43 research outputs found
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 brainâmachine 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
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Automated Cardiac Rhythm Diagnosis for Electrophysiological Studies, an Enhanced Classifier Approach
INTRODUCTION
Heart function can be impaired by rhythm disturbances (cardiac arrhythmia), illustrated by electrocardiogram (ECG) recordings. Computerised arrhythmia diagnosis is well established for ECGâs but less for intracardiac electrophysiological (EP) testing. Accurate diagnosis is pre-requisite for delivering appropriate treatment to patients however existing algorithms misdiagnose a proportion of arrhythmias. Studies suggested artificial intelligence (AI) classifiers are accurate using ECG and intracardiac electrogram features and reviews suggested new features might augment diagnosis. This study aimed to develop an accurate cardiac rhythm diagnostic algorithm for electrophysiological (EP) studies with potential application as a generic rhythm classifier.
METHOD
An ethically approved prospective clinical study collected clinical history, right atrial and right ventricular intracardiac electrograms, beat-to-beat cardiac stroke volume, body motion and body temperature data during EP studies. An iterative system development life-cycle was used, including knowledge management and classifier development sub-processes. Domain expert knowledge and clinical arrhythmia diagnosis were modelled, synthesised as AI classifiers and used to classify cardiac rhythms.
RESULTS
Data collected from 65 patients was pre-processed into instances for classifier inputs. Decision tree, naĂŻve Bayes, neural network, support vector machine and inference engine classifiers developed using Matlab showed good performance and were combined as a production system in a mixture-of-experts multi-classifier system. 18 different rhythms were classified, with the naĂŻve Bayes classifier used to classify 11 rhythms, decision tree 4 rhythms, neural network and support vector machine one each, unclassified instances by the inference engine classifier and final class allocation using decision rule. Production system showed overall correct clasification rate 0.960; error 0.040; mean sensitivity 0.855; mean specificity 0.977; mean Îș 0.767; mean positive predictive value 0.792; mean negative predictive value 0.975; mean Pearsonâs phi 0.787, with P 0.9 for sinus node dysfunction and atrio-ventricular nodal/ junctional tachycardias. Temperature, accelerometry and QT interval were assessed as features by a comparison of algorithm performances with each feature removed and found not to affect classification performance. An evaluation showed 10 beat analysis performed better than 5 beat analysis.
CONCLUSIONS
Modelling of the clinical diagnosis process produced an AI based mixture-of-experts multi-classifier system, which accurately diagnosed different 18 cardiac rhythms. The naĂŻve Bayes classifier performed best and classified 11 rhythms. Features for clinical symptoms and predisposing factors, atrial electrogram morphology and changes in stroke volume were found to influence rhythm classification. High performances encourage further development and potential future improvements include: a larger sample dataset; inclusion of His and coronary sinus electrograms; data mining for unknown features with significant influence on diagnosis; binary classification. The aim to classify rhythm using artificial intelligence suitable for use during EP studies was satisfied and the research hypothesis that it outperformed current algorithms was accepted. The system was likely to be able to accept updates but needs conversion as a precursor to use in a live clinical environment
Multidimensional embedded MEMS motion detectors for wearable mechanocardiography and 4D medical imaging
Background: Cardiovascular diseases are the number one cause of death. Of these deaths, almost 80% are due to coronary artery disease (CAD) and cerebrovascular disease. Multidimensional microelectromechanical systems (MEMS) sensors allow measuring the mechanical movement of the heart muscle offering an entirely new and innovative solution to evaluate cardiac rhythm and function. Recent advances in miniaturized motion sensors present an exciting opportunity to study novel device-driven and functional motion detection systems in the areas of both cardiac monitoring and biomedical imaging, for example, in computed tomography (CT) and positron emission tomography (PET).
Methods: This Ph.D. work describes a new cardiac motion detection paradigm and measurement technology based on multimodal measuring tools â by tracking the heartâs kinetic activity using micro-sized MEMS sensors â and novel computational approaches â by deploying signal processing and machine learning techniquesâfor detecting cardiac pathological disorders. In particular, this study focuses on the capability of joint gyrocardiography (GCG) and seismocardiography (SCG) techniques that constitute the mechanocardiography (MCG) concept representing the mechanical characteristics of the cardiac precordial surface vibrations.
