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Introduction: Understanding the interplay between cardiovascular parameters, cognitive stress induced by increasing load, and mental well-being is vital for the development of integrated health strategies today. By monitoring physiological signals like electrocardiogram (ECG) and photoplethysmogram (PPG) in real time, researchers can discover how cognitive tasks influence both cardiovascular and mental health. Cardiac biomarkers resulting from cognitive strain act as indicators of autonomic nervous system function, potentially reflecting conditions related to heart and mental health, including depression and anxiety. The purpose of this study is to investigate how cognitive load affects ECG and PPG measurements and whether these can signal early cardiovascular changes during depression and anxiety disorders. Methods: Ninety participants aged 18 to 45 years, ranging from symptom-free individuals to those with diverse psychological conditions, were assessed using psychological questionnaires and anamnesis. ECG and PPG monitoring were conducted as volunteers engaged in a cognitive 1-back task consisting of two separate blocks, each with six progressively challenging levels. The participants’ responses were analyzed to correlate physiological and psychological data with cognitive stressors and outcomes. Results: The study confirmed a notable interdependence between anxiety and depression, and cardiovascular responses. Task accuracy decreased with increased task difficulty. A strong relationship between PPG-measured heart rate and markers of depression and trait anxiety was observed. Increasing task difficulty corresponded to an increase in heart rate, linked with elevated levels of depression and trait anxiety. A strong relationship between ECG-measured heart rate and anxiety attacks was observed. Increasing task difficulty corresponded to an increase in heart rate, linked with elevated levels of anxiety attacks, although this association decreased under more challenging conditions. Discussion: The findings underscore the predictive importance of ECG and PPG heart rate parameters in mental health assessment, particularly depression and anxiety under cognitive stress induced by increasing load. We discuss mechanisms of sympathetic activation explaining these differences. Our research outcomes have implications for clinical assessments and wearable device algorithms for more precise, personalized mental health diagnostics. [ABSTRACT FROM AUTHOR]

The purpose of this study is to more quantitatively identify changes in body function through various bio-signal parameters. (1) Background: Forest therapy is effective in stabilizing cognitive, emotional, cardiovascular, and autonomic nervous systems. In particular, it is necessary to more quantitatively confirm changes in body functions through various bio signals. (2) Methods: As a forest therapy program (FTP) for the elderly, it consisted of strength training in the forest, respiratory aerobic exercises, and cognitive function training, and a total of 19 sessions were performed for 12 weeks. The electroencephalography (EEG) and Photoplethysmography (PPG) before and after the program were measured and compared between program participants (FTP group) and non-participants (control group). (3) Results: the FTP group showed increase in the alpha band power in EEG and a decrease in the PRV index, Tad, and Tae after the program compared to the control group; (4) Conclusions: Significant differences occurred in the physiological functioning of the elderly participants after the program. This is a result that can confirm the effectiveness of forest therapy more quantitatively. Forest therapy has a positive effect on mental stress reduction and cardiovascular function. [ABSTRACT FROM AUTHOR]

Respiratory rate (RR) is a vital indicator for assessing the bodily functions and health status of patients. RR is a prominent parameter in the field of biomedical signal processing and is strongly associated with other vital signs such as blood pressure, heart rate, and heart rate variability. Various physiological signals, such as photoplethysmogram (PPG) signals, are used to extract respiratory information. RR is also estimated by detecting peak patterns and cycles in the signals through signal processing and deep-learning approaches. In this study, we propose an end-to-end RR estimation approach based on a third-generation artificial neural network model—spiking neural network. The proposed model employs PPG segments as inputs, and directly converts them into sequential spike events. This design aims to reduce information loss during the conversion of the input data into spike events. In addition, we use feedback-based integrate-and-fire neurons as the activation functions, which effectively transmit temporal information. The network is evaluated using the BIDMC respiratory dataset with three different window sizes (16, 32, and 64 s). The proposed model achieves mean absolute errors of 1.37 ± 0.04, 1.23 ± 0.03, and 1.15 ± 0.07 for the 16, 32, and 64 s window sizes, respectively. Furthermore, it demonstrates superior energy efficiency compared with other deep learning models. This study demonstrates the potential of the spiking neural networks for RR monitoring, offering a novel approach for RR estimation from the PPG signal. [ABSTRACT FROM AUTHOR]

