Automatic identification of snoring and groaning segments in acoustic recordings
Author(s)
Kok, Xuen
Imtiaz, syed anas
Rodriguez Villegas, esther
Type
Conference Paper
Abstract
Sleep-related breathing disorders have severe im-
pact on the quality of lives of those suffering from them. These
disorders present with a variety of symptoms, out of which
snoring and groaning are very common. This paper presents
an algorithm to identify and classify segments of acoustic
respiratory sound recordings that contain both groaning and
snoring events. The recordings were obtained from a database
containing 20 subjects from which features based on the Mel-
frequency cepstral coefficients (MFCC) were extracted. In the
first stage of the algorithm, segments of recordings consisting of
either snoring or groaning episodes - without classifying them -
were identified. In the second stage, these segments were further
differentiated into individual groaning or snoring events. The
algorithm in the first stage achieved a sensitivity and specificity
of 90.5% ± 2.9% and 90.0% ± 1.6% respectively, using a
RUSBoost model. In the second stage, a random forest classifier
was used, and the accuracies for groan and snore events were
78.1% ± 4.7% and 78.4% ± 4.7% respectively.
pact on the quality of lives of those suffering from them. These
disorders present with a variety of symptoms, out of which
snoring and groaning are very common. This paper presents
an algorithm to identify and classify segments of acoustic
respiratory sound recordings that contain both groaning and
snoring events. The recordings were obtained from a database
containing 20 subjects from which features based on the Mel-
frequency cepstral coefficients (MFCC) were extracted. In the
first stage of the algorithm, segments of recordings consisting of
either snoring or groaning episodes - without classifying them -
were identified. In the second stage, these segments were further
differentiated into individual groaning or snoring events. The
algorithm in the first stage achieved a sensitivity and specificity
of 90.5% ± 2.9% and 90.0% ± 1.6% respectively, using a
RUSBoost model. In the second stage, a random forest classifier
was used, and the accuracies for groan and snore events were
78.1% ± 4.7% and 78.4% ± 4.7% respectively.
Date Issued
2022-09-08
Date Acceptance
2022-04-01
Citation
2022, pp.1993-1996
Publisher
IEEE
Start Page
1993
End Page
1996
Copyright Statement
Copyright © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Identifier
https://ieeexplore.ieee.org/document/9871863
Source
44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society
Publication Status
Published
Start Date
2022-07-11
Finish Date
2022-07-15
Coverage Spatial
Glasgow, UK
Date Publish Online
2022-09-08