Feature selection before EEG classification supports the diagnosis of Alzheimer’s disease

Trambaiolli, Lucas R.; Spolaôr, Newton; Lorena, Ana Carolina; Anghinah, Renato; Sato, João Ricardo

doi:10.1016/j.clinph.2017.06.251

Cited by 51 publications

(25 citation statements)

References 41 publications

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“…Furthermore, other studies using EEG power have found promising results (Claus et al, 1999, Lehmann et al, 2007, Snaedal et al, 2012 but had low sample size (Claus et al, 1999) or were not able to replicate the findings (Ommundsen et al, 2011). However, more advanced EEG techniques, such as feature (Trambaiolli et al, 2017), entropy (Abasolo et al, 2005), and amplitude modulation (Fraga et al, 2013), have shown promising results but with low sample sizes. In addition, the accuracies above 95% are higher than conventional Alzheimer's disease biomarkers, including fludeoxyglucose PET, cerebrospinal fluid biomarkers, or MRI, which has been shown to be >80% (Frisoni et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

Oscillatory connectivity as a diagnostic marker of dementia due to Alzheimer’s disease

Musaeus

Engedal

Høgh

et al. 2019

Clinical Neurophysiology

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Section: Discussionmentioning

confidence: 99%

Oscillatory connectivity as a diagnostic marker of dementia due to Alzheimer’s disease

Musaeus

Engedal

Høgh

et al. 2019

Clinical Neurophysiology

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“…In particular, Hagmann et al [47] revealed that 200 hours of single-channel EEG recording contains 12% noise leading to erroneous classification. Furthermore, 80% of the EEG features turned out irrelevant in the case of Alzheimer's disease diagnosis [48].…”

Section: Discussionmentioning

confidence: 99%

Artificial Neural Network Classification of Motor-Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity

et al. 2018

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We apply artificial neural network (ANN) for recognition and classification of electroencephalographic (EEG) patterns associated with motor imagery in untrained subjects. Classification accuracy is optimized by reducing complexity of input experimental data. From multichannel EEG recorded by the set of 31 electrodes arranged according to extended international 10-10 system, we select an appropriate type of ANN which reaches 80 ± 10% accuracy for single trial classification. Then, we reduce the number of the EEG channels and obtain an appropriate recognition quality (up to 73 ± 15%) using only 8 electrodes located in frontal lobe. Finally, we analyze the time-frequency structure of EEG signals and find that motor-related features associated with left and right leg motor imagery are more pronounced in the mu (8-13 Hz) and delta (1-5 Hz) brainwaves than in the high-frequency beta brainwave (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). Based on the obtained results, we propose further ANN optimization by preprocessing the EEG signals with a low-pass filter with different cutoffs. We demonstrate that the filtration of high-frequency spectral components significantly enhances the classification performance (up to 90 ± 5% accuracy using 8 electrodes only). The obtained results are of particular interest for the development of brain-computer interfaces for untrained subjects.

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“…In this study, the number of samples in all dataset is considered relatively small for ML applications. This issue is common among many studies which have less than 50 subjects in the dataset used [23]- [25], [27], [29], [32]. It is understood that data scarcity is a major issue in this field.…”

Section: ) Data Augmentationmentioning

confidence: 99%

“…It is understood that data scarcity is a major issue in this field. Segmenting the data samples into smaller, but still informative, segments is a possible remedy for this issue and has been adopted in previous work [27], [32]. A data augmentation process is therefore proposed in this framework.…”

Section: ) Data Augmentationmentioning

confidence: 99%

“…In terms of ML techniques, a multitude of well-established methods have been employed with supervised learning algorithms being the most widespread. Support Vector Machine (SVM) was the most widely used technique [18], [22], [25], [27]. However, simpler supervised learning algorithms such as Linear and Quadratic Discriminant Analysis (LDA, QDA) [28], [29], logistic regression [23], classification tree and random forest [18], [28] had also been used.…”

Section: Introductionmentioning

confidence: 99%

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A Dementia Classification Framework Using Frequency and Time-Frequency Features Based on EEG Signals

Durongbhan

Zhao

Chen

et al. 2019

IEEE Trans. Neural Syst. Rehabil. Eng.

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Alzheimer's Disease (AD) accounts for 60-70% of all dementia cases, and clinical diagnosis at its early stage is extremely difficult. As several new drugs aiming to modify disease progression or alleviate symptoms are being developed, to assess their efficacy, novel robust biomarkers of brain function are urgently required. This study aims to explore a routine to gain such biomarkers using the quantitative analysis of Electroencephalography (QEEG). This paper proposes a supervised classification framework which uses EEG signals to classify healthy controls (HC) and AD participants. The framework consists of data augmentation, feature extraction, K-Nearest Neighbour (KNN) classification, quantitative evaluation and topographic visualisation. Considering the human brain either as a stationary or a dynamical system, both frequency-based and time-frequency-based features were tested in 40 participants. Results: a) The proposed method can achieve up to 99% classification accuracy on short (4s) eyes open EEG epochs, with the KNN algorithm that has best performance when compared to alternative machine learning approaches; b) The features extracted using the wavelet transform produced better classification performance in comparison to the features based on FFT; c) In the spatial domain, the temporal and parietal areas offer the best distinction between healthy controls and AD. The proposed framework can effectively classify HC and AD participants with high accuracy, meanwhile offering identification and localisation of significant QEEG features. These important findings and the proposed classification framework could be used for the development of a biomarker for the diagnosis and monitoring of disease progression in AD.

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Feature selection before EEG classification supports the diagnosis of Alzheimer’s disease

Cited by 51 publications

References 41 publications

Oscillatory connectivity as a diagnostic marker of dementia due to Alzheimer’s disease

Oscillatory connectivity as a diagnostic marker of dementia due to Alzheimer’s disease

Artificial Neural Network Classification of Motor-Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity

A Dementia Classification Framework Using Frequency and Time-Frequency Features Based on EEG Signals

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