Electroencephalogram based diagnosis of Alzheimer Disease

Kulkarni, Nilesh; Bairagi, Vinayak K.

doi:10.1109/isco.2015.7282275

Cited by 11 publications

(7 citation statements)

References 22 publications

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“…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.…”

Section: Introductionmentioning

confidence: 99%

EEG-Based Alzheimer’s Disease Recognition Using Robust-PCA and LSTM Recurrent Neural Network

Alessandrini

Biagetti

Crippa

et al. 2022

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

EEG-Based Alzheimer’s Disease Recognition Using Robust-PCA and LSTM Recurrent Neural Network

Alessandrini

Biagetti

Crippa

et al. 2022

Sensors

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“…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.…”

Section: Methodsmentioning

confidence: 99%

Role of EEG for Diagnosis of Alzheimer Disease

Elgandelwar¹,

Bairagi²

2019

IJITEE

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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.

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“…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.…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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Support vector machine based alzheimer’s disease diagnosis using synchrony features

Kulkarni¹

2020

IJ-ICT

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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.

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Electroencephalogram based diagnosis of Alzheimer Disease

Cited by 11 publications

References 22 publications

EEG-Based Alzheimer’s Disease Recognition Using Robust-PCA and LSTM Recurrent Neural Network

EEG-Based Alzheimer’s Disease Recognition Using Robust-PCA and LSTM Recurrent Neural Network

Role of EEG for Diagnosis of Alzheimer Disease

Support vector machine based alzheimer’s disease diagnosis using synchrony features

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