AI Article Synopsis
- The detection and classification of abnormal lung sounds are crucial for diagnosing and managing lung diseases, potentially using diverse platforms like medical devices and smartphone applications.
- Smartphone applications are particularly appealing due to their widespread availability, making them an effective tool for automating the detection and classification of lung sounds such as coughs, wheezes, crackles, and snores.
- The paper reviews various methods and algorithms used for sound detection and classification, highlights their features, and discusses current challenges and future trends in this area.
Article Abstract
Detection and classification of adventitious acoustic lung sounds plays an important role in diagnosing, monitoring, controlling and, caring the patients with lung diseases. Such systems can be presented as different platforms like medical devices, standalone software or smartphone application. Ubiquity of smartphones and widespread use of the corresponding applications make such a device an attractive platform for hosting the detection and classification systems for adventitious lung sounds. In this paper, the smartphone-based systems for automatic detection and classification of the adventitious lung sounds are surveyed. Such adventitious sounds include cough, wheeze, crackle and, snore. Relevant sounds related to abnormal respiratory activities are considered as well. The methods are shortly described and the analyzing algorithms are explained. The analysis includes detection and/or classification of the sound events. A summary of the main surveyed methods together with the classification parameters and used features for the sake of comparison is given. Existing challenges, open issues and future trends will be discussed as well.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1109/RBME.2020.3002970 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Automated Classification of Cardiac Arrhythmia using Short-Duration ECG Signals and Machine Learning.
Biomed Phys Eng Express
January 2025
Electronics and Communication Engineering, Rajiv Gandhi University, Rono Hills, Doimukh, ITANAGAR, Itanagar, Arunachal Pradesh, 791112, INDIA.
Accurate detection of cardiac arrhythmias is crucial for preventing premature deaths. The current study employs a dual-stage Discrete Wavelet Transform (DWT) and a median filter to eliminate noise from ECG signals. Subsequently, ECG signals are segmented, and QRS regions are extracted for further preprocessing.
View Article and Find Full Text PDFAnalysis of Reddit Discussions on Motivational Factors for Physical Activity: Cross-Sectional Study.
J Med Internet Res
January 2025
Department of Health Promotion, School of Public Health, Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel.
Background: Despite the ample benefits of physical activity (PA), many individuals do not meet the minimum PA recommended by health organizations. Structured questionnaires and interviews are commonly used to study why individuals perform PA and their strategies to adhere to PA. However, certain biases are inherent to these tools that limit what can be concluded from their results.
View Article and Find Full Text PDFIntegrated of Hyperspectral Imaging and Machine Learning Algorithms for Nondestructive Detection of Therapeutic Properties of Plants.
Chem Biodivers
January 2025
Department of Horticultural Science, Faculty of Agriculture, Jahrom University, Jahrom, Iran.
The approaches used to determine the medicinal properties of the plants are often destructive, labor-intensive, time-consuming, and expensive, making it impossible to analyze their quality analysis online. Performance of hyperspectral imaging (HSI) integrated with intelligent techniques to overcome these problems was investigated in this research. For this purpose, three classification methods-support vector machine, random forest (RF), and extreme gradient boosting-were studied for the classification of plants in three classes of medicinal, edible, and ornamental for the organs of leaf, stem, flower, and root.
View Article and Find Full Text PDFLoop-mediated isothermal amplification assay coupled with lateral flow dipstick for the rapid detection of methicillin-resistant Staphylococcus pseudintermedius from dogs.
Vet Res Commun
January 2025
Faculty of Medical Technology, Prince of Songkla University, Songkhla, 90110, Thailand.
Staphylococcus pseudintermedius is a global animal pathogen. Traditional identification methods are time-consuming necessitating a more efficient approach. This study validated and enhanced the loop-mediated isothermal amplification (LAMP) technique by integration it with a lateral flow dipstick (LFD) assay for the detection of S.
View Article and Find Full Text PDFPreliminary Development and Validation of Automated Nociception Recognition Using Computer Vision in Perioperative Patients.
Anesthesiology
January 2025
Division of Anesthesia, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA.
Background: Effective pain recognition and treatment in perioperative environments reduce length of stay and decrease risk of delirium and chronic pain. We sought to develop and validate preliminary computer vision-based approaches for nociception detection in hospitalized patients.
Methods: Prospective observational cohort study using red-green-blue camera detection of perioperative patients.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!