Hypertension is one of the leading causes of cardiovascular disease morbidity in the world. If remains untreated, it may cause severe damage like heart attack or even death. Early detection is required to prevent the development of other cardiac abnormalities. Photoplethysmogram (PPG) is a bio signal that can be obtained optically by a sensor. It is studied to monitor the change of volume of blood and detect heart conditions. Previous studies have already applied PPG to detect hypertension at the software level. In this article, along with software-based detection, we have implemented a digital hardware-based system for detecting hypertension from signals recorded using PPG sensor. Xilinx ZedBoard Zynq-7000 field programmable gate array (FPGA) board is utilized for designing the embedded system. The hypertension detection accuracy is 98.02% at the software level while for the digital system, it is 96.05% consuming 0.374 W power. The study can be analyzed for other cardiac disease detection and medical equipment development. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this study, we conducted research on a deep learning‐based blood pressure (BP) estimation model suitable for wearable environments. To measure BP while wearing a wearable watch, it needs to be considered that computing power for signal processing is limited and the input signals are subject to noise interference. Therefore, we employed a convolutional neural network (CNN) as the BP estimation model and utilized time‐series electrocardiogram (ECG) and photoplethysmogram (PPG) signals, which are quantifiable in a wearable context. We generated periodic input signals and used differential and thresholding methods to decrease noise in the preprocessing step. We then applied a max‐pooling technique with filter sizes of 2 × 1 and 5 × 1 within a 3‐layer convolutional neural network to estimate BP. Our method was trained, validated, and tested using 2.4 million data samples from 49 patients in the intensive care unit. These samples, totaling 3.1 GB were obtained from the publicly accessible MIMIC database. As a result of a test with 480,000 data samples, the average root mean square error in BP estimation was 3.41, 5.80, and 2.78 mm Hg in the prediction of pulse pressure, systolic BP (SBP), and diastolic BP (DBP), respectively. The cumulative error percentage less than 5 mm Hg was 68% and 93% for SBP and DBP, respectively. In addition, the cumulative error percentage less than 15 mm Hg was 98% and 99% for SBP and DBP. Subsequently, we evaluated the impact of changes in input signal length (1 cycle vs. 30 s) and the introduction of noise on BP estimation results. The experimental results revealed that the length of the input signal did not significantly affect the performance of CNN‐based analysis. When estimating BP using noise‐added ECG signals, the mean absolute error (MAE) for SBP and DBP was 9.72 and 6.67 mm Hg, respectively. Meanwhile, when using noise‐added PPG signals, the MAE for SBP and DBP was 26.85 and 14.00 mm Hg, respectively. Therefore, this study confirmed that using ECG signals rather than PPG signals is advantageous for noise reduction in a wearable environment. Besides, short sampling frames without calibration can be effective as input signals. Furthermore, it demonstrated that using a model suitable for information extraction rather than a specialized deep learning model for sequential data can yield satisfactory results in BP estimation. We have developed a deep learning methodology to predict blood pressure, designed specifically for use in noisy wearable environments, and using input signals from electrocardiograms and photoplethysmograms. Through comprehensive experimentation, we determined which signals within electrocardiograms and photoplethysmograms are resilient to noise interference. Furthermore, we identified the optimal size, data type, and format of the input for an effective deep learning analysis. Based on these insights, we were able to construct a lightweight deep learning model that is well‐suited for these analytical tasks. [ABSTRACT FROM AUTHOR]

IntroductionUnderstanding the interplay between cardiovascular parameters, cognitive stress induced by increasing load, and mental well-being is vital for the development of integrated health strategies today. By monitoring physiological signals like electrocardiogram (ECG) and photoplethysmogram (PPG) in real time, researchers can discover how cognitive tasks influence both cardiovascular and mental health. Cardiac biomarkers resulting from cognitive strain act as indicators of autonomic nervous system function, potentially reflecting conditions related to heart and mental health, including depression and anxiety. The purpose of this study is to investigate how cognitive load affects ECG and PPG measurements and whether these can signal early cardiovascular changes during depression and anxiety disorders.MethodsNinety participants aged 18 to 45 years, ranging from symptom-free individuals to those with diverse psychological conditions, were assessed using psychological questionnaires and anamnesis. ECG and PPG monitoring were conducted as volunteers engaged in a cognitive 1-back task consisting of two separate blocks, each with six progressively challenging levels. The participants’ responses were analyzed to correlate physiological and psychological data with cognitive stressors and outcomes.ResultsThe study confirmed a notable interdependence between anxiety and depression, and cardiovascular responses. Task accuracy decreased with increased task difficulty. A strong relationship between PPG-measured heart rate and markers of depression and trait anxiety was observed. Increasing task difficulty corresponded to an increase in heart rate, linked with elevated levels of depression and trait anxiety. A strong relationship between ECG-measured heart rate and anxiety attacks was observed. Increasing task difficulty corresponded to an increase in heart rate, linked with elevated levels of anxiety attacks, although this association decreased under more challenging conditions.DiscussionThe findings underscore the predictive importance of ECG and PPG heart rate parameters in mental health assessment, particularly depression and anxiety under cognitive stress induced by increasing load. We discuss mechanisms of sympathetic activation explaining these differences. Our research outcomes have implications for clinical assessments and wearable device algorithms for more precise, personalized mental health diagnostics.

There is a growing demand for emotion recognition systems (ERS) to be adopted in everyday life from various fields, particularly automotive, education, and social security. Recently, the use of cardio-based physiological signals, electrocardiogram (ECG), and photoplethysmogram (PPG) in ERS has yielded promising results. Furthermore, the development of wearable devices equipped with cardio-based physiological sensors has significantly aided towards the adoption of ERS in daily life. This paper systematically reviews emotion recognition using cardio-based physiological signals, encompassing emotion models, emotion elicitation methods, and ERS development methods, emphasizing feature extraction, feature selection methods, feature dimension reduction methods, and classifiers. A summary and comparison of recent studies are presented to highlight existing studies’ gaps and suggest future research for better ERS especially using cardio-based signals.

