Role of EEG as Biomarker in the Early Detection and Classification of Dementia

The early detection and classification of dementia are important clinical support tasks for medical practitioners in customizing patient treatment programs to better manage the development and progression of these diseases. Efforts are being made to diagnose these degenerative disorders in the early stages. Indeed, early diagnosis helps patients to obtain the maximum treatment benefit before significant mental decline occurs. The use of electroencephalogram as a tool for the detection of changes in brain activities and clinical diagnosis is becoming increasingly popular for its capabilities in quantifying changes in brain degeneration in dementia. This paper reviews the role of electroencephalogram as a biomarker based on signal processing to detect dementia in early stages and classify its severity. The review starts with a discussion of dementia types and cognitive spectrum followed by the presentation of the effective preprocessing denoising to eliminate possible artifacts. It continues with a description of feature extraction by using linear and nonlinear techniques, and it ends with a brief explanation of vast variety of separation techniques to classify EEG signals. This paper also provides an idea from the most popular studies that may help in diagnosing dementia in early stages and classifying through electroencephalogram signal processing and analysis

Epilepsy

Epilepsy is the most common neurological disorder globally, affecting approximately 50 million people of all ages. It is one of the oldest diseases described in literature from remote ancient civilizations 2000-3000 years ago. Despite its long history and wide spread, epilepsy is still surrounded by myth and prejudice, which can only be overcome with great difficulty. The term epilepsy is derived from the Greek verb epilambanein, which by itself means to be seized and to be overwhelmed by surprise or attack. Therefore, epilepsy is a condition of getting over, seized, or attacked. The twelve very interesting chapters of this book cover various aspects of epileptology from the history and milestones of epilepsy as a disease entity, to the most recent advances in understanding and diagnosing epilepsy

Utilizzo del segnale elettroencefalografico per applicazioni diagnostiche: panoramica delle tecniche di analisi

L’elettroencefalografia (EEG) è un sistema diagnostico non invasivo che consiste nella registrazione dell’attività elettrica del cervello, generata dai potenziali post-sinaptici provenienti dai neuroni piramidali. A partire dagli anni 2000, tale tecnica ha assunto una fondamentale importanza al fine di diagnosticare patologie a carico del sistema nervoso tramite l’analisi di specifici pattern. In questo contesto, il presente elaborato si propone di analizzare la letteratura relativa all’utilizzo del segnale EEG in ambito clinico. Nello specifico, l’obiettivo della tesi è proprio quello di fare una panoramica delle principali tecniche di denoising e processing disponibili per individuare caratteristiche anomale nel tracciato e di darne una corretta interpretazione. Infatti, poiché il segnale rilevato presenta molti artefatti dovuti all’interferenza di rete, ai movimenti dei muscoli involontari del paziente (si pensi al battito del cuore), alla respirazione o ad un mal posizionamento degli elettrodi sullo scalpo, risulta di fondamentale importanza saper individuare eventuali sorgenti di artefatto al fine di poterle rimuovere ed ottenere le informazioni desiderate. Per fornire una panoramica completa, l’elaborato affronta innanzitutto le caratteristiche principali del sistema nervoso, sorgente del segnale EEG, le onde cerebrali coinvolte, i loro diversi ritmi e le principali tipologie di elettrodi e di setup utilizzati per l’acquisizione. Si procede quindi con una classificazione dei ritmi cerebrali, rilevabili mediante elettrodi, in onde alpha, beta, theta, delta, gamma: la loro differenziazione è essenziale per l’interpretazione del tracciato elettroencefalografico e permette il riconoscimento di eventuali patterns patologici. Una volta chiariti questi aspetti, vengono introdotte le principali patologie che si possono identificare tramite un esame EEG (l’epilessia, il coma, la depressione, la schizofrenia e l’autismo) e descritte alcune delle principali tecniche di denoising disponibili al giorno d’oggi nella letteratura, utili per tali ambiti clinici. Infine, nell’ultimo capitolo viene riportata una breve parte sperimentale dedicata a proporre un esempio di analisi di un segnale patologico attraverso uno dei metodi illustrati nell’elaborato, usufruendo del software Matlab.An electroencephalogram (EEG) is a non-invasive test that measures the electrical activity generated by post synaptic potentials which originate from pyramidal neurons. Since the early 2000s, this technique gained a crucial role in the diagnoses of several pathological state affecting the nervous system through the analysis of specific patterns. In this context, the purpose of this paper is to analyze the literature related to the use of the EEG signal in the clinical setting. In particular, the aim of the paper is to give an overview of the major techniques of de-noising and processing available to detect abnormal features in EEG, in order to give a proper interpretation of the signal. Indeed, since EEG is affected by multiple artifacts due to network interference, movements of involuntary muscles (e.g. heartbeat), breathing and electrode popping, it is fundamental a proper identification and removal of the possible interfering sources. In order to give a full overview, the thesis addresses the main features of the nervous system as source of the EEG signal, the main features of brain signals and rhythms, the several types of electrodes and the setup used to acquire the signal. After this overview, the classification of cerebral rhythms in alpha, beta, gamma, delta, theta waves will be provided: this distinction is meant to give an appropriate interpretation of the EEG acquisitions and allows the recognition of possible pathological patterns. Afterwards the main diseases identified through an electroencephalograph (epilepsy, depression, autism and schizophrenia) and the main denoising techniques exploited nowadays for clinical use of EEG are introduced. Finally, in the last chapter a brief experimental part is proposed. In detail, an example of the analysis of a pathological EEG signal is provided through one of the methods previously shown, using the software Matlab

