RESUMEN
The potential of microwave Doppler radar in non-contact vital sign detection is significant; however, prevailing radar-based heart rate (HR) and heart rate variability (HRV) monitoring technologies often necessitate data lengths surpassing 10 s, leading to increased detection latency and inaccurate HRV estimates. To address this problem, this paper introduces a novel network integrating a frequency representation module and a residual in residual module for the precise estimation and tracking of HR from concise time series, followed by HRV monitoring. The network adeptly transforms radar signals from the time domain to the frequency domain, yielding high-resolution spectrum representation within specified frequency intervals. This significantly reduces latency and improves HRV estimation accuracy by using data that are only 4 s in length. This study uses simulation data, Frequency-Modulated Continuous-Wave radar-measured data, and Continuous-Wave radar data to validate the model. Experimental results show that despite the shortened data length, the average heart rate measurement accuracy of the algorithm remains above 95% with no loss of estimation accuracy. This study contributes an efficient heart rate variability estimation algorithm to the domain of non-contact vital sign detection, offering significant practical application value.
Asunto(s)
Aprendizaje Profundo , Frecuencia Cardíaca , Radar , Determinación de la Frecuencia Cardíaca , AlgoritmosRESUMEN
Objective. Although inter-beat intervals (IBI) and the derived heart rate variability (HRV) can be acquired through consumer-grade photoplethysmography (PPG) wristbands and have been applied in a variety of physiological and psychophysiological conditions, their accuracy is still unsatisfactory.Approach.In this study, 30 healthy participants concurrently wore two wristbands (E4 and Honor 5) and a gold-standard electrocardiogram (ECG) device under four conditions: resting, deep breathing with a frequency of 0.17 Hz and 0.1 Hz, and mental stress tasks. To quantitatively validate the accuracy of IBI acquired from PPG wristbands, this study proposed to apply an information-based similarity (IBS) approach to quantify the pattern similarity of the underlying dynamical temporal structures embedded in IBI time series simultaneously recorded using PPG wristbands and the ECG system. The occurrence frequency of basic patterns and their rankings were analyzed to calculate the IBS distance from gold-standard IBI, and to further calculate the signal-to-noise ratio (SNR) of the wristband IBI time series.Main results.The accuracies of both HRV and mental state classification were not satisfactory due to the low SNR in the wristband IBI. However, by rejecting data segments of SNR < 25, the Pearson correlation coefficients between the wristbands' HRV and the gold-standard HRV were increased from 0.542 ± 0.235 to 0.922 ± 0.120 for E4 and from 0.596 ± 0.227 to 0.859 ± 0.145 for Honor 5. The average accuracy of four-class mental state classification increased from 77.3% to 81.9% for E4 and from 79.3% to 83.3% for Honor 5.Significance.Consumer-grade PPG wristbands are acceptable for HR and HRV monitoring when removing low SNR segments. The proposed method can be applied for quantifying the accuracies of IBI and HRV indices acquired via any non-ECG system.
Asunto(s)
Determinación de la Frecuencia Cardíaca , Fotopletismografía , Humanos , Fotopletismografía/métodos , Frecuencia Cardíaca/fisiología , Monitoreo Fisiológico , Electrocardiografía/métodosRESUMEN
Commercial photoplethysmography (PPG) sensors rely on the measurement of continuous-wave diffuse reflection signals (CW-DRS) to monitor heart rate. Using Monte Carlo modeling of light propagation in skin, we quantitatively evaluate the dependence of continuous-wave photoplethysmography (CW-PPG) in commercial wearables on source-detector distance (SDD). Specifically, when SDD increases from 0.5 mm to 3.3 mm, CW-PPG signal increases by roughly 846% for non-obese (NOB) skin and roughly 683% for morbidly obese (MOB) skin. Ultimately, we introduce the concept of time-of-flight PPG (TOF-PPG) which can significantly improve heart rate signals. Our model shows that the optimized TOF-PPG improves heart rate monitoring experiences by roughly 47.9% in NOB and 93.2% in MOB when SDD = 3.3 mm is at green light. Moving forward, these results will provide a valuable source for hypothesis generation in the scientific community to improve heart rate monitoring.
