Amyotrophic lateral sclerosis (ALS) is a rapidly progressive neurodegenerative disease of the motor system that leads to the impairment of speech and swallowing functions. The lack of a biomarker typically causes a diagnostic delay. To advance the current diagnostic process, we explored the feasibility of automatic detection of patients with ALS at an early stage from highly intelligible speech. A speech dataset was collected from thirteen newly diagnosed patients with ALS and thirteen age- and gender-matched healthy controls. Convolutional Neural Networks (CNNs), including time-domain CNN and frequency-domain CNN, were used to classify the intelligible speech produced by patients with ALS and those by healthy individuals. Experimental results indicated both time- and frequency-CNN outperformed standard neural network. The best sample-level sensitivity and specificity were obtained by time-CNN (71.6% and 80.9%, respectively). When multiple samples were used to vote to estimate a person-level performance, the best result was obtained by frequency-CNN (76.9% sensitivity and 92.3% specificity). Results demonstrated the possibility of early detection of ALS from intelligible speech signals.
DOI: 10.21437/Interspeech.2018-2496
Cite as: An, K., Kim, M., Teplansky, K., Green, J., Campbell, T., Yunusova, Y., Heitzman, D., Wang, J. (2018) Automatic Early Detection of Amyotrophic Lateral Sclerosis from Intelligible Speech Using Convolutional Neural Networks. Proc. Interspeech 2018, 1913-1917, DOI: 10.21437/Interspeech.2018-2496.
@inproceedings{An2018,
author={Kwanghoon An and Myungjong Kim and Kristin Teplansky and Jordan Green and Thomas Campbell and Yana Yunusova and Daragh Heitzman and Jun Wang},
title={Automatic Early Detection of Amyotrophic Lateral Sclerosis from Intelligible Speech Using Convolutional Neural Networks},
year=2018,
booktitle={Proc. Interspeech 2018},
pages={1913--1917},
doi={10.21437/Interspeech.2018-2496},
url={http://dx.doi.org/10.21437/Interspeech.2018-2496}
}