Inferring ECG Waveforms from PPG Signals with a Modified U-Net Neural Network.

There are two widely used methods to measure the cardiac cycle and obtain heart rate measurements: the electrocardiogram (ECG) and the photoplethysmogram (PPG). The sensors used in these methods have gained great popularity in wearable devices, which have extended cardiac monitoring beyond the hospital environment. However, the continuous monitoring of ECG signals via mobile devices is challenging, as it requires users to keep their fingers pressed on the device during data collection, making it unfeasible in the long term. On the other hand, the PPG does not contain this limitation. However, the medical knowledge to diagnose these anomalies from this sign is limited by the need for familiarity, since the ECG is studied and used in the literature as the gold standard. To minimize this problem, this work proposes a method, PPG2ECG, that uses the correlation between the domains of PPG and ECG signals to infer from the PPG signal the waveform of the ECG signal. PPG2ECG consists of mapping between domains by applying a set of convolution filters, learning to transform a PPG input signal into an ECG output signal using a U-net inception neural network architecture. We assessed our proposed method using two evaluation strategies based on personalized and generalized models and achieved mean error values of 0.015 and 0.026, respectively. Our method overcomes the limitations of previous approaches by providing an accurate and feasible method for continuous monitoring of ECG signals through PPG signals. The short distances between the infer-red ECG and the original ECG demonstrate the feasibility and potential of our method to assist in the early identification of heart diseases.

Remote Heart Rate Prediction in Virtual Reality Head-Mounted Displays Using Machine Learning Techniques.

Head-mounted displays are virtual reality devices that may be equipped with sensors and cameras to measure a patient's heart rate through facial regions. Heart rate is an essential body signal that can be used to remotely monitor users in a variety of situations. There is currently no study that predicts heart rate using only highlighted facial regions; thus, an adaptation is required for beats per minute predictions. Likewise, there are no datasets containing only the eye and lower face regions, necessitating the development of a simulation mechanism. This work aims to remotely estimate heart rate from facial regions that can be captured by the cameras of a head-mounted display using state-of-the-art EVM-CNN and Meta-rPPG techniques. We developed a region of interest extractor to simulate a dataset from a head-mounted display device using stabilizer and video magnification techniques. Then, we combined support vector machine and FaceMash to determine the regions of interest and adapted photoplethysmography and beats per minute signal predictions to work with the other techniques. We observed an improvement of 188.88% for the EVM and 55.93% for the Meta-rPPG. In addition, both models were able to predict heart rate using only facial regions as input. Moreover, the adapted technique Meta-rPPG outperformed the original work, whereas the EVM adaptation produced comparable results for the photoplethysmography signal.

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.

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.

A machine learning approach for hypertension detection based on photoplethysmography and clinical data.

High blood pressure early screening remains a challenge due to the lack of symptoms associated with it. Accordingly, noninvasive methods based on photoplethysmography (PPG) or clinical data analysis and the training of machine learning techniques for hypertension detection have been proposed in the literature. Nevertheless, several challenges arise when analyzing PPG signals, such as the need for high-quality signals for morphological feature extraction from PPG related to high blood pressure. On the other hand, another popular approach is to use deep learning techniques to avoid the feature extraction process. Nonetheless, this method requires high computational power and behaves as a black-box approach, which impedes application in a medical context. In addition, considering only the socio-demographic and clinical data of the subject does not allow constant monitoring. This work proposes to use the wavelet scattering transform as a feature extraction technique to obtain features from PPG data and combine it with clinical data to detect early hypertension stages by applying Early and Late Fusion. This analysis showed that the PPG features derived from the wavelet scattering transform combined with a support vector machine can classify normotension and prehypertension with an accuracy of 71.42% and an F1-score of 76%. However, classifying normotension and prehypertension by considering both the features extracted from PPG signals through wavelet scattering transform and clinical variables such as age, body mass index, and heart rate by either Late Fusion or Early Fusion did not provide better performance than considering each data type separately in terms of accuracy and F1-score.

