Blood Pressure Estimation from Electrocardiogram and Photoplethysmography Signals Using Continuous Wavelet Transform and Convolutional Neural Network

Cuff-less and continuous blood pressure (BP) measurement has recently become an active research area in the field of remote healthcare monitoring. There is a growing demand for automated BP estimation and monitoring for various long-term and chronic conditions. Automated BP monitoring can produce a good amount of rich health data, which increases the chance of early diagnosis and treatments that are critical for a long-term condition such as hypertension and Cardiovascular diseases (CVDs). However, mining and processing this vast amount of data is challenging, which is aimed to address in this research. We employed a continuous wavelet transform (CWT) and a deep convolutional neural network (CNN) to estimate the BP. The electrocardiogram (ECG), photoplethysmography (PPG) and arterial blood pressure (ABP) signals were extracted from the online Medical Information Mart for Intensive Care (MIMIC III) database. The scalogram of each signal was created and used for training and testing our proposed CNN model that can implicitly learn to extract the descriptive features from the training data. This study achieved a promising BP estimation approach has been achieved without employing engineered feature extraction that is comparable with previous works. Experimental results demonstrated a low root mean squere error (RMSE) rate of 3.36 mmHg and a high accuracy of 86.3% for BP estimations. The proposed CNN-based model can be considered as a reliable and feasible approach to estimate BP for continuous remote healthcare monitoring

Multimodal Photoplethysmography-Based Approaches for Improved Detection of Hypertension

Elevated blood pressure (BP) is a major cause of death, yet hypertension commonly goes undetected. Owing to its nature, it is typically asymptomatic until later in its progression when the vessel or organ structure has already been compromised. Therefore, noninvasive and continuous BP measurement methods are needed to ensure appropriate diagnosis and early management before hypertension leads to irreversible complications. Photoplethysmography (PPG) is a noninvasive technology with waveform morphologies similar to that of arterial BP waveforms, therefore attracting interest regarding its usability in BP estimation. In recent years, wearable devices incorporating PPG sensors have been proposed to improve the early diagnosis and management of hypertension. Additionally, the need for improved accuracy and convenience has led to the development of devices that incorporate multiple different biosignals with PPG. Through the addition of modalities such as an electrocardiogram, a final measure of the pulse wave velocity is derived, which has been proved to be inversely correlated to BP and to yield accurate estimations. This paper reviews and summarizes recent studies within the period 2010-2019 that combined PPG with other biosignals and offers perspectives on the strengths and weaknesses of current developments to guide future advancements in BP measurement. Our literature review reveals promising measurement accuracies and we comment on the effective combinations of modalities and success of this technology

Desarrollo de nuevos dispositivos y algoritmos para la monitorización ambulatoria de personas con epilepsia

La epilepsia es una enfermedad crónica con un enorme impacto sociosanitario. Aunque en la actualidad se dispone de una gran cantidad de fármacos antiepilépticos y de otros tratamientos más selectivos como la cirugía o la estimulación cerebral, un porcentaje considerable de pacientes no están controlados y continúan teniendo crisis epilépticas. Estas personas suelen vivir condicionadas por la posibilidad de un ataque epiléptico y sus posibles consecuencias, como accidentes, lesiones o incluso la muerte súbita inexplicable. En este contexto, un dispositivo capaz de monitorizar el estado de salud y avisar de un posible ataque epiléptico contribuiría a mejorar la calidad de vida de estas personas. La presente Tesis Doctoral se centra en el desarrollo de un novedoso sistema de monitorización ambulatoria que permita identificar y predecir los ataques epilépticos. Dicho sistema está compuesto por diferentes sensores capaces de registrar de forma sincronizada diferentes señales biomédicas. Mediante técnicas de aprendizaje automático supervisado, se han desarrollado diferentes modelos predictivos capaces de clasificar el estado de la persona epiléptica en normal, preictal (antes de la crisis) e ictal (crisis)

Effects of errorless learning on the acquisition of velopharyngeal movement control

Session 1pSC - Speech Communication: Cross-Linguistic Studies of Speech Sound Learning of the Languages of Hong Kong (Poster Session)The implicit motor learning literature suggests a benefit for learning if errors are minimized during practice. This study investigated whether the same principle holds for learning velopharyngeal movement control. Normal speaking participants learned to produce hypernasal speech in either an errorless learning condition (in which the possibility for errors was limited) or an errorful learning condition (in which the possibility for errors was not limited). Nasality level of the participants’ speech was measured by nasometer and reflected by nasalance scores (in %). Errorless learners practiced producing hypernasal speech with a threshold nasalance score of 10% at the beginning, which gradually increased to a threshold of 50% at the end. The same set of threshold targets were presented to errorful learners but in a reversed order. Errors were defined by the proportion of speech with a nasalance score below the threshold. The results showed that, relative to errorful learners, errorless learners displayed fewer errors (50.7% vs. 17.7%) and a higher mean nasalance score (31.3% vs. 46.7%) during the acquisition phase. Furthermore, errorless learners outperformed errorful learners in both retention and novel transfer tests. Acknowledgment: Supported by The University of Hong Kong Strategic Research Theme for Sciences of Learning © 2012 Acoustical Society of Americapublished_or_final_versio

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 349)

This bibliography lists 149 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during April, 1991. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

Model-based hemodynamic management of critically ill patients.