Introduction: Photoplethysmogram signals from wearable devices typically measure heart rate and blood oxygen saturation, but contain a wealth of additional information about the cardiovascular system. In this study, we compared two signal-processing techniques: fiducial point analysis and Symmetric Projection Attractor Reconstruction, on their ability to extract new cardiovascular information from a photoplethysmogram signal. The aim was to identify fiducial point analysis and Symmetric Projection Attractor Reconstruction indices that could classify photoplethysmogram signals, according to age, sex and physical activity. Methods: Three datasets were used: an in-silico dataset of simulated photoplethysmogram waves for healthy male participants (25–75 years old); an in-vivo dataset containing 10-min photoplethysmogram recordings from 57 healthy subjects at rest (18–39 or > 70 years old; 53% female); and an in-vivo dataset containing photoplethysmogram recordings collected for 4 weeks from a single subject, in daily life. The best-performing indices from the in-silico study (5/48 fiducial point analysis and 6/49 Symmetric Projection Attractor Reconstruction) were applied to the in-vivo datasets. Results: Key fiducial point analysis and Symmetric Projection Attractor Reconstruction indices, which showed the greatest differences between groups, were found to be consistent across datasets. These indices were related to systolic augmentation, diastolic peak positioning and prominence, and waveform variability. Both fiducial point analysis and Symmetric Projection Attractor Reconstruction techniques provided indices that supported the classification of age and physical activity, but not sex. Conclusions: Both fiducial point analysis and Symmetric Projection Attractor Reconstruction techniques demonstrated utility in identifying cardiovascular differences between individuals and within an individual over time. Future research should investigate the potential utility of these techniques for extracting information on fitness and disease, to support healthcare-decision making. [ABSTRACT FROM AUTHOR]

Respiratory rate (RR) is a critical vital sign that can provide valuable insights into various medical conditions, including pneumonia. Unfortunately, manual RR counting is often unreliable and discontinuous. Current RR estimation algorithms either lack the necessary accuracy or demand extensive window sizes. In response to these challenges, this study introduces a novel method for continuously estimating RR from photoplethysmogram (PPG) with a reduced window size and lower processing requirements. To evaluate and compare classical and deep learning algorithms, this study leverages the BIDMC and CapnoBase datasets, employing the Respiratory Rate Estimation (RRest) toolbox. The optimal classical techniques combination on the BIDMC datasets achieves a mean absolute error (MAE) of 1.9 breaths/min. Additionally, the developed neural network model utilises convolutional and long short-term memory layers to estimate RR effectively. The best-performing model, with a 50% train–test split and a window size of 7 s, achieves an MAE of 2 breaths/min. Furthermore, compared to other deep learning algorithms with window sizes of 16, 32, and 64 s, this study's model demonstrates superior performance with a smaller window size. The study suggests that further research into more precise signal processing techniques may enhance RR estimation from PPG signals. [ABSTRACT FROM AUTHOR]

Trace elements, often used as dietary supplements, are widely accessible without prescription at pharmacies. Pronutri has pioneered Nutripuncture®, a methodology that utilizes orally consumed trace elements to elicit a physiological response akin to that of acupuncture. Pronutri has empirically observed that the user's voice becomes deeper following an exclusive ingestion procedure. Given that alterations in vocal characteristics are often linked to stress, the Pronutri researchers postulated that the pills have the capacity to promptly alleviate stress upon ingestion. Nevertheless, there is a lack of scientific substantiation about the impact of these supplements on voice (or stress) indicators. The aim of this research was to determine whether there is a consistent impact of trace element ingestion on vocal characteristics, namely the fundamental frequency of the voice, as well as other physiological and psychological stress measurements.In order to achieve this objective, we have devised a unique methodology to examine this hypothesis. This involves conducting a monocentric crossover, randomized, triple-blind, placebo-controlled trial with a sample size of 43 healthy individuals.This study demonstrates that compared to placebo tablets, consuming 10 metal traces containing tablets at once is enough to cause noticeable changes in the vocal spectrum in the direction of an improvement of the voice timbre “richness”, and a decrease in the occurrence of spontaneous electrodermal activity, suggesting a stress reduction. However, there were no significant changes observed in the other parameters that were tested. These parameters include vocal measures such as voice frequency F0, standard deviation from this frequency, jitter, and shimmer. Additionally, physiological measures such as respiratory rate, oxygenation and heart rate variability parameters, as well as psychological measures such as self-assessment analogic scales of anxiety, stress, muscle tension, and nervous tension, did not show any significant changes.Ultimately, our research revealed that the ingestion of 10 trace elements pills may promptly elicit a targeted impact on both vocal spectrum and electrodermal activity. Despite the limited impact, these findings warrant more research to explore the long-term effects of trace elements on voice and stress reduction.

