Alzheimer’s Disease (AD
) is neurogenerative disease of unknown etiology with a great prevalence in western countries [1
]. Patients with AD
are characterized with a loss of memory, sleeping problems, mood disorders, and general confusion, which are caused by structural irregularities or damage in the synaptic connections, due to amyloid-β plaques and neurofibrillary tangles [2
]. In a recent Alzheimer’s report of 2018 [3
], the worldwide AD
prevalence was about 33 million patients out of 50 million people suffering from dementia, making AD
the most common type of dementia.
A variety of diagnostics procedures are performed to evaluate the cognitive and neuropsychological state of patients with dementia, including neuronal and physical examination, brain imaging, and electroencephalographic (EEG
) recording. The Mini-Mental State Examination (MMSE
] and the Clinical Dementia Rating (CDR
] score are a 30-point scale and a 5-point scale respectively, which are utilized by neurologists to evaluate the cognitive decline and functional performance of patients with AD
. Higher values of the CDR
score indicate a more severe condition, whereas higher values of the MMSE
score shows very mild dementia and a healthy condition (MMSE
An analysis of the EEG
recordings in AD
patients is of significant importance, since information of the brain dynamics may shed light on the exact mechanisms of AD
]. Research studies in AD
over the past 40 years have indicated the alterations in EEG
complexity, synchrony, and brain dynamics (the slowing of alpha rhythm and the diffuse dominance of theta or delta rhythm) [7
]. Several studies have been proposed aimed at finding a correlation between the MMSE
score and EEG
] or discriminating AD
patients from patients with other neurological conditions through their EEG
findings. In particular, methods have been proposed for the automated discrimination of AD
patients from healthy elderly subjects [10
], frontotemporal dementia [17
], vascular dementia [18
], Mild Cognitive Impairment (MCI
], or even epilepsy [21
]. Generally, the EEG
activity is analyzed from each electrode site [6
] or from electrode clusters [7
]. Studies concerning the structural and functional asymmetry have reported that an early onset of AD affects different lobes [24
]. Thus, an analysis of the EEG
based on electrode clusters that depict different cortical regions may reveal anatomical deficits or differences in the neuronal connection due to other mechanisms [8
detection from EEG
findings, researchers have suggested several different features, which represent EEG
complexity, synchrony, and regularity. Relative band power [12
], absolute band power [18
], Lempel–Ziv complexity [12
], Permutation entropy [10
], Sample entropy [17
], Spectral entropy [11
], Fuzzy entropy [20
], automutual information [17
], mean frequency [17
] amplitude modulation [10
], central tendency [17
], mean [12
], variance [12
], and zero-crossing [12
] are the most frequently extracted EEG
features for AD
detection. The features are extracted directly from raw EEG
] or after a signal decomposition with a Wavelet Analysis [25
], Power Spectral Density using Berg’s method [28
], Hilbert–Huang Transform [10
], or Multivariate Multiscale Analysis [11
]. Concerning the epoch duration in which the signal is segmented, there is no common agreement regarding the appropriate window length and there is a diversity among research studies [10
]. According to the literature, the EEG
window length is usually selected between 5 s to 12 s arbitrarily or based on literature survey.
In this study, a method for automated detection of Alzheimer’s Disease is proposed. EEG recordings from AD patients with moderate and mild AD are analyzed along with the EEG data from healthy, age-matched individuals in epochs of different length (ranging from 5 to 12 s). The features from both the time and frequency domains are extracted, forming the feature vector to train several classifiers. The evaluation of the window length shows that epochs of 12 s with Random Forests indicate the best classification performance for six classification problems and 5 different brain regions of interest. To the best of our knowledge, this is the first comprehensive study examining a variety of features over multiple window lengths and showing a high classification accuracy. The results of the methodology are presented below.
The paper is organized as follows: In Section 2
, the methodology and the extracted linear and nonlinear EEG
features are addressed. Section 3
presents the obtained results for six classification problems, and Section 4
discusses the obtained results compared to literature findings. Finally, in Section 5
, the conclusion and the future directions of this study are presented.