An Examination of the Bio-Philosophical Literature on the Definition and Criteria of Death: When is Dead Dead and Why Some Donation After Cardiac Death Donors Are Not

Human fascination with transplantation has been portrayed in mythology and legend as well as in art and literature throughout history. It has both horrified and captivated our collective conscience and has progressed, in a relatively short time period, from science fiction to a modern accomplishment that can improve longevity and enhance quality of life. The primary obstacle for transplantation is no longer scientific in nature but is predominantly one of supply and demand and carries with it attendant ethical concerns. Specific to this work is the question of whether a particular method of organ procurement known as Donation After Cardiac Death procures organs from the newly dead or from the imminently dying. Since the normative rules that guide transplantation require that one may not be killed for or by the removal of one\u27s organs determining the nature of death is of paramount importance. Accordingly, the primary question concerned herein is whether Donation After Cardiac Death donors are dead at the moment of organ recovery. This work focuses on the conceptual underpinnings of why a person is said to be dead according to particular definitions and when specific criteria and tests are fulfilled. Much attention is devoted to exploring why the irreversible loss of cardio-respiratory functions or the irreversible cessation of all functions of the entire brain signifies death and whether these two criteria represent distinct types of death or if they instantiate the same overarching definition. Transplantation saves lives and is a social good that society ought to continue to support. The aim of this dissertation is not to denigrate the field. On the contrary, if donation is to thrive we must ensure that our definition and criteria for death are coherent and that the methods for procurement operate accordingly

Development and applications of a smartphone-based mobile electroencephalography (EEG) system

Electroencephalography (EEG) is a clinical and research technique used to non-invasively acquire brain activity. EEG is performed using static systems in specialist laboratories where participant mobility is constrained. It is desirable to have EEG systems which enable acquisition of brain activity outside such settings. Mobile systems seek to reduce the constraining factors of EEG device and participant mobility to enable recordings in various environments but have had limited success due to various factors including low system specification. The main aim of this thesis was to design, build, test and validate a novel smartphone-based mobile EEG system.A literature review found that the term ‘mobile EEG’ has an ambiguous meaning as researchers have used it to describe many differing degrees of participant and device mobility. A novel categorisation of mobile EEG (CoME) scheme was derived from thirty published EEG studies which defined scores for participant and device mobilities, and system specifications. The CoME scheme was subsequently applied to generate a specification for the proposed mobile EEG system which had 24 channels, sampled at 24 bit at a rate of 250 Hz. Unique aspects of the EEG system were the introduction of a smartphone into the specification, along with the use of Wi-Fi for communications. The smartphone’s processing power was used to remotely control the EEG device so as to enable EEG data capture and storage as well as electrode impedance checking via the app. This was achieved by using the Unity game engine to code an app which provided the flexibility for future development possibilities with its multi-platform support.The prototype smartphone-based waist-mounted mobile EEG system (termed ‘io:bio’) was validated against a commercial FDA clinically approved mobile system (Micromed). The power spectral frequency, amplitude and area of alpha frequency waves were determined in participants with their eyes closed in various postures: lying, sitting, standing and standing with arms raised. Since a correlation analysis to compare two systems has interpretability problems, Bland and Altman plots were utilised with a priori justified limits of agreement to statistically assess the agreement between the two EEG systems. Overall, the results found similar agreements between the io:bio and Micromed systems indicating that the systems could be used interchangeably. Utilising the io:bio and Micromed systems in a walking configuration, led to contamination of EEG channels with artifacts thought to arise from movement and muscle-related sources, and electrode displacement.To enable an event related potential (ERP) capability of the EEG system, additional coding of the smartphone app was undertaken to provide stimulus delivery and associated data marking. Using the waist-mounted io:bio system, an auditory oddball paradigm was also coded into the app, and delivery of auditory tones (standard and deviant) to the participant (sitting posture) achieved via headphones connected to the smartphone. N100, N200 and P300 ERP components were recorded in participants sitting, and larger amplitudes were found for the deviant tones compared to the standard ones. In addition, when the paradigm was tested in individual participants during walking, movement-related artifacts impacted negatively upon the quality of the ERP components, although components were discernible in the grand mean ERP.The io:bio system was redesigned into a head-mounted configuration in an attempt to reduce EEG artifacts during participant walking. The initial approach taken to redesign the system involved using electronic components populated onto a flexible PCB proved to be non-robust. Instead, the rigid PCB form of the circuitry was taken from the io:bio waist-mounted system and placed onto the rear head section of the electrode cap via a bespoke cradle. Using this head-mounted system, in a preliminary auditory oddball paradigm study, ERP responses were obtained in participants whilst walking. Initial results indicate that artifacts are reduced in this head-mounted configuration, and N100, N200 and P300 components are clearly identifiable in some channels