The purpose of this study is to more quantitatively identify changes in body function through various bio-signal parameters. (1) Background: Forest therapy is effective in stabilizing cognitive, emotional, cardiovascular, and autonomic nervous systems. In particular, it is necessary to more quantitatively confirm changes in body functions through various bio signals. (2) Methods: As a forest therapy program (FTP) for the elderly, it consisted of strength training in the forest, respiratory aerobic exercises, and cognitive function training, and a total of 19 sessions were performed for 12 weeks. The electroencephalography (EEG) and Photoplethysmography (PPG) before and after the program were measured and compared between program participants (FTP group) and non-participants (control group). (3) Results: the FTP group showed increase in the alpha band power in EEG and a decrease in the PRV index, Tad, and Tae after the program compared to the control group; (4) Conclusions: Significant differences occurred in the physiological functioning of the elderly participants after the program. This is a result that can confirm the effectiveness of forest therapy more quantitatively. Forest therapy has a positive effect on mental stress reduction and cardiovascular function.

We propose for the first time a method for extracting the instantaneous phases of respiration from the signal of a photoplethysmogram (PPG). In addition to the instantaneous phases of respiration, this method allows for more accurately extracting the instantaneous frequencies of respiration from a PPG than other methods. The proposed method is based on a calculation of the element-wise product of the wavelet spectrum of a PPG and the sequence of intervals between the heartbeats extracted from a PPG, and a calculation of the skeleton of the resulting spectrum in the respiratory frequency range. It is shown that such an element-wise product makes it possible to extract the instantaneous phases and instantaneous frequencies of respiration more accurately than using the wavelet transform of a PPG signal or the sequence of the heartbeat intervals. The proposed method was verified by analyzing the signals from healthy subjects recorded during stress-inducing cognitive tasks. This method can be used in wearable devices for signal processing. [ABSTRACT FROM AUTHOR]

A theoretical analysis of the literature data on the pulse-wave propagation velocity using a combination of electrocardiographic and photoplethysmographic signals was carried out. Its diagnostic value in medical practice lies in its use as a noninvasive method for measuring blood pressure, as well as for determining arterial stiffness. Some studies have shown a relationship between the velocity of pulse-wave propagation and risk factors for cardiovascular diseases. Pulse-wave propagation velocity is an early marker and predictor of cardiovascular risk in both people with normal blood pressure and people with hypertension; it is used as the "gold standard" in determining arterial stiffness; however, the issue of accuracy and automation of measuring the pulse-wave propagation velocity remains unresolved. [ABSTRACT FROM AUTHOR]

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The respiratory rate (RR) is a significant indicator to evaluate a patient's prognosis and status; however, it requires specific instrumentation or estimates from other monitored signals. A photoplethysmogram (PPG) is extensively used in clinical environments as well as in intensive care units (ICUs) to primarily monitor peripheral circulation while capturing indirect information about intrathoracic pressure changes. This study aims to apply and evaluate several deep learning models using a PPG for the continuous and accurate estimation of the RRs of patients. The dataset was collected twice for 2 min each in 100 patients aged 18 years and older from the surgical intensive care unit of a tertiary referral hospital. The BIDMC and CapnoBase public datasets were also analyzed. The collected dataset was preprocessed and split according to the 5-fold cross-validation. We used seven deep learning models, including our own Dilated Residual Neural Network, to check how accurately the RR estimates match the ground truth using the mean absolute error (MAE). As a result, when validated using the collected dataset, our model showed the best results with a 1.2628 ± 0.2697 MAE on BIDMC and RespNet and with a 3.1268 ± 0.6363 MAE on our dataset, respectively. In conclusion, RR estimation using PPG-derived models is still challenging and has many limitations. However, if there is an equal amount of data from various breathing groups to train, we expect that various models, including our Dilated ResNet model, which showed good results, can achieve better results than the current ones. [ABSTRACT FROM AUTHOR]

A photoplethysmogram (PPG) is an optically-derived signal that records the variation in blood volume within the microvasculature. Certain cardiovascular diseases (CVDs) are symptomatic of damaged blood vessels and problems in blood flow, including hypertension. While software implementations for heart rate and blood pressure estimation exist, point-of-care systems demand hardware-based implementations for real-time estimations to be useful for CVD detection. In this study, digital field programmable gate array (FPGA) based systems are developed for heart rate and blood pressure estimation from PPG signals by means of linear regression. In addition to the blood pressure estimation system, we present a prototype hypertension level detection system that achieves 92.42% accuracy while consuming 0.364 W of power. The Mean Absolute Error (MAE) ± Standard Deviation (SD) for heart rate estimation is 3.17 ± 2.79 beat per minute. The corresponding results for systolic and diastolic blood-pressure estimation are 4.75 ± 2.78 and 3.34 ± 2.60, respectively. The prototype can be further extended to wearable devices and medical equipment in the future.