Results: Experimental analyses showed that integrating multisource sensory data resulted in precise estimation of heart rate with an accuracy of 99% (healthy, n=29), detection of heart arrhythmia (n=435) with an accuracy of 95-97%, ischemic disease indication with approximately 75% accuracy (n=22), as well as significantly improved quality of four-dimensional (4D) cardiac PET images by eliminating motion related inaccuracies using MEMS dual gating approach. Tissue Doppler imaging (TDI) analysis of GCG (healthy, n=9) showed promising results for measuring the cardiac timing intervals and myocardial deformation changes.
Conclusion: The findings of this study demonstrate clinical potential of MEMS motion sensors in cardiology that may facilitate in time diagnosis of cardiac abnormalities. Multidimensional MCG can effectively contribute to detecting atrial fibrillation (AFib), myocardial infarction (MI), and CAD. Additionally, MEMS motion sensing improves the reliability and quality of cardiac PET imaging.Moniulotteisten sulautettujen MEMS-liiketunnistimien kÀyttö sydÀnkardiografiassa sekÀ lÀÀketieteellisessÀ 4D-kuvantamisessa
Tausta: SydÀn- ja verisuonitaudit ovat yleisin kuolinsyy. NÀistÀ kuolemantapauksista lÀhes 80% johtuu sepelvaltimotaudista (CAD) ja aivoverenkierron hÀiriöistÀ. Moniulotteiset mikroelektromekaaniset jÀrjestelmÀt (MEMS) mahdollistavat sydÀnlihaksen mekaanisen liikkeen mittaamisen, mikÀ puolestaan tarjoaa tÀysin uudenlaisen ja innovatiivisen ratkaisun sydÀmen rytmin ja toiminnan arvioimiseksi. Viimeaikaiset teknologiset edistysaskeleet mahdollistavat uusien pienikokoisten liiketunnistusjÀrjestelmien kÀyttÀmisen sydÀmen toiminnan tutkimuksessa sekÀ lÀÀketieteellisen kuvantamisen, kuten esimerkiksi tietokonetomografian (CT) ja positroniemissiotomografian (PET), tarkkuuden parantamisessa.
MenetelmÀt: TÀmÀ vÀitöskirjatyö esittelee uuden sydÀmen kineettisen toiminnan mittaustekniikan, joka pohjautuu MEMS-anturien kÀyttöön. Uudet laskennalliset lÀhestymistavat, jotka perustuvat signaalinkÀsittelyyn ja koneoppimiseen, mahdollistavat sydÀmen patologisten hÀiriöiden havaitsemisen MEMS-antureista saatavista signaaleista. TÀssÀ tutkimuksessa keskitytÀÀn erityisesti mekanokardiografiaan (MCG), joihin kuuluvat gyrokardiografia (GCG) ja seismokardiografia (SCG). NÀiden tekniikoiden avulla voidaan mitata kardiorespiratorisen jÀrjestelmÀn mekaanisia ominaisuuksia.
Tulokset: Kokeelliset analyysit osoittivat, ettÀ integroimalla usean sensorin dataa voidaan mitata syketiheyttÀ 99% (terveillÀ n=29) tarkkuudella, havaita sydÀmen rytmihÀiriöt (n=435) 95-97%, tarkkuudella, sekÀ havaita iskeeminen sairaus noin 75% tarkkuudella (n=22). LisÀksi MEMS-kaksoistahdistuksen avulla voidaan parantaa sydÀmen 4D PET-kuvan laatua, kun liikeepÀtarkkuudet voidaan eliminoida paremmin. Doppler-kuvantamisessa (TDI, Tissue Doppler Imaging) GCG-analyysi (terveillÀ, n=9) osoitti lupaavia tuloksia sydÀnsykkeen ajoituksen ja intervallien sekÀ sydÀnlihasmuutosten mittaamisessa.
PÀÀtelmÀ: TÀmÀn tutkimuksen tulokset osoittavat, ettÀ kardiologisilla MEMS-liikeantureilla on kliinistÀ potentiaalia sydÀmen toiminnallisten poikkeavuuksien diagnostisoinnissa. Moniuloitteinen MCG voi edistÀÀ eteisvÀrinÀn (AFib), sydÀninfarktin (MI) ja CAD:n havaitsemista. LisÀksi MEMS-liiketunnistus parantaa sydÀmen PET-kuvantamisen luotettavuutta ja laatua
Diagnosing acute heart failure in the emergency department
Background: Acute, decompensated heart failure is a serious and common presentation in patients attending Emergency Departments. Diagnosis of this condition in this environment can be challenging.
