Screening of Mild Cognitive Impairment Subtypes Through the Training of 1D Convolutional Neural Network with QEEG Features

Abstract

AbstractBackgroundThe pre‐clinical stage of dementia, mild cognitive impairment (MCI) carries various pathological pathways and respective prognoses. The two foremost causes of MCI and dementia that have been identified through autopsy diagnoses are Amyloid plaques and Lewy body, commonly referred to as Alzheimer’s disease (AD) and Lewy body disease (LBD) respectively. Although correct identification of pathological pathways plays a crucial role in effective designing of treatment methods, MCI subtypes are underexploited in the current diagnostic paradigm due to multiple reasons, mainly cost‐effectiveness. Hence, we utilized quantitative electroencephalography (QEEG) that are cheap and convenient to record in the distinguishment of MCI subtypes.MethodA total of 151 MCI patients’ EEG data were aggregated into 3 groups, pure AD (n = 29), pure LBD (n = 88), and mixed (n = 34). The mixed type was further subdivided into main AD (n = 31) and main LBD (n = 3) groups, in accordance with AD/LBD tendency exhibited by the patients. The clinical labelling was brought by the experienced experts of Yonsei Severance hospital, South Korea.1D SE‐ResNet‐based classification model was established for quantitative investigation of AD and LBD propensity. The power spectrum density (PSD) in dB/Hz scale were computed from the EEG data and were used to train the model. Due to the small number of data, augmentation was applied to the training data. The final dataset was split into 8 to 1 to 1 ratio (Train n = 7092: 2274 pure AD + main AD; 3694 pure LBD + main LBD, Validation n = 14: 6 pure AD + main AD; 8 Pure LBD + main LBD, Test n = 15: 6 pure AD + main AD; 9 pure LBD + main LBD).ResultThe classification model showed validation results of 85.7% accuracy, 83.3% AD sensitivity and 87.5% LBD sensitivity. Test results were at 92.8% accuracy, 83.3% AD sensitivity and 88.9% LBD sensitivity.ConclusionQEEG‐based deep learning classifier developed in this study successfully distinguished the two main causes of cognitive degeneration in MCI patients. Our model can help identify subtype‐specific spectral trends, which could also make contributions in the establishment of effective treatment methods for MCI.