Assessing the quality of heart rate variability estimated from wrist and finger PPG: A novel approach based on cross-mapping method

The non-invasiveness of photoplethysmographic (PPG) acquisition systems, together with their cost-effectiveness and easiness of connection with IoT technologies, is opening up to the possibility of their widespread use. For this reason, the study of the reliability of PPG and pulse rate variability (PRV) signal quality has become of great scientific, technological, and commercial interest. In this field, sensor location has been demonstrated to play a crucial role. The goal of this study was to investigate PPG and PRV signal quality acquired from two body locations: finger and wrist. We simultaneously acquired the PPG and electrocardiographic (ECG) signals from sixteen healthy subjects (aged 28.5 ± 3.5, seven females) who followed an experimental protocol of affective stimulation through visual stimuli. Statistical tests demonstrated that PPG signals acquired from the wrist and the finger presented different signal quality indexes (kurtosis and Shannon entropy), with higher values for the wrist-PPG. Then we propose to apply the cross-mapping (CM) approach as a new method to quantify the PRV signal quality. We found that the performance achieved using the two sites was significantly different in all the experimental sessions (p < 0.01), and the PRV dynamics acquired from the finger were the most similar to heart rate variability (HRV) dynamics

Health management trends: The Internet of Things as a modernization and care tool

The modernization of health institutions is one of the biggest trends of our time. Part of the 4th industrial revolution, the Internet of Things, that allows a more personalized care and greater appreciation for the patient, can be a reality that is in the process of being consolidated worldwide. In Brazil, through the promulgation of decree number 9.854 of June 26th of 2019, the democratization and popularization of these technologies have started. This integrative review intends to verify the introduction of IoTs in the Brazilian scenario by investigating the potential as well as the weaknesses of this new technology. The research suggests that IoTs are a unique content that may improve productivity and process efficiencies but are still permeated by caveats that indicate system deficiencies and the need of further studies

O Uso de Tecnologias eHealth Integradas a IoT como Possibilidade para Aplicação em Ambientes Educacionais: uma revisão sistemática de literatura

Este artigo tem como objetivo apresentar uma revisão sistemática de literatura (RSL) de trabalhos que integram as temáticas de Saúde Eletrônica (eHealth) com a Internet das Coisas (IoT), direcionados para o monitoramento da frequência cardíaca de seres humanos. Tal estudo visa conhecer as características dos trabalhos, como as formas de desenvolvimento, materiais utilizados e avaliações, afim de identificar as possibilidades de aplicação em ambiente de ensino. Para a metodologia, foi utilizado o protocolo de RSL consolidado por Kitchenham (2004), realizando a triagem dos estudos com base nos elementos de pesquisa descritos neste trabalho. Como resultado, das 130 publicações acerca do tema, apenas 29 resultaram após as etapas de seleção, dentre as quais pode-se constatar na sua maioria desenvolvimento de soluções. Na questão da avaliação, cerca de 77% foram realizadas em seres humanos e 43% dos trabalhos não foram avaliados. Concluiu-se que na maioria dos estudos, as tecnologias desenvolvidas tiveram o objetivo de atender demandas de monitoramento da frequência cardíaca e eletrocardiografia (ECG), propondo sistemas de baixo custo e de usabilidade diária, fora do ambiente clínico ou hospitalar, possibilitando que estas tecnologias sejam utilizadas em ambiente de ensino, como em aulas de educação física

Using heart rate to tap into motivational and emotional processes during teaching and learning

A current ambition of research into motivation and emotion in teaching and learning is to investigate motivation and emotion in more holistic ways and to dive deeper into the dynamics of motivation and emotion processes in the classroom setting. Physiological measures have the potential to reach these goals by moving beyond between-person comparisons of habitual, often self-reported, levels of motivation and emotion. For a long time, tracking physiology was only possible in lab settings, which is problematic for studying authentic processes as they occur during teaching and learning. But recent technological innovations have enabled physiological measurement in ambulatory settings, such as the classroom. For many educational researchers interested in motivation and emotion, dealing with these measures can be challenging. This chapter provides a basic introduction to physiological measures in general and heart rate in particular. We also discuss the conceptual meaning of heart rate in studies on motivation and emotion. Furthermore, we present concrete tips for collecting heart rate data (i.e. study preparation, data cleaning, and data analyses). An important conclusion is that physiological measures open up some new aspects of human functioning to educational researchers and can complement (but not replace) behavioural and self-report measures of motivation and emotion

