The use of successive systolic differences in photoplethysmographic (PPG) signals for respiratory rate estimation.

Most PPG-based methods for extracting the respiratory rate (RR) rely on changes in the PPG signal's amplitude, baseline, or frequency. However, several other parameters may provide more valuable information for accurate RR computation. In this study, we explored the capabilities of the respiratory-induced variations in successive systolic differences (RISSDV) of PPG signals to estimate RR. We partitioned fifty-three publicly available recordings into eight 1-min segments and identified peaks and troughs of the PPG signals to quantify respiratory-induced variations in amplitude (RIAV), baseline (RIIV), frequency (RIFV), and peak-to-peak amplitude differences (RISSDV). RR values were extracted by determining the peak frequency of the power spectral density of the four variations and the reference respiratory signal. We assessed each feature's performance by computing the root-mean-squared (RMSE) and mean absolute errors (MAE). RISSDV errors were significantly lower than those of RIAV (RMSE and MAE: p < 0.001), RIIV (RMSE: p < 0.01; MAE p < 0.05), and RIFV (RMSE and MAE: p < 0.001), and it appeared less sensitive to absent or missed PPG pulses than respiratory-induced frequency variations. Further research is necessary to extrapolate these findings to subjects under ambulatory rather than stationary conditions, including pediatric and neonatal populations.

Non-invasive assessment of sublingual microcirculation using flow derived from green light PPG: evaluation and reference values.

Significance: The study of sublingual microcirculation offers valuable insights into vascular changes and overcomes some limitations of peripheral microcirculation assessment. Videomicroscopy and pulse oximetry have been used to assess microcirculation, providing insights into organ perfusion beyond macrohemodynamics parameters. However, both techniques have important limitations that preclude their use in clinical practice. Aim: To address this, we propose a non-invasive approach using photoplethysmography (PPG) to assess microcirculation. Approach: Two experiments were performed on different samples of 31 subjects. First, multi-wavelength, finger PPG signals were compared before and while applying pressure on the sensor to determine if PPG signals could detect changes in peripheral microcirculation. For the second experiment, PPG signals were acquired from the ventral region of the tongue, aiming to assess the microcirculation through features calculated from the PPG signal and its first derivative. Results: In experiment 1, 13 out of 15 features extracted from green PPG signals showed significant differences (p<0.05) before and while pressure was applied to the sensor, suggesting that green light could detect flow distortion in superficial capillaries. In experiment 2, 15 features showed potential application of PPG signal for sublingual microcirculation assessment. Conclusions: The PPG signal and its first derivative have the potential to effectively assess microcirculation when measured from the fingertip and the tongue. The assessment of sublingual microcirculation was done through the extraction of 15 features from the green PPG signal and its first derivative. Future studies are needed to standardize and gain a deeper understanding of the evaluated features.

Automatic Classification of Emotions Based on Cardiac Signals: A Systematic Literature Review.

Emotions play a pivotal role in human cognition, exerting influence across diverse domains of individuals' lives. The widespread adoption of artificial intelligence and machine learning has spurred interest in systems capable of automatically recognizing and classifying emotions and affective states. However, the accurate identification of human emotions remains a formidable challenge, as they are influenced by various factors and accompanied by physiological changes. Numerous solutions have emerged to enable emotion recognition, leveraging the characterization of biological signals, including the utilization of cardiac signals acquired from low-cost and wearable sensors. The objective of this work was to comprehensively investigate the current trends in the field by conducting a Systematic Literature Review (SLR) that focuses specifically on the detection, recognition, and classification of emotions based on cardiac signals, to gain insights into the prevailing techniques employed for signal acquisition, the extracted features, the elicitation process, and the classification methods employed in these studies. A SLR was conducted using four research databases, and articles were assessed concerning the proposed research questions. Twenty seven articles met the selection criteria and were assessed for the feasibility of using cardiac signals, acquired from low-cost and wearable devices, for emotion recognition. Several emotional elicitation methods were found in the literature, including the algorithms applied for automatic classification, as well as the key challenges associated with emotion recognition relying solely on cardiac signals. This study extends the current body of knowledge and enables future research by providing insights into suitable techniques for designing automatic emotion recognition applications. It emphasizes the importance of utilizing low-cost, wearable, and unobtrusive devices to acquire cardiac signals for accurate and accessible emotion recognition.