Hemodynamic monitoring and therapy aims to ensure adequate circulatory function, and thus organ perfusion. However, achieving this goal is challenging due to the inability to directly monitor organ perfusion, difficulty and ambiguity in ascertaining the most appropriate treatment strategy, and highly variable and complex patient response to therapy. Hence, effective measurements and protocols to clarify hemodynamic management and optimise outcomes are urgently needed to meet growing demand for intensive care arising from aging populations and rising rates of chronic disease. This thesis explores model-based solutions to provide real-time, non-additionally-invasive hemodynamic assessment for critical care. These models have the advantage of directly accounting for intra- and inter- patient variability by identifying patient-specific parameters from time-varying data, and monitoring their evolution over time and care. Model validation is performed using data from experimental pig trials, which provide high-fidelity / invasive monitoring not feasible in humans. Further validation is from two clinical databases: VitalDB, from surgical patients; and BedMasterDB, from Christchurch Hospital Intensive Care Unit. This personalised model-based approach is used to provide insight and objective assessments not possible from current measurements. Several clinically-applicable, non-additionally-invasive hemodynamic models are developed and validated in this thesis. Two beat-to-beat cardiac stroke volume estimation methods, the constant-Z windkessel and tube-load models, are shown to outperform existing methods for pig trial and clinical validation. Next, a model is presented to estimate preload changes, Frank-Starling curves, and contractility. The model performed well for the pig trial and for the VitalDB data had reasonable contractility estimation, but poor preload estimation accuracy, providing proof-of-concept. Finally, a model is developed for predicting stroke volume changes in response to fluid therapy. This model had good performance for the pig trial, and a proposed clinical trial design for further validation is presented. All these models together address key elements and insight required to simplify, personalise, and optimise hemodynamic management. This thesis also delivers the design and validation of a low-cost, open-source data acquisition system enabling direct recording of patient arterial pressure waveforms at the same time as a clinical monitor, and measurement of fluid infusion rate. The system is a useful tool for hemodynamic monitoring research, and reduces the high cost barrier of acquiring waveform data from medical monitoring systems, which stifles innovation. Overall, clinically-applicable model-based methods are developed in this thesis to deliver non-additionally-invasive stroke volume monitoring, assessment of cardiac preload responsiveness / contractility, and fluid responsiveness prediction in real-time at the patient bedside. Together, these outputs can provide patient-specific assessment of hemodynamic state and response to therapy. These outputs greatly enrich the information available to clinicians and potentially enable a smarter, more personalised approach to hemodynamic management to improve patient outcomes

Signal processing and machine learning techniques for Doppler ultrasound haemodynamic measurements

Haemodynamic monitoring is an invaluable tool for evaluating, diagnosing and treating the cardiovascular system, and is an integral component of intensive care units, obstetrics wards and other medical units. Doppler ultrasound provides a non-invasive, cost-effective and fast means of haemodynamic monitoring, which traditionally necessitates highly invasive methods such as Pulmonary artery catheter or transoesophageal echocardiography. However, Doppler ultrasound scan acquisition requires a highly experienced operator and can be very challenging. Machine learning solutions that quantify and guide the scanning process in an automatic and intelligent manner could overcome these limitations and lead to routine monitoring. Development of such methods is the primary goal of the presented work. In response to this goal, this thesis proposes a suite of signal processing and machine learning techniques. Among these is a new and real-time method of maximum frequency envelope estimation. This method, which is based on image-processing techniques and is highly adaptive to varying signal quality, was developed to facilitate automatic and consistent extraction of features from Doppler ultrasound measurements. Through a thorough evaluation, this method was demonstrated to be accurate and more stable than alternative state-of-art methods. Two novel real-time methods of beat segmentation, which operate using the maximum frequency envelope, were developed to enable systematic feature extraction from individual cardiac cycles. These methods do not require any additional hardware, such as an electrocardiogram machine, and are fully automatic, real-time and highly resilient to noise. These qualities are not available in existing methods. Extensive evaluation demonstrated the methods to be highly successful. A host of machine learning solutions were analysed, designed and evaluated. This led to a set of novel features being proposed for Doppler ultrasound analysis. In addition, a state of- the-art image recognition classification method, hitherto undocumented for Doppler ultrasound analysis, was shown to be superior to more traditional modelling approaches. These contributions facilitated the design of two innovative types of feedback. To reflect beneficial probe movements, which are otherwise difficult to distinguish, a regression model to quantitatively score ultrasound measurements was proposed. This feedback was shown to be highly correlated with an ideal response. The second type of feedback explicitly predicted beneficial probe movements. This was achieved using classification models with up to five categories, giving a more challenging scenario than those addressed in prior disease classification work. Evaluation of these, for the first time, demonstrated that Doppler scan information can be used to automatically indicate probe position. Overall, the presented work includes significant contributions for Doppler ultrasound analysis, it proposes valuable new machine learning techniques, and with continued work, could lead to solutions that unlock the full potential of Doppler ultrasound haemodynamic monitoring