Transcending conventional biometry frontiers: Diffusive Dynamics PPG Biometry

In the first half of the 20th century, a first pulse oximeter was available to measure blood flow changes in the peripheral vascular net. However, it was not until recent times the PhotoPlethysmoGraphic (PPG) signal used to monitor many physiological parameters in clinical environments. Over the last decade, its use has extended to the area of biometrics, with different methods that allow the extraction of characteristic features of each individual from the PPG signal morphology, highly varying with time and the physical states of the subject. In this paper, we present a novel PPG-based biometric authentication system based on convolutional neural networks. Contrary to previous approaches, our method extracts the PPG signal's biometric characteristics from its diffusive dynamics, characterized by geometric patterns image in the (p, q)-planes specific to the 0-1 test. The diffusive dynamics of the PPG signal are strongly dependent on the vascular bed's biostructure, which is unique to each individual, and highly stable over time and other psychosomatic conditions. Besides its robustness, our biometric method is anti-spoofing, given the convoluted nature of the blood network. Our biometric authentication system reaches very low Equal Error Rates (ERRs) with a single attempt, making it possible, by the very nature of the envisaged solution, to implement it in miniature components easily integrated into wearable biometric systems.Comment: 18 pages, 6 figures, 4 table

Abnormal Pattern Detection In Ppg Signals Using Time Series Analysis

The photoplethysmogram (PPG) signal is a data in continuous real-time series. It depicts the peripheral pulse wave that is produced due to heart activity, respiration, and other physiological effects. The time-series signal contains a lot of information which is difficult to be processed. The abnormal PPG signal is messy, non-periodic, and irregular. Several existing methods such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Deep Neural Network (DNN) and sensor had been used to detect abnormal pattern from PPG signal which can produce high performance and accuracy. However, these methods are higher in complexity or have uncertain repeatability. Therefore, this thesis proposed a method which is rule-based algorithm that is less complex, with quicker and more simple training, reducing the errors while still producing high accuracy. This project’s objectives are to implement rule-based algorithm method for abnormal pattern detection in PPG signals, and to investigate the accuracy and performance of rule-based algorithm in detecting the abnormal pattern. The signal processing, segmentation, feature extraction, training and testing for rule-based algorithm classifier, using wrist PPG during exercise dataset and pulse transmit time dataset, are done in this study to detect the abnormal pattern in PPG signals. The accuracy and coverage of rule for both training and testing process are recorded in order to determine the performance of the method used in this study. The abnormal PPG pattern detection using rule-based algorithm has produced accuracy of 87.30% in training process and 87.18% in testing process with coverage of rule for training and testing, 89.26% and 87.33%. The findings of this project can be further used for application of abnormal pattern in PPG signal such as healthcare and human activity recognition

Desarrollo de un filtro digital para señales foto pletismográficas obtenidas de una tarjeta de adquisición de datos en un entorno de laboratorio