Abstract This review article explores the transformative advancements in wearable biosignal sensors powered by machine learning, focusing on four notable biosignals: electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG), and photoplethysmogram (PPG). The integration of machine learning with these biosignals has led to remarkable breakthroughs in various medical monitoring and human–machine interface applications. For ECG, machine learning enables automated heartbeat classification and accurate disease detection, improving cardiac healthcare with early diagnosis and personalized interventions. EMG technology, combined with machine learning, facilitates real‐time prediction and classification of human motions, revolutionizing applications in sports medicine, rehabilitation, prosthetics, and virtual reality interfaces. EEG analysis powered by machine learning goes beyond traditional clinical applications, enabling brain activity understanding in psychology, neurology, and human–computer interaction, and holds promise in brain–computer interfaces. PPG, augmented with machine learning, has shown exceptional progress in diagnosing and monitoring cardiovascular and respiratory disorders, offering non‐invasive and accurate healthcare solutions. These integrated technologies, powered by machine learning, open new avenues for medical monitoring and human–machine interaction, shaping the future of healthcare.

The purpose of this study was to develop a mathematical model of the photoplethysmogram, which can be used to test methods that introduce the instantaneous phases of the modulating signals. The model must reproduce statistical and spectral characteristics of the real photoplethysmogram, and explicitly incorporate the instantaneous phases of the modulating signals, so they can be used as a reference during testing. Methods. Anacrotic and catacrotic phases of the photoplethysmogram pulse wave were modeled as a sum of two density distributions for the skew normal distribution. The modulating signals were introduced as harmonic functions taken from the experimental instantaneous phases of the VLF (0.015...0.04 Hz), LF (0.04...0.15 Hz) and HF (0.15...0.4 Hz) oscillations in the real photoplethysmogram. The spectral power in the VLF, LF, and HF frequency ranges was calculated to compare the model and experimental data. Results. The model qualitatively reproduces the shape of the experimental photoplethysmogram pulse wave and shows less than 1% error when simulating the spectral properties of the signal. Conclusion. The proposed mathematical model can be used to test the methods for introduction of the instantaneous phases of the modulating signals in photoplethysmogram time-series.

Abstract Background Monitoring blood pressure and peripheral capillary oxygen saturation plays a crucial role in healthcare management for patients with chronic diseases, especially hypertension and vascular disease. However, current blood pressure measurement methods have intrinsic limitations; for instance, arterial blood pressure is measured by inserting a catheter in the artery causing discomfort and infection. Method Photoplethysmogram (PPG) signals can be collected via non-invasive devices, and therefore have stimulated researchers’ interest in exploring blood pressure estimation using machine learning and PPG signals as a non-invasive alternative. In this paper, we propose a Transformer-based deep learning architecture that utilizes PPG signals to conduct a personalized estimation of arterial systolic blood pressure, arterial diastolic blood pressure, and oxygen saturation. Results The proposed method was evaluated with a subset of 1,732 subjects from the publicly available ICU dataset MIMIC III. The mean absolute error is 2.52 ± 2.43 mmHg for systolic blood pressure, 1.37 ± 1.89 mmHg for diastolic blood pressure, and 0.58 ± 0.79% for oxygen saturation, which satisfies the requirements of the Association of Advancement of Medical Instrumentation standard and achieve grades A for the British Hypertension Society standard. Conclusions The results indicate that our model meets clinical standards and could potentially boost the accuracy of blood pressure and oxygen saturation measurement to deliver high-quality healthcare.

Respiratory rate (RR) is a vital indicator for assessing the bodily functions and health status of patients. RR is a prominent parameter in the field of biomedical signal processing and is strongly associated with other vital signs such as blood pressure, heart rate, and heart rate variability. Various physiological signals, such as photoplethysmogram (PPG) signals, are used to extract respiratory information. RR is also estimated by detecting peak patterns and cycles in the signals through signal processing and deep-learning approaches. In this study, we propose an end-to-end RR estimation approach based on a third-generation artificial neural network model—spiking neural network. The proposed model employs PPG segments as inputs, and directly converts them into sequential spike events. This design aims to reduce information loss during the conversion of the input data into spike events. In addition, we use feedback-based integrate-and-fire neurons as the activation functions, which effectively transmit temporal information. The network is evaluated using the BIDMC respiratory dataset with three different window sizes (16, 32, and 64 s). The proposed model achieves mean absolute errors of 1.37 ± 0.04, 1.23 ± 0.03, and 1.15 ± 0.07 for the 16, 32, and 64 s window sizes, respectively. Furthermore, it demonstrates superior energy efficiency compared with other deep learning models. This study demonstrates the potential of the spiking neural networks for RR monitoring, offering a novel approach for RR estimation from the PPG signal.

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 F1-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.

