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Alzheimer Disease is one of the most common & tremendously growing neurological diseases in the world. Several biomarkers tools exist for diagnosis & disease progression in Alzheimer disease which can be assumed as key issues for clinical applications. Electroencephalogram signals (EEG) yields out powerful and relatively cheap tool of diagnosis of...
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... Numerous genetic, biochemical, neuroimaging and neurophysiological biomarkers have been associated with PD vulnerability, although no single biomarker is as yet specific enough for routine use in diagnostic or prognostic evaluation in clinical practice [12]. Electroencephalography (EEG) has emerged as a non-invasive, low-cost, and highly sensitive screening method for objectively monitoring of the disease's progression thanks to its high temporal resolution [13]. From a theoretical perspective, the EEG-based neurophysiological biomarker is a functional marker of neuronal and synaptic integrity, which may be sensitive to the subtle changes that precede the structural alterations of neurodegenerative diseases [14]. ...
... The average PSD per electrode per subject was then computed to obtain the relative power of the typical EEG components without separating oscillatory and non-oscillatory activity: delta (2.5-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12)(13), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and gamma (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45), total power being defined in the bandwidth 2.5-45 Hz. We also calculated the alpha/theta ratio, since this resting-state/ EEG ratio has been shown to be linked to neuropsychological test performance in PD [15]. ...
... The average PSD per electrode per subject was then computed to obtain the relative power of the typical EEG components without separating oscillatory and non-oscillatory activity: delta (2.5-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12)(13), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and gamma (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45), total power being defined in the bandwidth 2.5-45 Hz. We also calculated the alpha/theta ratio, since this resting-state/ EEG ratio has been shown to be linked to neuropsychological test performance in PD [15]. ...
While resting state electroencephalography (EEG) provides relevant information on pathological changes in Parkinson's disease, most studies focus on the eyes-closed EEG biomarkers. Recent evidence has shown that both eyes-open EEG and reactivity to eyes-opening can also differentiate Parkinson's disease from healthy aging, but no consensus has been reached on a discriminatory capability benchmark. The aim of this study was to determine the resting-state EEG biomarkers suitable for real-time application that can differentiate Parkinson's patients from healthy subjects under both eyes closed and open. For this, we analysed and compared the quantitative EEG analyses of 13 early-stage cognitively normal Parkinson's patients with an age and sex-matched healthy group. We found that Parkinson's disease exhibited abnormal excessive theta activity in eyes-closed, which was reflected by a significantly higher relative theta power, a higher time percentage with a frequency peak in the theta band and a reduced alpha/theta ratio, while Parkinson's patients showed a significantly steeper non-oscillatory spectral slope activity than that of healthy subjects. We also found considerably less alpha and beta reactivity to eyes-opening in Parkinson's disease plus a significant moderate correlation between these EEG-biomarkers and the MDS-UPDRS score, used to assesses the clinical symptoms of Parkinson's Disease. Both EEG recordings with the eyes open and reactivity to eyes-opening provided additional information to the eyes-closed condition.
We thus strongly recommend that both eyes open and closed be used in clinical practice recording protocols to promote EEG as a complementary non-invasive screening method for the early detection of Parkinson's disease, which would allow clinicians to design patient-oriented treatment and improve the patient's quality of life.
... The electrode was labeled as frontal (F), temporal (T), parietal (P), occipital (O), and central (C), respectively. Additionally, the FTOPC abbreviation was followed by a number, to define the detailed position for the EEG measurement [111,112]. Since an EEG waveform measured in the human brain is visually confusing and difficult to analyze, the complex signal is generally interpreted with the frequency, using the power spectrum method (Figure 7b) [113,114]. First, the delta waves are vibrations below 4 Hz and appear in meditation or coma. ...
Electrophysiological signals are collected to characterize human health and applied in various fields, such as medicine, engineering, and pharmaceuticals. Studies of electrophysiological signals have focused on accurate signal acquisition, real-time monitoring, and signal interpretation. Furthermore, the development of electronic devices consisting of biodegradable and biocompatible materials has been attracting attention over the last decade. In this regard, this review presents a timely overview of electrophysiological signals collected with biodegradable polymer electrodes. Candidate polymers that can constitute biodegradable polymer electrodes are systemically classified by their essential properties for collecting electrophysiological signals. Moreover, electrophysiological signals, such as electrocardiograms, electromyograms, and electroencephalograms subdivided with human organs, are discussed. In addition, the evaluation of the biodegradability of various electrodes with an electrophysiology signal collection purpose is comprehensively revisited.
