Neurological Tremor: Sensors, Signal Processing and Emerging Applications

Neurological tremor is the most common movement disorder, affecting more than 4% of elderly people. Tremor is a non linear and non stationary phenomenon, which is increasingly recognized. The issue of selection of sensors is central in the characterization of tremor. This paper reviews the state-of-the-art instrumentation and methods of signal processing for tremor occurring in humans. We describe the advantages and disadvantages of the most commonly used sensors, as well as the emerging wearable sensors being developed to assess tremor instantaneously. We discuss the current limitations and the future applications such as the integration of tremor sensors in BCIs (brain-computer interfaces) and the need for sensor fusion approaches for wearable solutions

Development of EEG-based technologies for the characterization and treatment of neurological diseases affecting the motor function

This thesis presents a set of studies applying signal processing and data mining techniques in real-time working systems to register, characterize and condition the movement-related cortical activity of healthy subjects and of patients with neurological disorders affecting the motor function. Patients with two of the most widespread neurological affections impairing the motor function are considered here: patients with essential tremor and patients who have suffered a cerebro-vascular accident. The different chapters in the presented thesis show results regarding the normal cortical activity associated with the planning and execution of motor actions with the upper-limb, and the pathological activity related to the patients' motor dysfunction (measurable with muscle electrodes or movement sensors). The initial chapters of the book present i) a revision of the basic concepts regarding the role of the cerebral cortex in the motor control and the way in which the electroencephalographic activity allows its analysis and conditioning, ii) a study on the cortico-muscular interaction at the tremor frequency in patients with essential tremor under the effects of a drug reducing their tremor, and finally iii) a study based on evolutionary algorithms that aims to identify cortical patterns related to the planning of a number of motor tasks performed with a single arm. In the second half of the thesis book, two brain-computer interface systems to be used in rehabilitation scenarios with essential tremor patients and with patients with a stroke are proposed. In the first system, the electroencephalographic activity is used to anticipate voluntary movement actions, and this information is integrated in a multimodal platform estimating and suppressing the pathological tremors. In the second case, a conditioning paradigm for stroke patients based on the identification of the motor intention with temporal precision is presented and tested with a cohort of four patients along a month during which the patients undergo eight intervention sessions. The presented thesis has yielded advances from both the technological and the scientific points of view in all studies proposed. The main contributions from the technological point of view are: ¿ The design of an integrated upper-limb platform working in real-time. The platform was designed to acquire information from different types of noninvasive sensors (EEG, EMG and gyroscopic sensors) characterizing the planning and execution of voluntary movements. The platform was also capable of processing online the acquired data and generating an electrical feedback. ¿ The development of signal processing and classifying techniques adapted to the kind of signal recorded in the two kinds of patients considered in this thesis (patients with essential tremor and patients with a stroke) and to the requirements of online processing and real-time single-trial function desired for BCI applications. Especially in this regard, an original methodology to detect onsets of voluntary movements using slow cortical potentials and cortical rhythms has been presented. ¿ The design and validation in real-time of asynchronous BCI systems using motor planning EEG segments to anticipate or detect when patients begin a voluntary movement with the upper-limb. ¿ The proof of concept of the advantages of an EEG system integrated in a multimodal human-robot interface architecture that constitutes the first multimodal interface using the combined acquisition of EEG, EMG and gyroscopic data, which allows the concurrent characterization of different parts of the body associated with the execution of a movement. The main scientific contributions of this thesis are: ¿ The study of the EEG-based anticipation of voluntary movements presented in Chapter 5 of the thesis was the first demonstration (to the author's knowledge) of the capacity of the EEG signal to provide reliable movement predictions based on single-trial classification of online data of healthy subjects and ET patients. This study also provides, for the first time, the results of a BCI system tested in ET patients and it represents an original approach to BCI applications for this group of patients. ¿ It has been presented the first neurophysiological study using EEG and EMG data to analyze the effects of a drug on cortical activity and tremors of patients with ET. In addition, the obtained results have shown for the first time that a significant correlation exists between the dynamics of specific cortical oscillations and pathological tremor manifestation as a consequence of the drug effects. ¿ It has been proposed for the first time an experiment to inspect whether the EEG signal carries enough information to classify up to seven different tasks performed with a single limb. Both the methodology applied and the validation procedure are also innovative in this sort of studies. ¿ It has been demonstrated for the first time the relevance of combining different cortical sources of information (such as BP and ERD) to estimate the initiation of voluntary movements with the upper-limb. In this line, special relevance may be given to the positive results achieved with stroke patients, improving the results presented by similar previous EEG-based studies by other research groups. It has also been proposed for the first time an upper-limb intervention protocol for stroke patients using BP and ERD patterns to provide proprioceptive feedback tightly associated with the patients' expectations of movement. The effects of the proposed intervention have been studied with a small group of patients

Review of medical data analysis based on spiking neural networks

Medical data mainly includes various types of biomedical signals and medical images, which can be used by professional doctors to make judgments on patients' health conditions. However, the interpretation of medical data requires a lot of human cost and there may be misjudgments, so many scholars use neural networks and deep learning to classify and study medical data, which can improve the efficiency and accuracy of doctors and detect diseases early for early diagnosis, etc. Therefore, it has a wide range of application prospects. However, traditional neural networks have disadvantages such as high energy consumption and high latency (slow computation speed). This paper presents recent research on signal classification and disease diagnosis based on a third-generation neural network, the spiking neuron network, using medical data including EEG signals, ECG signals, EMG signals and MRI images. The advantages and disadvantages of pulsed neural networks compared with traditional networks are summarized and its development orientation in the future is prospected