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Determinación de la Frecuencia Cardíaca , Obesidad Mórbida , Humanos , Fotopletismografía/métodos , Monitoreo Fisiológico , Frecuencia Cardíaca/fisiología , Procesamiento de Señales Asistido por ComputadorRESUMEN
INTRODUCTION: In clinical practice, fetal heart rate monitoring is performed intermittently using Doppler ultrasound, typically for 30 minutes. In case of a non-reassuring heart rate pattern, monitoring is usually prolonged. Noninvasive fetal electrocardiography may be more suitable for prolonged monitoring due to improved patient comfort and signal quality. This study evaluates the performance and patient experience of four noninvasive electrocardiography devices to assess candidate devices for prolonged noninvasive fetal heart rate monitoring. MATERIAL AND METHODS: Non-critically sick women with a singleton pregnancy from 24 weeks of gestation were eligible for inclusion. Fetal heart rate monitoring was performed during standard care with a Doppler ultrasound device (Philips Avalon-FM30) alone or with this Doppler ultrasound device simultaneously with one of four noninvasive electrocardiography devices (Nemo Fetal Monitoring System, Philips Avalon-Beltless, Demcon Dipha-16 and Dräger Infinity-M300). Performance was evaluated by: success rate, positive percent agreement, bias, 95% limits of agreement, regression line, root mean square error and visual agreement using FIGO guidelines. Patient experience was captured using a self-made questionnaire. RESULTS: A total of 10 women were included per device. For fetal heart rate, Nemo performed best (success rate: 99.4%, positive percent agreement: 94.2%, root mean square error 5.1 BPM, bias: 0.5 BPM, 95% limits of agreement: -9.7 - 10.7 BPM, regression line: y = -0.1x + 11.1) and the cardiotocography tracings obtained simultaneously by Nemo and Avalon-FM30 received the same FIGO classification. Comparable results were found with the Avalon-Beltless from 36 weeks of gestation, whereas the Dipha-16 and Infinity-M300 performed significantly worse. The Avalon-Beltless, Nemo and Infinity-M300 closely matched the performance of the Avalon-FM30 for maternal heart rate, whereas the performance of the Dipha-16 deviated more. Patient experience scores were higher for the noninvasive electrocardiography devices. CONCLUSIONS: Both Nemo and Avalon-Beltless are suitable devices for (prolonged) noninvasive fetal heart rate monitoring, taking their intended use into account. But outside its intended use limit of 36 weeks' gestation, the Avalon-Beltless performs less well, comparable to the Dipha-16 and Infinity-M300, making them currently unsuitable for (prolonged) noninvasive fetal heart rate monitoring. Noninvasive electrocardiography devices appear to be preferred due to greater comfort and mobility.
Asunto(s)
Cardiotocografía , Determinación de la Frecuencia Cardíaca , Embarazo , Femenino , Humanos , Cardiotocografía/métodos , Monitoreo Fetal/métodos , Electrocardiografía , Frecuencia Cardíaca Fetal/fisiología , Evaluación del Resultado de la Atención al PacienteRESUMEN
INTRODUCTION: Perinatal asphyxia, a condition that results from compromised placental or pulmonary gas exchange during the birth process, is rare but can lead to serious neonatal and long-term consequences. The visual analysis of cardiotocography (CTG) is designed to avoid perinatal asphyxia, but its interpretation can be difficult. Our aim was to test the impact of an e-learning training program for interpreting CTG on the rate of avoidable perinatal asphyxia at term. METHOD: We conducted a retrospective multicenter before-after study comparing two periods, before and after the implementation of e-learning training program from July 1, 2016 to December 31, 2016, in CTG interpretation for midwives and obstetricians in five maternity hospitals in the Paris area, France. The training involved theoretical aspects such as fetal physiology and heart rhythm abnormalities, followed by practical exercises using real case studies to enhance skills in interpreting CTG. We included all term births that occurred between the "before" period (July 1 to December 31, 2014) and the "after period (January 1 to June 30, 2017). We excluded multiple pregnancies, antenatal detection of congenital abnormalities, breech births and all scheduled caesarean sections. Perinatal asphyxia cases were analyzed by a pair of experts consisting of midwives and obstetricians, and avoidability of perinatal asphyxia was estimated. The main criterion was the prevalence of avoidable perinatal asphyxia. RESULTS: The e-learning program was performed by 83 % of the obstetrician-gynecologists and 65 % of the midwives working in the delivery rooms of the five centers. The prevalence of perinatal asphyxia was 0.45 % (29/7902 births) before the training and 0.54 % (35/7722) after. The rate of perinatal asphyxia rated as avoidable was 0.30 % of live births before the training and 0.28 % after (p = 0.870). The main causes of perinatal asphyxia deemed avoidable were delay in reactions to severe CTG anomalies and errors in the analysis and interpretation of the CTG. These causes did not differ between the two periods. CONCLUSION: One session of e-learning training to analyze CTG was not associated with a reduction in avoidable perinatal asphyxia. Other types of e-learning, repeated and implemented over a longer period should be evaluated.
Asunto(s)
Asfixia , Instrucción por Computador , Femenino , Embarazo , Recién Nacido , Humanos , Determinación de la Frecuencia Cardíaca , Placenta , AprendizajeRESUMEN
Heart rate is a key vital sign that can be used to understand an individual's health condition. Recently, remote sensing techniques, especially acoustic-based sensing, have received increasing attention for their ability to non-invasively detect heart rate via commercial mobile devices such as smartphones and smart speakers. However, due to signal interference, existing methods have primarily focused on monitoring a single user and required a large separation between them when monitoring multiple people. These limitations hinder many common use cases such as couples sharing the same bed or two or more people located in close proximity. In this paper, we present an approach that can minimize interference and thereby enable simultaneous heart rate monitoring of multiple individuals in close proximity using a commonly available smart speaker prototype. Our user study, conducted under various real-life scenarios, demonstrates the system's accuracy in sensing two users' heart rates when they are seated next to each other with a median error of 0.66 beats per minute (bpm). Moreover, the system can successfully monitor up to four people in close proximity.