Blood-pressure-waveform monitoring without interruptions due to changes in arterial compliance: The use of the vibrational and volume-clamp methods.

The arterial-blood-pressure (ABP) waveform can be monitored by the volume-clamp method. The photoplethysmography (PPG) signal is measured and clamped at maximum arterial compliance (PPGcmax) by controlling the external pressure (EP) with a cuff. PPGcmax is determined by the volume-oscillometric method though ABP measurement is regularly interrupted. To overcome this drawback, the vibrational method superimposes high-frequency vibrations on EP and measures the PPG response to estimate the "vibrational" compliance (Cv) and the PPGcmax. This method, though, has never been validated or implemented simultaneously with the volume-clamp method because the control has always been unstable. We implemented a custom-made device with a novel control system, monitoring stability and adapting the gain at high frequencies, plus lower-amplitude EP vibrations. We compared, in eleven volunteers, the EP at PPGcmax determined by the volume-oscillometric and the vibrational methods. Both exhibited a good linear correlation (r2 >0.92) and Bland-Altman agreement (95% confidence interval <15 mmHg). Moreover, in three volunteers, the vibrational and volume-clamp methods were implemented together while experimentally changing the ABP and/or Cv without manifesting control-system instability. Cv measured with the vibrational method could be used by the volume-clamp method to measure the ABP waveform without any interruptions due to changes in arterial compliance.

La relación de la edad con los componentes espectrales de las realizaciones sin ruido de señales fotopletismográficas: resultados de un enfoque de identificación no lineal / Relationship of Age with Spectral Components of Noise-Free Realizations of Photoplethysmographic Signals: Outcomes of a Nonlinear Identification Approach

Background: Age-related changes in the vascular network have been widely documented, however, nonlinear identification has been poorly applied to the analysis of cardiovascular signals. Objective: To determine the impact of age on spectral components of Noise-free realizations (NFR) obtained from photoplethysmographic signals, summarized in the Kernel Complexity Regressive Index (KCRIndex). Methods: With 190 apparently healthy participants (9 to 89 years) from Orense, Spain, Photoplethysmographic signals were recorded during 5 minutes in supine position using Nellcor-395 pulse oximeter; signals were digitized at 1000 Hz, and furtherly submitted to nonlinear identification via a kernel nonlinear autoregressive estimator. KCRIndex is defined as the average of at least three negative slope values at the NFR log-log spectrum in the 9 to 25 Hz frequency region. Results: KCRIndex decreased with age in a linear fashion and did not differ between genders. The regression line obtained was KCRIndex=-0.025*age+6.868 (r=-0.751). Conclusions: KCRIndex, is strongly correlated with age, thus opening up new possibilities for cardiovascular exploration at primary health care settings and even on open field conditions(AU)

Antecedentes: los cambios relacionados con la edad en la red vascular se han documentado ampliamente, sin embargo, la identificación no lineal solo se ha aplicado de manera esporádica al análisis de las señales cardiovasculares. Objetivo: determinar los cambios con la edad en los componentes espectrales de las realizaciones sin ruido (NFR) obtenidas a partir de señales fotopletismográficas, resumidas en el índice regresivo de la complejidad por núcleos (KCRIndex). Métodos: Con 190 participantes aparentemente sanos (de 9 a 89 años) residentes en Orense, España, se registraron señales fotopletismográficas durante 5 minutos en posición supina usando un oxímetro de pulso Nellcor-395; las señales se digitalizaron a 1000 Hz, y se sometieron a identificación no lineal a través de un estimador autorregresivo no lineal por núcleos. El KCRIndex se define como el promedio de al menos tres valores de pendiente negativos en el espectro log-log de NFR en la región de frecuencia de 9 a 25 Hz. Resultados: KCRIndex disminuyó con la edad de forma lineal y no difirió entre géneros. La línea de regresión obtenida fue KCRIndex = -0.025 * edad + 6.868 (r = -0.751). Conclusiones: Este índice propuesto está fuertemente correlacionado con la edad, lo que abre nuevas posibilidades para la exploración cardiovascular en entornos de atención primaria de salud e incluso en condiciones de campo(AU)

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.

