous technological limitations. In this paper, we propose a novel method to continuously estimate blood pressure from the pulse morphology of the photoplethysmographic signals, acquired by a commercial oximeter. We conducted cold-pressor tests for 70 healthy subjects, simultaneously acquiring the photoplethysmograms from the oximeter and the continuous blood pressure from a commercial sphygmomanometer as a reference. The shape of each pulse was extracted, normalized, and regressed with the reference blood pressure using support vector machines. The performance of the regression model for each subject was evaluated using 10-fold cross-validation. Results showed that the predicted values from the regression model were highly correlated with the reference measurements from the commercial device. The mean correlation coefficients were 0.748, 0.754, and 0.775 for systolic, diastolic, and mean blood pressures, respectively. The mean errors were 0.043 mmHg, 0.011 mmHg, and 0.008 mmHg, with standard deviations of 5.001 mmHg, 3.689 mmHg, and 4.140 mmHg for systolic, diastolic, and mean blood pressures, respectively. These results suggest high feasibility of the proposed method to be employed in wearable medical and home healthcare devices.

A photoplethysmogram (PPG) sensor has been broadly used for smart watches and bands because it is easy to measure and contains many health information, such as heart rate (HR) and respiratory rate (RR). Because the PPG sensor blinks an LED at sampling instants, the rate for sampling and LED flashing should be reduced to extend the battery life. To reduce the rate, we employed two different compressive covariance sensing (CCS) techniques and applied them to HR and RR estimation. The CCS cannot recover a signal itself but reconstructs its covariance. We designed a signal processing technique to extract HR and RR from the reconstructed covariance. The estimation performance was evaluated by using the open-source data and the experimental data, and the power consumption of a wrist-type PPG sensor with respect to the compression ratio was evaluated. The proposed method acquired the covariance of the PPG with an average sampling rate of 1.79 Hz below the Nyquist rate for HR (10 Hz), and it significantly reduced the energy consumption for the PPG sampling. Moreover, its estimation accuracy was sufficient to be used for a wearable healthcare system. As a result, the proposed method showed that HR and RR can be estimated with an ultra-low-power PPG sensor.

Continuous blood pressure (BP) monitoring in daily life is needed to enable early detection of hypertension and improve its control. Although pulse transit time (PTT)-based BP estimation represents a promising approach, it still lacks of performance in systolic blood pressure (SBP) estimation and its use in daily life is limited owing to the requirement of bulky systems for measurement of PTT. This study aims at developing a wearable system providing continuous PTT measurement and enhanced SBP estimation for continuous BP monitoring. A single chest-worn device was developed, which measures the photoplethysmogram and seismocardiogram simultaneously, and thereby obtains PTT by time difference between two signals. A multivariate model using the seismocardiogram amplitude (SA) in conjunction with PTT was proposed for SBP estimation, and validated against 30 healthy males (31.47 ± 7.23 years old). Performance of the proposed model was compared with that of conventional univariate models using PTT or pulse arrival time in two types of BP interventions, and for the verification of real use in daily life, performance assessment with calibration and BP monitoring in daily life were conducted. The results suggested that the proposed model (1) outperformed the conventional models, (2) showed potential to be generalized with just a simple calibration, and (3) demonstrated the potential of continuous BP monitoring in daily life. In conclusion, the presented system provides an improved performance of continuous BP monitoring by using a combination of PTT and SA with a convenient and compact single chest-worn device, and thus, it can contribute to mobile healthcare services.

Photoplethysmography is a widely used technique to noninvasively assess heart rate, blood pressure, and oxygen saturation. This technique has considerable potential for further applications—for example, in the field of physiological and mental health monitoring. However, advanced applications of photoplethysmography have been hampered by the lack of accurate and reliable methods to analyze the characteristics of the complex nonlinear dynamics of photoplethysmograms. Methods of nonlinear time series analysis may be used to estimate the dynamical characteristics of the photoplethysmogram, but they are highly influenced by the length of the time series, which is often limited in practical photoplethysmography applications. The aim of this study was to evaluate the error in the estimation of the dynamical characteristics of the photoplethysmogram associated with the limited length of the time series. The dynamical properties were evaluated using recurrence quantification analysis, and the estimation error was computed as a function of the length of the time series. Results demonstrated that properties such as determinism and entropy can be estimated with an error lower than 1% even for short photoplethysmogram recordings. Additionally, the lower limit for the time series length to estimate the average prediction time was computed.

Currently, in terms of reducing the infection risk of the COVID-19 virus spreading all over the world, the development of touchless blood pressure (BP) measurement has potential benefits. The pulse transit time (PTT) has a high relation with BP, which can be measured by electrocardiogram (ECG) and photoplethysmogram (PPG). The ballistocardiogram (BCG) reflects the mechanical vibration (or displacement) caused by the heart contraction/relaxation (or heart beating), which can be measured from multiple degrees of the body. The goal of this study is to develop a cuffless and touchless BP-measurement method based on a commercial weight scale combined with a PPG sensor when measuring body weight. The proposed method was that the PTTBCG-PPGT was extracted from the BCG signal measured by a weight scale, and the PPG signal was measured from the PPG probe placed at the toe. Four PTT models were used to estimate BP. The reference method was the PTTECG-PPGF extracted from the ECG signal and PPG signal measured from the PPG probe placed at the finger. The standard BP was measured by an electronic blood pressure monitor. Twenty subjects were recruited in this study. By the proposed method, the root-mean-square error (ERMS) of estimated systolic blood pressure (SBP) and diastolic blood pressure (DBP) are 6.7 ± 1.60 mmHg and 4.8 ± 1.47 mmHg, respectively. The correlation coefficients, r2, of the proposed model for the SBP and DBP are 0.606 ± 0.142 and 0.284 ± 0.166, respectively. The results show that the proposed method can serve for cuffless and touchless BP measurement.