Asunto(s)
Determinación de la Frecuencia Cardíaca , Telemetría , Humanos , Frecuencia Cardíaca , Acústica , Computadoras de ManoRESUMEN
Monitoring the fetal heartbeat underpins assessment of fetal wellbeing in labour. Although commonly employed in clinical practice, shortcomings remain. A recent review of clinical practice guidelines highlights the variation in definitions of the fetal heart rate that will lead to differences in interpretation. Will intrapartum care be improved by greater consensus around clinical practice guidelines through rationalisation or refinement of guidelines, or will the future see this technique replaced by more accurate forms of fetal monitoring?
Asunto(s)
Cardiotocografía , Trabajo de Parto , Embarazo , Femenino , Humanos , Cardiotocografía/métodos , Determinación de la Frecuencia Cardíaca , Monitoreo Fetal/métodos , Predicción , Frecuencia Cardíaca FetalRESUMEN
Introducción: la exposición a estímulos dolorosos y estrés en la etapa neonatal, sin un correcto tratamiento, tiene consecuencias a corto y largo plazo. El diagnóstico adecuado es un desafío, ya que las escalas clínicas son subjetivas y se requieren herramientas de detección con mejor objetividad y capacidad de interpretación del disconfort/dolor neonatal. Newborn Infant Parasympathetic Evaluation (NIPE™) es una tecnología no invasiva de monitorización continua del dolor en neonatos, desarrollada recientemente dada la dificultad de objetivar el dolor mediante los métodos convencionales en la práctica clínica. Esta tecnología se basa en el análisis de la variabilidad de la frecuencia cardíaca (FC), lo que permite aproximarse a la actividad del sistema parasimpático. Objetivos: el objetivo de esta investigación fue valorar el disconfort en un modelo de cerdo recién nacido (RN) y en humanos neonatos expuestos a maniobras nociceptivas con la utilización de tecnología no invasiva (NIPE), en la maternidad del Hospital Universitario. Material y métodos: se realizó un estudio observacional, longitudinal, en seis cerdos RN, anestesiados, monitorizados hemodinámicamente y sometidos a un procedimiento quirúrgico mayor (toracotomía lateral izquierda con abordaje cardíaco, pericardiostomía y acceso vascular pulmonar transventricular derecho) y 12 procedimientos mínimamente invasivos de la práctica clínica habitual, como vacunación BCG, hemoglucotest y pesquisa, que generan un estímulo nociceptivo en ocho RN de término sanos. Se incluyeron RN sanos de término (EG entre 37-41 semanas más seis días) internados en el alojamiento conjunto madre-hijo, se excluyeron de la muestra los RN que presentaron alguna patología y aquellos cuyos padres no aceptaron la participación en el estudio. Resultados: se comparó la variabilidad de la FC mediante detección automatizada (NIPE™) para estimación objetiva del dolor/disconfort. Se comparó en la clínica con una escala validada y ampliamente utilizada en neonatos: Premature Infant Pain Profile (PIPP). Para valorar la asociación entre variables NIPE™ y FC se utilizaron correlación de Spearman, el test de Kruskall-Wallis o test de chi cuadrado con corrección de Fisher, según correspondiera. Se encontró una correlación negativa entre FC y NIPE™ tanto para el grupo de neonatos humanos (r=-1; p=0,008) como para el modelo animal (r=-0,6; p=0,0004). No se encontró asociación significativa entre NIPE™ y la escala PIPP. La variación entre valores de NIPE™ pre y posestímulo en RN humanos fue significativa (p=0,008). Conclusiones: determinamos que en ambos escenarios explorados los valores de NIPE™ descienden ante estímulos nociceptivos y los cambios en la FC se relacionan con sus valores, independientemente de la especie o la agresividad de la maniobra. Este trabajo es el primero a nivel nacional incorporando el uso de esta tecnología, creemos que tendrá impacto en la forma de evaluar y abordar el dolor por parte de los equipos asistenciales y de experimentación.