Photoplethysmographic characterization of vascular tone mediated changes in arterial pressure: an observational study.

To determine whether a classification based on the contour of the photoplethysmography signal (PPGc) can detect changes in systolic arterial blood pressure (SAP) and vascular tone. Episodes of normotension (SAP 90-140 mmHg), hypertension (SAP > 140 mmHg) and hypotension (SAP < 90 mmHg) were analyzed in 15 cardiac surgery patients. SAP and two surrogates of the vascular tone, systemic vascular resistance (SVR) and vascular compliance (Cvasc = stroke volume/pulse pressure) were compared with PPGc. Changes in PPG amplitude (foot-to-peak distance) and dicrotic notch position were used to define 6 classes taking class III as a normal vascular tone with a notch placed between 20 and 50% of the PPG amplitude. Class I-to-II represented vasoconstriction with notch placed > 50% in a small PPG, while class IV-to-VI described vasodilation with a notch placed < 20% in a tall PPG wave. 190 datasets were analyzed including 61 episodes of hypertension [SAP = 159 (151-170) mmHg (median 1st-3rd quartiles)], 84 of normotension, SAP = 124 (113-131) mmHg and 45 of hypotension SAP = 85(80-87) mmHg. SAP were well correlated with SVR (r = 0.78, p < 0.0001) and Cvasc (r = 0.84, p < 0.0001). The PPG-based classification correlated well with SAP (r = - 0.90, p < 0.0001), SVR (r = - 0.72, p < 0.0001) and Cvasc (r = 0.82, p < 0.0001). The PPGc misclassified 7 out of the 190 episodes, presenting good accuracy (98.4% and 97.8%), sensitivity (100% and 94.9%) and specificity (97.9% and 99.2%) for detecting episodes of hypotension and hypertension, respectively. Changes in arterial pressure and vascular tone were closely related to the proposed classification based on PPG waveform.Clinical Trial Registration NTC02854852.

Comparação do índice tornozelo-braquial com parâmetros de rigidez e resistência arterial periférica avaliados por fotopletismografia em idosos / Comparison of the ankle-brachial index with parameters of stiffness and peripheral arterial resistance assessed by photoplethysmography in elderly patients

O índice tornozelo-braquial (ITB) utiliza a razão entre a pressão arterial sistólica do tornozelo e do braço para diagnosticar de forma não invasiva a doença arterial periférica (DAP). A fotopletismografia (photoplethysmography, PPG) faz a medição e o registro das modificações de volume sanguíneo do corpo humano por meio de técnicas ópticas. Objetivos O objetivo deste estudo foi comparar o ITB com parâmetros de rigidez arterial e resistência periférica avaliados pela PPG em idosos e propor um modelo de predição para o ITB. Métodos Foi realizado um estudo transversal quantitativo. A amostra foi composta por idosos atendidos no ambulatório médico de especialidades da Universidade do Sul de Santa Catarina (UNISUL). Foram verificados: idade, sexo, índice de massa corporal (IMC), presença de comorbidades, tabagismo e atividade física. Para comparação das variáveis obtidas com a PPG com o ITB, foi realizada regressão linear bivariada e multivariada, considerando erro α = 0,05. Resultados Foram avaliados 93 idosos, sendo 63,4% mulheres. Em 98,9% dos casos, o ITB apresentou-se dentro da normalidade. Na comparação do ITB e variáveis derivadas da PPG em relação à idade, foram demonstradas associações significativas. Contudo, não foram observadas associações significativas entre ITB e PPG. O modelo multivariado indicou que apenas idade, sexo e tabagismo foram associados ao ITB. Conclusões Como conclusão, o ITB e a PPG demonstraram associação com o envelhecimento arterial, tendo em vista sua correlação com a idade; contudo, o ITB foi relacionado apenas com idade, sexo e tabagismo. Mais estudos são necessários para avaliar o potencial uso da PPG como rastreio de doenças vasculares em rotinas ambulatórias

