Novel Approach Combining Shallow Learning and Ensemble Learning for the Automated Detection of Swallowing Sounds in a Clinical Database

Abstract

Cervical auscultation is a simple, noninvasive method for diagnosing dysphagia, although the reliability of the method largely depends on the subjectivity and experience of the evaluator. Recently developed methods for the automatic detection of swallowing sounds facilitate a rough automatic diagnosis of dysphagia, although a reliable method of detection specialized in the peculiar feature patterns of swallowing sounds in actual clinical conditions has not been established. We investigated a novel approach for automatically detecting swallowing sounds by a method wherein basic statistics and dynamic features were extracted based on acoustic features: Mel Frequency Cepstral Coefficients and Mel Frequency Magnitude Coefficients, and an ensemble learning model combining Support Vector Machine and Multi-Layer Perceptron were applied. The evaluation of the effectiveness of the proposed method, based on a swallowing-sounds database synchronized to a video fluorographic swallowing study compiled from 74 advanced-age patients with dysphagia, demonstrated an outstanding performance. It achieved an F1-micro average of approximately 0.92 and an accuracy of 95.20%. The method, proven effective in the current clinical recording database, suggests a significant advancement in the objectivity of cervical auscultation. However, validating its efficacy in other databases is crucial for confirming its broad applicability and potential impact.

Keywords: automated detection; swallowing sound.

Figure 1

Example of swallowing sounds detected from video fluorographic swallowing study (VFSS).

Figure 2

Steps from recording data to loud event detection. (a) Recording data waveform, including swallowing during jelly ingestion (red vertical line area is the swallowing-sound interval). (b) Waveform of recording data after bandpass filter processing. (c) Spectrogram for recording data waveform. (d) Recording of loud events and swallowing events (light-colored highlighted sections).

Figure 3

Steps for calculating the MFCC and MFMC.

Figure 4

Comparison of number of feature dimensions and swallowing-sound classification performance.