TY - GEN
T1 - LungHeart-AtMe
T2 - 5th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2023
AU - Chend, Changyan
AU - Zhang, Qing
AU - Sheng, Shirui
AU - Huang, Huajie
AU - Zhang, Yuhang
AU - Li, Yongfu
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Adventitious cardiopulmonary (lung and heart) sound detection and classification through a digital stethoscope plays a vital role in early diagnosis and telehealth services. However, automatically detecting the adventitious sounds is challenging since they are easily susceptible to each other's influence and noises. In this paper, for the first time, we simultaneously classify adventitious lung and heart sounds using our proposed LungHeart-AtMe model based on a mixed dataset of the ICBHI 2017 lung sounds dataset and the PhysioNet 2016 heart sounds dataset. Based on characteristics of lung and heart sounds, Wavelet Decomposition is applied first to perform noise reduction, then two time-frequency feature extraction techniques, which are Short Time Fourier Transform (STFT) and Mel Frequency Cepstral Coefficients (MFCCs), are chosen to extract preliminary features of sounds and transform sounds data to spectrograms that are easy to analyze. Our LungHeart-AtMe model is improved by introducing MMoE structure and by using the attention mechanism-based CNN model to extend its global feature extraction capability. From our experimental result, LungHeart-AtMe has achieved a Sensitivity of 71.55% and a Specificity of 28.06% for cardiopulmonary sounds classification.
AB - Adventitious cardiopulmonary (lung and heart) sound detection and classification through a digital stethoscope plays a vital role in early diagnosis and telehealth services. However, automatically detecting the adventitious sounds is challenging since they are easily susceptible to each other's influence and noises. In this paper, for the first time, we simultaneously classify adventitious lung and heart sounds using our proposed LungHeart-AtMe model based on a mixed dataset of the ICBHI 2017 lung sounds dataset and the PhysioNet 2016 heart sounds dataset. Based on characteristics of lung and heart sounds, Wavelet Decomposition is applied first to perform noise reduction, then two time-frequency feature extraction techniques, which are Short Time Fourier Transform (STFT) and Mel Frequency Cepstral Coefficients (MFCCs), are chosen to extract preliminary features of sounds and transform sounds data to spectrograms that are easy to analyze. Our LungHeart-AtMe model is improved by introducing MMoE structure and by using the attention mechanism-based CNN model to extend its global feature extraction capability. From our experimental result, LungHeart-AtMe has achieved a Sensitivity of 71.55% and a Specificity of 28.06% for cardiopulmonary sounds classification.
KW - Cardiopulmonary Sound Classification
KW - Data Augmentation
KW - Feature Extraction
KW - Mel Frequency Cepstral Coefficients
KW - Neural Network
KW - Short Time Fourier Transform
UR - https://www.scopus.com/pages/publications/85166377079
U2 - 10.1109/AICAS57966.2023.10168624
DO - 10.1109/AICAS57966.2023.10168624
M3 - 会议稿件
AN - SCOPUS:85166377079
T3 - AICAS 2023 - IEEE International Conference on Artificial Intelligence Circuits and Systems, Proceeding
BT - AICAS 2023 - IEEE International Conference on Artificial Intelligence Circuits and Systems, Proceeding
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 11 June 2023 through 13 June 2023
ER -