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

Area Triangulation Method for Automatic Detection of Venous Emptying Maneuvers

Abstract Venous refilling time (VRT) can diagnose the presence of venous diseases in lower limbs. In order to calculate VRT it is necessary to determine the End of the Emptying Maneuvers (EEM). First Derivative Method (FDM) can be employed for automatic detection of the EEM, but its sensitivity to artifacts and noise can degrade its performance. In contrast, studies report that Area Triangulation Method (ATM) evinces effectiveness in biosignals point finding. This work compares the exactness of ATM and FDM for recognition of the EEM. The annotations made by 3 trained human observers on 37 photoplethysmography records were used as a reference. Bland-Altman graphics supported the analysis of agreement among human observers and methods, which was complemented with Analysis of variance and Multiple Comparisons statistical tests. Results showed that ATM is more accurate than FDM for automatic detection of the EEM, with statistically significant differences (p-value < 0.01).

Non-Contact, Simple Neonatal Monitoring by Photoplethysmography.

This paper presents non-contact vital sign monitoring in neonates, based on image processing, where a standard color camera captures the plethysmographic signal and the heart and breathing rates are processed and estimated online. It is important that the measurements are taken in a non-invasive manner, which is imperceptible to the patient. Currently, many methods have been proposed for non-contact measurement. However, to the best of the authors' knowledge, it has not been possible to identify methods with low computational costs and a high tolerance to artifacts. With the aim of improving contactless measurement results, the proposed method based on the computer vision technique is enhanced to overcome the mentioned drawbacks. The camera is attached to an incubator in the Neonatal Intensive Care Unit and a single area in the neonate's diaphragm is monitored. Several factors are considered in the stages of image acquisition, as well as in the plethysmographic signal formation, pre-filtering and filtering. The pre-filter step uses numerical analysis techniques to reduce the signal offset. The proposed method decouples the breath rate from the frequency of sinus arrhythmia. This separation makes it possible to analyze independently any cardiac and respiratory dysrhythmias. Nine newborns were monitored with our proposed method. A Bland-Altman analysis of the data shows a close correlation of the heart rates measured with the two approaches (correlation coefficient of 0.94 for heart rate (HR) and 0.86 for breath rate (BR)) with an uncertainty of 4.2 bpm for HR and 4.9 for BR (k = 1). The comparison of our method and another non-contact method considered as a standard independent component analysis (ICA) showed lower central processing unit (CPU) usage for our method (75% less CPU usage).

Advances in Photopletysmography Signal Analysis for Biomedical Applications.

Heart Rate Variability (HRV) is an important tool for the analysis of a patient’s physiological conditions, as well a method aiding the diagnosis of cardiopathies. Photoplethysmography (PPG) is an optical technique applied in the monitoring of the HRV and its adoption has been growing significantly, compared to the most commonly used method in medicine, Electrocardiography (ECG). In this survey, definitions of these technique are presented, the different types of sensors used are explained, and the methods for the study and analysis of the PPG signal (linear and nonlinear methods) are described. Moreover, the progress, and the clinical and practical applicability of the PPG technique in the diagnosis of cardiovascular diseases are evaluated. In addition, the latest technologies utilized in the development of new tools for medical diagnosis are presented, such as Internet of Things, Internet of Health Things, genetic algorithms, artificial intelligence and biosensors which result in personalized advances in e-health and health care. After the study of these technologies, it can be noted that PPG associated with them is an important tool for the diagnosis of some diseases, due to its simplicity, its cost⁻benefit ratio, the easiness of signals acquisition, and especially because it is a non-invasive technique.

A novel and low-complexity peak detection algorithm for heart rate estimation from low-amplitude photoplethysmographic (PPG) signals.

