TY - GEN
T1 - Robust Deep Learning Framework for Predicting Respiratory Anomalies and Diseases
AU - Pham, Lam
AU - McLoughlin, Ian
AU - Phan, Huy
AU - Tran, Minh
AU - Nguyen, Truc
AU - Palaniappan, Ramaswamy
PY - 2020/7
Y1 - 2020/7
N2 - This paper presents a robust deep learning framework developed to detect respiratory diseases from recordings of respiratory sounds. The complete detection process firstly involves front end feature extraction where recordings are transformed into spectrograms that convey both spectral and temporal information. Then a back-end deep learning model classifies the features into classes of respiratory disease or anomaly. Experiments, conducted over the ICBHI benchmark dataset of respiratory sounds, evaluate the ability of the framework to classify sounds. Two main contributions are made in this paper. Firstly, we provide an extensive analysis of how factors such as respiratory cycle length, time resolution, and network architecture, affect final prediction accuracy. Secondly, a novel deep learning based framework is proposed for detection of respiratory diseases and shown to perform extremely well compared to state of the art methods.
AB - This paper presents a robust deep learning framework developed to detect respiratory diseases from recordings of respiratory sounds. The complete detection process firstly involves front end feature extraction where recordings are transformed into spectrograms that convey both spectral and temporal information. Then a back-end deep learning model classifies the features into classes of respiratory disease or anomaly. Experiments, conducted over the ICBHI benchmark dataset of respiratory sounds, evaluate the ability of the framework to classify sounds. Two main contributions are made in this paper. Firstly, we provide an extensive analysis of how factors such as respiratory cycle length, time resolution, and network architecture, affect final prediction accuracy. Secondly, a novel deep learning based framework is proposed for detection of respiratory diseases and shown to perform extremely well compared to state of the art methods.
UR - http://www.scopus.com/inward/record.url?scp=85090995751&partnerID=8YFLogxK
U2 - 10.1109/EMBC44109.2020.9175704
DO - 10.1109/EMBC44109.2020.9175704
M3 - Conference paper
AN - SCOPUS:85090995751
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 164
EP - 167
BT - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
PB - Institute of Electrical and Electronics Engineers
T2 - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
Y2 - 20 July 2020 through 24 July 2020
ER -