To evaluate the EEG window length and the proposed methodology, 6 classification problems are created. In the first problem, the group of 10 healthy subjects forms the class “controls” (CN), whereas the EEG features of all of the 14 AD patients are merged and forms the class “Alzheimer’s” (AD), resulting in the problem CN/AD. In the second problem (CN/mild/moderate), the AD group is further divided into the “mild” and “moderate” classes, corresponding to the groups of patients with mild AD (8 patients) and moderate AD (6 patients), respectively. The third problem is a 2-class problem between the controls and mild AD patients (CN/mild), whereas the forth problem consists of EEG features of the controls and moderate AD patients (CN/moderate). The fifth problem is a classification between two groups. The first group includes the moderate AD patients, and the second group consists of EEG data from the controls and patients with mild AD (CN-mild/moderate). Finally, the sixth problem corresponds to the classification among mild and moderate AD patients (mild/moderate).
The classifier’s performance is evaluated with Accuracy, Precision, F1-score, and kappa statistics. The accuracy of the classification shows the ability of the classifier to differentiate AD
subjects from healthy subjects, healthy subjects from AD
stages, and mild AD
patients from moderate AD
patients. The precision of the classification between AD
patients and healthy subjects examines whether the correctly classified instances of AD
patients are actual AD
patients and whether the rest are healthy subjects incorrectly labeled as AD
. On the other hand, the F1-score expresses the average of the precision and recall, wherein the recall shows whether the instances that should have been classified as AD
are actually labeled as AD
patients. The Kappa statistic evaluates the correctly classified instances and those that have been classified randomly owing to uncertainty [33
]. The results for the six classification problems for 8 different window lengths (ranging from 5 to 12 s) are depicted in Table 3
. For the 3-class problem (CN/mild/moderate), the average values are presented.
The best window length is 12 s for all classification problems with the classification accuracy ranging from 88.79% to 96.76% for the CN/mild/moderate and CN/moderate problems. The CN-mild/moderate problem indicates the second highest value of accuracy (94.99%), followed by CN/AD (91.80%), CN/mild (91.77%), and mild/moderate (91.71%). On the other hand, the worst classification results are obtained for epochs of 5 s. Likewise, CN/moderate shows the highest accuracy (94.68%), followed by the CN-mild/moderate (92.59%), mild/moderate (87.63%), CN/AD (86.98%), CN/mild (86.60%), and the 3-class problem CN/mild/moderate that succeeded the worst accuracy (82.34%). The classification accuracies for epochs of 6, 7, 8, 9, 10, and 11 s are gradually increased.
In Figure 2
, a visualization of the obtained accuracy for each classification problem over different window lengths is presented.
Τhe rest of the analysis is conducted solely for the 12-s window length, which is the best classification window length according to the analysis. Table 4
presents the classification results (Accuracy, Precision, F1-score, and kappa statistics) as obtained for the best window length.
The best classification accuracy (96.76%), which also shows the highest kappa statistic (0.9069) and F1-score = 0.9277, is obtained for the 2-class problem CN/moderate, followed by the CN-mild/moderate (94.99%) with a kappa statistic of 0.8079 and an F1-score = 0.8372 and the CN-AD (91.80%) with a kappa statistic of 0.8340 and an F1-score = 0.9077. The 3-class problem CN-mild-moderate indicates the worse classification accuracy (88.79%) with a kappa of 0.8860 and an F1-score = 0.8474. The discrimination between the controls from mild Alzheimer’s (CN-mild) and between mild AD from moderate AD (mild-moderate) presents almost the same classification accuracy (91.77% and 91.71%, respectively) with the kappa statistics being 0.8132 and 0.8194, respectively, and the F1-scores equal to 0.8739 and 0.8837, respectively.
Furthermore, since the examination of different cortical regions is significant in AD
, the electrodes are grouped in 5 groups, as proposed in previous studies [8
] in order to capture the differences in the brain activities among subject groups in different brain regions. Thus, the 6 classification problems are also examined for epochs of 12 s for the anterior (Fp1, F3, Fz, Fp2, and F4), central (C3, Cz, and C4), left temporal (F7, T3, and T5), right temporal (F8, T4, and T6), and posterior (O1, O2, P3, Pz, and P4) clusters. The results are presented in Table 5
. For the 3-class problem (CN/mild/moderate), the average values are presented.