Low-amplitude PPG signals are more affected by noise contamination and other undesirable effects because the signal strength is comparable to noise power. Although several authors claim that decreases in the amplitude of the PPG wave should be addressed from signal acquisition and conditioning stages such decreases can also be associated with changes in the patient condition. In that instance, it is important to ensure continuous and reliable HR monitoring which, in turn, depends on how robust is the peak detection method. Numerous efforts have been made to develop algorithms for accurate PPG peak detection under high motion artefact conditions. However, little has been done regarding peak detection in low-amplitude PPG signals. In an attempt to address this issue, a novel and simple peak detection algorithm for PPG signals was proposed. Results show that our method could be a good contribution for robust strategies that can dynamically adapt their peak detection method to circumstances in which a decrease in the amplitude of the PPG signal is expected. Still, more extensive testing under a wide range of conditions (e.g. intensive physical exercise) is needed to perform a rigorous validation.

Aumento en la efectividad de la identificación de cimas y pies en el pulso fotopletismográfico al reconstruirlo mediante filtrado adaptativo / Increase in the effectiveness of identifying peaks and feet of the photoplethysmographic pulse to be reconstructed it using adaptive filtering

Resumen: Objetivo: Mejorar la identificación de cimas y pies en el pulso fotopletismográfico (PPG, por sus siglas en inglés), deformado por efecto del ruido miocinético, mediante la implementación de un dedal modificado y filtrado adaptativo. Método: Se obtuvo el PPG en 10 voluntarios sanos empleando 2 sistemas de fotopletismografía colocados en el dedo índice de cada mano, y registrándolos simultáneamente durante 3 min. Durante el primer minuto de registro, ambas manos estuvieron en reposo, y durante los 2 min posteriores, solo la mano izquierda realizó movimientos cuasi-periódicos para añadir ruido miocinético. Se emplearon 2 metodologías para procesar las señales fuera de línea, en una se usó un filtro con el algoritmo de mínimos cuadrados promediados (LMS, por sus siglas en inglés) y en la otra se hizo un preprocesamiento adicional al filtrado LMS. Ambas metodologías fueron comparadas y la de menor error porcentual en la señal recuperada se utilizó para valorar la mejora en la identificación de cimas y pies del PPG. Resultados: El error promedio obtenido fue del 22.94% para la primera metodología, y del 3.72% para la segunda. Los errores en la identificación de cimas y pies antes de filtrar el PPG fueron del 24.26 y 48.39%, respectivamente, una vez filtrados, disminuyeron a 2.02 y 3.77%, respectivamente. Conclusiones: El filtrado adaptativo basado en el algoritmo LMS, más una etapa de preprocesamiento, permite atenuar el ruido miocinético en el PPG, y aumentar la efectividad en la identificación de cimas y pies de pulso, que resultan de gran importancia para una valoración médica.

Abstract: Objective: To improve the identification of peaks and feet in photoplethysmographic (PPG) pulses deformed by myokinetic noise, through the implementation of a modified fingertip and applying adaptive filtering. Method: PPG signals were recordedfrom 10 healthy volunteers using two photoplethysmography systems placed on the index finger of each hand. Recordings lasted three minutes andwere done as follows: during the first minute, both handswere at rest, and for the lasting two minutes only the left hand was allowed to make quasi-periodicmovementsin order to add myokinetic noise. Two methodologies were employed to process the signals off-line. One consisted on using an adaptive filter based onthe Least Mean Square (LMS) algorithm, and the other includeda preprocessing stage in addition to the same LMS filter. Both filtering methods were compared and the one with the lowest error was chosen to assess the improvement in the identification of peaks and feet from PPG pulses. Results: Average percentage errorsobtained wereof 22.94% with the first filtering methodology, and 3.72% withthe second one. On identifying peaks and feet from PPG pulsesbefore filtering, error percentages obtained were of 24.26% and 48.39%, respectively, and once filtered error percentageslowered to 2.02% for peaks and 3.77% for feet. Conclusions: The attenuation of myokinetic noise in PPG pulses through LMS filtering, plusa preprocessing stage, allows increasingthe effectiveness onthe identification of peaks and feet from PPG pulses, which are of great importance for medical assessment.