... The use of electroencephalography (EEG) has recently grown as a means to diagnose neurodegenerative pathologies such as AD [7][8][9][10][11][12][13]. EEG is a non-invasive test that records the electrical activity of the brain measured at different sites on patient's scalp, resulting in indirect information about the physiological conditions of the brain. ...
The use of electroencephalography (EEG) has recently grown as a means to diagnose neurodegenerative pathologies such as Alzheimer’s disease (AD). AD recognition can benefit from machine learning methods that, compared with traditional manual diagnosis methods, have higher reliability and improved recognition accuracy, being able to manage large amounts of data. Nevertheless, machine learning methods may exhibit lower accuracies when faced with incomplete, corrupted, or otherwise missing data, so it is important do develop robust pre-processing techniques do deal with incomplete data. The aim of this paper is to develop an automatic classification method that can still work well with EEG data affected by artifacts, as can arise during the collection with, e.g., a wireless system that can lose packets. We show that a recurrent neural network (RNN) can operate successfully even in the case of significantly corrupted data, when it is pre-filtered by the robust principal component analysis (RPCA) algorithm. RPCA was selected because of its stated ability to remove outliers from the signal. To demonstrate this idea, we first develop an RNN which operates on EEG data, properly processed through traditional PCA; then, we use corrupted data as input and process them with RPCA to filter outlier components, showing that even with data corruption causing up to 20% erasures, the RPCA was able to increase the detection accuracy by about 5% with respect to the baseline PCA.
... Es posible apreciar cómo la variación de correlación a lo largo de las iteraciones de ataque es similar en todas las bandas consideradas. Las bandas de frecuencia están asociadas a diferentes procesos cognitivos [11]. Sin embargo en el estado de reposo, en el que se realizaron los registros EEG, es posible que la configuración neuronal dominante sea la conocida como default mode network [12] y, en consecuencia, no se aprecien diferencias en la respuesta de las bandas. ...
Resumen La enfermedad de Alzheimer (EA) es una enfermedad de-generativa con alta prevalencia en la sociedad actual. El diagnóstico de la misma es complejo y costoso. Por ello, se investiga en nuevas formas de caracterización de las altera-ciones que esta enfermedad provoca en la red neuronal. En el presente trabajo se pretende evaluar la robustez de la red neuronal funcional obtenida a partir de registros de electro-encefalograma. Se emplearon dos bases de datos con 195 y 202 sujetos, respectivamente. Ambas, formadas por sujetos cognitivamente sanos, pacientes con deterioro cognitivo leve y pacientes con demencia por EA. La red neuronal funcional construida a nivel de fuente con la medida de conectivi-dad amplitude envelope correlation (AEC) fue atacada (i.e., reducción del peso de los enlaces) a lo largo de 100 iteracio-nes. Tras cada ataque, se calculó la correlación de Spearman entre la red atacada y la red original. La variación de co-rrelación, como la medida de robustez propuesta, permitió replicar los resultados en ambas bases de datos. Las tenden-cias entre grupos mostraron una dicotomía entre bandas de frecuencia bajas y altas, con una disminución de robustez en las frecuencias bajas a medida que la EA progresa y un in-cremento en las frecuencias altas. Se observa, en definitiva, una dependencia de la de robustez de la red con la frecuencia. 1. Introducción La enfermedad de Alzheimer (EA) es un trastorno neu-rodegenerativo que provoca cambios cerebrales estruc-turales y funcionales [1]. La severidad del deterioro en la función cognitiva de los pacientes con EA varía a me-dida que esta avanza. Las primeras manifestaciones lle-van asociadas alteraciones subjetivas, que no se mani-fiestan en los test cognitivos convencionales [2]. El dete-rioro cognitivo leve (DCL) por EA, estado prodrómico de la enfermedad, se caracteriza fundamentalmente por una pérdida de memoria, mientras que las capacidades funcionales se conservan prácticamente en su totalidad [1, 2]. La EA en sus fases posteriores destaca por un de-terioro cognitivo suficiente para alterar la independencia y afectar claramente a la vida cotidiana de los pacientes [1]. En la actualidad, el diagnóstico de la EA es com-plejo, siendo necesarios protocolos que incluyen diferen-tes tipos de evaluaciones por parte de neurólogos [2]. El marco de referencia diagnóstico empleado es el estable-cido por el National Institute on Aging and Alzheimer's Association (NIA-AA) [3]. La actividad electromagnética cerebral muestra patro-nes alterados como consecuencia de la EA [2]. La exis-tencia de técnicas para el registro de esta actividad, co-mo el electroencefalograma (EEG), abren la puerta a su uso diagnóstico, con el objetivo de determinar poten-ciales biomarcadores de la enfermedad que permitan su detección de forma precoz, precisa y con un bajo cos-te [1, 2]. A partir de la actividad registrada, es posible obtener patrones de conectividad funcional entre las di-ferentes regiones cerebrales. Estudios previos han mos-trado cómo estos patrones de conectividad son alterados