Intelligent Biosignal Analysis Methods

This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others

Sitting behaviour-based pattern recognition for predicting driver fatigue

The proposed approach based on physiological characteristics of sitting behaviours and sophisticated machine learning techniques would enable an effective and practical solution to driver fatigue prognosis since it is insensitive to the illumination of driving environment, non-obtrusive to driver, without violating driver’s privacy, more acceptable by drivers

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 204

This bibliography lists 140 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1980

Brain Tumour Classification Using Deep Features from Structural MRI

C

Doctor of Philosophy

dissertationHeart failure (HF) is a significant health care problem in the United States. Many patients advance towards end stage HF despite optimal medical therapy. For patients with end stage HF, unfortunately, therapeutic options are limited. While heart transplantation is the most proven treatment for improving survival, it is only performed in approximately 2,500 cases annually due to a shortage of donor hearts. Left ventricular assist device (LVAD) implantation is an FDA-approved therapy and is clinically indicated for two applications: (i) bridge-to-transplantation (BTT) for patients who are awaiting heart transplantation and (ii) destination therapy (DT) for patients who are ineligible for heart transplantation. Unexpectedly, patients in BTT and DT experience cardiac functional recovery after LVAD-induced unloading, which led to an investigational concept called bridge-to-recovery (BTR). For successful clinical translation, it is important to identify reliable predictors and discriminate responders from non-responders. Myocardial fibrosis, as a marker of adverse structural remodeling, is a proven predictor of poor outcomes. Cardiac magnetic resonance (CMR) is a proven and safe imaging modality for non-invasive assessment of myocardial fibrosis. Particularly, cardiac T1 mapping has been widely used for assessment of diffuse myocardial fibrosis. However, current cardiac T1 mapping techniques are unlikely to produce accurate results in LVAD candidates due to three obstacles: arrhythmia, limited breath-hold capacity, and implantable defibrillators. In response, this dissertation describes the development of new cardiac T1 mapping methods that overcome these obstacles. To overcome arrhythmia and limited breath-hold capacity, we developed a new arrhythmia-insensitive-rapid (AIR) cardiac T1 mapping pulse sequence using a robust saturation radio-frequency (RF) pulse that is inherently insensitive to arrhythmia. We also made the AIR pulse sequence rapid by acquiring only one proton-density and one T1-weighted image within a short breath-hold duration of only 2-3 heartbeats. To overcome the challenge of suppressing image artifacts induced by implantable defibrillators, we developed a new wideband AIR cardiac T1 mapping pulse sequence by incorporating a new saturation RF pulse that extends the frequency bandwidth to off-resonant spins induced by defibrillators. The AIR and wideband AIR pulse sequences are validated extensively through in vitro and in vivo experiments

Chromatic driver fatigue monitoring system

Sleep related vehicle accidents have been under publicised but remains as one of the main causes of road traffic accidents, as much as drink driving. This research aims to reduce this worldwide problem by developing a system to monitor fatigue driving. The thesis describes the research into the application of chromatic data processing techniques to detect early physiological and physical indicators of fatigue. Physiological factors that influence drivers are based on the duration of the drive, how much rest they have throughout the journey and the quality of sleep they had prior to the drive. The physiological indicator algorithm of the system is developed to take account of these factors and calculates the tiredness level. The chromatic technique is then used to analyse the results to establish trends and signatures of early fatigue situations where a warning system can be introduced. The chromatic signatures of fatigue have been established using results from 20 road tests conducted by professional drivers. Physical indicators such as early drowsy driving are detected by monitoring the behaviour of the vehicle. Micro sleep (e.g. head nodding, slow eye-blinking) can lead to lane drifting and vehicle swerving. These events are being regarded as early physical signs of sleepy driving. The main sensor for detecting the lateral yaw motion of the vehicle is a miniaturised gyroscope. Chromatic analysis is applied to the gyroscope output to identify and differentiate fatigue related events (e.g. swerves and lane drifting) from normal driving (e.g. left and right turning, roundabouts and bumpy roads) Combining the extracted information of the physiological and physical indicators, a Chromatic Fatigue Driving System can be developed as a tail safe system which monitors and alerts driver during critical fatigue conditions

NASA Tech Briefs Index, 1978

Approximately 601 announcements of new technology derived from the research and development activities of the National Aeronautics and Space Administration are presented. Emphasis is placed on information considered likely to be transferrable across industrial, regional, or disciplinary lines. Subject matter covered includes: electronic components and circuits; electron systems; physical sciences; materials; life sciences; mechanics; machinery; fabrication technology; and mathematics and information sciences