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Hypertension. --- Heart --- Blood pressure --- Systolic blood pressure. --- Systolic pressure --- Regulation of blood pressure --- Biological control systems --- Baroreceptors --- Renin-angiotensin system --- Contraction of the heart --- Heart contraction --- Myocardial contraction --- Myocardium --- Systole (Cardiology) --- Systolic time interval --- Heart beat --- Blood pressure, High --- High blood pressure --- Vascular hypertension --- Blood circulation disorders --- Contraction. --- Regulation. --- Contraction

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Cardiac Mechano-Electric Coupling and Arrhythmias offers a thoroughly reviewed compendium written by leading experts in the field on the mechanism and consequences of cardiac mechano-electrical coupling. Its coverage ranges from stretch-activated ion channels to mechanically induced arrhythmias and mechanical interventions for heart rhythm correction. Information is grouped into logical sections, from molecular mechanisms, to cell, tissue and whole organ responses, rightthrough to patient-based observations and insight emerging from clinical trials. The information provided carefully highlight

Heart --- Arrhythmia. --- Heart conduction system. --- Biological control systems. --- Contraction of the heart --- Heart contraction --- Myocardial contraction --- Myocardium --- Systole (Cardiology) --- Systolic time interval --- Heart beat --- Biocybernetics --- Biofeedback --- Biological cybernetics --- Biological regulation --- Control biophysics --- Control systems, Biological --- Control theory in biology --- Regulation, Biological --- Biological systems --- Control theory --- Cybernetics --- Feedback control systems --- Nervous system --- Cardiac conduction system --- Conducting system of the heart --- Conduction system of the heart --- Conductive system of the heart --- Connecting system of the heart --- Allorhythmia --- Arhythmia --- Arrhythmias --- Cardiac arrhythmia --- Dysrhythmia --- Irregular heart beats --- Electric countershock --- Palpitation --- Contraction. --- Contraction --- Conduction system --- Diseases

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This book presents the latest advances in applications of continuous, objective, and automated sensing technologies and computer tools for sustainable and efficient poultry production, and it offers solutions to the poultry industry to address challenges in terms of poultry management, the environment, nutrition, automation and robotics, health, welfare assessment, behavior monitoring, waste management, etc. The reader will find original research papers that address, on a global scale, the sustainability and efficiency of the poultry industry and explore the above-mentioned areas through applications of PPF solutions in poultry meat and egg production

broiler --- activity index --- time interval --- age --- image processing --- poultry --- cage-free --- preening behavior --- mask R-CNN --- residual network --- broiler chicken --- machine vision --- image restoring --- precision poultry farming --- feeding system --- pecking force --- precision livestock farming --- poultry farming --- information management --- cloud database --- disease detection --- acoustic --- audio --- frequency --- behavior --- image analysis --- animal welfare --- movement analysis --- LED --- comfort index --- manure area --- manure coverage proportion --- environment control --- ammonia emission --- layer house --- laying hen --- daily behavior --- machine learning --- inertia sensor --- walking ability --- animal behavior --- precision livestock --- PLF --- precise feeding --- ideal protein --- animal health --- immune system --- productive parameters --- management

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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.

