6 research outputs found
Recent Trends in Computational Intelligence
Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications
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
Epilepsy
With the vision of including authors from different parts of the world, different educational backgrounds, and offering open-access to their published work, InTech proudly presents the latest edited book in epilepsy research, Epilepsy: Histological, electroencephalographic, and psychological aspects. Here are twelve interesting and inspiring chapters dealing with basic molecular and cellular mechanisms underlying epileptic seizures, electroencephalographic findings, and neuropsychological, psychological, and psychiatric aspects of epileptic seizures, but non-epileptic as well
New approaches for EEG signal processing: artifact EOG removal by ICA-RLS scheme and tracks extraction method
Localizing the bioelectric phenomena originating from the cerebral cortex
and evoked by auditory and somatosensory stimuli are clear objectives to
both understand how the brain works and to recognize different pathologies.
Diseases such as Parkinsonâs, Alzheimerâs, schizophrenia and epilepsy are intensively
studied to find a cure or accurate diagnosis.
Epilepsy is considered the disease with major prevalence within disorders
with neurological origin. The recurrent and sudden incidence of seizures can
lead to dangerous and possibly life-threatening situations. Since disturbance
of consciousness and sudden loss of motor control often occur without any
warning, the ability to predict epileptic seizures would reduce patientsâ anxiety,
thus considerably improving quality of life and safety.
The common procedure for epilepsy seizure detection is based on brain
activity monitorization via electroencephalogram (EEG) data. This process
consumes a lot of time, especially in the case of long recordings, but the major
problem is the subjective nature of the analysis among specialists when
analyzing the same record. From this perspective, the identification of hidden
dynamical patterns is necessary because they could provide insight into
the underlying physiological mechanisms that occur in the brain.
Time-frequency distributions (TFDs) and adaptive methods have demonstrated
to be good alternatives in designing systems for detecting neurodegenerative
diseases. TFDs are appropriate transformations because they offer
the possibility of analyzing relatively long continuous segments of EEG data
even when the dynamics of the signal are rapidly changing. On the other
hand, most of the detection methods proposed in the literature assume a
clean EEG signal free of artifacts or noise, leaving the preprocessing problem
opened to any denoising algorithm.
In this thesis we have developed two proposals for EEG signal processing:
the first approach consists in electrooculogram (EOG) removal method based
on a combination of ICA and RLS algorithms which automatically cancels
the artifacts produced by eyes movement without the use of external âad
hocâ electrode. This method, called ICA-RLS has been compared with other
techniques that are in the state of the art and has shown to be a good
alternative for artifacts rejection. The second approach is a novel method
in EEG features extraction called tracks extraction (LFE features). This
method is based on the TFDs and partial tracking. Our results in pattern
extractions related to epileptic seizures have shown that tracks extraction is
appropriate in EEG detection and classification tasks, being practical, easily applicable in medical environment and has acceptable computational cost