por la EA [4]. La teoría de grafos ha jugado un papel fundamental en la caracterización de las alteraciones de la conectividad cerebral [5]. Sin embargo, la inherente complejidad de la EA, con los mecanismos neurodege-nerativos asociados, hace necesarias nuevas formas de caracterización que contribuyan a la determinación de nuevos biomarcadores partiendo de la actividad eléctri-ca registrada [1, 2]. Por esto, surge la necesidad de em-plear en las redes neuronales el concepto de robustez previamente utilizado en redes como Internet [6, 7]. Se entiende robustez como la habilidad de una red para per-manecer inalterada ante perturbaciones [7]. La robustez se ha evaluado tradicionalmente a través de la alteración de parámetros que caracterizan la red tras ser perturba-da (i.e., eficiencia) [6, 7]. De esta forma, se obtiene una robustez desagregada, dependiente de los aspectos que evalúa cada parámetro. En este trabajo se plantea la hipótesis de que la EA pro-voca alteraciones de la actividad neuronal que afectan a la red; lo que a su vez debería afectar a la robustez e integridad de la misma. En consecuencia, el objeti-vo de este trabajo es, simulando alteraciones en la red neuronal, presentar una metodología de evaluación de la robustez independiente de parámetros de red indivi-duales y analizar cómo esa robustez evoluciona con la severidad de la EA. 2. Materiales 2.1. Sujetos En el presente trabajo se han empleado dos bases de datos. La primera (BD1), registrada en el Hospital Uni-versitario Río Hortega de Valladolid, está formada por 195 sujetos: 45 sujetos de control cognitivamente sanos, 69 pacientes con DCL por EA y 81 pacientes con de-1
... The bioelectric cells are non-invasively examined by series of electrodes placed over patients head as per international 10/20 standards. EEG signal is simply a multivariate signal transmitted over different channels with variation of different sampling frequencies depending on each application [4,5]. This tool takes more time as it generates more data which needs to be analyzed. ...
... EEG signals are key diagnostic tools not only for neurologists, but also for clinicians and doctors. Today, EEG recordings are also used in many Brain Computer Interface (BCI) applications [4]. Several research findings have identified the potential of EEG for diagnosing dementia and Alzheimer's disease in recent years. ...
... In upcoming years, they can be majorly used for identification of several neuro-degenerative diseases such as Alzheimer's disease, Epilepsy, Huntington disease and many more. Several dynamical changes related to normal ageing can be easily observed using EEG signals [4]. ...
Previous research work has highlighted that neuro-signals of Alzheimer’s disease patients are least complex and have low synchronization as compared to that of healthy and normal subjects. The changes in EEG signals of Alzheimer’s subjects start at early stage but are not clinically observed and detected. To detect these abnormalities, three synchrony measures and wavelet-based features have been computed and studied on experimental database. After computing these synchrony measures and wavelet features, it is observed that Phase Synchrony and Coherence based features are able to distinguish between Alzheimer’s disease patients and healthy subjects. Support Vector Machine classifier is used for classification giving 94% accuracy on experimental database used. Combining, these synchrony features and other such relevant features can yield a reliable system for diagnosing the Alzheimer’s disease.
... EEG straightforwardly relates the brain activity which is used for observing the various function of brain. Different neurological disease is diagnosed based on the significant features computed from the various non-linear and linear analyses of sampled EEG Signals [10]. Different methods for diagnosing the AD are compared in the table I and literature summary for the detection of AD based on EEG signals summarized in the table II. ...
In the recent years, costliest Alzheimer disease (AD) is now primary reason for the cause of death. An early finding is essential as there is no cure for severe AD. Despite recent advances, early finding of Alzheimer disease from electroencephalography (EEG) remains a difficult job. In this paper, we focus a spectral and signal complexity measures through which such early findings can possibly be improved. Power spectral and nonlinear features, which have been utilized for classification of Alzheimer disease subjects (ADS) from the normal healthy subject (NHS). So far, the power in the various EEG bands has been intensely analyzed. The main aim of this research article is to study the power and nonlinear analysis for the finding of AD to consider as a probable biomarker to recognize AD subject and normal healthy subject. Relative power (RP) was independently calculated from various EEG bands which indicate the slowing of EEG signals acknowledge the Alzheimer disease subjects. In this study, EEGs signal had been acquired at the rest condition from 20 normal healthy subject whose age around 60 years along with same number of Alzheimer disease subjects. The result shows that relative power is increased towards lower frequencies while decreased towards higher frequencies in AD. Such analysis of power may additionally explore to differentiate Alzheimer disease’s stages.