Real-Time Evaluation of Time-Domain Pulse Rate Variability Parameters in Different Postures and Breathing Patterns Using Wireless Photoplethysmography Sensor: Towards Remote Healthcare in Low-Resource Communities.

Photoplethysmography (PPG) signals have been widely used in evaluating cardiovascular biomarkers, however, there is a lack of in-depth understanding of the remote usage of this technology and its viability for underdeveloped countries. This study aims to quantitatively evaluate the performance of a low-cost wireless PPG device in detecting ultra-short-term time-domain pulse rate variability (PRV) parameters in different postures and breathing patterns. A total of 30 healthy subjects were recruited. ECG and PPG signals were simultaneously recorded in 3 min using miniaturized wearable sensors. Four heart rate variability (HRV) and PRV parameters were extracted from ECG and PPG signals, respectively, and compared using analysis of variance (ANOVA) or Scheirer-Ray-Hare test with post hoc analysis. In addition, the data loss was calculated as the percentage of missing sampling points. Posture did not present statistical differences across the PRV parameters but a statistical difference between indicators was found. Strong variation was found for the RMSSD indicator in the standing posture. The sitting position in both breathing patterns demonstrated the lowest data loss (1.0 ± 0.6 and 1.0 ± 0.7) and the lowest percentage of different factors for all indicators. The usage of commercial PPG and BLE devices can allow the reliable extraction of the PPG signal and PRV indicators in real time.

Photoplethysmography waveform analysis for classification of vascular tone and arterial blood pressure: Study based on neural networks.

BACKGROUND: To test whether a Shallow Neural Network (S-NN) can detect and classify vascular tone dependent changes in arterial blood pressure (ABP) by advanced photopletysmographic (PPG) waveform analysis. METHODS: PPG and invasive ABP signals were recorded in 26 patients undergoing scheduled general surgery. We studied the occurrence of episodes of hypertension (systolic arterial pressure (SAP) >140 mmHg), normotension and hypotension (SAP < 90 mmHg). Vascular tone according to PPG was classified in two ways: 1) By visual inspection of changes in PPG waveform amplitude and dichrotic notch position; where Classes I-II represent vasoconstriction (notch placed >50% of PPG amplitude in small amplitude waves), Class III normal vascular tone (notch placed between 20-50% of PPG amplitude in normal waves) and Classes IV-V-VI vasodilation (notch <20% of PPG amplitude in large waves). 2) By an automated analysis, using S-NN trained and validated system that combines seven PPG derived parameters. RESULTS: The visual assessment was precise in detecting hypotension (sensitivity 91%, specificity 86% and accuracy 88%) and hypertension (sensitivity 93%, specificity 88% and accuracy 90%). Normotension presented as a visual Class III (III-III) (median and 1st-3rd quartiles), hypotension as a Class V (IV-VI) and hypertension as a Class II (I-III); all p < .0001. The automated S-NN performed well in classifying ABP conditions. The percentage of data with correct classification by S-ANN was 83% for normotension, 94% for hypotension, and 90% for hypertension. CONCLUSIONS: Changes in ABP were correctly classified automatically by S-NN analysis of the PPG waveform contour.

Effect of dental tissue thickness on the measurement of oxygen saturation by two different pulse oximeters

Abstract This study aimed to evaluate the influence of different dental tissue thickness on the measurement of oxygen saturation (SpO2) levels in high (HP) and low (LP) blood perfusion by comparing the values obtained from two different pulse oximeters (POs) - BCI and Sense 10. Thirty freshly extracted human teeth had their crowns interposed between the POs and an optical simulator, which emulated the SpO2 and heart beats per minute (bpm) at HP (100% SpO2/75 bpm) and LP (86% SpO2/75 bpm) modes. Afterwards, the palatine/lingual surfaces of the dental crowns were worn with diamond drills. The reading of SpO2 was performed again using the POs alternately through the buccal surface of each dental crown. Data were analyzed by the Wilcoxon, Mann-Whitney and Kendall Tau-b tests (α=5%). The results showed significant difference at the HP and LP modes in the SpO2 readouts through the different dental thicknesses with the use of BCI, and at the LP mode with the use of Sense 10, which had a significant linear correlation (p<0.0001) and lower SpO2 readout values in relation to the increase of the dental thickness. Irrespective of tooth thickness, Sense 10 had significantly higher readout values (p<0.0001) than BCI at both perfusion modes. The interposition of different thicknesses of enamel and dentin influenced the POs measurement of SpO2, specially at the low perfusion mode. The POs were more accurate in SpO2 measurement when simulated perfusion levels were higher.

