The Duration of the Cycle to Get the P Amplitude on A Discrete Electrocardiogram

The P amplitude value for each cycle has not been carried out even though it is related to indications of atrial hypertrophy. The basic interpretation of the maximum P amplitude under normal conditions is 2.5 small squares on electrocardiogram (ECG) paper which is equivalent to 2.5 mV. Apart from these interpretations, an amplitude value is required that corresponds to the amount of depolarization of the atrial muscle cells. The difficulty faced by researchers is the lack of discrete ecg data available for experiments, so it only depends on amplitude data as a function of Physionet output time. An ECG is produced using discrete data but there is no electrocardiograph that displays discrete data yet. This study aims to obtain the P amplitude value based on discrete electrocardiogram data. The cycle duration value obtained from R to R is used to obtain the initial position of the cycle (sc) with the formula RN+1-1.5dR for each cycle. The P amplitude value can be obtained by filtering the maximum amplitude value between the sc and RN positions. The results of research on 10 physionet samples and 10 RSSA samples showed that all samples had an amplitude R, cycle duration and P amplitude value in each cycle

Design techniques for smart and energy-efficient wireless body sensor networks

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadores y Automática, leída el 26/10/2012Las redes inalámbricas de sensores corporales (en inglés: "wireless body sensor networks" o WBSNs) para monitorización, diagnóstico y detección de emergencias, están ganando popularidad y están llamadas a cambiar profundamente la asistencia sanitaria en los próximos años. El uso de estas redes permite una supervisión continua, contribuyendo a la prevención y el diagnóstico precoz de enfermedades, al tiempo que mejora la autonomía del paciente con respecto a otros sistemas de monitorización actuales. Valiéndose de esta tecnología, esta tesis propone el desarrollo de un sistema de monitorización de electrocardiograma (ECG), que no sólo muestre continuamente el ECG del paciente, sino que además lo analice en tiempo real y sea capaz de dar información sobre el estado del corazón a través de un dispositivo móvil. Esta información también puede ser enviada al personal médico en tiempo real. Si ocurre un evento peligroso, el sistema lo detectará automáticamente e informará de inmediato al paciente y al personal médico, posibilitando una rápida reacción en caso de emergencia. Para conseguir la implementación de dicho sistema, se desarrollan y optimizan distintos algoritmos de procesamiento de ECG en tiempo real, que incluyen filtrado, detección de puntos característicos y clasificación de arritmias. Esta tesis también aborda la mejora de la eficiencia energética de la red de sensores, cumpliendo con los requisitos de fidelidad y rendimiento de la aplicación. Para ello se proponen técnicas de diseño para reducir el consumo de energía, que permitan buscar un compromiso óptimo entre el tamaño de la batería y su tiempo de vida. Si el consumo de energía puede reducirse lo suficiente, sería posible desarrollar una red que funcione permanentemente. Por lo tanto, el muestreo, procesamiento, almacenamiento y transmisión inalámbrica tienen que hacerse de manera que se suministren todos los datos relevantes, pero con el menor consumo posible de energía, minimizando así el tamaño de la batería (que condiciona el tamaño total del nodo) y la frecuencia de recarga de la batería (otro factor clave para su usabilidad). Por lo tanto, para lograr una mejora en la eficiencia energética del sistema de monitorización y análisis de ECG propuesto en esta tesis, se estudian varias soluciones a nivel de control de acceso al medio y sistema operativo.Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEunpu

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

Algorithms and systems for home telemonitoring in biomedical applications

During the past decades, the interest of the healthcare community shifted from the simple treatment of the diseases towards the prevention and maintenance of a healthy lifestyle. This approach is associated to a reduced cost for the Health Systems, having to face the constantly increased expenditures due to the reduced mortality for chronical diseases and to the progressive population ageing. Nevertheless, the high costs related to hospitalization of patients for monitoring procedures that could be better performed at home hamper the full implementation of this approach in a traditional way. Information and Communication Technology can provide a solution to implement a care model closer to the patient, crossing the physical boundaries of the hospitals and thus allowing to reach also those patients that, for a geographical or social condition, could not access the health services as other luckier subjects. This is the case of telemonitoring systems, whose aim is that of providing monitoring services for some health-related parameters at a distance, by means of custom-designed electronic devices. In this thesis, the specific issues associated to two telemonitoring applications are presented, along with the proposed solutions and the achieved results. The first telemonitoring application considered is the fetal electrocardiography. Non-invasive fetal electrocardiography is the recording of the fetal heart electrical activity using electrodes placed on the maternal abdomen. It can provide important diagnostic parameters, such as the beat-to-beat heart rate variability, whose recurring analysis would be useful in assessing and monitoring fetal health during pregnancy. Long term electrocardiographic monitoring is sustained by the absence of any collateral effects for both the mother and the fetus. This application has been tackled from several perspectives, mainly acquisition and processing. From the acquisition viewpoint a study on different skin treatments, disposable commercial electrodes and textile electrodes has been performed with the aim of improving the signal acquisition quality, while simplifying the measurement setup. From the processing viewpoint, different algorithms have been developed to allow extracting the fetal ECG heart rate, starting from an on-line ICA algorithm or exploiting a subtractive approach to work on recordings acquired with a reduced number of electrodes. The latter, took part to the international "Physionet/Computing in Cardiology Challenge" in 2013 entering into the top ten best-performing open-source algorithms. The improved version of this algorithm is also presented, which would mark the 5th and 4th position in the final ranking related to the fetal heart rate and fetal RR interval measurements performance, reserved to the open-source challenge entries, taking into account both official and unofficial entrants. The research in this field has been carried out in collaboration with the Pediatric Cardiology Unit of the Hospital G. Brotzu in Cagliari, for the acquisition of non-invasive fetal ECG signals from pregnant voluntary patients. The second telemonitoring application considered is the telerehabilitation of the hand. The execution of rehabilitation exercises has been proven to be effective in recovering hand functionality in a wide variety of invalidating diseases, but the lack of standardization and continuous medical control cause the patients neglecting this therapeutic procedures. Telemonitoring the rehabilitation sessions would allow the physician to closely follow the patients' progresses and compliance to the prescribed adapted exercises. This application leads to the development of a sensorized telerehabilitation system for the execution and objective monitoring of therapeutic exercises at the patients' home and of the telemedicine infrastructure that give the physician the opportunity to monitor patients' progresses through parameters summarizing the patients' performance. The proposed non-CE marked medical device, patent pending, underwent a clinical trial, reviewed and approved by the Italian Public Health Department, involving 20 patients with Rheumatoid Arthritis and 20 with Systemic Sclerosis randomly assigned to the experimental or the control arm, enrolled for 12 weeks in a home rehabilitation program. The trial, carried out with the collaboration of the Rheumatology Department of the Policlinico Universitario of Cagliari, revealed promising results in terms of hand functionality recovering, highlighting greater improvements for the patients enrolled in the experimental arm, that use the proposed telerehabilitation system, with respect to those of the control arm, which perform similar rehabilitation exercises using common objects

Low-voltage embedded biomedical processor design

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 180-190).Advances in mobile electronics are fueling new possibilities in a variety of applications, one of which is ambulatory medical monitoring with body-worn or implanted sensors. Digital processors on such sensors serve to analyze signals in real-time and extract key features for transmission or storage. To support diverse and evolving applications, the processor should be flexible, and to extend sensor operating lifetime, the processor should be energy-efficient. This thesis focuses on architectures and circuits for low power biomedical signal processing. A general-purpose processor is extended with custom hardware accelerators to reduce the cycle count and energy for common tasks, including FIR and median filtering as well as computing FFTs and mathematical functions. Improvements to classic architectures are proposed to reduce power and improve versatility: an FFT accelerator demonstrates a new control scheme to reduce datapath switching activity, and a modified CORDIC engine features increased input range and decreased quantization error over conventional designs. At the system level, the addition of accelerators increases leakage power and bus loading; strategies to mitigate these costs are analyzed in this thesis. A key strategy for improving energy efficiency is to aggressively scale the power supply voltage according to application performance demands. However, increased sensitivity to variation at low voltages must be mitigated in logic and SRAM design. For logic circuits, a design flow and a hold time verification methodology addressing local variation are proposed and demonstrated in a 65nm microcontroller functioning at 0.3V. For SRAMs, a model for the weak-cell read current is presented for near-V supply voltages, and a self-timed scheme for reducing internal bus glitches is employed with low leakage overhead. The above techniques are demonstrated in a 0.5-1.OV biomedical signal processing platform in 0.13p-Lm CMOS. The use of accelerators for key signal processing enabled greater than 10x energy reduction in two complete EEG and EKG analysis applications, as compared to implementations on a conventional processor.by Joyce Y. S. Kwong.Ph.D