Background: Monitoring blood pressure and peripheral capillary oxygen saturation plays a crucial role in healthcare management for patients with chronic diseases, especially hypertension and vascular disease. However, current blood pressure measurement methods have intrinsic limitations; for instance, arterial blood pressure is measured by inserting a catheter in the artery causing discomfort and infection. Method: Photoplethysmogram (PPG) signals can be collected via non-invasive devices, and therefore have stimulated researchers' interest in exploring blood pressure estimation using machine learning and PPG signals as a non-invasive alternative. In this paper, we propose a Transformer-based deep learning architecture that utilizes PPG signals to conduct a personalized estimation of arterial systolic blood pressure, arterial diastolic blood pressure, and oxygen saturation. Results: The proposed method was evaluated with a subset of 1,732 subjects from the publicly available ICU dataset MIMIC III. The mean absolute error is 2.52 ± 2.43 mmHg for systolic blood pressure, 1.37 ± 1.89 mmHg for diastolic blood pressure, and 0.58 ± 0.79% for oxygen saturation, which satisfies the requirements of the Association of Advancement of Medical Instrumentation standard and achieve grades A for the British Hypertension Society standard. Conclusions: The results indicate that our model meets clinical standards and could potentially boost the accuracy of blood pressure and oxygen saturation measurement to deliver high-quality healthcare. [ABSTRACT FROM AUTHOR]

Atrial fibrillation is a prevalent cardiac arrhythmia that poses significant health risks to patients. The use of non-invasive methods for AF detection, such as Electrocardiogram and Photoplethysmogram, has gained attention due to their accessibility and ease of use. However, there are challenges associated with ECG-based AF detection, and the significance of PPG signals in this context has been increasingly recognized. The limitations of ECG and the untapped potential of PPG are taken into account as this work attempts to classify AF and non-AF using PPG time series data and deep learning. In this work, we emploted a hybrid deep neural network comprising of 1D CNN and BiLSTM for the task of AF classification. We addressed the under-researched area of applying deep learning methods to transmissive PPG signals by proposing a novel approach. Our approach involved integrating ECG and PPG signals as multi-featured time series data and training deep learning models for AF classification. Our hybrid 1D CNN and BiLSTM model achieved an accuracy of 95% on test data in identifying atrial fibrillation, showcasing its strong performance and reliable predictive capabilities. Furthermore, we evaluated the performance of our model using additional metrics. The precision of our classification model was measured at 0.88, indicating its ability to accurately identify true positive cases of AF. The recall, or sensitivity, was measured at 0.85, illustrating the model's capacity to detect a high proportion of actual AF cases. Additionally, the F1 score, which combines both precision and recall, was calculated at 0.84, highlighting the overall effectiveness of our model in classifying AF and non-AF cases. [ABSTRACT FROM AUTHOR]

Recently, photoplethysmography sensors in smart watches have been frequently used to monitor heart rate. Because a photoplethysmography sensor flashes light on the skin and measures the reflected light, the rate of flashing and sampling is directly related to its energy consumption. It is necessary to reduce the rate to extend the battery life of a smart watch. In this study, to satisfy sub-Nyquist sampling, real-time, and high accuracy requirements, we propose a novel heart rate tracking method that consists of online compressive covariance sensing, signal subspace tracking, and spectral peak tracking. The proposed method (average sampling rate: 1.4 Hz) showed better frequency tracking performance than the conventional spectral peak tracking algorithm (sampling rate: 10 Hz) in time-varying heart rate simulations, and a statistically significant difference between them was not observed in the real photoplethysmogram data. The trimmed average absolute error was 1.04 beats/min. As a result, the proposed real-time heart rate tracking method with sub-Nyquist sampling showed high accuracy.

We perform a novel comparative analysis between optically and mechanically derived pulse transit time (PTT) that is universally employed technique for cuffless blood pressure (BP) estimation. For data collection two inline photoplethysmogram (PPG) sensors were mounted at the distal and proximal phalanxes of the index finger of each subject and top each PPG sensor fixture a finger ballistocardiogram (BPP) sensors were clamped. The clamped stacking of the BPP sensors over the PPG sensors provided vertical aligned acquisition of the blood flow waveform through the radial artery for both sensors. The analysis of variance (ANOVA) between PTT derived from the BPP and PPG sensors resulted in a statistically significant difference at $p < 0.05$ . The PTT derived from the BPP sensors showed higher values,17.8 milliseconds on average, than the PTT derived from the PPG sensors. Higher accuracy PTT values will improve the estimation of cuffless BP and thus has the potential to revolutionize the technology.

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.

Respiratory rate (RR) is a critical vital sign that can provide valuable insights into various medical conditions, including pneumonia. Unfortunately, manual RR counting is often unreliable and discontinuous. Current RR estimation algorithms either lack the necessary accuracy or demand extensive window sizes. In response to these challenges, this study introduces a novel method for continuously estimating RR from photoplethysmogram (PPG) with a reduced window size and lower processing requirements. To evaluate and compare classical and deep learning algorithms, this study leverages the BIDMC and CapnoBase datasets, employing the Respiratory Rate Estimation (RRest) toolbox. The optimal classical techniques combination on the BIDMC datasets achieves a mean absolute error (MAE) of 1.9 breaths/min. Additionally, the developed neural network model utilises convolutional and long short-term memory layers to estimate RR effectively. The best-performing model, with a 50% train–test split and a window size of 7 s, achieves an MAE of 2 breaths/min. Furthermore, compared to other deep learning algorithms with window sizes of 16, 32, and 64 s, this study’s model demonstrates superior performance with a smaller window size. The study suggests that further research into more precise signal processing techniques may enhance RR estimation from PPG signals.