[Increase in the effectiveness of identifying peaks and feet of the photoplethysmographic pulse to be reconstructed it using adaptive filtering]. / Aumento en la efectividad de la identificación de cimas y pies en el pulso fotopletismográfico al reconstruirlo mediante filtrado adaptativo.

OBJECTIVE: To improve the identification of peaks and feet in photoplethysmographic (PPG) pulses deformed by myokinetic noise, through the implementation of a modified fingertip and applying adaptive filtering. METHOD: PPG signals were recordedfrom 10 healthy volunteers using two photoplethysmography systems placed on the index finger of each hand. Recordings lasted three minutes andwere done as follows: during the first minute, both handswere at rest, and for the lasting two minutes only the left hand was allowed to make quasi-periodicmovementsin order to add myokinetic noise. Two methodologies were employed to process the signals off-line. One consisted on using an adaptive filter based onthe Least Mean Square (LMS) algorithm, and the other includeda preprocessing stage in addition to the same LMS filter. Both filtering methods were compared and the one with the lowest error was chosen to assess the improvement in the identification of peaks and feet from PPG pulses. RESULTS: Average percentage errorsobtained wereof 22.94% with the first filtering methodology, and 3.72% withthe second one. On identifying peaks and feet from PPG pulsesbefore filtering, error percentages obtained were of 24.26% and 48.39%, respectively, and once filtered error percentageslowered to 2.02% for peaks and 3.77% for feet. CONCLUSIONS: The attenuation of myokinetic noise in PPG pulses through LMS filtering, plusa preprocessing stage, allows increasingthe effectiveness onthe identification of peaks and feet from PPG pulses, which are of great importance for medical assessment.

Hemodynamic responses during and after multiple sets of stretching exercises performed with and without the Valsalva maneuver.

OBJECTIVE: This study investigated the acute hemodynamic responses to multiple sets of passive stretching exercises performed with and without the Valsalva maneuver. METHODS: Fifteen healthy men aged 21 to 29 years with poor flexibility performed stretching protocols comprising 10 sets of maximal passive unilateral hip flexion, sustained for 30 seconds with equal intervals between sets. Protocols without and with the Valsalva maneuver were applied in a random counterbalanced order, separated by 48-hour intervals. Hemodynamic responses were measured by photoplethysmography pre-exercise, during the stretching sets, and post-exercise. RESULTS: The effects of stretching sets on systolic and diastolic blood pressure were cumulative until the fourth set in protocols performed with and without the Valsalva maneuver. The heart rate and rate pressure product increased in both protocols, but no additive effect was observed due to the number of sets. Hemodynamic responses were always higher when stretching was performed with the Valsalva maneuver, causing an additional elevation in the rate pressure product. CONCLUSIONS: Multiple sets of unilateral hip flexion stretching significantly increased blood pressure, heart rate, and rate pressure product values. A cumulative effect of the number of sets occurred only for systolic and diastolic blood pressure, at least in the initial sets of the stretching protocols. The performance of the Valsalva maneuver intensified all hemodynamic responses, which resulted in significant increases in cardiac work during stretching exercises.

Evaluation of quality of life and photoplethysmography in patients with chronic venous insufficiency treated with foam sclerotherapy

BACKGROUND: Ultrasound-guided foam sclerotherapy plays a major role in treatment of chronic venous insufficiency, providing clinical and hemodynamic improvement to patients undergoing treatment.OBJECTIVES: To examine the relationships between venous refilling time and impact of venous disease on quality of life and between changes in venous refilling time and improvement of symptoms after ultrasound-guided foam sclerotherapy for chronic venous insufficiency. METHODS: Thirty-two patients classified as C4, C5 or C6 answered a questionnaire on quality of life and symptoms and their venous filling time was measured using photoplethysmography before and 45 days after treatment of chronic venous insufficiency with ultrasound-guided foam sclerotherapy.RESULTS: Statistically significant improvements were observed in quality of life scores and in venous filling time and in the following symptoms: aching, heavy legs, restless legs, swelling, burning sensations, and throbbing (p<0.0001). A similar improvement was also seen in the work and social domains of quality of life (p<0.0001).CONCLUSIONS: As confirmed by questionnaire scores and venous refilling times, ultrasound-guided foam sclerotherapy demonstrated efficacy and resulted in high satisfaction levels and low rates of major complications.