Análise de registros eletroencefalográficos de pacientes comatosos: estudo quantitativo para definir estatisticamente a maneira mais apropriada de agrupar pacientes em coma

The electroencephalogram (EEG) clinical examination is essential in the Intensive Care Unit (ICU) environments, and is commonly used in comatose patients, since it provides essential information regarding brain electrical activity. The main causes of hospitalization of patients in ICU’s are caused by the etiologies of Cranioencephalic Trauma (TBI), Stroke (Stroke) and Metabolic Coma (CM), which together with EEG techniques, are assessed by the degree of consciousness using scales such as the Glasgow Scale (ECG) and the Ritchmond Agitation Sedation Scale (RASS). Based on the EEG signals collected from these patients, with different characteristics, the present study aims to verify quantitatively, through the use of three different quantifiers, the degree of linear correlation between patient data, when they are submitted to two criteria distinct, in order to identify the best among them. 75 EEG signals (0-40Hz) measured in comatose patients at Hospital das Clínicas, Federal University of Uberlândia, and 30 neurologically healthy individuals were analyzed. The EEG stretches were measured by different quantifiers that measure normalized and percentage power, as well as the symmetry of the brain signal. The correlation calculation was used in order to measure the degree of correlation in the groups performed, using two criteria, the etiology, and the degree of consciousness (assessed by the Glasgow and RASS scales), being compared with the control group. The results obtained show that the groups formed by level of consciousness lead to a better level of statistical correlation than when grouped by etiology, with the control group as the maximum correlation pattern. The conclusions suggest that the quantitative assessment of EEG’s of comatose patients of different characteristics is based on the grouping by level of consciousness, where it will be possible to obtain a greater similarity of the data of each group, and thus present more reliable results.Trabalho de Conclusão de Curso (Graduação)O exame clínico eletroencefalograma (EEG) é fundamental nos ambientes de Unidade de Terapia Intensiva (UTI), e é comumente utilizado em pacientes comatosos, tendo em vista que ele fornece informações essenciais a respeito da atividade elétrica cerebral. As principais causas da internação de pacientes em UTI’s são causadas pelas etiologias Trauma Cranioencefálico (TCE), Acidente Vascular Encefálico (AVE) e Coma Metabólico (CM), que juntamente com as técnicas de EEG, são avaliados pelo grau de consciência por meio de escalas como, Escala de Glasgow (ECG) e Escala de Sedação por Agitação de Ritchmond (RASS). A partir dos sinais EEG coletados nesses pacientes, com diferentes características, o presente estudo tem como objetivo verificar quantitativamente, por meio da utilização de três distintos quantificadores, o grau de correlação linear entre os dados dos pacientes, quando os mesmos são submetidos a dois critérios distintos, a fim de identificar o melhor entre eles. Foram analisados 75 sinais EEG (0-40Hz) medidos em pacientes comatosos no Hospital das Clínicas da Universidade Federal de Uberlândia, e 30 indivíduos neurologicamente saudáveis. Os trechos do EEG foram mensurados por quantificadores distintos que medem potência normalizada e percentual, assim como a simetria do sinal cerebral. Foi utilizado o cálculo de correlação a fim de mensurar o grau de correlação nos grupos realizados, utilizando dois critérios, a etiologia, e o grau de consciência (avaliado pelas escalas Glasgow e RASS), sendo confrontada com o grupo controle. Os resultados obtidos evidenciam que os grupos formados por nível de consciência levam a um nível de correlação estatística melhor do que quando agrupados por etiologia, tendo o grupo controle como padrão máximo de correlação. As conclusões sugerem que a avaliação quantitativa de EEG’s de pacientes comatosos de diferentes características seja baseada no agrupamento por nível de consciência, onde será possível obter uma maior similaridade dos dados de cada grupo, e assim apresentar resultados mais confiáveis.2021-06-1

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