Clinical feasibility of a contactless multiparameter continuous monitoring technology for neonates in a large public maternity hospital in Nairobi, Kenya

Multiparameter continuous physiological monitoring (MCPM) technologies are critical in the clinical management of high-risk neonates; yet, these technologies are frequently unavailable in many African healthcare facilities. We conducted a prospective clinical feasibility study of EarlySense’s novel under-mattress MCPM technology in neonates at Pumwani Maternity Hospital in Nairobi, Kenya. To assess feasibility, we compared the performance of EarlySense’s technology to Masimo’s Rad-97 pulse CO-oximeter with capnography technology for heart rate (HR) and respiratory rate (RR) measurements using up-time, clinical event detection performance, and accuracy. Between September 15 and December 15, 2020, we collected and analyzed 470 hours of EarlySense data from 109 enrolled neonates. EarlySense’s technology’s up-time per neonate was 2.9 (range 0.8, 5.3) hours for HR and 2.1 (range 0.9, 4.0) hours for RR. The difference compared to the reference was a median of 0.6 (range 0.1, 3.1) hours for HR and 0.8 (range 0.1, 2.9) hours for RR. EarlySense’s technology identified high HR and RR events with high sensitivity (HR 81%; RR 83%) and specificity (HR 99%; RR 83%), but was less sensitive for low HR and RR (HR 0%; RR 14%) although maintained specificity (HR 100%; RR 95%). There was a greater number of false negative and false positive RR events than false negative and false positive HR events. The normalized spread of limits of agreement was 9.6% for HR and 28.6% for RR, which met the a priori-identified limit of 30%. EarlySense’s MCPM technology was clinically feasible as demonstrated by high percentage of up-time, strong clinical event detection performance, and agreement of HR and RR measurements compared to the reference technology. Studies in critically ill neonates, assessing barriers and facilitators to adoption, and costing analyses will be key to the technology’s development and potential uptake and scale-up

Extraction of respiratory signals from the electrocardiogram and photoplethysmogram: technical and physiological determinants.

OBJECTIVE: Breathing rate (BR) can be estimated by extracting respiratory signals from the electrocardiogram (ECG) or photoplethysmogram (PPG). The extracted respiratory signals may be influenced by several technical and physiological factors. In this study, our aim was to determine how technical and physiological factors influence the quality of respiratory signals. APPROACH: Using a variety of techniques 15 respiratory signals were extracted from the ECG, and 11 from PPG signals collected from 57 healthy subjects. The quality of each respiratory signal was assessed by calculating its correlation with a reference oral-nasal pressure respiratory signal using Pearson's correlation coefficient. MAIN RESULTS: Relevant results informing device design and clinical application were obtained. The results informing device design were: (i) seven out of 11 respiratory signals were of higher quality when extracted from finger PPG compared to ear PPG; (ii) laboratory equipment did not provide higher quality of respiratory signals than a clinical monitor; (iii) the ECG provided higher quality respiratory signals than the PPG; (iv) during downsampling of the ECG and PPG significant reductions in quality were first observed at sampling frequencies of  <250 Hz and  <16 Hz respectively. The results informing clinical application were: (i) frequency modulation-based respiratory signals were generally of lower quality in elderly subjects compared to young subjects; (ii) the qualities of 23 out of 26 respiratory signals were reduced at elevated BRs; (iii) there were no differences associated with gender. SIGNIFICANCE: Recommendations based on the results are provided regarding device designs for BR estimation, and clinical applications. The dataset and code used in this study are publicly available

Caracterização quantitativa do sistema nervoso autônomo : estimação da taxa respiratória a partir do sinal de eletrocardiograma (ECG)

