Collaborative framework for automatic classification of respiratory sounds

Ntalampiras, Stavros

doi:10.1049/iet-spr.2019.0487

Cited by 18 publications

(9 citation statements)

References 27 publications

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“…It basically depicts a comparison study between the two algorithms. In machine learning, preprocessing, feature extraction, and feature selection [115,116] exist, followed by classification. However, no such blocks exist in the deep-learning model; only the deep model, followed by the classification part, is there.…”

Section: Deep-learning-based Heartmentioning

confidence: 99%

Recent Advances in PCG Signal Analysis using AI: A Review

Roy,

Mandal

et al. 2024

International Journal on Smart Sensing and Intelligent Systems

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The paper reviews the milestones and various modern-day approaches in developing phonocardiogram (PCG) signal analysis. It also explains the different phases and methods of the Heart Sound signal analysis. Many physicians depend heavily on ECG experts, inviting healthcare costs and ignorance of stethoscope skills. Hence, auscultation is not a simple solution for the detection of valvular heart disease; therefore, doctors prefer clinical evaluation using Doppler Echo-cardiogram and another pathological test. However, the benefits of auscultation and other clinical evaluation can be associated with computer-aided diagnosis methods that can help considerably in measuring and analyzing various Heart Sounds. This review covers the most recent research for segmenting valvular Heart Sound during preprocessing stages, like adaptive fuzzy system, Shannon energy, time-frequency representation, and discrete wavelet distribution for analyzing and diagnosing various heart-related diseases. Different Convolutional Neural Network (CNN) based deep-learning models are discussed for valvular Heart Sound analysis, like LeNet-5, AlexNet, VGG16, VGG19, DenseNet121, Inception Net, Residual Net, Google Net, Mobile Net, Squeeze Net, and Xception Net. Among all deep-learning methods, the Xception Net claimed the highest accuracy of 99.43 + 0.03% and sensitivity of 98.58 + 0.06%. The review also provides the recent advances in the feature extraction and classification techniques of Cardiac Sound, which helps researchers and readers to a great extent.

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Section: Deep-learning-based Heartmentioning

confidence: 99%

Recent Advances in PCG Signal Analysis using AI: A Review

Roy,

Mandal

et al. 2024

International Journal on Smart Sensing and Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…ing speech emotion recognition [13], acoustic scene analysis [20], music analysis [10], and medical acoustics [17]. First, the audio signal was converted in a log-amplitude spectrogram using windows lasting 40ms and overlapping by 50%; Hamming windowing function was applied before of computing the FFT-based log-amplitude spectrum.…”

Section: Preprocessing Of Respiratory Soundsmentioning

confidence: 99%

Variational Autoencoders for Anomaly Detection in Respiratory Sounds

Cozzatti¹,

Simonetta²,

Ntalampiras³

2022

Preprint

Self Cite

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This paper proposes a weakly-supervised machine learningbased approach aiming at a tool to alert patients about possible respiratory diseases. Various types of pathologies may affect the respiratory system, potentially leading to severe diseases and, in certain cases, death. In general, effective prevention practices are considered as major actors towards the improvement of the patient's health condition. The proposed method strives to realize an easily accessible tool for the automatic diagnosis of respiratory diseases. Specifically, the method leverages Variational Autoencoder architectures permitting the usage of training pipelines of limited complexity and relatively small-sized datasets. Importantly, it offers an accuracy of 57%, which is in line with the existing strongly-supervised approaches.

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“…Such a scheme could support the classification precision and outperform to distinguish respiratory sound with 66.7% precision. Other subclasses of respiratory sounds were not engaged, i.e., St, Sq, and Rh ( 16 ). As research tells us that lung disease is the third most ordinary cause of death worldwide, so it is important to classify the RS abnormality in a true way to overcome the death rate.…”

Section: Related Workmentioning

confidence: 99%

Abnormal Respiratory Sounds Classification Using Deep CNN Through Artificial Noise Addition

et al. 2021

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Respiratory sound (RS) attributes and their analyses structure a fundamental piece of pneumonic pathology, and it gives symptomatic data regarding a patient's lung. A couple of decades back, doctors depended on their hearing to distinguish symptomatic signs in lung audios by utilizing the typical stethoscope, which is usually considered a cheap and secure method for examining the patients. Lung disease is the third most ordinary cause of death worldwide, so; it is essential to classify the RS abnormality accurately to overcome the death rate. In this research, we have applied Fourier analysis for the visual inspection of abnormal respiratory sounds. Spectrum analysis was done through Artificial Noise Addition (ANA) in conjunction with different deep convolutional neural networks (CNN) to classify the seven abnormal respiratory sounds—both continuous (CAS) and discontinuous (DAS). The proposed framework contains an adaptive mechanism of adding a similar type of noise to unhealthy respiratory sounds. ANA makes sound features enough reach to be identified more accurately than the respiratory sounds without ANA. The obtained results using the proposed framework are superior to previous techniques since we simultaneously considered the seven different abnormal respiratory sound classes.

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Collaborative framework for automatic classification of respiratory sounds

Cited by 18 publications

References 27 publications

Recent Advances in PCG Signal Analysis using AI: A Review

Recent Advances in PCG Signal Analysis using AI: A Review

Variational Autoencoders for Anomaly Detection in Respiratory Sounds

Abnormal Respiratory Sounds Classification Using Deep CNN Through Artificial Noise Addition

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