Introduction: exposure to painful stimuli and stress in the neonatal stage, without correct treatment, has short and long-term consequences. Proper diagnosis is a challenge since clinical scales are subjective, and we require more objective screening tools and a better ability to interpret neonatal discomfort/pain. Newborn Infant Parasympathetic Evaluation (NIPE™) is a non-invasive technology for continuous pain monitoring in neonates, recently developed given the difficulty to objectify pain using conventional methods in clinical practice. This technology is based on the analysis of heart rate variability (HR), which allows us to approximate the activity of the parasympathetic system. Objectives: the objective of this research was to assess discomfort in a newborn pig model (NB) and in human neonates exposed to nociceptive maneuvers with the use of non-invasive technology (NIPE), in the maternity ward of the University Hospital. Material and methods: an observational, longitudinal study was carried out in 6 NB pigs, anesthetized, hemodynamically monitored and subjected to a major surgical procedure (left lateral thoracotomy with cardiac approach, pericardiostomy and right trans ventricular pulmonary vascular access) and 12 minimally invasive procedures, from clinical practice. routine such as BCG vaccination, hemoglucotest and screening, which generate a nociceptive stimulus in 8 healthy term newborns. Healthy term newborns (GA between 37-41 weeks plus 6 days) admitted to the mother-child joint accommodation were included, We excluded those NB patients who presented some pathology or whose parents did not accept participation in the study. Results: HR variability was compared using automated detection (NIPETM) for objective estimation of pain/discomfort. It was compared in the clinic with a validated and widely used scale in neonates: Premature Infant Pain Profile (PIPP). We used the Spearman's correlation, the Kruskall-Wallis test or the Chi square test with Fisher's correction to assess the association between NIPE™ variables and HR, as needed. A negative correlation was found between HR and NIPETM for both the group of neonates. humans (r=-1; p=0.008) and for the animal model (r=-0.6; p=0.0004). No significant association was found between NIPETM and the PIPP scale. The variation between pre- and post-stimulus NIPE™ values in human NBs was significant (p=0.008). Conclusions: we conclude that in both scenarios explored, NIPE™ values decrease when faced with nociceptive stimuli and changes in HR are related to its values, regardless of the species or the aggressiveness of the maneuver. This paper is the first at a national level to incorporate the use of this technology, we believe it will have an impact on the way pain is assessed and addressed by healthcare and experimental teams.
Introdução: a exposição a estímulos dolorosos e ao estresse na fase neonatal, sem tratamento correto, traz consequências a curto e longo prazo. O diagnóstico adequado é um desafio, uma vez que as escalas clínicas são subjetivas e são necessárias ferramentas de triagem com melhor objetividade e capacidade de interpretar o desconforto/dor neonatal. A Avaliação Parassimpática do Recém-Nascido (NIPE™) é uma tecnologia não invasiva para monitoramento contínuo da dor em neonatos, desenvolvida recentemente devido à dificuldade de objetivar a dor usando métodos convencionais na prática clínica. Esta tecnologia baseia-se na análise da variabilidade da frequência cardíaca (FC), o que nos permite aproximar a atividade do sistema parassimpático. Objetivos: o objetivo desta pesquisa foi avaliar o desconforto em recém-nascido modelo suíno (RN) e em neonatos humanos expostos a manobras nociceptivas com uso de tecnologia não invasiva (NIPE), na maternidade do Hospital Universitário. Material e métodos: foi realizado um estudo observacional, longitudinal, em 6 suínos RN, anestesiados, monitorados hemodinamicamente e submetidos a um procedimento cirúrgico de grande porte (toracotomia lateral esquerda com abordagem cardíaca, pericardiostomia e acesso vascular pulmonar transventricular direito) e 12 procedimentos minimamente invasivos, da prática clínica. rotina como vacinação BCG, hemoglicoteste e triagem, que geram estímulo nociceptivo em 8 recém-nascidos a termo saudáveis. Foram incluídos recém-nascidos a termo saudáveis (IG entre 37-41 semanas mais 6 dias) internados no alojamento conjunto mãe-filho, foram excluídos do A amostra incluiu RNs que apresentavam alguma patologia e aqueles cujos pais não aceitaram a participação no estudo. Resultados: a variabilidade da FC foi comparada por meio de detecção automatizada (NIPETM) para estimativa objetiva de dor/desconforto. Foi comparado na clínica com uma escala validada e amplamente utilizada em neonatos: Premature Infant Pain Profile (PIPP). Para avaliar a associação entre as variáveis do NIPE™ e a FC, utilizou-se a correlação de Spearman, o teste de Kruskall-Wallis ou o teste Qui-quadrado com correção de Fisher, conforme apropriado. Foi encontrada correlação negativa entre a FC e o NIPETM para ambos os grupos de neonatos. (r=-1; p=0,008) e para o modelo animal (r=-0,6; p=0,0004). Não foi encontrada associação significativa entre o NIPETM e a escala PIPP. A variação entre os valores de NIPE™ pré e pós-estímulo em RN humanos foi significativa (p=0,008). Conclusões: concluímos que em ambos os cenários explorados, os valores do NIPE™ diminuem diante de estímulos nociceptivos e as alterações na FC estão relacionadas aos seus valores, independente da espécie ou da agressividade da manobra. Este trabalho é o primeiro a nível nacional a incorporar a utilização desta tecnologia, acreditamos que terá impacto na forma como a dor é avaliada e abordada pelas equipas de saúde e experimentais.