Design for energy-efficient and reliable fog-assisted healthcare IoT systems

Cardiovascular disease and diabetes are two of the most dangerous diseases as they are the leading causes of death in all ages. Unfortunately, they cannot be completely cured with the current knowledge and existing technologies. However, they can be effectively managed by applying methods of continuous health monitoring. Nonetheless, it is difficult to achieve a high quality of healthcare with the current health monitoring systems which often have several limitations such as non-mobility support, energy inefficiency, and an insufficiency of advanced services. Therefore, this thesis presents a Fog computing approach focusing on four main tracks, and proposes it as a solution to the existing limitations. In the first track, the main goal is to introduce Fog computing and Fog services into remote health monitoring systems in order to enhance the quality of healthcare. In the second track, a Fog approach providing mobility support in a real-time health monitoring IoT system is proposed. The handover mechanism run by Fog-assisted smart gateways helps to maintain the connection between sensor nodes and the gateways with a minimized latency. Results show that the handover latency of the proposed Fog approach is 10%-50% less than other state-of-the-art mobility support approaches. In the third track, the designs of four energy-efficient health monitoring IoT systems are discussed and developed. Each energy-efficient system and its sensor nodes are designed to serve a specific purpose such as glucose monitoring, ECG monitoring, or fall detection; with the exception of the fourth system which is an advanced and combined system for simultaneously monitoring many diseases such as diabetes and cardiovascular disease. Results show that these sensor nodes can continuously work, depending on the application, up to 70-155 hours when using a 1000 mAh lithium battery. The fourth track mentioned above, provides a Fog-assisted remote health monitoring IoT system for diabetic patients with cardiovascular disease. Via several proposed algorithms such as QT interval extraction, activity status categorization, and fall detection algorithms, the system can process data and detect abnormalities in real-time. Results show that the proposed system using Fog services is a promising approach for improving the treatment of diabetic patients with cardiovascular disease

Artificial Intelligence for the Edge Computing Paradigm.

With modern technologies moving towards the internet of things where seemingly every financial, private, commercial and medical transaction being carried out by portable and intelligent devices; Machine Learning has found its way into every smart device and application possible. However, Machine Learning cannot be used on the edge directly due to the limited capabilities of small and battery-powered modules. Therefore, this thesis aims to provide light-weight automated Machine Learning models which are applied on a standard edge device, the Raspberry Pi, where one framework aims to limit parameter tuning while automating feature extraction and a second which can perform Machine Learning classification on the edge traditionally, and can be used additionally for image-based explainable Artificial Intelligence. Also, a commercial Artificial Intelligence software have been ported to work in a client/server setups on the Raspberry Pi board where it was incorporated in all of the Machine Learning frameworks which will be presented in this thesis. This dissertation also introduces multiple algorithms that can convert images into Time-series for classification and explainability but also introduces novel Time-series feature extraction algorithms that are applied to biomedical data while introducing the concept of the Activation Engine, which is a post-processing block that tunes Neural Networks without the need of particular experience in Machine Leaning. Also, a tree-based method for multiclass classification has been introduced which outperforms the One-to-Many approach while being less complex that the One-to-One method.\par The results presented in this thesis exhibit high accuracy when compared with the literature, while remaining efficient in terms of power consumption and the time of inference. Additionally the concepts, methods or algorithms that were introduced are particularly novel technically, where they include: • Feature extraction of professionally annotated, and poorly annotated time-series. • The introduction of the Activation Engine post-processing block. • A model for global image explainability with inference on the edge. • A tree-based algorithm for multiclass classification

Smart Sensors for Healthcare and Medical Applications

This book focuses on new sensing technologies, measurement techniques, and their applications in medicine and healthcare. Specifically, the book briefly describes the potential of smart sensors in the aforementioned applications, collecting 24 articles selected and published in the Special Issue “Smart Sensors for Healthcare and Medical Applications”. We proposed this topic, being aware of the pivotal role that smart sensors can play in the improvement of healthcare services in both acute and chronic conditions as well as in prevention for a healthy life and active aging. The articles selected in this book cover a variety of topics related to the design, validation, and application of smart sensors to healthcare

Aplicaciones de sensores vestibles y teléfonos inteligentes en el bienestar personal: Cuantificación de la actividad física y control de la práctica de mindfulness