Resumo Este estudo avaliou a influência de diferentes espessuras de esmalte e dentina na medição dos níveis de saturação de oxigênio (SpO2) em alta (HP) e baixa (LP) perfusão sanguínea, comparando os valores obtidos em dois oxímetros de pulso (OPs) diferentes, BCI e Sense 10. Trinta dentes recém-extraídos de humanos tiveram suas coroas interpostas entre os OPs e um simulador óptico, que simulava a SpO2 e os batimentos cardíacos por minuto (bpm) nos modos de HP (100% SpO2 / 75 bpm) e LP (86% SpO2 / 75 bpm). Após, as superfícies palatinas / linguais dos dentes foram desgastadas com brocas de diamantadas. A leitura da SpO2 foi realizada novamente usando os dois OPs alternadamente através da face vestibular de cada coroa dental. Os dados foram analisados ​​pelos testes Wilcoxon, Mann-Whitney e Kendall Tau-b (α = 5%). Os resultados mostraram diferença significativa nos modos HP e LP nas leituras de SpO2 através das diferentes espessuras dentárias com o uso do BCI, e no modo LP com o uso do Sense 10, que teve correlação linear significativa (p <0,0001) e menores valores de leitura de SpO2 em relação ao aumento da espessura dentária. Independentemente da espessura do dente, o Sense 10 apresentou valores de leitura significativamente maiores (p <0,0001) do que o BCI em ambos os modos de perfusão. A interposição de diferentes espessuras de esmalte e dentina influenciaram a mensuração da SpO2 pelos OPs, especialmente no modo de baixa perfusão. Os POs foram mais precisos na mensuração da SpO2 quando os níveis simulados de perfusão foram maiores.

Measuring Heart Rate Variability Using Facial Video.

Heart Rate Variability (HRV) has become an important risk assessment tool when diagnosing illnesses related to heart health. HRV is typically measured with an electrocardiogram; however, there are multiple studies that use Photoplethysmography (PPG) instead. Measuring HRV with video is beneficial as a non-invasive, hands-free alternative and represents a more accessible approach. We developed a methodology to extract HRV from video based on face detection algorithms and color augmentation. We applied this methodology to 45 samples. Signals obtained from PPG and video recorded an average mean error of less than 1 bpm when measuring the heart rate of all subjects. Furthermore, utilizing PPG and video, we computed 61 variables related to HRV. We compared each of them with three correlation metrics (i.e., Kendall, Pearson, and Spearman), adjusting them for multiple comparisons with the Benjamini-Hochberg method to control the false discovery rate and to retrieve the q-value when considering statistical significance lower than 0.5. Using these methods, we found significant correlations for 38 variables (e.g., Heart Rate, 0.991; Mean NN Interval, 0.990; and NN Interval Count, 0.955) using time-domain, frequency-domain, and non-linear methods.

A deep learning approach to estimate pulse rate by remote photoplethysmography.

Objective.This study proposes a U-net shaped Deep Neural Network (DNN) model to extract remote photoplethysmography (rPPG) signals from skin color signals to estimate Pulse Rate (PR).Approach.Three input window sizes are used in the DNN: 256 samples (5.12 s), 512 samples (10.24 s), and 1024 (20.48 s). A data augmentation algorithm based on interpolation is also used here to artificially increase the number of training samples.Main results.The proposed model outperformed a prior-knowledge rPPG method by using input signals with window of 256 and 512 samples. Also, it was found that the data augmentation procedure only increased the performance for the window of 1024 samples. The trained model achieved a Mean Absolute Error (MAE) of 3.97 Beats per Minute (BPM) and Root Mean Squared Error (RMSE) of 6.47 BPM, for the 256 samples window, and MAE of 3.00 BPM and RMSE of 5.45 BPM for the window of 512 samples. On the other hand, the prior-knowledge rPPG method got a MAE of 8.04 BPM and RMSE of 16.63 BPM for the window of 256 samples, and MAE of 3.49 BPM and RMSE of 7.92 BPM for the window of 512 samples. For the longest window (1024 samples), the concordance of the predicted PRs from the DNNs and the true PRs was higher when applying the data augmentation procedure.Significance.These results demonstrate a big potential of this technique for PR estimation, showing that the DNN proposed here may generate reliable rPPG signals even with short window lengths (5.12 s and 10.24 s), suggesting that it needs less data for a faster rPPG measurement and PR estimation.

