MCI detection using transformers for EEG-HRV fusion

The need for multi-modal data in detecting complex relationships between physiological processes to improve anomaly characterization becomes evident with the growing of medical records modalities. Robust fusion techniques are not often used for biomedical data, and current multi-modal approaches need to be employed more effectively. This makes it possible to early identify a variety of anomalies as Alzheimer's disease (AD). To possibly prevent AD, it is essential to identify mild cognitive impairment (MCI) patients who are more likely to develop this disease. Our study makes use of the Consortium to Establish a Registry for Alzheimer's Disease (CERAD). This protocol has become a prevalent clinical method for a variety of dementias, including MCI. Numerous modalities including electroencephalography (EEG) and heart rate variability (HRV) can be used as helpful tools for MCI diagnosis. In this research, we developed a new deep learning (DL) method using the fusion of EEG and HRV signals acquired during the CERAD task. Using our data, a Transformer architecture was adapted to categorize participants into MCI and healthy control (HC). Our experimental results show that the proposed method has a benefit over the state-of-the-art in terms of classification accuracy. An accuracy test of 95.02%, a sensitivity of 95.47%, an F1 score of 95.03%, and a precision of 94.57% were achieved due to the proposed method.