Because the process of atherosclerosis is almost irreversible, early prevention and detection have become increasingly important. However, existing noninvasive methods for assessing the risk of arterial stiffness, such as PWV (pulse wave velocity), are unsuitable for daily use. But, the photoplethysmogram (PPG) sensor used in PWV measurement devices is less constraining and easy to use, and is also used in wearable devices. Therefore, this study investigated the possibility of using PPG to evaluate the arterial stiffness placed on the radial artery, where the wearable device is worn. [ABSTRACT FROM AUTHOR]

The novel noninvasive method to diagnose microvascular lesions has been developed􀀃 using time constants obtained from a radial artery pulse (RAPW) and 􀀃 photoplethysmogram (PPG) utilizing the Windkessel model. There are two methods 􀀃 for measuring RAPW: The first is reflectance photoelectric and the second is air conduction. The purpose of this study is to investigate which method was better. Time constants were calculated from each frequency component, with fixtures􀀃 attached to the palm and forearm. Pressure applied to the RAPW sensor was varied 6 􀀃 points. Furthermore, the time constant calculability rate and variation rate calculated􀀃 when the optical and air conduction sensor were used. The maximum value of the 􀀃 calculability rate for the former and latter were 88% and 100%, respectively. The 􀀃 minimum value of variability of the time constant was 18% and 11% for the former􀀃 and latter, respectively. The results demonstrate that air conduction was the superior method. [ABSTRACT FROM AUTHOR]

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

Hypertension is a leading risk factor for cardiovascular disease (CVD) and the overlap of diabetes mellitus (DM) with hypertension can lead to severe complications. There is a need for early diagnosis and risk stratification to implement an overall risk management strategy. Presently, the conventional method is not suitable for large-scale screening. The primary aim of this study is to develop an automated diagnostic system that uses Photoplethysmogram (PPG) signals for the non-invasive diagnosis of hypertension and DM-II. The proposed model uses a statistical feature extracted by decomposing the PPG signal up to level 11 into a sub-band using Discrete wavelet transform (DWT), and a variety of classifiers are used for the classification of hypertension and detection of DM-II patients. Three feature selection techniques used are Spearman correlation, ReliefF and Minimum Redundancy Maximum Relevance (mRMR) to select 20 top features out of 130 features using correlation with systole blood pressure (SBP), diastole blood pressure (DBP) values and D-II. The highest accuracy attained by the Adaptive neural fuzzy system (ANFIS) for classification categories such as normal (NT) vs prehypertension(PHT), NT vs. hypertension type 1 (HT-I), NT vs hypertension type 2 (HT-II) in terms of F1 score are 92.%, 98.5%, 98.3% (SBP) and 83.1%, 95.6%, 86.8% (DBP),respectively. The accuracy achieved by the adaptive-network-based fuzzy inference system (ANFIS) for the classification of normal (non-diabetic) vs. diabetic patients is 99.1%. The hybrid learning algorithm-based classifier achieved higher accuracy for hypertension risk stratification as compared to the hard computing classifier, which requires parameter tuning and DWT decomposition is robust to the noisy signal, overcoming the limitation of the morphological feature-based model.

Photoplethysmography (PPG) is used to measure blood volume changes in the microvascular bed of tissue. Information about these changes along time can be used for estimation of various physiological parameters, such as heart rate variability, arterial stiffness, and blood pressure, to name a few. As a result, PPG has become a popular biological modality and is widely used in wearable health devices. However, accurate measurement of various physiological parameters requires good-quality PPG signals. Therefore, various signal quality indexes (SQIs) for PPG signals have been proposed. These metrics have usually been based on statistical, frequency, and/or template analyses. The modulation spectrogram representation, however, captures the second-order periodicities of a signal and has been shown to provide useful quality cues for electrocardiograms and speech signals. In this work, we propose a new PPG quality metric based on properties of the modulation spectrum. The proposed metric is tested using data collected from subjects while they performed various activity tasks contaminating the PPG signals. Experiments on this multi-wavelength PPG dataset show the combination of proposed and benchmark measures significantly outperforming several benchmark SQIs with improvements of 21.3% BACC (balanced accuracy) for green, 21.6% BACC for red, and 19.0% BACC for infrared wavelengths, respectively, for PPG quality detection tasks. The proposed metrics also generalize for cross-wavelength PPG quality detection tasks. [ABSTRACT FROM AUTHOR]