Skin Pigmentation Influence on Pulse Oximetry Accuracy: A Systematic Review and Bibliometric Analysis.

Nowadays, pulse oximetry has become the standard in primary and intensive care units, especially as a triage tool during the current COVID-19 pandemic. Hence, a deeper understanding of the measurement errors that can affect precise readings is a key element in clinical decision-making. Several factors may influence the accuracy of pulse oximetry, such as skin color, body temperature, altitude, or patient movement. The skin pigmentation effect on pulse oximetry accuracy has long been studied reporting some contradictory conclusions. Recent studies have shown a positive bias in oxygen saturation measurements in patients with darkly pigmented skin, particularly under low saturation conditions. This review aims to study the literature that assesses the influence of skin pigmentation on the accuracy of these devices. We employed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement to conduct a systematic review retrospectively since February 2022 using WOS, PubMed, and Scopus databases. We found 99 unique references, of which only 41 satisfied the established inclusion criteria. A bibliometric and scientometrics approach was performed to examine the outcomes of an exhaustive survey of the thematic content and trending topics.

Towards a Non-Contact Method for Identifying Stress Using Remote Photoplethysmography in Academic Environments.

Stress has become a common condition and is one of the chief causes of university course disenrollment. Most of the studies and tests on academic stress have been conducted in research labs or controlled environments, but these tests can not be extended to a real academic environment due to their complexity. Academic stress presents different associated symptoms, anxiety being one of the most common. This study focuses on anxiety derived from academic activities. This study aims to validate the following hypothesis: by using a non-contact method based on the use of remote photoplethysmography (rPPG), it is possible to identify academic stress levels with an accuracy greater than or equal to that of previous works which used contact methods. rPPG signals from 56 first-year engineering undergraduate students were recorded during an experimental task. The results show that the rPPG signals combined with students' demographic data and psychological scales (the State-Trait Anxiety Inventory) improve the accuracy of different classification methods. Moreover, the results demonstrate that the proposed method provides 96% accuracy by using K-nearest neighbors, J48, and random forest classifiers. The performance metrics show better or equal accuracy compared to other contact methods. In general, this study demonstrates that it is possible to implement a low-cost method for identifying academic stress levels in educational environments.

Validation study of the use of the HRVCAM software for the evaluation of heart rate and heart rate variability at rest.

Aims: The existing instruments for assessing heart rate (HR) and heart rate variability (HRV) require contact area. This is difficult to obtain from specific groups of patients and from those moving. The aim of this study was to validate the use of the HRVCam software for measuring HR and HRV in healthy adults. Methods and results: The HR and HRV variables were evaluated in terms of time and frequency using a webcam and Polar® S810i. The Shapiro-Wilk test was used to test the normality of the data, and the Pearson's correlation coefficient (r) was used to identify the possible correlation between the two instruments. The size of the effect was calculated based on a generalized linear model, and the Bland-Altman plots were used to analyse the agreement between the methods. The level of significance for all analyses was set at P < 0.05. We evaluated 102 participants, of whom 52% were men; 83.3% were aged between 18 and 29.9 years; and 84.3% were single. Conclusion: There was a good agreement and moderate to strong correlations among all analysed variables. The biases were low, except for the low frequency/high frequency measures. Moreover, the difference between the samples was small to moderate. The results of this study corroborate the use of HRVCam for measuring HR and HRV.

Blood Pressure Morphology Assessment from Photoplethysmogram and Demographic Information Using Deep Learning with Attention Mechanism.

