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

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

Multivariate classification of gene expression microarray data

L'expressiódels gens obtinguts de l'anàliside microarrays s'utilitza en molts casos, per classificar les cèllules. En aquestatesi, unaversióprobabilística del mètodeDiscriminant Partial Least Squares (p-DPLS)s'utilitza per classificar les mostres de les expressions delsseus gens. p-DPLS esbasa en la regla de Bayes de la probabilitat a posteriori. Aquestsclassificadorssónforaçats a classficarsempre.Per superaraquestalimitaciós'haimplementatl'opció de rebuig.Aquestaopciópermetrebutjarlesmostresamb alt riscd'errors de classificació (és a dir, mostresambigüesi outliers).Aquestaopció de rebuigcombinacriterisbasats en els residuals x, el leverage ielsvalorspredits. A més,esdesenvolupa un mètode de selecció de variables per triarels gens mésrellevants, jaque la majoriadels gens analitzatsamb un microarraysónirrellevants per al propòsit particular de classificacióI podenconfondre el classificador. Finalment, el DPLSs'estenen a la classificació multi-classemitjançant la combinació de PLS ambl'anàlisidiscriminant lineal

Computer Aided Dysplasia Grading for Barrett’s Oesophagus Virtual Slides

Dysplasia grading in Barrett’s Oesophagus has been an issue among pathologist worldwide. Despite of the increasing number of sufferers every year especially for westerners, dysplasia in Barrett’s Oesophagus can only be graded by a trained pathologist with visual examination. Therefore, we present our work on extracting textural and spatial features from the tissue regions. Our first approach is to extract only the epithelial layer of the tissue, based on the grading rules by pathologists. This is carried out by extracting sub images of a certain window size along the tissue epithelial layer. The textural features of these sub images were used to grade regions into dysplasia or not-dysplasia and we have achieved 82.5% AP with 0.82 precision and 0.86 recall value. Therefore, we have managed to overcame the ‘boundary-effect’ issues that have usually been avoided by selecting or cropping tissue image without the boundary. Secondly, the textural and spatial features of the whole tissue in the region were investigated. Experiments were carried out using Grey Level Co-occurrence Matrices at the pixel-level with a brute-force approach experiment, to cluster patches based on its texture similarities.Then, we have developed a texture-mapping technique that translates the spatial arrangement of tissue texture within a tissue region on the patch-level. As a result, three binary decision tree models were developed from the texture-mapping image, to grade each annotated regions into dysplasia Grade 1, Grade 3 and Grade 5 with 87.5%, 75.0% and 81.3% accuracy percentage with kappa score 0.75, 0.5 and 0.63 respectively. A binary decision tree was then used on the spatial arrangement of the tissue texture types with respect to the epithelial layer to help grade the regions. 75.0%, 68.8% and 68.8% accuracy percentage with kappa value of 0.5, 0.37 and 0.37 were achieved respectively for dysplasia Grade 1, Grade 3 and Grade 5. Based on the result achieved, we can conclude that the spatial information of tissue texture types with regards to the epithelial layer, is not as strong as is on the whole region. The binary decision tree grading models were applied on the broader tissue area; the whole virtual pathology slides itself. The consensus grading for each tissue is calculated with positivity table and scoring method. Finally, we present our own thresholded frequency method to grade virtual slides based on frequency of grading occurrence; and the result were compared to the pathologist’s grading. High agreement score with 0.80 KV was achieved and this is a massive improvement compared to a simple frequency scoring, which is only 0.47 KV

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

Platelet Diagnostics:A novel liquid biomarker

The aim of this thesis is to find a novel liquid biomarker for the detection of cancer and to optimize treatment. The first chapter gives an introduction to the oncology biomarker field and focuses on platelets and their role in cancer. In part 1, we evaluate extracellular vesicles (EVs). EVs are small vesicles released by all types of cells, including tumor cells, into the circulation. They carry protein kinases and can be isolated from plasma. We demonstrate that AKT and ERK kinase protein levels in EVs reflect the cellular expression levels and treatment with kinase inhibitors alters their concentration, depending on the clinical response to the drug. Therefore, EVs may provide a promising biomarker biosource for monitoring of treatment responses. Part 2 starts with reviews describing the function and role of platelets in greater depth. Chapter 3 focusses on thrombocytogenesis and several biological processes in which platelets play a role. Furthermore, the RNA processing machineries harboured by platelets are discussed. Both chapter 3 and 4 evaluate the change platelets undergo after being exposed to tumor and its environment. The exchange of biomolecules with tumor cells results in educated platelets, so-called tumor educated platelets (TEPs). TEPs play a role in several hallmarks of cancer and have the ability to respond to systemic alterations making them an interesting biomarker. In chapter 5 the diagnostic potential of platelets is first discussed. We determine their potential by sequencing the RNA of 283 platelet samples, of which 228 are patients with cancer, and 55 are healthy controls. We reach an accuracy of 96%. Furthermore, we are able to pinpoint the location of the primary tumor with an accuracy of 71%. In part 3, our developed thromboSeq platform is taken to the next level. Several potential confounding factors are taken into account such as age and comorbidity. We show that particle-swarm optimization (PSO)-enhanced algorithms enable efficient selection of RNA biomarker panels. In a validation cohort we apply these algorithms to non-small-cell lung cancer and reach an accuracy of 88% in late stage (n=518) and early-stage 81% accuracy. Finally, in chapter 7 we describe our wet- and dry-lab protocols in detail. This includes platelet RNA isolation, mRNA amplification, and preparation for next-generation sequencing. The dry-lab protocol describes the automated FASTQ file pre-processing to quantified gene counts, quality controls, data normalization and correction, and swarm intelligence-enhanced support vector machine (SVM) algorithm development. Part 4 focuses on central nervous system (CNS) malignancies especially on glioblastoma. Chapter 8 gives an overview of the different liquid biomarkers for diffuse glioma, the most common primary CNS malignancy. In chapter 9 we assess the specificity of the platelet education due to glioblastoma by comparing the RNA profile of TEPs from glioblastoma patients with a neuroinflammatory disease and brain metastasis patients. This results in a detection accuracy of 80%. Secondly, analysis of patients with glioblastoma versus healthy controls in an independent validation series provide a detection accuracy of 95%. Furthermore, we describe the potential value of platelets as a monitoring biomarker for patients with glioma, distinguishing pseudoprogression from real tumor progression. In part 5 thromboSeq is applied to breast cancer diagnostics both as a screening tool in the general population and in a high risk population, BRCA mutated women. In chapter 11 we first apply our technique to an inflammatory condition, multiple sclerosis (MS). Platelet RNA is used as input for the development of a diagnostic MS classifier capable of detecting MS with 80% accuracy in the independent validation series. In the final part we conclude this thesis with a general discussion of the main findings and suggestions for future research