In the head-mounted display environment for experiencing metaverse or virtual reality, conventional input devices cannot be used, so a new type of nonintrusive and continuous biometric authentication technology is required. Since the wrist wearable device is equipped with a photoplethysmogram sensor, it is very suitable for use for nonintrusive and continuous biometric authentication purposes. In this study, we propose a one-dimensional Siamese network biometric identification model using a photoplethysmogram. To maintain the unique characteristics of each person and reduce noise in preprocessing, we adopted a multicycle averaging method without using a bandpass or low-pass filter. In addition, to verify the effectiveness of the multicycle averaging method, the number of cycles was changed and the results were compared. Genuine and impostor data were used to verify the biometric identification. We used the one-dimensional Siamese network to verify the similarity between the classes and found that the method with five overlapping cycles was the most effective. Tests were conducted on the overlapping data of five single-cycle signals and excellent identification results were observed, with an AUC score of 0.988 and an accuracy of 0.9723. Thus, the proposed biometric identification model is time-efficient and shows excellent security performance, even in devices with limited computational capabilities, such as wearable devices. Consequently, our proposed method has the following advantages compared with previous works. First, the effect of noise reduction and information preservation through multicycle averaging was experimentally verified by varying the number of photoplethysmogram cycles. Second, by analyzing authentication performance through genuine and impostor matching analysis based on a one-dimensional Siamese network, the accuracy that is not affected by the number of enrolled subjects was derived. [ABSTRACT FROM AUTHOR]

The rapid evolution of wearable technology in healthcare sectors has created the opportunity for people to measure their blood pressure (BP) using a smartwatch at any time during their daily activities. Several commercially-available wearable devices have recently been equipped with a BP monitoring feature. However, concerns about recalibration remain. Pulse transit time (PTT)-based estimation is required for initial calibration, followed by periodic recalibration. Recalibration using arm-cuff BP monitors is not practical during everyday activities. In this study, we investigated recalibration using PTT-based BP monitoring aided by a deep neural network (DNN) and validated the performance achieved with more practical wrist-cuff BP monitors. The PTT-based prediction produced a mean absolute error (MAE) of 4.746 ± 1.529 mmHg for systolic blood pressure (SBP) and 3.448 ± 0.608 mmHg for diastolic blood pressure (DBP) when tested with an arm-cuff monitor employing recalibration. Recalibration clearly improved the performance of both DNN and conventional linear regression approaches. We established that the periodic recalibration performed by a wrist-worn BP monitor could be as accurate as that obtained with an arm-worn monitor, confirming the suitability of wrist-worn devices for everyday use. This is the first study to establish the potential of wrist-cuff BP monitors as a means to calibrate BP monitoring devices that can reliably substitute for arm-cuff BP monitors. With the use of wrist-cuff BP monitoring devices, continuous BP estimation, as well as frequent calibrations to ensure accurate BP monitoring, are now feasible. [ABSTRACT FROM AUTHOR]

[Display omitted] Epitaxially grown photodiodes are the foundation of infrared photodetection technology; however, their rigid structure and limited area scaling limit their use in advanced applications. Colloidal-quantum-dot (CQD) infrared photodiodes have increased active areas through solution processing, and are thus potential candidates for large-area flexible photodetection, but these large-area photodiodes have disadvantages such as large dark current density, poor homogeneity, and poor stability. Therefore, this study established a fabrication strategy for large-area flexible CQD photodiodes that involves introducing polyimide to CQD ink to improve CQD passivation, monodisperse ink persistence, and film morphology. The resulting CQD photodiodes exhibited reduced dark current density and improved homogeneity and work stability. Furthermore, the as-prepared photodiodes exhibited a detectivity (D *) of greater than 1013 Jones, which was higher than other reported CQD photodetectors. The CQD photodiodes developed in this study can be used for wearable photoplethysmogram (PPG) signal measurement under ambient light at reduced cost and power consumption. [ABSTRACT FROM AUTHOR]

Heart rate is an extremely important physiological parameter to measure in critically unwell infants, as it is the main physiological marker that changes in response to a change in infant condition. Heart rate is routinely measured peripherally on a limb with a pulse oximeter. However, when infants are critically unwell, the blood supply to these peripheries is reduced in preference for central perfusion of vital organs such as the brain and heart. Measurement of heart rate with a reflection mode photoplethysmogram (PPG) sensor on the forehead could help minimise this problem and make it easier for other important medical equipment, such as cannulas, to be placed on the limbs. This study compares heart rates measured with a forehead-based PPG sensor against a wrist-based PPG sensor in 19 critically unwell infants in neonatal intensive care collecting 198 h of data. The two heart rates were compared using positive percentage agreement, Spearman's correlation coefficient and Bland-Altman analysis. The forehead PPG sensor showed good agreement with the wrist-based PPG sensor with limits of agreement of 8.44 bpm, bias of -0.22 bpm; positive percentage agreement of 98.87%; and Spearman's correlation coefficient of 0.9816. The analysis demonstrates that the forehead is a reliable alternative location for measuring vital signs using the PPG. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 r c = 0.88 and r c = 0.97, compared to OSA and hypopnea, respectively, when p < 0.001) and arousal ( r c = 0.93 and r c = 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, r c = 0.98), and regular and irregular pulse waves (p < 0.001, r c = 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., r c = 0.97, r c = 0.97, r c = 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. [ABSTRACT FROM AUTHOR]