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

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies

Accurate telemonitoring of Parkinson's disease symptom severity using nonlinear speech signal processing and statistical machine learning

This study focuses on the development of an objective, automated method to extract clinically useful information from sustained vowel phonations in the context of Parkinson’s disease (PD). The aim is twofold: (a) differentiate PD subjects from healthy controls, and (b) replicate the Unified Parkinson’s Disease Rating Scale (UPDRS) metric which provides a clinical impression of PD symptom severity. This metric spans the range 0 to 176, where 0 denotes a healthy person and 176 total disability. Currently, UPDRS assessment requires the physical presence of the subject in the clinic, is subjective relying on the clinical rater’s expertise, and logistically costly for national health systems. Hence, the practical frequency of symptom tracking is typically confined to once every several months, hindering recruitment for large-scale clinical trials and under-representing the true time scale of PD fluctuations. We develop a comprehensive framework to analyze speech signals by: (1) extracting novel, distinctive signal features, (2) using robust feature selection techniques to obtain a parsimonious subset of those features, and (3a) differentiating PD subjects from healthy controls, or (3b) determining UPDRS using powerful statistical machine learning tools. Towards this aim, we also investigate 10 existing fundamental frequency (F_0) estimation algorithms to determine the most useful algorithm for this application, and propose a novel ensemble F_0 estimation algorithm which leads to a 10% improvement in accuracy over the best individual approach. Moreover, we propose novel feature selection schemes which are shown to be very competitive against widely-used schemes which are more complex. We demonstrate that we can successfully differentiate PD subjects from healthy controls with 98.5% overall accuracy, and also provide rapid, objective, and remote replication of UPDRS assessment with clinically useful accuracy (approximately 2 UPDRS points from the clinicians’ estimates), using only simple, self-administered, and non-invasive speech tests. The findings of this study strongly support the use of speech signal analysis as an objective basis for practical clinical decision support tools in the context of PD assessment.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Developing Machine Learning Algorithms for Behavior Recognition from Deep Brain Signals

Parkinson’s disease (PD) is a neurodegenerative condition and movement disorder that appears with symptoms such as tremor, rigidity of muscles and slowness of movements. Deep brain stimulation (DBS) is an FDA-approved surgical therapy for essential tremor and PD. Despite the fact that DBS substantially alleviates the motor signs of PD, it can cause cognitive side effects and speech malfunction mainly due to the lack of adaptivity and optimality of the stimulation signal to the patients’ current state. A behavior-adapted closed-loop DBS system may reduce the side effects and power consumption by adjusting the stimulation parameters to patients’ need. Behavior recognition based on physiological feedbacks plays a key role in designing the next generation of closed-loop DBS systems. Hence, this dissertation is concentrated on: 1. Investigating the capability of local field potential (LFP) signals recorded from Subthalamic nucleus (STN) in identifying behavioral activities 2. Developing advanced machine learning algorithms to recognize behavioral activities using LFP signals 3. Investigating the effects of medication and stimulation pulse on the behavior recognition task as well as characteristics of the LFP signal. STN-LFP is a great physiological signal candidate since the stimulation device itself can record it, eliminating the need for additional sensors. Continuous wavelet transform is utilized for time-frequency analysis of STN-LFPs. Experimental results demonstrate that different behaviors create different modulation patterns in STN within the beta frequency range. A hierarchical classification structure is proposed to perform the behavior classification through a multi-level framework. The beta frequency components of STN-LFPs recorded from all contacts of DBS leads are combined through an MKL-based SVM classifier for behavior classification. Alternatively, the inter-hemispheric synchronization of the LFP signals measured by an FFT-based synchronization approach is utilized to pair up the LFP signals from left and right STNs. Using these rearranged LFP signals reduces the computational cost significantly while keeping the classification ability almost unchanged. LFP-Net, a customized deep convolutional neural network (CNN) approach for behavior classification, is also proposed. CNNs learn different feature maps based on the beta power patterns associated with different behaviors. The features extracted by CNNs are passed through fully connected layers, and, then to the softmax layer for classification. The effect of medication and stimulation “off/on” conditions on characteristics of LFP signals and the behavior classification performance is studied. The beta power of LFP signals under different stimulation and medication paradigms is investigated. Experimental results confirm that the beta power is suppressed significantly when the patients take medication or therapeutic stimulation. The results also show that the behavior classification performance is not impacted by different medication or stimulation conditions. Identifying human behavioral activities from physiological signals is a stepping-stone toward adaptive closed-loop DBS systems. To design such systems, however, there are other open questions that need to be addressed, which are beyond the scope of this dissertation, such as developing event-related biomarkers, customizing the parameter of DBS system based on the patients’ current state, investigating the power consumption and computational complexity of the behavior recognition algorithms

Advanced Sensing and Image Processing Techniques for Healthcare Applications

This Special Issue aims to attract the latest research and findings in the design, development and experimentation of healthcare-related technologies. This includes, but is not limited to, using novel sensing, imaging, data processing, machine learning, and artificially intelligent devices and algorithms to assist/monitor the elderly, patients, and the disabled population

Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy. This edition celebrates twenty years of uninterrupted and succesfully research in the field of voice analysis

Computational Intelligence in Electromyography Analysis

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research

Biomedical Sensing and Imaging

This book mainly deals with recent advances in biomedical sensing and imaging. More recently, wearable/smart biosensors and devices, which facilitate diagnostics in a non-clinical setting, have become a hot topic. Combined with machine learning and artificial intelligence, they could revolutionize the biomedical diagnostic field. The aim of this book is to provide a research forum in biomedical sensing and imaging and extend the scientific frontier of this very important and significant biomedical endeavor