Inclusión de la información respiratoria en el análisis de la variabilidad del ritmo cardiaco para la identificación de estrés

Se define el estrés como una respuesta fisiológica ante una amenaza. Sin embargo, si la respuesta ante el estrés se mantiene durante mucho tiempo o se inicia muy continuamente desemboca en una situación no saludable para el sujeto. Los problemas sociales y médicos asociados al estrés están creciendo claramente y afectando seriamente tanto a adultos como a niños, siendo considerado el estrés como la epidemia del siglo XXI. El principal problema del estrés es la no existencia de una medida objetiva del mismo. Éste es el objetivo del proyecto ES3, el estudio de señales fisiológicas, de marcadores bioquímicos y de cuestionarios psicométricos para analizar la respuesta del organismo ante el estrés. La variabilidad del ritmo cardiaco (HRV) es considerada una medida no invasiva de la regulación del Sistema Nervioso Autonomo (ANS) sobre el corazón, por lo que es ampliamente usada para caracterizar la respuesta al estrés. La respiración también varía ante estrés mental y tareas que requieren atención. Dentro del proyecto ES3, este trabajo se centra en el análisis de la respiración y de la variabilidad del ritmo cardiaco ante estrés emocional agudo. La primera parte de este trabajo comprende la grabación de la base de datos de voluntarios jóvenes y sanos sometidos a un protocolo destinado a originar estrés emocional agudo. Varias señales fisiológicas, incluídas la señal del ECG y la señal respiratoria, han sido grabadas. La segunda parte del trabajo ha includo el análisis espectral de la HRV en las bandas de frecuencia clásicas, asociadas comúnmente con los sistemas simpático y parasimpático. Se realiza la representacion tiempo-frecuencia de la señal moduladora que contiene la información del ANS y se definen las siguientes bandas frecuenciales: baja frecuencia (LF, de 0.04 a 0.15 Hz) y alta frecuencia (HF, de 0.15 a 0.4 Hz). Varios índices, que se usan como medidas del balance simpato-vagal, han sido extraídos para estudiar su capacidad de discriminar si el sujeto está estresado o no. Los índices del dominio frecuencial de la HRV, calculados segun los términos clásicos, apenas muestran diferencias signicativas con la presencia de estrés. La tercera parte del proyecto se ha centrado en el análisis de la información respiratoria, específicamente en su estabilidad y su frecuencia. En este trabajo, la estabilidad respiratoria es medida como la picudez del espectro respiratorio, que se calcula como el porcentaje de potencia alrededor del pico máximo respecto a la potencia del espectro total. Los resultados muestran mayor potencia discriminativa considerando la información de la frecuencia respiratoria, sugiriendo que puede ser un marcador para discriminar la presencia de estrés entre las distintas etapas de la prueba. Esto, sin embargo, se consigue a costa de perder algunas excepciones donde no se puede estimar la frecuencia respiratoria. La útlima parte considera el análisis de la HRV teniendo en cuenta la información respiratoria. La frecuencia respiratoria se usa para definir la banda de HF y evitar la medida de potencia en ambas bandas cuando la frecuencia respiratoria es tan baja que cae dentro de la banda de LF. Esto evita la sobresestimación de la actividad simpática y la infraestimación de la actividad parasimpática que ocurre cuando la frecuencia respiratoria cae en la banda de baja frecuencia. La combinación de los análisis de la HRV y la respiración aumenta el poder discriminativo entre las diferentes etapas del test, mostrando una mayor dominancia simpática cuando se está en una situación de estrés

A Reproducible Study on Remote Heart Rate Measurement

This paper studies the problem of reproducible research in remote photoplethysmography (rPPG). Most of the work published in this domain is assessed on privately-owned databases, making it difficult to evaluate proposed algorithms in a standard and principled manner. As a consequence, we present a new, publicly available database containing a relatively large number of subjects recorded under two different lighting conditions. Also, three state-of-the-art rPPG algorithms from the literature were selected, implemented and released as open source free software. After a thorough, unbiased experimental evaluation in various settings, it is shown that none of the selected algorithms is precise enough to be used in a real-world scenario