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2019.Este trabalho propõe o estudo de uma abordagem alternativa e não invasiva para se obter características oriundas do sinal de respiração e de suas influências sobre o Sistema Nervoso Autônomo (SNA). Especificamente, procura-se verificar a validade do uso de um sinal respiratório extraído de um sinal de eletrocardiograma em um sistema que correlaciona sinais cardíacos e respiratórios e o grau de apneia do sono de um certo indivíduo. Para se alcançar tal objetivo, usaram-se dois conjuntos de dados: o primeiro consiste na seleção de 6 pacientes de um banco de 53 pacientes de unidades de tratamento intensivo; o segundo continha sinais de eletrocardiograma e respiração de 8 pacientes, divididos em 4 indívíduos com apneia e 4 pacientes sem apneia. Por meio da estimação da resposta ao impulso pelos métodos autoregressivo (ARX) e de funções base de Laguerre (FBL), foi possível obter parâmetros quantitativos que refletem a modulação do SNA a partir de um sistema com o sinal de respiração estimado. A partir desses modelos, foi possível compará-los com os parâmetros extraídos de um sistema com sinais de respiração obtidos por métodos convencionais, os quais foram tratados nesse estudo como sinais de referência. Assim, compararam-se esses sinais estimados e de referência por diferentes métricas: uma correlação cruzada foi aplicada entre os sinais estimado e de referência para se obter o nível de semelhança entre eles. Em seguida, realizou-se uma correlação entre os parâmetros de descrição da resposta ao impulso (RI) tanto para os modelos estimados quanto os de referência, com o intuito de se obter uma medida de similaridade no comportamento entre eles. Finalmente, procurou-se utilizar as técnicas ANOVA e K-Means com a finalidade de verificar a possibilidade de distinção entre pacientes apneicos e não-apneicos para esses parâmetros estimados e de referência. Nos resultados obtidos, observou-se uma diferença sensível na correlação cruzada dos sinais e na correlação dos parâmetros para bases de dados diferentes. Para o método ANOVA, foi possível observar que os parâmetros oriundos dos modelos criados com os sinais de respiração estimados não apresentaram diferença estatística na distinção entre pacientes apneicos e não-apneicos, algo que foi alcançado para os parâmetros provindos dos modelos com sinais de referência. Todavia, essa incerteza poderia ser possivelmente explicada por um número reduzido de amostras. Ademais, o método K-Means obteve 100% de acertos na segregação dos pacientes apneicos e não apneicos utilizando os parâmetros descritivos de RI dos modelos de referência; para os modelos com sinais estimados, dois pacientes apneicos foram incorretamente classificados. Pelos resultados obtidos, sugere-se a aplicação das metodologias descritas em uma base de dados maior e em diferentes bases de dados, a fim de se verificar algumas da questões levantadas ao longo dessa pesquisa.This work proposes the study of an alternative and non-invasive approach to acquire characteristics from the respiratory signal and its influences to the Autonomic Nervous System. Specifically, this work seeks to verifiy the validity of the application of a respiratory signal extracted from an electrocardiogram signal in a system that correlates cardiac and respiratory signals and the index of sleep apnea of a certain subject. In order to reach this goal, two databases were used: the first one consists of a selection of 6 subjects from a database of 53 critically-ill patients in intensive care units; the second data set contain electrocardiogram and respiratory signals of 8 subjects, divided between 4 individuals without apnea and 4 subjects with apnea. By means of the impulse response with the autregressive (ARX) and Laguerre basis functions (LBF) models, it was possible to extract quantitative parameters that reflect the Autonomic Nervous System modulation from a system with the estimated respiratory signal. These models made it possible to compare these parameters with the ones from a system with respiratory signals obtained from more conventional methods, which were treated in this study as reference signals. Therefore, the estimated and reference respiratory signals were compared by different metrics: a cross-correlation analysis was applied between these signals in order to measure the level of similarity between them. Then, the correlation technique was applied to the descriptive parameters of the impulse response for both the estimated and reference models. This was done with the purpose of obtaining a measure of similarity in the behavior of these parameters. Finally, the ANOVA and K-Means techniques were used in order to verify the possibility of distinction between apneic and non-apneic patients with the estimated and reference parameters. In the results obtained from these metrics, a sensible difference was observed in the cross-correlation for the signals and in the correlation for the parameters for the two different databases. In the ANOVA method, the parameters from the models with estimated respiratory signals weren’t statistically different in the distinction between apneic and non-apneic patients, unlike the models with the reference signals. However, this statistical uncertainty can possibly be explained by a reduced number of samples. Moreover, the K-Means method obtained 100% accuracy in the process of distinguishing patients with and without apnea using the impulse response parameters from the reference models; for the estimated models, however, two apneic patients were incorrectly classified as non-apneic. With the results obtained in this study, the application of the procedures used in this study in a larger database and in different databases is suggested, in order to verify some of the observations raised throughout this research