Technology: general issues --- History of engineering & technology --- electrocardiogram --- deep metric learning --- k-nearest neighbors classifier --- premature ventricular contraction --- dimensionality reduction --- classifications --- Laplacian eigenmaps --- locality preserving projections --- compressed sensing --- convolutional neural network --- EEG --- epileptic seizure detection --- RISC-V --- ultra-low-power --- sepsis --- atrial fibrillation --- prediction --- heart rate variability --- feature extraction --- random forest --- annotations --- myoelectric prosthesis --- sEMG --- grasp phases analysis --- grasp classification --- machine learning --- electronic nose --- liver dysfunction --- cirrhosis --- semiconductor metal oxide gas sensor --- vagus nerve --- intraneural --- decoding --- intrafascicular --- recording --- carbon nanotube --- artificial intelligence --- lens-free shadow imaging technique --- cell-line analysis --- cell signal enhancement --- deep learning --- ECG signal --- reconstruction dictionaries --- projection matrices --- signal classifications --- osteopenia --- sarcopenia --- XAI --- SHAP --- IMU --- gait analysis --- sensors --- convolutional neural networks --- Parkinson's disease --- biomedical monitoring --- accelerometer --- pressure sensor --- disease management --- electromyography --- correlation --- high blood pressure --- hypertension --- photoplethysmography --- electrocardiography --- calibration --- classification models --- COVID-19 --- ECG trace image --- transfer learning --- Convolutional Neural Networks (CNN) --- feature selection --- sympathetic activity (SNA) --- skin sympathetic nerve activity (SKNA) --- electrodes --- electrocardiogram (ECG) --- cardiac time interval --- dynamic time warping --- fiducial point detection --- heart failure --- seismocardiography --- wearable electroencephalography --- motor imagery --- motor execution --- beta rebound --- brain-machine interface --- EEG classification --- electrocardiogram --- deep metric learning --- k-nearest neighbors classifier --- premature ventricular contraction --- dimensionality reduction --- classifications --- Laplacian eigenmaps --- locality preserving projections --- compressed sensing --- convolutional neural network --- EEG --- epileptic seizure detection --- RISC-V --- ultra-low-power --- sepsis --- atrial fibrillation --- prediction --- heart rate variability --- feature extraction --- random forest --- annotations --- myoelectric prosthesis --- sEMG --- grasp phases analysis --- grasp classification --- machine learning --- electronic nose --- liver dysfunction --- cirrhosis --- semiconductor metal oxide gas sensor --- vagus nerve --- intraneural --- decoding --- intrafascicular --- recording --- carbon nanotube --- artificial intelligence --- lens-free shadow imaging technique --- cell-line analysis --- cell signal enhancement --- deep learning --- ECG signal --- reconstruction dictionaries --- projection matrices --- signal classifications --- osteopenia --- sarcopenia --- XAI --- SHAP --- IMU --- gait analysis --- sensors --- convolutional neural networks --- Parkinson's disease --- biomedical monitoring --- accelerometer --- pressure sensor --- disease management --- electromyography --- correlation --- high blood pressure --- hypertension --- photoplethysmography --- electrocardiography --- calibration --- classification models --- COVID-19 --- ECG trace image --- transfer learning --- Convolutional Neural Networks (CNN) --- feature selection --- sympathetic activity (SNA) --- skin sympathetic nerve activity (SKNA) --- electrodes --- electrocardiogram (ECG) --- cardiac time interval --- dynamic time warping --- fiducial point detection --- heart failure --- seismocardiography --- wearable electroencephalography --- motor imagery --- motor execution --- beta rebound --- brain-machine interface --- EEG classification

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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.

Technology: general issues --- History of engineering & technology --- electrocardiogram --- deep metric learning --- k-nearest neighbors classifier --- premature ventricular contraction --- dimensionality reduction --- classifications --- Laplacian eigenmaps --- locality preserving projections --- compressed sensing --- convolutional neural network --- EEG --- epileptic seizure detection --- RISC-V --- ultra-low-power --- sepsis --- atrial fibrillation --- prediction --- heart rate variability --- feature extraction --- random forest --- annotations --- myoelectric prosthesis --- sEMG --- grasp phases analysis --- grasp classification --- machine learning --- electronic nose --- liver dysfunction --- cirrhosis --- semiconductor metal oxide gas sensor --- vagus nerve --- intraneural --- decoding --- intrafascicular --- recording --- carbon nanotube --- artificial intelligence --- lens-free shadow imaging technique --- cell-line analysis --- cell signal enhancement --- deep learning --- ECG signal --- reconstruction dictionaries --- projection matrices --- signal classifications --- osteopenia --- sarcopenia --- XAI --- SHAP --- IMU --- gait analysis --- sensors --- convolutional neural networks --- Parkinson’s disease --- biomedical monitoring --- accelerometer --- pressure sensor --- disease management --- electromyography --- correlation --- high blood pressure --- hypertension --- photoplethysmography --- electrocardiography --- calibration --- classification models --- COVID-19 --- ECG trace image --- transfer learning --- Convolutional Neural Networks (CNN) --- feature selection --- sympathetic activity (SNA) --- skin sympathetic nerve activity (SKNA) --- electrodes --- electrocardiogram (ECG) --- cardiac time interval --- dynamic time warping --- fiducial point detection --- heart failure --- seismocardiography --- wearable electroencephalography --- motor imagery --- motor execution --- beta rebound --- brain–machine interface --- EEG classification --- n/a --- Parkinson's disease --- brain-machine interface