Asunto(s)
Humanos , Animales , Masculino , Femenino , Recién Nacido , Dimensión del Dolor , Dolor Nociceptivo/diagnóstico , Nocicepción , Porcinos , Estudios Longitudinales , Determinación de la Frecuencia CardíacaRESUMEN
Contactless monitoring of heart rate (HR) can improve passive and continuous tracking of cardiovascular activities and overall people's health. Remote photoplethysmography (rPPG) using a camera eliminates the need for a wearable device. rPPG-based HR has shown promising results to be accurate and comparable to conventional methods such as contact PPG. Most experiments use stationary subjects while motion is known to affect the accuracy of remote PPG. In this paper, a novel methodology is introduced to enhance the accuracy and reliability of HR monitoring based on rPPG in the presence of physical activities like Yoga. This method quickly and accurately tracks HR and analyzes head motion to exclude unreliable data within short windows of rPPG signals. The method was tested with smartphone video data collected from 60 subjects when they are doing activities with varying levels of movement. Results show that our method without motion removal improves the accuracy of the HR readings by 0.7 bpm, reaching 3.57 bpm on average for a 30-sec-window. The accuracy is further improved by another 1.3 bpm after removing the motion artifacts, and reaches 2.29 bpm.Clinical relevance- The enhancement of HR readings from shorter rPPG signal with motion tolerance during physical activities can ultimately help with a more reliable HR tracking of people in uncontrolled settings like home which is a critical step towards remote health-care or wellness tracking.
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Artefactos , Determinación de la Frecuencia Cardíaca , Humanos , Reproducibilidad de los Resultados , Algoritmos , Ejercicio Físico/fisiología , Fotopletismografía/métodosRESUMEN
In the Neonatal Intensive Care Unit (NICU), infants' vital signs are monitored on a continuous basis via wired devices. These often interfere with patient care and pose increased risks of skin damage, infection, and tangling around the body. Recently, a wireless system for neonatal monitoring called ANNEâ One (Sibel Health, Chicago, USA) was developed. We designed an ongoing study to evaluate the feasibility, reliability and accuracy, of using this system in the NICU. Vital signals were simultaneously acquired by using the standard, wired clinical monitor and the ANNEâ device. Data from 10 NICU infants were recorded for 8 hours per day during 4 consecutive days. Initial analysis of the heart rate (HR) data revealed four problems in comparing the signals: 1) gaps in the signals - periods of time for which data were unavailable, 2) wired and wireless signals were sampled at different rates, 3) a delay between the sampled values of wired and wireless signals, and 4) this delay increased with time. To address these problems, we developed a pre-processing algorithm that interpolated samples in short gaps, resampled the signals to an equal rate, estimated the delay and drift rate between corresponding signals, and aligned the signals. Applications of the pre-processing algorithm to 40 recordings demonstrated that it was very effective. A strong agreement between wireless and wired HR signals was seen, with an average correlation of 0.95±0.04, a slope of 1.00, and a variance accounted for 89.56±7.62%. Bland-Altman analysis showed a low bias across the ensemble, with an average difference of 0.11 (95% confidence interval of -0.02 to 0.24) bpm.Clinical relevance- This algorithm provides the means for a detailed comparison of wired and wireless monitors in the NICU.
Asunto(s)
Determinación de la Frecuencia Cardíaca , Unidades de Cuidado Intensivo Neonatal , Recién Nacido , Humanos , Reproducibilidad de los Resultados , Tecnología Inalámbrica , Monitoreo FisiológicoRESUMEN
Fetal heart rate monitoring is a crucial element in determining the health of the fetus during pregnancy. In this paper, we evaluate the fetal heart rate (FHR) and maternal heart rate (MHR) between our non-invasive fetal monitoring system, Femom, developed by a Biorithm and the Huntleigh computerized cardiotocography (cCTG) together with the Sonicaid FetalCare3 software by comparing the accuracy, sensitivity, and reliability through using Bland-Altman analysis, Positive Percent Agreement (PPA) and Intraclass Correlation Coefficient (ICC) respectively. Femom device is a part of the Femom system which collects abdominal electrocardiogram (aECG) signals. Femom sever then processes the collected signals to generate FHR and MHR using novel algorithms. We collected data from 285 pregnant participants who were at least of 28 weeks of gestational age. FHR accuracy consists of mean bias and limit-of-agreement (LoA). The FHR bias is 0.05 beat per minute (BPM) and LoA is [-8.7 8.8] with 95% confidence interval (95% CI) measured using Bland Altman analysis. The PPA of 90.9% reflects FHR sensitivity. Reliability is measured with absolute ICC and consistency ICC. The absolute ICC is of 88% and consistency ICC of 94%. For MHR evaluation, accuracy is measured using Bland Altman analysis which provided a bias of 0.35 BPM and LoA of [-7 6.2] with 95% CI. The MHR sensitivity calculated using PPA is 98% while the MHR reliability is with the absolute value of 99% and consistency ICC of 99%.