A review of automated sleep disorder detection

Automated sleep disorder detection is challenging because physiological symptoms can vary widely. These variations make it difficult to create effective sleep disorder detection models which support hu-man experts during diagnosis and treatment monitoring. From 2010 to 2021, authors of 95 scientific papers have taken up the challenge of automating sleep disorder detection. This paper provides an expert review of this work. We investigated whether digital technology and Artificial Intelligence (AI) can provide automated diagnosis support for sleep disorders. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines during the content discovery phase. We compared the performance of proposed sleep disorder detection methods, involving differ-ent datasets or signals. During the review, we found eight sleep disorders, of which sleep apnea and insomnia were the most studied. These disorders can be diagnosed using several kinds of biomedical signals, such as Electrocardiogram (ECG), Polysomnography (PSG), Electroencephalogram (EEG), Electromyogram (EMG), and snore sound. Subsequently, we established areas of commonality and distinctiveness. Common to all reviewed papers was that AI models were trained and tested with labelled physiological signals. Looking deeper, we discovered that 24 distinct algorithms were used for the detection task. The nature of these algorithms evolved, before 2017 only traditional Machine Learning (ML) was used. From 2018 onward, both ML and Deep Learning (DL) methods were used for sleep disorder detection. The strong emergence of DL algorithms has considerable implications for future detection systems because these algorithms demand significantly more data for training and testing when compared with ML. Based on our review results, we suggest that both type and amount of labelled data is crucial for the design of future sleep disorder detection systems because this will steer the choice of AI algorithm which establishes the desired decision support. As a guiding principle, more labelled data will help to represent the variations in symptoms. DL algorithms can extract information from these larger data quantities more effectively, therefore; we predict that the role of these algorithms will continue to expand

Survey of Machine Learning Techniques in the Analysis of EEG Signals for Parkinson’s Disease: A Systematic Review.

Background: Parkinson’s disease (PD) affects 7–10 million people worldwide. Its diagnosis is clinical and can be supported by image-based tests, which are expensive and not always accessible. Electroencephalograms (EEG) are non-invasive, widely accessible, low-cost tests. However, the signals obtained are difficult to analyze visually, so advanced techniques, such as Machine Learning (ML), need to be used. In this article, we review those studies that consider ML techniques to study the EEG of patients with PD. Methods: The review process was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, which are used to provide quality standards for the objective evaluation of various studies. All publications before February 2022 were included, and their main characteristics and results were evaluated and documented through three key points associated with the development of ML techniques: dataset quality, data preprocessing, and model evaluation. Results: 59 studies were included. The predominating models were Support Vector Machine (SVM) and Artificial Neural Networks (ANNs). In total, 31 articles diagnosed PD with a mean accuracy of 97.35 ± 3.46%. There was no standard cleaning protocol for EEG and a great heterogeneity in EEG characteristics was shown, although spectral features predominated by 88.37%. Conclusions: Neither the cleaning protocol nor the number of EEG channels influenced the classification results. A baseline value was provided for the PD diagnostic problem, although recent studies focus on the identification of cognitive impairment.post-print1392 K

Advances in Clinical Neurophysiology

Including some of the newest advances in the field of neurophysiology, this book can be considered as one of the treasures that interested scientists would like to collect. It discusses many disciplines of clinical neurophysiology that are, currently, crucial in the practice as they explain methods and findings of techniques that help to improve diagnosis and to ensure better treatment. While trying to rely on evidence-based facts, this book presents some new ideas to be applied and tested in the clinical practice. Advances in Clinical Neurophysiology is important not only for the neurophysiologists but also for clinicians interested or working in wide range of specialties such as neurology, neurosurgery, intensive care units, pediatrics and so on. Generally, this book is written and designed to all those involved in, interpreting or requesting neurophysiologic tests

Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges

In recent years, new research has brought the field of EEG-based Brain-Computer Interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely,“Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices

Signal Processing Using Non-invasive Physiological Sensors

Non-invasive biomedical sensors for monitoring physiological parameters from the human body for potential future therapies and healthcare solutions. Today, a critical factor in providing a cost-effective healthcare system is improving patients' quality of life and mobility, which can be achieved by developing non-invasive sensor systems, which can then be deployed in point of care, used at home or integrated into wearable devices for long-term data collection. Another factor that plays an integral part in a cost-effective healthcare system is the signal processing of the data recorded with non-invasive biomedical sensors. In this book, we aimed to attract researchers who are interested in the application of signal processing methods to different biomedical signals, such as an electroencephalogram (EEG), electromyogram (EMG), functional near-infrared spectroscopy (fNIRS), electrocardiogram (ECG), galvanic skin response, pulse oximetry, photoplethysmogram (PPG), etc. We encouraged new signal processing methods or the use of existing signal processing methods for its novel application in physiological signals to help healthcare providers make better decisions

Deep learning for healthcare applications based on physiological signals: A review

Background and objective: We have cast the net into the ocean of knowledge to retrieve the latest scientific research on deep learning methods for physiological signals. We found 53 research papers on this topic, published from 01.01.2008 to 31.12.2017. Methods: An initial bibliometric analysis shows that the reviewed papers focused on Electromyogram(EMG), Electroencephalogram(EEG), Electrocardiogram(ECG), and Electrooculogram(EOG). These four categories were used to structure the subsequent content review. Results: During the content review, we understood that deep learning performs better for big and varied datasets than classic analysis and machine classification methods. Deep learning algorithms try to develop the model by using all the available input. Conclusions: This review paper depicts the application of various deep learning algorithms used till recently, but in future it will be used for more healthcare areas to improve the quality of diagnosi