Long-term monitoring of respiratory metrics using wearable devices

Recently, there has been an increased interest in monitoring health using wearable sensors technologies however, few have focused on breathing. The utility of constant monitoring of breathing is currently not well understood, both for general health as well as respiratory conditions such as asthma and chronic obstructive pulmonary disease (COPD) that have significant prevalence in society. Having a wearable device that could measure respiratory metrics continuously and non-invasively with high adherence would allow us to investigate the significance of ambulatory breathing monitoring in health and disease management. The purpose of this thesis was to determine if it was feasible to continuously monitor respiratory metrics. To do this, we identified pulse oximetry to provide the best balance between use of mature signal processing methods, commercial availability, power efficiency, monitoring site and perceived wearability. Through a survey, it was found users would monitor their breathing, irrespective of their health status using a smart watch. Then it was found that reducing the duty cycle and power consumption adversely affected the reliability to capture accurate respiratory rate measurements through pulse oximetry. To account for the decreased accuracy of PPG derived respiratory rate at higher rates, a long short-term memory (LSTM) network and a U-Net were proposed, characterised and implemented. In addition to respiratory rate, inspiration time, expiration time, inter-breath intervals and the Inspiration:Expiration ratio were also predicted. Finally, the accuracy of these predictions was validated using pilot data from 11 healthy participants and 11 asthma participants. While percentage bias was low, the 95\% limits of agreement was high. While there is likely going to be enthusiastic uptake in wearable device use, it remains unseen whether clinical utility can be achieved, in particular the ability to forecast respiratory status. Further, the issues of sensor noise and algorithm performance during activity was not calculated. However, this body of work has investigated and developed the use of pulse oximetry, classical signal processing and machine learning methodologies to extract respiratory metrics to lay a foundation for both the hardware and software requirements in future clinical research

An algorithm for extracting the PPG Baseline Drift in real-time

Photoplethysmography is an optical technique for measuring the perfusion of blood in skin and tissue arterial vessels. Due to its simplicity, accessibility and abundance of information on an individual’s cardiovascular system, it has been a pervasive topic of research within recent years. With these benefits however there are many challenges concerning the processing and conditioning of the signal in order to allow information to be extracted. One such challenge is removing the baseline drift of the signal, which is caused by respiratory rate, muscle tremor and physiological changes within the body as a response to various stimuli. Over the years there have been many methods developed in order to condition the signal such as Wavelet Transform, Cubic Spline Interpolation, Morphological Operators and Fourier-Based filtering techniques. All have their own individual benefits and drawbacks. These drawbacks are that they are unsuitable for real-time usage due to the computation power needed, or have the trade-off of being real-time at the cost of deforming the signal which is unideal for accurate analysis. This thesis aims to explore these techniques in order to develop an algorithm that can be used to condition the signal against the baseline drift in real-time, while being able to achieve good computational efficiency and the preservation of the signal form

Contributions of Human Prefrontal Cortex to the Recogitation of Thought

Human beings have a unique ability to not only verbally articulate past and present experiences, as well as potential future ones, but also evaluate the mental representations of such things. Some evaluations do little good, in that they poorly reflect facts, create needless emotional distress, and contribute to the obstruction of personal goals, whereas some evaluations are the converse: They are grounded in logic, empiricism, and pragmatism and, therefore, are functional rather than dysfunctional. The aim of non-pharmacological mental health interventions is to revise dysfunctional thoughts into more adaptive, healthier ones; however, the neurocognitive mechanisms driving cognitive change have hitherto remained unclear. Therefore, this thesis examines the role of the prefrontal cortex (PFC) in this aspect of human higher cognition using the relatively new method of functional near-infrared spectroscopy (fNIRS). Chapter 1 advances recogitation as the mental ability on which cognitive restructuring largely depends, concluding that, as a cognitive task, it is a form of open-ended human problem-solving that uses metacognitive and reasoning faculties. Because these faculties share similar executive resources, Chapter 2 discusses the systems in the brain involved in controlled information processing, specifically the nature of executive functions and their neural bases. Chapter 3 builds on these ideas to propose an information-processing model of recogitation, which predicts the roles of different subsystems localized within the PFC and elsewhere in the context of emotion regulation. This chapter also highlights several theoretical and empirical challenges to investigating this neurocognitive theory and proposes some solutions, such as to use experimental designs that are more ecologically valid. Chapter 4 focuses on a neuroimaging method that is best suited to investigating questions of spatial localization in ecological experiments, namely functional near-infrared spectroscopy (fNIRS). Chapter 5 then demonstrates a novel approach to investigating the neural bases of interpersonal interactions in clinical settings using fNIRS. Chapter 6 explores physical activity as a ‘bottom-up’ approach to upregulating the PFC, in that it might help clinical populations with executive deficits to regulate their mental health from the ‘top-down’. Chapter 7 addresses some of the methodological issues of investigating clinical interactions and physical activity in more naturalistic settings by assessing an approach to recovering functional events from observed brain data. Chapter 8 draws several conclusions about the role of the PFC in improving psychological as well as physiological well-being, particularly that rostral PFC is inextricably involved in the cognitive effort to modulate dysfunctional thoughts, and proposes some important future directions for ecological research in cognitive neuroscience; for example, psychotherapy is perhaps too physically stagnant, so integrating exercise into treatment environments might boost the effectiveness of intervention strategies