SEGNET-BASED DEEP REPRESENTATION LEARNING FOR DYSPHAGIA CLASSIFICATION

Subramani, S and Rao, AMV and Roy, A and Hegde, PS and Ghosh, PK (2022) SEGNET-BASED DEEP REPRESENTATION LEARNING FOR DYSPHAGIA CLASSIFICATION. In: 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022, 23 - 27 May 2022, Virtual, Online at Singapore, pp. 831-835.

PDF IEEE_ICASSP 2022_2022_831-835_2022.pdf - Published Version Restricted to Registered users only Download (1MB) | Request a copy

Abstract

Swallowing disorders, broadly known as Dysphagia, are difficulties in the process of swallowing food. Many currently available methods for classifying healthy and dysphagic swallows typically use hand-picked acoustic features. This article presents a SegNet-based method for classifying healthy and dysphagic swallow signals by learning mel-spectrogram features. Swallow sounds were recorded from a total of 24 subjects using a microphone based cervical auscultation (CA) system. Each subject swallowed multiple samples of water of volumes 5ml, 10ml and 15ml, and also performed multiple dry swallows. The experiments investigated the significance of temporal structures in the SegNet-learnt representations. The classification performance was evaluated at different model depths in order to identify the optimum feature time-scale that maximized the classification performance. The proposed method was found to be more robust to variations in the signatures of swallow signals across multiple volumes of water, against a baseline method across a single volume of water. The best performing model yielded a mean test F1-score of 80.13 (±4.62) in a 5-fold cross validation setup.

Item Type:	Conference Paper
Publication:	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to the Institute of Electrical and Electronics Engineers Inc.
Keywords:	Biological organs, 'Dry' ; Acoustic features; Cervical auscultation; Classification performance; Dysphagia classification; Multiple samples; Segnet; Spectrograms; Swallowing disorders; Two-step training, Deep learning
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	04 Aug 2022 11:41
Last Modified:	04 Aug 2022 11:41
URI:	https://eprints.iisc.ac.in/id/eprint/75349

Actions (login required)

View Item