A discrimination among the healthy subjects and Moderate AD patients (CN/moderate) indicates the best classification accuracy for all electrode clusters, ranging from 96.39% to 97.72% with kappa statistics from 0.8957 to 0.9338 and F1-scores from 0.9188 to 0.9469 for the anterior cluster, the right side of the temporal region, the left side of the temporal region, the central region, and the posterior region.
For the 2-class problem “CN-mild/moderate”, the central region shows the best classification results (ACC = 97.19%, kappa = 0.8796, and F1-score = 0.9163), followed by the posterior region (ACC = 96.95%, kappa = 0.8492, and F1-score = 0.9425), the left side of the temporal region (ACC = 95.71%, kappa = 0.8348, and F1-score = 0.8599), the right side of the temporal region (ACC = 95.23%, kappa = 0.8156, and F1-score = 0.9480), and the anterior cluster (ACC = 94.37%, kappa = 0.7833, and F1-score = 0.8161). For the 2-class problem “mild-moderate”, the best classification accuracy is 96.24% (kappa = 0.921 and F1-score = 0.9518) for the central cluster, followed by 94.66% (kappa = 0.8828 and F1-score = 0.9239) for the posterior cluster, 94.28% (kappa = 0.8778 and F1-score = 0.9234) for the temporal/left, 92.57% (kappa = 0.8339 and F1-score = 0.8884) for the temporal/right, and the worse accuracy 90.03% (kappa = 0.7883 and F1-score = 0.8610) for the anterior cluster.
For the classification problem “CN/mild”, the highest accuracy is 94.87% for the central cluster (kappa = 0.8807 and F1-score = 0.9179), followed by 93.55% (kappa = 0.8566 and F1-score = 0.9055) for the posterior cluster, 92.18% (kappa = 0.8186 and F1-score = 0.8754) for the temporal/left, 91.02% (kappa = 0.8065 and F1-score = 0.8769), and 90.84% (kappa = 0.7894 and F1-score = 0.8561) for both the temporal/right and anterior clusters.
The classification of Alzheimer’s concerning controls group (CN/AD) presents good classification results with accuracies ranging from 90.99% to 94.76% (temporal/right, anterior, temporal/left, posterior, and central), with kappa statistics from 0.8194 to 0.8936, and with F1-scores from 0.9148 to 0.9534. The worst classification performance is obtained for the 3-class problem (CN/mild/moderate) with an accuracy ranging from 87.67% to 93.80%, with kappa from 0.7861 to 0.8930, and with an F1-score from 0.8041 to 0.9051 for the anterior cluster, the right side of the temporal region, the left side of the temporal region, the posterior region, and the central region. A visualization of the obtained accuracy range for each classification problem is depicted in Figure 3
. Figure 4
represents the classification accuracy in each cluster for each classification problem.
In this study, a methodology for the detection of AD-related dynamics from the whole brain and from specific brain regions of interest was presented. The statistical and spectral features were calculated from the EEG segments of different lengths acquired from 14 patients with AD and 10 healthy subjects, which were used to train and test a Random Forests classifier. Six different classification problems were conducted for the evaluation of the proposed method.
The proposed methodology showed significant results in the discrimination between healthy elderly and AD-related patient groups and in the characterization of the disease (mild/moderate). With regard to the window length, the results showed a high classification accuracy as the length of the window was gradually increasing, and the best classification results were obtained for epochs of 12 s.
