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Published on: March 1, 2015
Swallowing detection by sonic and subsonic frequencies: a comparison
Juan M Fontana1, Pedro L Melo, Edward S Sazonov
1Department of Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL 35487, USA. jmfontana@bama.ua.edu
Summary
Acoustic detection of swallowing events is crucial for diagnosing swallowing disorders. This study found that subsonic frequencies did not significantly reduce artifacts from speech or chewing compared to sonic frequencies, indicating no improvement in detection accuracy.
Area of Science:
- Biomedical Engineering
- Acoustics
- Speech and Hearing Science
Background:
- Acoustic detection of swallowing is vital for diagnosing swallowing disorders and monitoring ingestive behavior.
- Swallowing sound detection can be compromised by artifacts from speech, chewing, and ambient noise.
- Investigating frequency ranges for improved acoustic swallowing detection is necessary.
Purpose of the Study:
- To compare the accuracy of swallowing detection using acoustic signals in subsonic (≤ 5 Hz) versus sonic (20-2500 Hz) frequency ranges.
- To determine if subsonic frequencies are less susceptible to artifacts from intrinsic sounds like speech and chewing.
Main Methods:
- Employed a simple method using throat and ambient microphones to capture acoustic signals.
- Acquired and analyzed swallowing sounds in both subsonic and sonic frequency ranges.
- Calculated and compared averaged recall and precision values for each frequency range.
Main Results:
- Averaged recall values exceeded 85% for both subsonic and sonic frequency ranges.
- Averaged precision values were 50% for subsonic and 42% for sonic frequencies, with high false positive rates in both.
- No significant difference in averaged precision values was observed between the two frequency ranges.
Conclusions:
- Subsonic frequencies did not demonstrate a significant advantage over sonic frequencies in reducing intrinsic sound artifacts for swallowing detection.
- The current acoustic detection method using these frequency ranges shows similar susceptibility to artifacts.
- Future research should incorporate a signal classification layer to potentially improve artifact differentiation and detection accuracy.

