An Ensemble of Deep Learning Frameworks for Predicting Respiratory Anomalies

Pham, Lam; Ngo, Dat; Tran, Khoa; Hoang, Thong; Strobel, Alexander; McLoughlin, Ian

doi:10.1109/embc48229.2022.9871440

Cited by 9 publications

(4 citation statements)

References 19 publications

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“…Pham et al [14] introduced a novel approach by merging deep learning frameworks to predict respiratory anomalies, including inception-based and transfer learning-based models. The results demonstrated that this ensemble approach outperformed existing state-of-the-art systems, promising advancements in respiratory anomaly detection.…”

Section: Related Workmentioning

confidence: 99%

Predicting Abnormal Respiratory Patterns in Older Adults Using Supervised Machine Learning on Internet of Medical Things Respiratory Frequency Data

Santana-Mancilla,

Castrejón-Mejía,

Fajardo-Flores

et al. 2023

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Wearable Internet of Medical Things (IoMT) technology, designed for non-invasive respiratory monitoring, has demonstrated considerable promise in the early detection of severe diseases. This paper introduces the application of supervised machine learning techniques to predict respiratory abnormalities through frequency data analysis. The principal aim is to identify respiratory-related health risks in older adults using data collected from non-invasive wearable devices. This article presents the development, assessment, and comparison of three machine learning models, underscoring their potential for accurately predicting respiratory-related health issues in older adults. The convergence of wearable IoMT technology and machine learning holds immense potential for proactive and personalized healthcare among older adults, ultimately enhancing their quality of life.

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Section: Related Workmentioning

confidence: 99%

Predicting Abnormal Respiratory Patterns in Older Adults Using Supervised Machine Learning on Internet of Medical Things Respiratory Frequency Data

Santana-Mancilla,

Castrejón-Mejía,

Fajardo-Flores

et al. 2023

Information

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“…The use of machine learning has become increasingly promising for the detection and monitoring of respiratory illnesses. A recent work (Pham et al, 2022) presented an exploration of various deep-learning models for detecting respiratory anomalies from auditory recordings. Authors used the ICBHI 2017 (Rocha et al, 2019) There are also recent datasets for respiratory diseases, such as COUGHVID (Orlandic, Teijeiro, & Atienza, 2021) and Coswara (N. Sharma et al, 2020).…”

Section: Machine Learning Datasets For Respiratory Diseasesmentioning

confidence: 99%

Sound-Dr: Reliable Sound Dataset and Baseline Artificial Intelligence System for Respiratory Illnesses

Hoang,

Nguyen,

Nguyen

et al. 2023

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As the burden of respiratory diseases continues to fall on society worldwide, this paper proposes a high-quality and reliable dataset of human sounds for studying respiratory illnesses, including pneumonia and COVID-19. It consists of coughing, mouth breathing, and nose breathing sounds together with metadata on related clinical characteristics. We also develop a proof-of-concept system for establishing baselines and benchmarking against multiple datasets, such as Coswara and COUGHVID. Our comprehensive experimentsshow that the Sound-Dr dataset has richer features, better performance, and is more robust to dataset shifts in various machine learning tasks. It is promising for a wide range of real-time applications on mobile devices. The proposed dataset and system will serve as practical tools to support healthcare professionals in diagnosing respiratory disorders. The dataset and code are publicly available here: https://github.com/ReML-AI/Sound-Dr/.

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“…In this case, typical features are matrix-like representations of the signal in a frequency-time space. Usually, FFT-based representations are extracted and used as an input for training deep learning networks, such as MLP [9], CNN [8,9,21], RNN [22], and other neural architectures [23,24]. In some works, multiple type of features are used with exceptional results [9,29].…”

Section: # Of Observationsmentioning

confidence: 99%

“…STFT+Wavelet [28] 0.78 0.20 0.49 Boosted Tree [6] 0.78 0.21 0.49 Ensemble DL [24] 0.86 0.30 0.57 Proposed Method 0.33 0.80 0.57 * 2 classes LSTM [22] 0.85 0.62 0.74 80/20 To the best of our knowledge, this is the first time that the detection of respiratory diseases is faced from an anomaly detection perspective. Interestingly, the proposed model achieved state of the art results in a patient-independent experimental protocol even though it is only weakly-supervised.…”

Section: /40 4 Classesmentioning

confidence: 99%

Variational Autoencoders for Anomaly Detection in Respiratory Sounds

Cozzatti¹,

Simonetta²,

Ntalampiras³

2022

Preprint

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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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An Ensemble of Deep Learning Frameworks for Predicting Respiratory Anomalies

Cited by 9 publications

References 19 publications

Predicting Abnormal Respiratory Patterns in Older Adults Using Supervised Machine Learning on Internet of Medical Things Respiratory Frequency Data

Predicting Abnormal Respiratory Patterns in Older Adults Using Supervised Machine Learning on Internet of Medical Things Respiratory Frequency Data

Sound-Dr: Reliable Sound Dataset and Baseline Artificial Intelligence System for Respiratory Illnesses

Variational Autoencoders for Anomaly Detection in Respiratory Sounds

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