TY - GEN
T1 - Noise-Robust Sleep States Classification Model Using Sound Feature Extraction and Conversion
AU - Ko, Sangkeun
AU - Min, Seongho
AU - Choi, Ye Shin
AU - Kim, Woo Je
AU - Lee, Suan
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - This study proposes an effective state classification model for sleep, which is crucial for improving daily functioning and overall quality of life. We delve into the extraction of auditory features from sleep-related sounds, such as snoring and teeth grinding, and apply five distinct image transformations-Recurrence Plots (RP), Markov Transition Fields (MTF), Gramian Angular Summation Fields (GASF), Gramian Angular Difference Fields (GAD F), and Short-Time Fourier Transform (STFT)-to accurately delineate sleep states. Our research introduces an innovative deep learning model adept at classifying these states based on the images obtained from these transformations. Furthermore, we rigorously test the model's resilience to noise by introducing varying levels (0%, 25%, 50%, and 75%) and observe that the Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model, particularly when combined with the STFT technique, consistently outperforms under all noise conditions, achieving accuracies between 99.55% and 98.98%. The findings of this research significantly contribute to the fields of sleep analysis and the study of sleep disorders, offering a robust framework for understanding and classifying sleep states.
AB - This study proposes an effective state classification model for sleep, which is crucial for improving daily functioning and overall quality of life. We delve into the extraction of auditory features from sleep-related sounds, such as snoring and teeth grinding, and apply five distinct image transformations-Recurrence Plots (RP), Markov Transition Fields (MTF), Gramian Angular Summation Fields (GASF), Gramian Angular Difference Fields (GAD F), and Short-Time Fourier Transform (STFT)-to accurately delineate sleep states. Our research introduces an innovative deep learning model adept at classifying these states based on the images obtained from these transformations. Furthermore, we rigorously test the model's resilience to noise by introducing varying levels (0%, 25%, 50%, and 75%) and observe that the Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model, particularly when combined with the STFT technique, consistently outperforms under all noise conditions, achieving accuracies between 99.55% and 98.98%. The findings of this research significantly contribute to the fields of sleep analysis and the study of sleep disorders, offering a robust framework for understanding and classifying sleep states.
KW - CNN
KW - Deep Learning
KW - Image Representation
KW - LSTM
KW - Sound Feature Extraction
UR - http://www.scopus.com/inward/record.url?scp=85191481507&partnerID=8YFLogxK
U2 - 10.1109/BigComp60711.2024.00051
DO - 10.1109/BigComp60711.2024.00051
M3 - Conference contribution
AN - SCOPUS:85191481507
T3 - Proceedings - 2024 IEEE International Conference on Big Data and Smart Computing, BigComp 2024
SP - 281
EP - 286
BT - Proceedings - 2024 IEEE International Conference on Big Data and Smart Computing, BigComp 2024
A2 - Unger, Herwig
A2 - Chae, Jinseok
A2 - Lee, Young-Koo
A2 - Wagner, Christian
A2 - Wang, Chaokun
A2 - Bennis, Mehdi
A2 - Ketcham, Mahasak
A2 - Suh, Young-Kyoon
A2 - Kwon, Hyuk-Yoon
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE International Conference on Big Data and Smart Computing, BigComp 2024
Y2 - 18 February 2024 through 21 February 2024
ER -