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Monitoreo Fetal , Determinación de la Frecuencia Cardíaca , Embarazo , Femenino , Humanos , Reproducibilidad de los Resultados , Frecuencia Cardíaca Fetal/fisiología , ElectrocardiografíaRESUMEN
Heart rate variability (HRV) is a measurement of the fluctuation of time between each heartbeat and reflects the function of the autonomic nervous system. HRV is an important indicator for both physical and mental status and for broad-scope diseases. In this review, we discuss how wearable devices can be used to monitor HRV, and we compare the HRV monitoring function among different devices. In addition, we have reviewed the recent progress in HRV tracking with wearable devices and its value in health monitoring and disease diagnosis. Although many challenges remain, we believe HRV tracking with wearable devices is a promising tool that can be used to improve personal health.
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Dispositivos Electrónicos Vestibles , Frecuencia Cardíaca/fisiología , Sistema Nervioso Autónomo/fisiología , Monitoreo Fisiológico , Determinación de la Frecuencia CardíacaRESUMEN
BACKGROUND: Health care professionals, particularly those in surgical settings, face high stress levels, impacting their well-being. Traditional monitoring methods, like using Holter electrocardiogram monitors, are impractical in the operating room, limiting the assessment of physicians' health. Wrist-worn heart rate monitors, like the Apple Watch, offer promise but are restricted in surgeries due to sterility issues. OBJECTIVE: This study aims to assess the feasibility and accuracy of using an upper arm-worn Apple Watch for heart rate monitoring during robotic-assisted surgeries, comparing its performance with that of a wrist-worn device to establish a reliable alternative monitoring site. METHODS: This study used 2 identical Apple Watch Series 8 devices to monitor the heart rate of surgeons during robotic-assisted surgery. Heart rate data were collected from the wrist-worn and the upper arm-worn devices. Statistical analyses included calculating the mean difference and SD of difference between the 2 devices, constructing Bland-Altman plots, assessing accuracy based on mean absolute error and mean absolute percentage error, and calculating the intraclass correlation coefficient. RESULTS: The mean absolute errors for the whole group and for participants A, B, C, and D were 3.63, 3.58, 2.70, 3.93, and 4.28, respectively, and the mean absolute percentage errors were 3.58%, 3.34%, 2.42%, 4.58%, and 4.00%, respectively. Bland-Altman plots and scatter plots showed no systematic error when comparing the heart rate measurements obtained from the upper arm-worn and the wrist-worn Apple Watches. The intraclass correlation coefficients for participants A, B, C, and D were 0.559, 0.651, 0.508, and 0.563, respectively, with a significance level of P<.001, indicating moderate reliability. CONCLUSIONS: The findings of this study suggest that the upper arm is a viable alternative site for monitoring heart rate during surgery using an Apple Watch. The agreement and reliability between the measurements obtained from the upper arm-worn and the wrist-worn devices were good, with no systematic error and a high level of accuracy. These findings have important implications for improving data collection and management of the physical and mental demands of operating room staff during surgery, where wearing a watch on the wrist may not be feasible.
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Procedimientos Quirúrgicos Robotizados , Cirujanos , Humanos , Brazo , Determinación de la Frecuencia Cardíaca , Estudios de Factibilidad , Reproducibilidad de los Resultados , Frecuencia CardíacaRESUMEN
Previous studies have shown a correlation between resting heart rate (HR) measured by wearable devices and serum free thyroxine concentration in patients with thyroid dysfunction. We have developed a machine learning (ML)-assisted system that uses HR data collected from wearable devices to predict the occurrence of thyrotoxicosis in patients. HR monitoring data were collected using a wearable device for a period of 4 months in 175 patients with thyroid dysfunction. During this period, 3 or 4 thyroid function tests (TFTs) were performed on each patient at intervals of at least one month. The HR data collected during the 10 days prior to each TFT were paired with the corresponding TFT results, resulting in a total of 662 pairs of data. Our ML-assisted system predicted thyrotoxicosis of a patient at a given time point based on HR data and their HR-TFT data pair at another time point. Our ML-assisted system divided the 662 cases into either thyrotoxicosis and non-thyrotoxicosis and the performance was calculated based on the TFT results. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of our system for predicting thyrotoxicosis were 86.14%, 85.92%, 52.41%, and 97.18%, respectively. When subclinical thyrotoxicosis was excluded from the analysis, the sensitivity, specificity, PPV, and NPV of our system for predicting thyrotoxicosis were 86.14%, 98.28%, 94.57%, and 95.32%, respectively. Our ML-assisted system used the change in mean, relative standard deviation, skewness, and kurtosis of HR while sleeping, and the Jensen-Shannon divergence of sleep HR and TFT distribution as major parameters for predicting thyrotoxicosis. Our ML-assisted system has demonstrated reasonably accurate predictions of thyrotoxicosis in patients with thyroid dysfunction, and the accuracy could be further improved by gathering more data. This predictive system has the potential to monitor the thyroid function status of patients with thyroid dysfunction by collecting heart rate data, and to determine the optimal timing for blood tests and treatment intervention.