En la presente investigación se hizo un estudio de diversos filtros digitales que puedan cumplir con la tarea de filtrar, en tiempo real, y usando una tarjeta de adquisición de datos (TAD), señales PPG obtenidas para calcular la hemoglobina en la sangre de una persona. Es por esto que, la tarea de filtrar estas señales fotopletismográficas (PPG), es crucial, ya que un mal filtrado puede terminar en un mal cálculo de hemoglobina. El primer paso fue estudiar el estado del arte alrededor del filtrado de señales PPG y así determinar cuáles pueden ser las opciones para hacer el filtrado. Posteriormente, se obtuvieron señales PPG sin filtrar de pacientes para su estudio, lo que permitió determinar los parámetros para los filtros elegidos. Luego de ello se determinaron las ecuaciones y los algoritmos para poder hacer la comparación necesaria para la determinación del filtro. Una vez determinadas las ecuaciones y algoritmos, se procedió a hacer su implementación en PyCharm, usando el lenguaje de programación Python, lo que permitió determinar los indicadores para la comparación de los filtros y la determinación del más eficiente, es decir, que optimice los recursos computacionales disponibles sin consumo excesivo. Una vez realizada la comparación, se determinó, según las necesidades del proyecto, cuál es el filtro que cumplía los requerimientos, lo que resultó en el filtro Butterworth de orden 6. Con la determinación del filtro, se procedió a desarrollarlo en lenguaje C para luego implementarse en el microcontrolador del proyecto, validando que el filtro, funciona según los requerimientos previamente establecidos.In the present investigation a study of many digital filters was made that may accomplish the task of filtering, in real time, and using a data acquisition board (DAQ), PPG signals obtained to calculate hemoglobin in a person’s blood. This is why, the task of filtering these PPG signals, is crucial, because, a bad filtering, may result in a bad hemoglobin calculation. The first step was studying the state of art surrounding photoplethysmographic signal (PPG) filtering, that way, determining which options may do the filtering task. After that, unfiltered PPG signals were obtained from patients, for its study, that way, the parameters needed, could be determined for the study of the chosen filters. After that, the equations and algorithms needed were determined for making the comparison for the filter determination. Once the equations and algorithms needed were determined, the implementation in PyCharm was done, using Python programming language, which allowed us to determine the indicators for the filter’s comparison and the determination of the most efficient one, that it optimizes the available computational resources without excessive consumption. When the comparative table was done, it was determined that, following the project needs, the most adequate filter, turned to be order 6 Butterworth filter. With this result, it was developed the filter in C language so it could be implemented in the microprocessor of the project, validating that this filter, works according to the previously established requirements.Tesi

Development, Design, and Utilization of a Reflective Based Photoplethysmography Sensor

Plethysmography refers to the dynamic measurement of biological tissue volumes that, for example, may change due to fluctuations in blood volume. Photoplethysmography (PPG) makes use of the attenuation of light penetrating into vascular tissues to determine these changes in blood volume. Modern PPG is an optical technique involving low cost photosensors and light emitting diodes (LED), and is capable of measuring multiple biological vitals simultaneously. For example, in addition to heart rate determination, PPG devices can be used as pulse oximeters, capable of calculating the blood oxygen saturation (SpO2) through a series of simple optical calculations performed on either reflectance or transmittance data. In this project, a reflectance-based PPG pulse oximeter was designed to collect blood volume measurements on the foot of a patient. This project also involves using the PPG sensor to determine the effect of vibrational signal on vasoconstriction in the tissue, to provide more information on biological properties, including diabetic nerve damage. The device is constructed via dual wavelength light sources and a phototransistor where the light sources are determined based on the isosbestic point, for oxygenated and deoxygenated hemoglobin

Sinus rhythm restoration with electrical cardioversion: acute effect of shock configuration and subsequent modifications in peripheral flow and sleep.

Atrial fibrillation (AF) is a widespread arrhythmia, associated with higher risk of stroke, sleep disorders and dementia. In some conditions, electrical cardioversion (ECV) represents the best choice for rhythm control. Nowadays, there is a growing interest in developing new devices for screening and monitoring of AF patients. We aimed to improve acute efficacy of ECV procedure and to explore the feasibility of the use of new wearable devices for monitoring in candidates to AF ECV. We compared antero-apical pads vs antero-posterior patches approach for AF ECV, and we elaborated a decision algorithm to improve acute efficacy. After, we evaluated the feasibility of the use of new wearable devices for monitoring of candidates to AF ECV. In particular, we analysed the effect of AF ECV on heart rate variability and vascular age parameters derived from PPG signals registered with Empatica (CE 1876/MDD 93/42/EEC), and on EEG pattern registered with Neurosteer (Israel). From December 2005 to September 2019, 492 patients were enrolled. We evaluated acute efficacy of the two approaches for AF ECV and we elaborated a decision algorithm based on body surface area, weight, and height. The decision algorithm improved first shock efficacy (93.2% vs. 87.2%, p=0.025). From 1st November 2021 to 1st April 2022, 24 patients were enrolled in PPEEG-AF pilot study. Considering vascular age parameters, a significant reduction in TPR and a wave was observed (p<0.001). Considering sleep patterns, a tendency to higher coherence was observed in registrations acquired during AF, or considering signals registered for each patient independently from AF. The new decision algorithm improved acute efficacy and reduced costs associated with adhesive patches. Significant modifications were observed on vascular age parameters measured before and after ECV, and a possible AF effect on sleep pattern was noticed. More data are necessary to confirm these preliminary results