Multi-scale neural networks classification of mild cognitive impairment using functional near-infrared spectroscopy

Abstract

Mild cognitive impairment (MCI) is recognized as an early stage preceding Alzheimer’s disease. Functional near-infrared spectroscopy (fNIRS) has recently been used to differentiate MCI patients from healthy controls (HCs) by analyzing their hemodynamic responses. This paper proposes a new method that uses the entire time series data from all fNIRS channels, skipping the feature extraction step. It involves a multi-scale convolutional neural network (CNN) integrated with long short-term memory (LSTM) layers to extract spatial and temporal features simultaneously. The study involves 64 participants (37 MCI patients and 27 HCs) performing three mental tasks: N-back, Stroop, and verbal fluency tests (VFT). The algorithm’s performance was assessed using 10-fold cross-validation across oxyhemoglobin (HbO), deoxyhemoglobin (HbR), and total hemoglobin (HbT). The highest classification accuracies were achieved with HbT, reaching 93.22 % for the N-back task, 91.14 % for the Stroop task, and 89.58 % for the VFT. It was found that using all types of hemodynamic signals from all channels provides better results than analyzing the region of interest data, eliminating the need for data segmentation and feature extraction procedures. Additionally, HbR (or HbT) gives better classification accuracy than HbO. The developed method can be implemented online for clinical applications and real-time monitoring of cognitive disorders.