Arterial blood pressure (ABP) is an important vital sign from which it can be extracted valuable information about the subject's health. After studying its morphology it is possible to diagnose cardiovascular diseases such as hypertension, so ABP routine control is recommended. The most common method of controlling ABP is the cuff-based method, from which it is obtained only the systolic and diastolic blood pressure (SBP and DBP, respectively). This paper proposes a cuff-free method to estimate the morphology of the average ABP pulse (ABPM¯) through a deep learning model based on a seq2seq architecture with attention mechanism. It only needs raw photoplethysmogram signals (PPG) from the finger and includes the capacity to integrate both categorical and continuous demographic information (DI). The experiments were performed on more than 1100 subjects from the MIMIC database for which their corresponding age and gender were consulted. Without allowing the use of data from the same subjects to train and test, the mean absolute errors (MAE) were 6.57 ± 0.20 and 14.39 ± 0.42 mmHg for DBP and SBP, respectively. For ABPM¯, R correlation coefficient and the MAE were 0.98 ± 0.001 and 8.89 ± 0.10 mmHg. In summary, this methodology is capable of transforming PPG into an ABP pulse, which obtains better results when DI of the subjects is used, potentially useful in times when wireless devices are becoming more popular.

Multi-Sensor Wearable Health Device Framework for Real-Time Monitoring of Elderly Patients Using a Mobile Application and High-Resolution Parameter Estimation.

Automatized scalable healthcare support solutions allow real-time 24/7 health monitoring of patients, prioritizing medical treatment according to health conditions, reducing medical appointments in clinics and hospitals, and enabling easy exchange of information among healthcare professionals. With recent health safety guidelines due to the COVID-19 pandemic, protecting the elderly has become imperative. However, state-of-the-art health wearable device platforms present limitations in hardware, parameter estimation algorithms, and software architecture. This paper proposes a complete framework for health systems composed of multi-sensor wearable health devices (MWHD), high-resolution parameter estimation, and real-time monitoring applications. The framework is appropriate for real-time monitoring of elderly patients' health without physical contact with healthcare professionals, maintaining safety standards. The hardware includes sensors for monitoring steps, pulse oximetry, heart rate (HR), and temperature using low-power wireless communication. In terms of parameter estimation, the embedded circuit uses high-resolution signal processing algorithms that result in an improved measure of the HR. The proposed high-resolution signal processing-based approach outperforms state-of-the-art HR estimation measurements using the photoplethysmography (PPG) sensor.

Blood Pressure Increase in Hypertensive Individuals During Resistance Training Protocols With Equated Work to Rest Ratio.

Introduction: Despite growing evidence regarding the benefits of resistance training in hypertension, the large and abrupt rise of systolic blood pressure (SBP) observed during resistance exercise execution has resulted in concern about its safety. However, the manipulation of the resistance training protocol (RTP) organization, maintaining the work to rest ratio equated between protocols (W:R-equated), may reduce the SBP increase. Purpose: To compare cardiovascular responses during two W:R-equated RTPs (3 × 15:88 s vs. 9 × 5:22 s - sets × reps: rest between sets) performed in exercises for the lower and upper limbs. Methods: Twelve medicated hypertensives (48 ± 8 years) randomly performed two RTPs in the bilateral leg extension (BLE) and unilateral elbow flexion (UEF) exercises at 50% 1RM. Increases (Δ) of SBP, heart rate (HR) and rate pressure product (RPP) during the exercises were measured by photoplethysmography. Results: In both BLE and UEF exercises, Δ SBP was significantly greater during 3 × 15:88 s than 9 × 5:22 s (peak values: BLE = + 84 ± 39 vs. + 67 ± 20 mm Hg, and UEF = + 46 ± 25 vs. + 37 ± 18 mm Hg, respectively, both p < 0.05). ΔHR and ΔRPP were significantly higher in the 3 × 15:88 s than 9 × 5:22 s in BLE (peak values + 45 ± 17 vs. + 30 ± 8 bpm, and + 15,559 ± 5570 vs. + 10,483 ± 2614 mm Hg. bpm). Conclusion: In medicated hypertensives, a RTP combining more sets with less repetitions per set and shorter rest intervals between sets (i.e., 9 × 5:22 s) produced a smaller increase in cardiovascular load (ΔSBP, ΔHR and ΔRPP) during its execution than a protocol with fewer longer sets (i.e., 3 × 15:88 s).

Mobile Personal Health Care System for Noninvasive, Pervasive, and Continuous Blood Pressure Monitoring: Development and Usability Study.

