Higuchi fractal dimension of the electroencephalogram as a biomarker for early detection of Alzheimer's disease

Abstract

It is widely accepted that early diagnosis of Alzheimer's disease (AD) makes it possible for patients to gain access to appropriate health care services and would facilitate the development of new therapies. AD starts many years before its clinical manifestations and a biomarker that provides a measure of changes in the brain in this period would be useful for early diagnosis of AD. Given the rapid increase in the number of older people suffering from AD, there is a need for an accurate, low-cost and easy to use biomarkers that could be used to detect AD in its early stages. Potentially, the electroencephalogram (EEG) can play a vital role in this but at present, no reliable EEG biomarker exists for early diagnosis of AD. The gradual slowing of brain activity caused by AD starts from the back of the brain and spreads out towards other parts. Consequently, determining the brain regions that are first affected by AD may be useful in its early diagnosis. Higuchi fractal dimension (HFD) has characteristics which make it suited to capturing region-specific neural changes due to AD. The aim of this study is to investigate the potential of HFD of the EEG as a biomarker which is associated with the brain region first affected by AD. Mean HFD value was calculated for all channels of EEG signals recorded from 52 subjects (20-AD and 32-normal). Then, p-values were calculated between the two groups (AD and normal) to detect EEG channels that have a significant association with AD. k-nearest neighbor (KNN) algorithm was used to compute the distance between AD patients and normal subjects in the classification. Our results show that AD patients have significantly lower HFD values in the parietal areas. HFD values for channels in these areas were used to discriminate between AD and normal subjects with a sensitivity and specificity values of 100% and 80%, respectively.

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

“…In this case, the results show that HFD values for AD patients are lower than those for normal subjects. This result is consistent with the finding in other studies [46,80]. As with the TsEn, the differences between HFD biomarkers for AD patients and normal subjects for the EEG frequency bands (i.e., delta, theta, and alpha bands) were Fp1 Fp2 F7 F3 FZ F4 F8 T3 C3 CZ C4 T4 T5 P3 PZ P4 T6 O1 O2 EEG channel Whole EEG …”

“…In addition, HFD provides a more accurate measure of the complexity of signals compared to other methods [75,78,79] and it has been shown to be an efficient method for discriminating between AD patients and normal subjects [31,80]. HFD of the EEG is potentially a good biomarker of AD diagnosis as it is significantly lower in AD patients than in normal subjects [46,80,81]. Smits et al found that HFD is sensitive to neural changes selectively related to AD patients and normal subjects.…”

“…Smits et al found that HFD is sensitive to neural changes selectively related to AD patients and normal subjects. Al-Nuaimi et al [46] investigated HFD of EEG for AD diagnosis and they found that HFD is a promising EEG biomarker that captures changes in the regions of the brain thought to be affected first by AD and it could be used to detect AD with sensitivity and specificity values of 100% and 80%, respectively.…”

“…AD biomarkers based on the slowing in EEG and a decrease in EEG coherence are often derived using linear analysis methods (i.e., spectral analysis of the EEG signal) [37,44,45], while biomarkers extracted by analysing the complexity of the EEG are based on nonlinear approaches (e.g., entropy methods, fractal dimension, and Lempel-Ziv complexity). The EEG complexity approaches have shown promising results in AD diagnosis [11,35,46] and appear to be appropriate for AD diagnosis [38,47,48]. Complexity is a measure of the extent to which the dynamic behavior of a given sequence resembles a random one [49].…”

Classification Methods Based on Complexity and Synchronization of Electroencephalography Signals in Alzheimer’s Disease

“…To evaluate the complexity in the EEG signals in patients with AD, some studies have investigated an approach focusing on deterministic chaos and fractal dimension such as correlation dimension and Lyapunov exponent (24). These studies reported reduced complexity in the neural activity of patients with AD (8,(25)(26)(27)(28)(29)(30). While, EEG dynamics plays a different role at each temporal-scale, such as memory function, cognitive function, and perceptual function in the theta, beta, and gamma bands, respectively (31).…”