On the Early Diagnosis of Alzheimer’s Disease from EEG Signals: A Mini-Review

Dauwels, Justin; Vialatte, François-Benoît; Cichocki, Andrzej

doi:10.1007/978-90-481-9695-1_106

Cited by 26 publications

(26 citation statements)

References 63 publications

(94 reference statements)

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“…It has been widely accepted that at least 3 types of changes occur in AD patients' EEG signals: they slow down (i.e. the power of low frequencies is found to be increased while the power of the high frequencies is decreased), their complexity, which is the measure of the number of different patterns in the signal [4], is reduced and synchrony or correlation between EEG signals of the different parts of the brain is reduced [4,194].…”

Section: Electroencephalogram (Eeg)mentioning

confidence: 99%

On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey

Alberdi

Aztiria

Basarab

2016

Artificial Intelligence in Medicine

162

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Section: Electroencephalogram (Eeg)mentioning

confidence: 99%

On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey

Alberdi

Aztiria

Basarab

2016

Artificial Intelligence in Medicine

162

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“…It is widely accepted that AD causes a decrease in the power of high frequencies (alpha, beta, and gamma) and an increase in the power of low frequencies (delta and theta) [11,33,34,38,41]. We hypothesized that complexity measures based on the EEG frequency bands would provide better results than those derived directly from the whole EEG record.…”

Section: Introductionmentioning

confidence: 97%

“…AD causes a reduction in neuronal activity of the brain [52] resulting in decreased capability of the brain to process information [53][54][55] and this may be reflected in the EEG signals [52]. EEG complexity can potentially be a good biomarker for AD diagnosis [38] as AD patients have a significant reduction in EEG complexity [38,40,41,52,56,57]. Several studies have investigated EEG complexity as a potential AD biomarker using whole EEG record with the objective of achieving a high performance.…”

Section: Introductionmentioning

confidence: 99%

Complexity Measures for Quantifying Changes in Electroencephalogram in Alzheimer’s Disease

et al. 2018

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Alzheimer's disease (AD) is a progressive disorder that affects cognitive brain functions and starts many years before its clinical manifestations. A biomarker that provides a quantitative measure of changes in the brain due to AD in the early stages would be useful for early diagnosis of AD, but this would involve dealing with large numbers of people because up to 50% of dementia sufferers do not receive formal diagnosis. Thus, there is a need for accurate, low-cost, and easy to use biomarkers that could be used to detect AD in its early stages. Potentially, electroencephalogram (EEG) based biomarkers can play a vital role in early diagnosis of AD as they can fulfill these needs. This is a cross-sectional study that aims to demonstrate the usefulness of EEG complexity measures in early AD diagnosis. We have focused on the three complexity methods which have shown the greatest promise in the detection of AD, Tsallis entropy (TsEn), Higuchi Fractal Dimension (HFD), and Lempel-Ziv complexity (LZC) methods. Unlike previous approaches, in this study, the complexity measures are derived from EEG frequency bands (instead of the entire EEG) as EEG activities have significant association with AD and this has led to enhanced performance. The results show that AD patients have significantly lower TsEn, HFD, and LZC values for specific EEG frequency bands and for specific EEG channels and that this information can be used to detect AD with a sensitivity and specificity of more than 90%.

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“…It is of great significance for those severely paralyzed patients, because the brain signals extracted from the subjects' scalp can be used to interpret the patients' wishes so that they can achieve communication and control with the outside world by the brain-computer interface (BCI) system [2,3]. The analysis of the EEG signals has also been used to diagnose the presence of diseases [4,5].…”

Section: Introductionmentioning

confidence: 99%

Analyze EEG signals with extreme learning machine based on PMIS feature selection

Zhao¹,

Guo²,

Wang

et al. 2015

Int. J. Mach. Learn. & Cyber.

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An electroencephalogram (EEG)-based braincomputer interface (BCI) system can be used to determine the intents for a paralyzed user by analyzing the EEG signals recorded from his scalp. The key technology of BCI systems is feature extraction and pattern recognition algorithm. Many of the BCI research show that nonlinear classification algorithms perform better than the linear, but they are usually far slower than required. Besides, many of these methods employ less significant features and could make the classification process less efficient. In this paper, a method is provided to identify different patterns of EEG signals. A fast nonlinear classification algorithm extreme learning machine (ELM) is provided to identify two different EEG signals. After feature extraction, we adopt a nonlinear feature selection algorithm partial mutual information-based feature selection (PMIS) to eliminate the less significant features. The experimental result shows that (1) ELM can discriminate the two patterns of EEG signals with a relatively satisfactory accuracy and (2) PMIS feature selection can find out the important features effectively and improve the classification accuracy. The final accuracy is 91.5 % against the best existed result of 88.7 % for the tested data set. The classification performance may be further improved by adding complementary features from EEG. This approach may eventually lead to a reliable EEG-based BCI studies.

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On the Early Diagnosis of Alzheimer’s Disease from EEG Signals: A Mini-Review

Abstract: Abstract. In recent years, various computational approaches have been proposed to diagnose Alzheimer's disease (AD) from EEG recordings. In this paper, we review some of those approaches, and discuss their limitations and potential.

Cited by 26 publications

References 63 publications

On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey

On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey

Complexity Measures for Quantifying Changes in Electroencephalogram in Alzheimer’s Disease

Analyze EEG signals with extreme learning machine based on PMIS feature selection

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