BACKGROUND: Smartphone-based blood pressure (BP) monitoring using photoplethysmography (PPG) technology has emerged as a promising approach to empower users with self-monitoring for effective diagnosis and control of hypertension. OBJECTIVE: This study aimed to develop a mobile personal health care system for noninvasive, pervasive, and continuous estimation of BP level and variability, which is user friendly for elderly people. METHODS: The proposed approach was integrated by a self-designed cuffless, calibration-free, wireless, and wearable PPG-only sensor and a native purposely designed smartphone app using multilayer perceptron machine learning techniques from raw signals. We performed a development and usability study with three older adults (mean age 61.3 years, SD 1.5 years; 66% women) to test the usability and accuracy of the smartphone-based BP monitor. RESULTS: The employed artificial neural network model had good average accuracy (>90%) and very strong correlation (>0.90) (P<.001) for predicting the reference BP values of our validation sample (n=150). Bland-Altman plots showed that most of the errors for BP prediction were less than 10 mmHg. However, according to the Association for the Advancement of Medical Instrumentation and British Hypertension Society standards, only diastolic blood pressure prediction met the clinically accepted accuracy thresholds. CONCLUSIONS: With further development and validation, the proposed system could provide a cost-effective strategy to improve the quality and coverage of health care, particularly in rural zones, areas lacking physicians, and areas with solitary elderly populations.

The Hemodynamic Mass Action of a Central Pattern Generator.

The hemodynamic response is a neurovascular and metabolic process in which there is rapid delivery of blood flow to a neuronal tissue in response to neuronal activation. The functional magnetic resonance imaging (fMRI) and the functional near-infrared spectroscopy (fNIRS), for instance, are based on the physiological principles of such hemodynamic responses. Both techniques allow the mapping of active neuronal regions in which the neurovascular and metabolic events are occurring. However, although both techniques have revolutionized the neurosciences, they are mostly employed for neuroimaging of the human brain but not for the spinal cord during functional tasks. Moreover, little is known about other techniques measuring the hemodynamic response in the spinal cord. The purpose of the present study was to show for the first time that a simple optical system termed direct current photoplethysmography (DC-PPG) can be employed to detect hemodynamic responses of the spinal cord and the brainstem during the functional activation of the spinal central pattern generator (CPG). In particular, we positioned two DC-PPG systems directly on the brainstem and spinal cord during fictive scratching in the cat. The optical DC-PPG systems allowed the trial-by-trial recording of massive hemodynamic signals. We found that the "strength" of the flexor-plus-extensor motoneuron activities during motor episodes of fictive scratching was significantly correlated to the "strengths" of the brainstem and spinal DC-PPG signals. Because the DC-PPG was robustly detected in real-time, we claim that such a functional signal reflects the hemodynamic mass action of the brainstem and spinal cord associated with the CPG motor action. Our findings shed light on an unexplored hemodynamic observable of the spinal CPGs, providing a proof of concept that the DC-PPG can be used for the assessment of the integrity of the human CPGs.

Analysis of Relevant Features from Photoplethysmographic Signals for Atrial Fibrillation Classification.

Atrial Fibrillation (AF) is the most common cardiac arrhythmia found in clinical practice. It affects an estimated 33.5 million people, representing approximately 0.5% of the world's population. Electrocardiogram (ECG) is the main diagnostic criterion for AF. Recently, photoplethysmography (PPG) has emerged as a simple and portable alternative for AF detection. However, it is not completely clear which are the most important features of the PPG signal to perform this process. The objective of this paper is to determine which are the most relevant features for PPG signal analysis in the detection of AF. This study is divided into two stages: (a) a systematic review carried out following the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) statement in six databases, in order to identify the features of the PPG signal reported in the literature for the detection of AF, and (b) an experimental evaluation of them, using machine learning, in order to determine which have the greatest influence on the process of detecting AF. Forty-four features were found when analyzing the signal in the time, frequency, or time-frequency domains. From those 44 features, 27 were implemented, and through machine learning, it was found that only 11 are relevant in the detection process. An algorithm was developed for the detection of AF based on these 11 features, which obtained an optimal performance in terms of sensitivity (98.43%), specificity (99.52%), and accuracy (98.97%).