Furthermore, in this study, the brain asymmetry was examined since it was highly related to EEG information processing [34
]. Generally, healthy elderly individuals showed a cortical atrophy which was predominantly affected by age and gradually resulted in MCI
without significant functional alterations. Brain asymmetry in healthy individuals was present mainly in the right temporal lobe due to cortical thinning, and higher dynamics were shown. On the other hand, in AD
patients, diffuse cortical atrophy, brain disfunction, and lower dynamics over the cerebral cortex were shown. The symptoms of patients with AD
were due to pathological alterations in many regions of the cerebral cortex and became more severe as the disease progressed. The hippocampus was predominantly affected by AD
, and hippocampal asymmetry was significantly reduced in AD
patients. Also, functional magnetic resonance imaging (fMRI) studies [36
] have shown additional atrophy in AD
patients with AD
in the medial temporal cortex, and it was evidence that the degree of brain asymmetry progressively decreased in AD
]. The obtained results were consistent with the literature findings regarding functional abnormalities in AD
patients compared to healthy, age-matched individuals. The results of the study indicated that AD
was diagnosed better from EEG
signals at the central and occipitoparietal regions and the left side of the temporal lobe than at the frontal area and at the right side of the temporal lobe. AD
-related brain dynamics were discriminated from the ones acquired from healthy subjects better at the central and posterior regions for all classification problems (CN/mild, CN/moderate, CN/AD, CN-mild/moderate, and CN/mild/moderate) and the 2-class disease severity (mild-moderate). This outcome is in line with literature that suggests that the occipitoparietal area [1
] and the left side of the brain [11
] are more affected in AD
than the frontal area and right hemisphere.
Also, a classification between healthy elderly subjects and dementia patients with moderate AD (CN-mild/moderate and CN/moderate) showed the best classification accuracy for a whole-brain classification and for each cluster separately. Undoubtedly, it was easier for the classifier to capture EEG changes between healthy elderly and AD patients with more severe disease progress, than between healthy individuals and mild AD patients, who showed less cognitive decline. Furthermore, the most challenging classification problem was the 3-class problem (CN/mild/moderate), which presented the worse performance in both the entire-brain classification and for each cluster. The low accuracy of this problem is mainly attributed to the misclassification of the mild AD group as the control group.
Most of previous studies [19
] dealt with healthy elderly subjects, patients with AD
, and patients with MCI
, which is a prodromal stage of AD
, not a category [19
]. In this study, MCI
patients were not included in the analysis. Therefore, it was not straightforward to compare the results of this study with previous reports related to MCI
, and so, these studies were excluded from the comparison. The proposed method with statistical, spectral, and nonlinear features and Random Forests outperformed in the classification accuracy of a previous study [10
] for all of the four binary classification problems (CN/AD, CN/mild, CN/moderate, and mild/moderate). Falk et al. [10
] proposed a method wherein the Hilbert–Huang Transform was used to decompose EEG
signals in 5 frequency bands, and then, the percentage modulation energy (PME) was extracted for each EEG
rhythm. Support Vector Machines (SVM) were trained and tested with PME and obtained a 90.60% classification accuracy for the CN/AD problem. For the same classification problem, a Linear Discriminant Analysis classifier in a study [13
] indicated a 90% accuracy with a maximum detrended cross-correlation coefficient when the C3-P3 channels were used as the input.
High levels of accuracy above 96% were obtained in References [11
]. Kulkarni et al. [12
] extracted wavelet, spectral, and complexity features from 50 AD patients and 50 healthy, age-matched subjects. The feature vector of the complexity features with SVM obtained a classification accuracy of 96% for the discrimination of AD
patients from the controls (AD-CN); however, the MMSE
score was not reported. Also, in Reference [14
], the authors proposed a brain functional network construction method based on the calculation of multiscale entropy and evaluated several classifiers. The classification accuracy for the CN/AD problem with the k-Nearest Neighbor was above 96%. Nevertheless, the MMSE
score of the AD
subjects included in this study ranged from moderate AD
score = 21.3 ± 5.8). Therefore, since, in our study, no MCI
patients were included, a comparison with Reference [14
] was not straightforward.
detection method was proposed in Reference [11
], in which the proposed Multivariate Multiscale Weighted Permutation Entropy method with ROC
curves achieved a 96.70% accuracy in the right frontal to the left occipitoparietal regions. However, the MMSE
score of AD
patients in this study ranged from 12–15, indicating a moderate AD
stage. Thus, it was feasible to compare the abovementioned classification with the results of the “CN/moderate” problem of the proposed methodology, which showed a slightly better classification accuracy.
Simons and Abasolo [15
] proposed a distance-based Lempel–Ziv complexity (dLZC) method to characterize the changes between pairs of electrodes and succeeded with a 78.25% accuracy for the O1–O2 pair. A comparison of the proposed methodology with previous studies is presented in Table 6