Deep learning in the EEG diagnosis of Alzheimer’s disease

Abstract

EEG (electroencephalogram) has a lot of advantages compared to other methods in the analysis of Alzheimer’s disease such as diagnosing Alzheimer’s disease in an early stage. Traditional EEG analysis method needs a lot of artificial works such as calculating coherence between different pair of electrodes. In our work we applied deep learning network in the analysis of EEG data of Alzheimer’s disease to fully use the advantage of the unsupervised feature learning. We studied EEG based deep learning on 15 clinically diagnosed Alzheimer’s disease patients and 15 healthy people. Each person has 16 electrodes. The time domain EEG data of each electrode is cut into 40 data units according to the data size in a period. In our work we first train the deep learning network with 25 data units on each electrode separately and then test with 15 data units to get the accuracy on each electrode. Finally we will combine the learning results on 16 electrodes and train them with SVM and get a final result. We report a 92%accuracy after combining 16 electrodes of each person. In order to improve the deep learning model on Alzheimer’s disease with the upcoming new data, we use incremental learning to make full use of the existing data while decrease the expenses on memory space and computing time by replacing the exising data with new data. We report a 0.5% improvement in accuracy with incremental learning.