Game-Calibrated and User-Tailored Remote Detection of Stress and Boredom in Games.

Emotion detection based on computer vision and remote extraction of user signals commonly rely on stimuli where users have a passive role with limited possibilities for interaction or emotional involvement, e.g., images and videos. Predictive models are also trained on a group level, which potentially excludes or dilutes key individualities of users. We present a non-obtrusive, multifactorial, user-tailored emotion detection method based on remotely estimated psychophysiological signals. A neural network learns the emotional profile of a user during the interaction with calibration games, a novel game-based emotion elicitation material designed to induce emotions while accounting for particularities of individuals. We evaluate our method in two experiments ( n = 20 and n = 62 ) with mean classification accuracy of 61.6%, which is statistically significantly better than chance-level classification. Our approach and its evaluation present unique circumstances: our model is trained on one dataset (calibration games) and tested on another (evaluation game), while preserving the natural behavior of subjects and using remote acquisition of signals. Results of this study suggest our method is feasible and an initiative to move away from questionnaires and physical sensors into a non-obtrusive, remote-based solution for detecting emotions in a context involving more naturalistic user behavior and games.

Comparison of the ankle-brachial index with parameters of stiffness and peripheral arterial resistance assessed by photoplethysmography in elderly patients.

BACKGROUND: The ankle-brachial index (ABI) uses the ratio between systolic blood pressures at the ankle and the arm to diagnose peripheral arterial disease (PAD) noninvasively. Photoplethysmography (PPG) measures and records changes to the blood volume in the human body using optical techniques. OBJECTIVES: The objective of this study was to compare ABI with arterial stiffness and peripheral resistance parameters assessed using PPG in elderly patients and to propose a model for prediction of ABI. METHODS: A cross-sectional, quantitative study was conducted. The sample comprised elderly patients seen at a medical specialties clinic at the Universidade do Sul de Santa Catarina (UNISUL), Brazil. Age, sex, body mass index (BMI), comorbidities, smoking, and physical activity were recorded. The variables obtained using PPG and ABI were compared using bivariate and multivariate linear regression, with an α error of 0.05. RESULTS: A total of 93 elderly patients were assessed, 63.4% of whom were women. In 98.9% of cases, ABI was within normal limits. Comparison of ABI with variables acquired by PPG revealed significant associations with age. However, no significant associations were observed between ABI and PPG. The multivariate model indicated that only age, sex, and smoking were associated with ABI. CONCLUSIONS: In conclusion, ABI and PPG exhibited associations with arterial aging, considering its correlation with age. However, ABI was only related to age, sex, and smoking. More studies are needed to evaluate the potential uses of PPG for screening for vascular diseases in ambulatory settings.

Systematic Review on Features Extracted from PPG Signals for the Detection of Atrial Fibrillation.

PROBLEM: Atrial Fibrillation (AF) is the most common sustained cardiac arrhythmia. It constitutes one of the leading cardiovascular health problems, affecting 33.5 million people of the world's population. AF detection is commonly made by an Electrocardiogram (EEG). Nevertheless, with the advances in biomedical sensors, innovative approaches have emerged on detecting AF based on the analysis of signals acquired by photoplethysmography (PPG) sensors. OBJECTIVE: This paper aims to provide a systematic review to determine the features that have been used to detect Atrial Fibrillation in PPG signals. METHODS: A systematic review of six databases (Pubmed, Science Direct, Scopus, IEEE Xplore, Engineering Village y Mendeley) was carried out following the PRISMA-DTA statement (Preferred Reporting Items for Systematic Reviews and Meta-Analyses on Diagnostic Test Accuracy). RESULTS: This article provides an analysis of the features extracted for the detection of Atrial Fibrillation in photoplethysmography signals from 16 studies. It was found 44 features: 29 were extracted from the signal analyzed in the time domain, 12 from the signal analyzed in the frequency domain, and 3 from the signal analyzed in the time-frequency domain. CONCLUSIONS: The systematic review allowed obtaining the features reported in the literature with higher performance in the detection of AF in terms of sensitivity, specificity, and accuracy. It was possible to observe a clear tendency to analyze the PPG signal in the time domain, although some studies have obtained better performance in the classification of AF when analyzing features in the frequency and time-frequency domains.