A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Units

The management of invasive mechanical ventilation, and the regulation of sedation and analgesia during ventilation, constitutes a major part of the care of patients admitted to intensive care units. Both prolonged dependence on mechanical ventilation and premature extubation are associated with increased risk of complications and higher hospital costs, but clinical opinion on the best protocol for weaning patients off of a ventilator varies. This work aims to develop a decision support tool that uses available patient information to predict time-to-extubation readiness and to recommend a personalized regime of sedation dosage and ventilator support. To this end, we use off-policy reinforcement learning algorithms to determine the best action at a given patient state from sub-optimal historical ICU data. We compare treatment policies from fitted Q-iteration with extremely randomized trees and with feedforward neural networks, and demonstrate that the policies learnt show promise in recommending weaning protocols with improved outcomes, in terms of minimizing rates of reintubation and regulating physiological stability

Consistency of pacing and metabolic responses during 2000-m rowing ergometry

PURPOSE: This study investigated the pacing strategy adopted and the consistency of performance and related physiological parameters across three 2000-m rowing-ergometer tests. METHODS: Fourteen male well-trained rowers took part in the study. Each participant performed three 2000-m rowing-ergometer tests interspersed by 3-7 d. Throughout the trials, respiratory exchange and heart rate were recorded and power output and stroke rate were analyzed over each 500 m of the test. At the completion of the trial, assessments of blood lactate and rating of perceived exertion were measured. RESULTS: Ergometer performance was unchanged across the 3 trials; however, pacing strategy changed from trial 1, which featured a higher starting power output and more progressive decrease in power, to trials 2 and 3, which were characterized by a more conservative start and an end spurt with increased power output during the final 500 m. Mean typical error (TE; %) across the three 2000-m trials was 2.4%, and variability was low to moderate for all assessed physiological variables (TE range = 1.4-5.1%) with the exception of peak lactate (TE = 11.5%). CONCLUSIONS: Performance and physiological responses during 2000-m rowing ergometry were found to be consistent over 3 trials. The variations observed in pacing strategy between trial 1 and trials 2 and 3 suggest that a habituation trial is required before an intervention study and that participants move from a positive to a reverse-J-shaped strategy, which may partly explain conflicting reports in the pacing strategy exhibited during 2000-m rowing-ergometer trials

Prediction of fluid responsiveness using respiratory variations in left ventricular stroke area by transoesophageal echocardiographic automated border detection in mechanically ventilated patients.

BackgroundLeft ventricular stroke area by transoesophageal echocardiographic automated border detection has been shown to be strongly correlated to left ventricular stroke volume. Respiratory variations in left ventricular stroke volume or its surrogates are good predictors of fluid responsiveness in mechanically ventilated patients. We hypothesised that respiratory variations in left ventricular stroke area (DeltaSA) can predict fluid responsiveness.MethodsEighteen mechanically ventilated patients undergoing coronary artery bypass grafting were studied immediately after induction of anaesthesia. Stroke area was measured on a beat-to-beat basis using transoesophageal echocardiographic automated border detection. Haemodynamic and echocardiographic data were measured at baseline and after volume expansion induced by a passive leg raising manoeuvre. Responders to passive leg raising manoeuvre were defined as patients presenting a more than 15% increase in cardiac output.ResultsCardiac output increased significantly in response to volume expansion induced by passive leg raising (from 2.16 +/- 0.79 litres per minute to 2.78 +/- 1.08 litres per minute; p < 0.01). DeltaSA decreased significantly in response to volume expansion (from 17% +/- 7% to 8% +/- 6%; p < 0.01). DeltaSA was higher in responders than in non-responders (20% +/- 5% versus 10% +/- 5%; p < 0.01). A cutoff DeltaSA value of 16% allowed fluid responsiveness prediction with a sensitivity of 92% and a specificity of 83%. DeltaSA at baseline was related to the percentage increase in cardiac output in response to volume expansion (r = 0.53, p < 0.01).ConclusionDeltaSA by transoesophageal echocardiographic automated border detection is sensitive to changes in preload, can predict fluid responsiveness, and can quantify the effects of volume expansion on cardiac output. It has potential clinical applications