Contactless methods for measuring respiratory rate: A review
Recent advances in understanding the importance of respiratory frequency (f R) as a
sensitive marker of a variety of physiopathological stressors are fostering growing interest in …
sensitive marker of a variety of physiopathological stressors are fostering growing interest in …
Self-supervised contrastive learning for medical time series: A systematic review
Medical time series are sequential data collected over time that measures health-related
signals, such as electroencephalography (EEG), electrocardiography (ECG), and intensive …
signals, such as electroencephalography (EEG), electrocardiography (ECG), and intensive …
Deep neural network for respiratory sound classification in wearable devices enabled by patient specific model tuning
The primary objective of this paper is to build classification models and strategies to identify
breathing sound anomalies (wheeze, crackle) for automated diagnosis of respiratory and …
breathing sound anomalies (wheeze, crackle) for automated diagnosis of respiratory and …
A lightweight CNN model for detecting respiratory diseases from lung auscultation sounds using EMD-CWT-based hybrid scalogram
Listening to lung sounds through auscultation is vital in examining the respiratory system for
abnormalities. Automated analysis of lung auscultation sounds can be beneficial to the …
abnormalities. Automated analysis of lung auscultation sounds can be beneficial to the …
An open access database for the evaluation of respiratory sound classification algorithms
Objective: Over the last few decades, there has been significant interest in the automatic
analysis of respiratory sounds. However, currently there are no publicly available large …
analysis of respiratory sounds. However, currently there are no publicly available large …
Convolutional neural networks based efficient approach for classification of lung diseases
Abstract Treatment of lung diseases, which are the third most common cause of death in the
world, is of great importance in the medical field. Many studies using lung sounds recorded …
world, is of great importance in the medical field. Many studies using lung sounds recorded …
Lung sound classification using co-tuning and stochastic normalization
T Nguyen, F Pernkopf - IEEE Transactions on Biomedical …, 2022 - ieeexplore.ieee.org
Computational methods for lung sound analysis are beneficial for computer-aided diagnosis
support, storage and monitoring in critical care. In this paper, we use pre-trained ResNet …
support, storage and monitoring in critical care. In this paper, we use pre-trained ResNet …
Data augmentation using Variational Autoencoders for improvement of respiratory disease classification
Computerized auscultation of lung sounds is gaining importance today with the availability
of lung sounds and its potential in overcoming the limitations of traditional diagnosis …
of lung sounds and its potential in overcoming the limitations of traditional diagnosis …
Recognition of pulmonary diseases from lung sounds using convolutional neural networks and long short-term memory
In this paper, a study is conducted to explore the ability of deep learning in recognizing
pulmonary diseases from electronically recorded lung sounds. The selected data-set …
pulmonary diseases from electronically recorded lung sounds. The selected data-set …
Respirenet: A deep neural network for accurately detecting abnormal lung sounds in limited data setting
Auscultation of respiratory sounds is the primary tool for screening and diagnosing lung
diseases. Automated analysis, coupled with digital stethoscopes, can play a crucial role in …
diseases. Automated analysis, coupled with digital stethoscopes, can play a crucial role in …