In recent years, research on emotion classification based on physiological signals has actively attracted scholars’ attention worldwide. Several studies and experiments have been conducted to analyze human emotions based on physiological signals, including the use of electrocardiograms (ECGs), electroencephalograms (EEGs), and photoplethysmograms (PPGs). Although the achievements with ECGs and EEGs are progressive, reaching higher accuracies over 90%, the number of studies utilizing PPGs are limited and their accuracies are relatively lower than other signals. One of the difficulties in studying PPGs for emotional analysis is the lack of open datasets (there is a single dataset to the best of the authors). This study introduces a new PPG dataset for emotional analysis. A total of 72 PPGs were recorded from 18 participants while watching short video clips and analyzed in time and frequency domains. Moreover, emotional classification accuracies with the presented dataset were presented with various neural network structures. The results prove that this dataset can be used for further emotional analysis with PPGs.

The whole of society faces with the problems of sub‐health and population aging. Every person has concerned how to get rid of sub‐health status. It is particularly important to prevent and cure disease for everyone. Monitoring physiological status becomes extremely important to accurately assess health status. In this paper, we establish a system to monitor the physiological status during mid‐ and long‐distance running. The mid‐ and long‐distance running is a common exercise and easy to practice. In order to monitor physiological status, we first collect photoplethysmogram signals, blood pressure, and temperature by using wearable devices. For photoplethysmogram signals, a fast median filtering algorithm is used to remove the noises. The denoised signals are extracted 11 features. Then, the 11 extracted features from photoplethysmogram signals, blood pressure, and temperature are input into a weighted one‐class support vector machine (WOC‐SVM) for training. The abnormal status can be identified 86.4% by the trained WOC‐SVM model. [ABSTRACT FROM AUTHOR]

Objective: Toward the ultimate goal of cuff-less blood pressure (BP) trend tracking via pulse transit time (PTT) using wearable ballistocardiogram (BCG) signals, we present a unified approach to the gating of wearable BCG and the localization of wearable BCG waves. Methods: We present a unified approach to localize wearable BCG waves suited to various gating and localization reference signals. Our approach gates individual wearable BCG beats and identifies candidate waves in each wearable BCG beat using a fiducial point in a reference signal, and exploits a pre-specified probability distribution of the time interval between the BCG wave and the fiducial point in the reference signal to accurately localize the wave in each wearable BCG beat. We tested the validity of our approach using experimental data collected from 17 healthy volunteers. Results: We showed that our approach could localize the J wave in the wearable wrist BCG accurately with both the electrocardiogram (ECG) and the wearable wrist photoplethysmogram (PPG) signals as reference, and that the wrist BCG-PPG PTT thus derived exhibited high correlation to BP. Conclusion: We demonstrated the proof-of-concept of a unified approach to localize wearable BCG waves suited to various gating and localization reference signals compatible with wearable measurement. Significance: Prior work using the BCG itself or the ECG to gate the BCG beats and localize the waves to compute PTT are not ideally suited to the wearable BCG. Our approach may foster the development of cuff-less BP monitoring technologies based on the wearable BCG. [ABSTRACT FROM AUTHOR]

The purpose of this work is to study the interaction between the autonomic regulatory loops of blood circulation from long time series. Methods ― We simultaneously recorded four-hour signals of electrocardiogram and photoplethysmogram from the ear and finger of ten healthy adults. We determined the intervals of phase synchronization of the studied regulatory loops and analyzed the dependence of their length on the recording time. The deviations of the total percentage of phase synchronization (index S) from its mean value were estimated in moving non-overlapping windows. Results ― For studied signals we found no significant correlation between the length of synchronization epoch and the time of its beginning. A sharp increase in the deviation of the index S from its mean was shown at the end of the experiment. Conclusion ― The increase in the deviation from the mean at the end of our records is most likely associated more with psychosomatic influences than with hormonal regulation or immobilization stress.

This work presents an overview of the main strategies that have been proposed for non-invasive monitoring of heart rate (HR) in extramural and home settings. We discuss three categories of sensing according to what physiological effect is used to measure the pulsatile activity of the heart, and we focus on an illustrative sensing modality for each of them. Therefore, electrocardiography, photoplethysmography, and mechanocardiography are presented as illustrative modalities to sense electrical activity, mechanical activity, and the peripheral effect of heart activity. In this paper, we describe the physical principles underlying the three categories and the characteristics of the different types of sensors that belong to each class, and we touch upon the most used software strategies that are currently adopted to effectively and reliably extract HR. In addition, we investigate the strengths and weaknesses of each category linked to the different applications in order to provide the reader with guidelines for selecting the most suitable solution according to the requirements and constraints of the application.