Asunto(s)
Enfermedades de la Tiroides , Tirotoxicosis , Humanos , Estudios Retrospectivos , Determinación de la Frecuencia Cardíaca , Tirotoxicosis/diagnóstico , Tirotoxicosis/tratamiento farmacológico , Pruebas de Función de la Tiroides , Tirotropina , TiroxinaRESUMEN
Cardio-mechanical monitoring techniques, such as Seismocardiography (SCG) and Gyrocardiography (GCG), have received an ever-growing interest in recent years as potential alternatives to Electrocardiography (ECG) for heart rate monitoring. Wearable SCG and GCG devices based on lightweight accelerometers and gyroscopes are particularly appealing for continuous, long-term monitoring of heart rate and its variability (HRV). Heartbeat detection in cardio-mechanical signals is usually performed with the support of a concurrent ECG lead, which, however, limits their applicability in standalone cardio-mechanical monitoring applications. The complex and variable morphology of SCG and GCG signals makes the ECG-free heartbeat detection task quite challenging; therefore, only a few methods have been proposed. Very recently, a template matching method based on normalized cross-correlation (NCC) has been demonstrated to provide very accurate detection of heartbeats and estimation of inter-beat intervals in SCG and GCG signals of pathological subjects. In this study, the accuracy of HRV indices obtained with this template matching method is evaluated by comparison with ECG. Tests were performed on two public datasets of SCG and GCG signals from healthy and pathological subjects. Linear regression, correlation, and Bland-Altman analyses were carried out to evaluate the agreement of 24 HRV indices obtained from SCG and GCG signals with those obtained from ECG signals, simultaneously acquired from the same subjects. The results of this study show that the NCC-based template matching method allowed estimating HRV indices from SCG and GCG signals of healthy subjects with acceptable accuracy. On healthy subjects, the relative errors on time-domain indices ranged from 0.25% to 15%, on frequency-domain indices ranged from 10% to 20%, and on non-linear indices were within 8%. The estimates obtained on signals from pathological subjects were affected by larger errors. Overall, GCG provided slightly better performances as compared to SCG, both on healthy and pathological subjects. These findings provide, for the first time, clear evidence that monitoring HRV via SCG and GCG sensors without concurrent ECG is feasible with the NCC-based template matching method for heartbeat detection.
Asunto(s)
Electrocardiografía , Corazón , Humanos , Frecuencia Cardíaca/fisiología , Corazón/fisiología , Monitoreo Fisiológico , Determinación de la Frecuencia CardíacaRESUMEN
Heart rate (HR) is a crucial physiological signal that can be used to monitor health and fitness. Traditional methods for measuring HR require wearable devices, which can be inconvenient or uncomfortable, especially during sleep and meditation. Noncontact HR detection methods employing microwave radar can be a promising alternative. However, the existing approaches in the literature usually use high-gain antennas and require the sensor to face the user's chest or back, making them difficult to integrate into a portable device and unsuitable for sleep and meditation tracking applications. This study presents a novel approach for noncontact HR detection using a miniaturized Soli radar chip embedded in a portable device (Google Nest Hub). The chip has a [Formula: see text] dimension and can be easily integrated into various devices. The proposed approach utilizes advanced signal processing and machine learning techniques to extract HRs from radar signals. The approach is validated on a sleep dataset (62 users, 498 h) and a meditation dataset (114 users, 1131 min). The approach achieves a mean absolute error (MAE) of 1.69 bpm and a mean absolute percentage error (MAPE) of [Formula: see text] on the sleep dataset. On the meditation dataset, the approach achieves an MAE of 1.05 bpm and a MAPE of [Formula: see text]. The recall rates for the two datasets are [Formula: see text] and [Formula: see text], respectively. This study represents the first application of the noncontact HR detection technology to sleep and meditation tracking, offering a promising alternative to wearable devices for HR monitoring during sleep and meditation.
Asunto(s)
Meditación , Humanos , Frecuencia Cardíaca/fisiología , Sueño , Monitoreo Fisiológico/métodos , Determinación de la Frecuencia CardíacaRESUMEN
Noncontact vital sign monitoring based on radar has attracted great interest in many fields. Heart Rate Variability (HRV), which measures the fluctuation of heartbeat intervals, has been considered as an important indicator for general health evaluation. This paper proposes a new algorithm for HRV monitoring in which frequency-modulated continuous-wave (FMCW) radar is used to separate echo signals from different distances, and the beamforming technique is adopted to improve signal quality. After the phase reflecting the chest wall motion is demodulated, the acceleration is calculated to enhance the heartbeat and suppress the impact of respiration. The time interval of each heartbeat is estimated based on the smoothed acceleration waveform. Finally, a joint optimization algorithm was developed and is used to precisely segment the acceleration signal for analyzing HRV. Experimental results from 10 participants show the potential of the proposed algorithm for obtaining a noncontact HRV estimation with high accuracy. The proposed algorithm can measure the interbeat interval (IBI) with a root mean square error (RMSE) of 14.9 ms and accurately estimate HRV parameters with an RMSE of 3.24 ms for MEAN (the average value of the IBI), 4.91 ms for the standard deviation of normal to normal (SDNN), and 9.10 ms for the root mean square of successive differences (RMSSD). These results demonstrate the effectiveness and feasibility of the proposed method in emotion recognition, sleep monitoring, and heart disease diagnosis.
