Alzheimer's Disease Classification With a Cascade Neural Network

Abstract

Classification of Alzheimer's Disease (AD) has been becoming a hot issue along with the rapidly increasing number of patients. This task remains tremendously challenging due to the limited data and the difficulties in detecting mild cognitive impairment (MCI). Existing methods use gait [or EEG (electroencephalogram)] data only to tackle this task. Although the gait data acquisition procedure is cheap and simple, the methods relying on gait data often fail to detect the slight difference between MCI and AD. The methods that use EEG data can detect the difference more precisely, but collecting EEG data from both HC (health controls) and patients is very time-consuming. More critically, these methods often convert EEG records into the frequency domain and thus inevitably lose the spatial and temporal information, which is essential to capture the connectivity and synchronization among different brain regions. This paper proposes a cascade neural network with two steps to achieve a faster and more accurate AD classification by exploiting gait and EEG data simultaneously. In the first step, we propose attention-based spatial temporal graph convolutional networks to extract the features from the skeleton sequences (i.e., gait) captured by Kinect (a commonly used sensor) to distinguish between HC and patients. In the second step, we propose spatial temporal convolutional networks to fully exploit the spatial and temporal information of EEG data and classify the patients into MCI or AD eventually. We collect gait and EEG data from 35 cognitively health controls, 35 MCI, and 17 AD patients to evaluate our proposed method. Experimental results show that our method significantly outperforms other AD diagnosis methods (91.07 vs. 68.18%) in the three-way AD classification task (HC, MCI, and AD). Moreover, we empirically found that the lower body and right upper limb are more important for the early diagnosis of AD than other body parts. We believe this interesting finding can be helpful for clinical researches.

Resting state electroencephalography contributes to the detection of disease progression and prediction of repetitive transcranial magnetic stimulation modulations in patients with subjective cognitive decline

“…Aberrant EEG oscillations in the β rhythm suggest possible cognitive and sensorimotor functional alterations in the SCD stage in this study. Gait-related literature supports the notion that impaired motor function occurs as early as in the MCI stage 42 .…”

“…Cortical regions of interest (ROIs) here were de ned as the 116 areas of the AAL. The frequency-series of the current density was extracted for all cortical ROIs in ve frequency bands: δ (2-4 Hz), θ (4-8 Hz), α (8-13 Hz), β (14-30 Hz), and low-gamma (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45). The source activities of each frequency band were extracted by ltering through Hamming windowed FIR lters.…”

Machine Learning for Diagnosis of Alzheimer’s Disease and Early Stages

“…The large number of samples (6,028) used it also relevant when compared with similar investigations [10]. The results show that our model has outperformed other modern experiments [5][6][7]14,24,25]. A more detailed comparison with some of the most promising investigations conducted to date is made in Table 8.…”

An EEG-based systematic explainable detection framework for probing and localizing abnormal patterns in Alzheimer’s disease

“…We reproduced the state-of-the-art algorithms to automatically detect AD patterns proposed in recent articles [81][82][83][84]. For those methods, the configurations clearly stated in the original content were set according to their papers, the flexible parameters were investigated to obtain the best performance on our data.…”