... Electroencephalogram (EEG) signals are widely used in the diagnosis of mental disease and neurodegenerative diseases such as Epilepsy, Alzheimer's disease and many more. It is more extensively used presently in bioengineering research [5][6][7]. EEG-based interfaces are used in many applications including psychophysiology, psychology and many more. EEG signal measures the brain activity within the brain. ...
Stress is one of the major contributing factors which lead to various diseases including cardiovascular diseases. To avoid this, stress monitoring is very essential for clinical intervention and disease prevention. In present study, the feasibility of exploiting Electroencephalography (EEG) signals to monitor stress in mental arithmetic tasks is investigated. This paper presents a novel hardware system along with software system which provides a method for determining stress level with the help of a Theta sub-band of EEG signals. The proposed system performs a signal-processing of EEG signals, which recognizes the peaks of the Theta sub-band above a certain threshold value. It finds the first order difference information to identify the peak. This proposed method of EEG based stress detection can be used as quick, noninvasive, portable and handheld tool for determining the stress level of a person.
... The reasons behind the use of Daubechies wavelet is discussed in Section 3.2. The EEG signal is decomposed using the above Daubechies wavelet at level 5. [27,28] Accordingly, EEG signal is decomposed into five bands with the following frequencies: delta (0.5-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12)(13), and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). Further, the power in each sub-band of the EEG signal is computed by means of power density functions. ...
... The reasons behind the use of Daubechies wavelet is discussed in Section 3.2. The EEG signal is decomposed using the above Daubechies wavelet at level 5. [27,28] Accordingly, EEG signal is decomposed into five bands with the following frequencies: delta (0.5-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12)(13), and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). Further, the power in each sub-band of the EEG signal is computed by means of power density functions. ...
Alzheimer's disease (AD) is one of the most common and fastest growing neurodegenerative diseases in the western countries. Development of different biomarkers tools are key issues for the diagnosis of AD and its progression. Prediction of cognitive performance of subjects from electroencephalography (EEG) and identification of relevant biomarkers are some of the research problems. Although EEG is a powerful and relatively cheap tool for the diagnosis of AD and dementia, it does not achieve the standards of clinical performance in terms of sensitivity and specificity to accept as a reliable technique for the screening of AD. Hence, there is an immense need to develop an efficient system and algorithms for diagnosis. Accordingly, the objective of this research paper is to analyze different features for the diagnosis of AD to serve as a possible biomarker to distinguish between AD subject and normal subject. The research is carried out on an experimental database. Three different features such as spectral-, wavelet-, and complexity-based features are computed and compared on the basis of classification accuracy obtained. The classification is carried out using support vector machine classifier giving 96% accuracy on complexity-based features and increased performance in terms of sensitivity and specificity. The results show the improved performance in the diagnosis of AD. It is observed that the severity of AD is depicted in EEG complexity. These features used in research work can be considered as the benchmark for AD diagnosis.
In the last decades the health care developments highly rise the level of ages of world population. This improvement was accompanied by increasing the diseases related with elder like Dementia, which Alzheimer’s disease represents the most common form. The present studies aim to design and implementation a medical system for improving the life of Alzheimer’s disease persons and ease the burden of their caregivers. AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patient’s future health, and recommend better treatments. AI goes beyond the foundations of deep learning to give you insight into the nuances of applying AI to medical use cases. Diagnosis is about identifying disease. By building an algorithm we can diagnosis chest X-ray and determine whether it contains disease, another algorithm that will look at brain MRIs and identify the location of tumours in those brain MRIs health of the patients lab values and their demographics and use those to predict the risk of an event. A Smart IOT Interactive Assistance is a technological device that continuously monitors the stability of an Alzheimer’s patient, indicates their position on a map, automatically reminds them to take their prescriptions, and has a call button for any emergencies they could experience during the day. The system has two components, one of which the patient wears and the other of which is an IoT platform application utilized by the caregiver. The motion processing unit sensor, GPS, heart rate sensor with microcontrollers, and LCD display were used to construct the wearable device. An Internet of Things (IoT) platform supports this device, allowing the caregiver to communicate with the patient from any location.