Asunto(s)
Determinación de la Frecuencia Cardíaca , Respiración , Humanos , Frecuencia Cardíaca/fisiología , Monitoreo Fisiológico/métodos , Algoritmos , Procesamiento de Señales Asistido por ComputadorRESUMEN
Motivation: The advancement of preventive medicine and, subsequently, telemedicine drives the need for noninvasive and remote measurements in patients' natural environments. Heart rate (HR) measurements are particularly promising and extensively researched due to their quick assessment and comprehensive representation of patients' conditions. However, in scenarios such as endurance training or emergencies, where HR measurement was not anticipated and direct access to victims is limited, no method enables obtaining HR results that are suitable even for triage. Methods: This paper presents the possibility of remotely measuring of human HR from a series of in-flight videos using videoplethysmography (VPG) along with skin detection, human pose estimation and image stabilization methods. An unmanned aerial vehicle (UAV) equipped with a camera captured ten segments of video footage featuring volunteers engaged in free walking and running activities in natural sunlight. The human pose was determined using the OpenPose algorithm, and subsequently, skin areas on the face and forearms were identified and tracked in consecutive frames. Ultimately, HR was estimated using several VPG methods: the green channel (G), green-red difference (GR), excess green (ExG), independent component analysis (ICA), and a plane orthogonal to the skin (POS). Results: When compared to simultaneous readings from a reference ECG-based wearable recorder, the root-mean-squared error ranged from 17.7 (G) to 27.7 (POS), with errors of less than 3.5 bpm achieved for the G and GR methods. Conclusions: These results demonstrate the acceptable accuracy of touchless human pulse measurement with the accompanying UAV-mounted camera. The method bridges the gap between HR-transmitting wearables and emergency HR recorders, and it has the potential to be advantageous in training or rescue scenarios in mountain, water, disaster, or battlefield settings.
Asunto(s)
Desastres , Determinación de la Frecuencia Cardíaca , Humanos , Dispositivos Aéreos No Tripulados , Frecuencia Cardíaca , AlgoritmosRESUMEN
In order to make the teaching and training of aerobics more standardized, it is necessary to use scientific means to detect and monitor the movement standardization in teaching and training and the change of human heart rate in the training process, but at present, there are some difficulties in both detection and monitoring, Therefore, this paper proposes to use the advantages of convolutional neural network to solve the current aerobics teaching problems of motion detection and heart rate monitoring. In the process of operation, the complete aerobics video needs to be divided into several different images, the standardized action image background needs to be eliminated, and then the visual error caused by the difficult action image needs to be corrected. On the premise of image processing, the convolutional neural network is used to pre train the image, and the skeleton map of the human body is constructed in the computer. In the process of practical operation, the use of convolutional neural network for heart rate monitoring has many advantages. It can not only save the time of contact with the human body, but also integrate various information of the time dimension, reducing a lot of computing steps, saving a lot of computing resources for practical work, and promoting the improvement of system output signal quality to a certain extent. The result of the experiment also proves that the convolutional neural network can improve the accuracy of students' movement detection and heart rate change monitoring in aerobics teaching and training.
Asunto(s)
Determinación de la Frecuencia Cardíaca , Redes Neurales de la Computación , Humanos , Procesamiento de Imagen Asistido por Computador/métodos , Frecuencia Cardíaca , AlgoritmosRESUMEN
Instantaneous heart rate (IHR) has been investigated for sleep applications, such as sleep apnea detection and sleep staging. To ensure the comfort of the patient during sleep, it is desirable for IHR to be measured in a contact-free fashion. In this work, we use speckle vibrometry (SV) to perform on-skin and on-textile IHR monitoring in a sleep setting. Minute motions on the laser-illuminated surface can be captured by a defocused camera, enabling the detection of cardiac motions even on textiles. We investigate supine, lateral, and prone sleeping positions. Based on Bland-Altman analysis between SV cardiac measurements and electrocardiogram (ECG), with respect to each position, we achieve the best limits of agreement with ECG values of [-8.65, 7.79] bpm, [-9.79, 9.25] bpm, and [-10.81, 10.23] bpm, respectively. The results indicate the potential of using speckle vibrometry as a contact-free monitoring method for instantaneous heart rate in a setting where the participant is allowed to rest in a spontaneous position while covered by textile layers.