El teléfono móvil inteligente (Smartphone) se ha convertido en un dispositivo con una amplia aceptación entre la población y ha logrado cambiar nuestras vidas en muchos aspectos. Sus aplicaciones van más allá de la simple comunicación, llegando a acuñarse en los últimos años el término “mHealth”, como referencia al uso de dispositivos móviles (en particular teléfonos), en el ámbito de la salud.En el ámbito de la salud, los teléfonos móviles pueden servir como: Elementos de aprendizaje y formación, a través de la visualización de texto, vídeos, audios, etc. Elementos de monitorización, a través de los propios sensores del móvil (geolocalización, sensores inerciales), de sensores que se conectan al móvil o mediante encuestas automatizadas. De una forma u otra, el teléfono inteligente aporta varias características, entre otras, la posibilidad de recopilar una gran cantidad de datos, muchas veces de forma ubicua y transparente al usuario. La posibilidad de extraer información relevante de esos datos es un gran campo de investigación, con fundamento en aspectos como sensores vestibles, reconocimiento de patrones y aprendizaje automático, “big data”, entre otros.La capacidad de monitorización de los teléfonos inteligentes se complementa con los sensores vestibles (wearable) no integrados en el propio teléfono inteligente, que en diversos formatos permiten la medida de variables físicas y fisiológicas. Generalmente suelen ser complementos, componentes que se sujetan a la ropa, sensores integrados directamente en los tejidos u otros. En muchas ocasiones se conectan a una aplicación móvil para tratar y visualizar los resultados.En esta tesis se realizan varias aportaciones en el campo de la salud móvil y sensores vestibles, dentro de las actividades realizadas en el grupo EduQTech (grupo de referencia reconocido por la DGA ref. T49_17R) (EduQTech, 2018). En concreto se plantea avanzar en dos aplicaciones para bienestar: la cuantificación de la actividad física y el control de la práctica de mindfulness.Cuantificación de la actividad física: Para cuantificar la actividad física se ha utilizado el acelerómetro de un smartphone de gama media-baja (acelerómetros con un rango normal de ± 2g), el cual registra los movimientos realizados por el usuario. Posteriormente se ha hecho un análisis de los datos (creación de algoritmos) y los resultados se han comparado con los resultados aportados por un acelerómetro comercial dedicado para medir la actividad física (GT3X+, acelerómetro con un rango normal de ± 6g). Las recomendaciones de actividad física se establecen en función de la salida del acelerómetro en unidades llamadas “counts”. Nuestros resultados demuestran que es factible el uso de los acelerómetros incorporados en los smartphones comerciales. Uno de los algoritmos obtuvo una correlación Kappa ponderada de 0.874 (p-valor Control de la práctica de mindfulness: Mindfulness es una técnica de intervención basada en la meditación budista y que ha demostrado ser eficiente tanto en el mantenimiento del bienestar físico y mental personal, como en el apoyo a pacientes para el tratamiento de distintas enfermedades. Su monitorización puede ayudar a los profesionales a evaluar la eficacia de la práctica y, en consecuencia, aumentar los beneficios esperados de la misma, especialmente en el ámbito de la salud. En esta tesis se han desarrollado dos prototipos: El primer kit fue desarrollado para medir la estabilidad de los meditadores durante sus sesiones de mindfulness. En dicho estudio participaron 31 sujetos, de los cuales 27 no tenían experiencia meditando. Los resultados mostraron que no hubo diferencias significativas con respecto a qué ubicación era la mejor para medir la estabilidad salvo la región lumbar, que es menos sensible. Sin embargo, sí que se pudo ver que la cabeza y el dedo pulgar fueron los más sensibles a los movimientos de los practicantes. Además, se comprobó que el zafú (cojín de meditación) presenta una ligera ventaja sobre otros asientos. La medición del ritmo cardíaco y su variabilidad son también de gran importancia. La variabilidad del ritmo cardíaco es un indicador general de salud y varios estudios han mostrado que puede haber cambios durante la meditación. El kit propuesto para medir la variabilidad se basó en un sensor Amped usando el método de fotopletismiografía. En este estudio se contó con la participación de 10 meditadores expertos y 20 noveles, en el cual el objetivo era ver si había diferencias significativas entre los dos grupos. Los resultados mostraron que en los parámetros de la variabilidad de la frecuencia cardiaca SDNN, NN50, RMSSD, VLF y HF hay diferencias significativas con un p-valor <br /