Using Spectral Sequence-to-Sequence Autoencoders to Assess Mild Cognitive Impairment

• Published in: Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 6467 - 6471
• Main Authors: Vetrab, Mercedes, Egas-Lopez, Jose Vicente, Balogh, Reka, Imre, Nora, Hoffmann, Ildiko, Toth, Laszlo, Pakaski, Magdolna, Kalman, Janos, Gosztolya, Gabor
• Format: Conference Proceeding
• Language: English
• Published: IEEE 23.05.2022
• Subjects: Alzheimer's disease; Cognition; dementia; Feature extraction; mild cognitive impairment; sequence-to-sequence autoencoders; Signal processing; Speech analysis; Speech coding; Training data
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP43922.2022.9746148

Dementia is a chronic or progressive clinical syndrome, mainly characterized by the deterioration of memory, thinking, reasoning and language. In Mild cognitive impairment (MCI), often considered as the prodromal stage of dementia, there is also a subtle deterioration of these functions, but they do not affect the daily life of the patient. However, due to the slight nature of the changes, it is quite hard to diagnose MCI. In this study, we employ sequence-to-sequence deep autoencoders in order to extract compact, robust and efficient attributes from the spontaneous speech of 25 MCI subjects and 25 healthy controls. From our results, this approach gives a competitive performance, as we significantly outperformed x-vectors even though they were trained on more data. Our additional efforts to identify mild Alzheimer's (mAD) subjects as well were less successful; but since the focus is on the early detection of dementia, this is not a limitation of the methodology from a practical point of view.