CONTEXTO: A escleroterapia com espuma guiada por ultrassom (EGUS) ocupa lugar de destaque no tratamento da insuficiência venosa crônica (IVC), proporcionando melhora clínica e hemodinâmica aos pacientes submetidos ao tratamento.OBJETIVOS: Verificar a correlação entre dados obtidos por questionário de qualidade de vida e de sintomas com dados obtidos por fotopletismografia (FPG), antes e depois do tratamento por escleroterapia com espuma guiada por ultrassom (EGUS) da insuficiência venosa crônica (IVC). MÉTODOS: Um grupo de 32 pacientes, classificados como C4, C5 e C6, foi submetido à aplicação de questionário de qualidade de vida e sintomas, sendo aferido o tempo de enchimento venoso (TEV) por FPG antes e 45 dias depois do tratamento da IVC através de EGUS. O teste do sinal foi utilizado para análise estatística da melhora dos escores dos questionários e do TEV. O teste de McNemar foi utilizado para avaliação da melhora nos sintomas e do impacto do tratamento nas atividades laborais e sociais dos pacientes.RESULTADOS: Houve melhora nos escores dos questionários de qualidade de vida e no TEV, com significância estatística (p<0,0001). Houve melhora estatisticamente significativa nos sintomas: dor, cansaço, edema, queimação, pernas inquietas e latejamento (p<0,0001). Incremento na qualidade laboral e social após o tratamento apresentou melhora estatisticamente significativa (p<0,0001). Não ocorreram complicações maiores ou efeitos adversos nesta série.CONCLUSÕES: A EGUS mostrou-se eficaz, com alto índice de satisfação e baixas taxas de complicacões maiores, ratificada pelos escores dos questionários e pelos TEVs aferidos pela FPG.

Aplicación a señales fotopletismográficas del modelo de Cámara de aire con tres elementos: diferencias en cuanto a género y correlación con la edad / Applying the 3-element windkessel model to photoplethysmographic signals: gender differences and age correlation

Automatically averaged photoplethysmographic (PPG) signals were fit to a 3-element windkessel model using a Gauss-Newton optimization algorithm. Data corresponded to 78 healthy subjects (ages from 8 to 87 years). Unlike other reports, two phase velocities are also estimated from the model. Gender differences were found, particularly respect to individual parameters correlation with age. When a nonlinear model was fit to the two first principal components, a high correlation with age was found for both females (r=0.69) and male subjects (r=0.77). Our results further support the idea that the PPG signal is a valuable source of information about the cardiovascular system, comparable to the much more expensive continuous pressure signal(AU)

Señales fotopletismográficas automáticamente promediadas fueron ajustadas a un modelo de bomba hidráulica de tres elementos. Para ello se utilizó un algoritmo de optimización del tipio Gauss-Newton. Los datos fueron obtenidos de 78 individuos sanos con edades entre 8 y 87 años. A diferencia de otros reportes, en el presente trabajo se estimaron dos velocidades de fase a partir del modelo. Al aplicar un modelo no lineal respecto los dos primeros componentes principales, se obtuvo una elevada correlación con la edad tanto para los sujetos femeninos (r=0.69) como para los masculinos (r=0.77). Nuestros resultados ofrecen un apoyo adicional a la idea de que la señal fotopletismográfica es una fuente importante de información acerca del sistema cardiovascular, comparable a la señal de presión continua, aun cuando esta última es mucho más costosa(AU)