Main Objective:
To assess the diagnostic utility of variables related to the diagnosis of decompensated heart failure in the Emergency Department setting.
To create a Clinical Decision Rule to facilitate the diagnosis of decompensated heart failure in the Emergency Department setting.
Methods:
A systematic review and meta-analysis of literature related to the diagnosis of acute heart failure in the Emergency Department was performed to assess and rank the diagnostic utility of all potential predictor variables. Bivariate meta-analysis was performed where appropriate to provide summary statistics for variable utility.
A diagnostic study performed in a single, urban Emergency Department to allow multivariable analysis of the data to derive a Clinical Decision Rule. Logistic regression, Random Forest analysis and CART analysis were used in the analysis.
Results: 44 papers were included in the systematic review providing data on 41 potential diagnostic variables. A history of heart failure, clinical opinion of heart failure, natriuretic peptide levels and the findings of cardiomegaly, pulmonary oedema or cephalisation of vessels on the chest x-ray had the greatest diagnostic utility.
105 patients were recruited in the diagnostic study and 62 potential variables were assessed against a reference standard of two cardiologists opinions. Cardiothoracic ratio, natriuretic peptide levels, the presence of pulmonary oedema, clinical impression of heart failure and urea levels were found to be the best performing variables.
Two or more of these variables, using selected cut-off values for continuous variables, had a specificity of nearly 100% for definite or possible heart failure. Different cut-off values could be used with these variables to provide high sensitivity for this condition.
Conclusion: The clinical and investigative findings with the greatest diagnostic utility for the acutely dyspnoeic patient with heart failure have been identified and can be used to rule in heart failure
Workshop on Countering Space Adaptation with Exercise: Current Issues
The proceedings represent an update to the problems associated with living and working in space and the possible impact exercise would have on helping reduce risk. The meeting provided a forum for discussions and debates on contemporary issues in exercise science and medicine as they relate to manned space flight with outside investigators. This meeting also afforded an opportunity to introduce the current status of the Exercise Countermeasures Project (ECP) science investigations and inflight hardware and software development. In addition, techniques for physiological monitoring and the development of various microgravity countermeasures were discussed
National Aeronautics and Space Administration (NASA)/American Society for Engineering Education (ASEE) Summer Faculty Fellowship Program, 1993, volume 2
The JSC NASA/ASEE Summer Faculty Fellowship Program was conducted by Texas A&M University and JSC. The objectives of the program, which began nationally in 1964 and at JSC in 1965, are (1) to further the professional knowledge of qualified engineering and science faculty members; (2) to stimulate an exchange of ideas between participants and NASA; (3) to enrich and refresh the research and teaching activities of participant's institutions; and (4) to contribute to the research objectives of the NASA centers. Each faculty fellow spent at least 10 weeks at JSC engaged in a research project in collaboration with a NASA/JSC colleague. A compilation of the final reports on the research projects completed by the faculty fellows during the summer of 1993 is presented
Characterization, Classification, and Genesis of Seismocardiographic Signals
Seismocardiographic (SCG) signals are the acoustic and vibration induced by cardiac activity measured non-invasively at the chest surface. These signals may offer a method for diagnosing and monitoring heart function. Successful classification of SCG signals in health and disease depends on accurate signal characterization and feature extraction. In this study, SCG signal features were extracted in the time, frequency, and time-frequency domains. Different methods for estimating time-frequency features of SCG were investigated. Results suggested that the polynomial chirplet transform outperformed wavelet and short time Fourier transforms. Many factors may contribute to increasing intrasubject SCG variability including subject posture and respiratory phase. In this study, the effect of respiration on SCG signal variability was investigated. Results suggested that SCG waveforms can vary with lung volume, respiratory flow direction, or a combination of these criteria. SCG events were classified into groups belonging to these different respiration phases using classifiers, including artificial neural networks, support vector machines, and random forest. Categorizing SCG events into different groups containing similar events allows more accurate estimation of SCG features. SCG feature points were also identified from simultaneous measurements of SCG and other well-known physiologic signals including electrocardiography, phonocardiography, and echocardiography. Future work may use this information to get more insights into the genesis of SCG
Aerospace Medicine and Biology: A cumulative index to the 1974 issues of a continuing bibliography
This publication is a cumulative index to the abstracts contained in supplements 125 through 136 of Aerospace Medicine and Biology: A Continuing Bibliography. It includes three indexes--subject, personal author, and corporate source