Interpretation of Lung Sounds Using Spectrogram-Based Statistical Features

2021 ◽  
pp. 815-823
Author(s):  
G. Shanthakumari ◽  
E. Priya
Keyword(s):  
Statistical Features ◽  
Lung Sounds

scholarly journals A dataset of lung sounds recorded from the chest wall using an electronic stethoscope

Data in Brief ◽  
2021 ◽  
pp. 106913
Author(s):  
Mohammad Fraiwan ◽  
Luay Fraiwan ◽  
Basheer Khassawneh ◽  
Ali Ibnian
Keyword(s):  
Chest Wall ◽  
Lung Sounds ◽  
Electronic Stethoscope

scholarly journals RSSI-Based for Device-Free Localization Using Deep Learning Technique

Smart Cities ◽  
2020 ◽  
Vol 3 (2) ◽  
pp. 444-455
Author(s):  
Abdul Syafiq Abdull Sukor ◽  
Latifah Munirah Kamarudin ◽  
Ammar Zakaria ◽  
Norasmadi Abdul Rahim ◽  
Sukhairi Sudin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Statistical Features ◽  
Received Signal Strength Indicator ◽  
Privacy Concerns ◽  
Physical Discomfort ◽  
Learning Technique ◽  
Device Free ◽  
The Internet Of Things

Device-free localization (DFL) has become a hot topic in the paradigm of the Internet of Things. Traditional localization methods are focused on locating users with attached wearable devices. This involves privacy concerns and physical discomfort especially to users that need to wear and activate those devices daily. DFL makes use of the received signal strength indicator (RSSI) to characterize the user’s location based on their influence on wireless signals. Existing work utilizes statistical features extracted from wireless signals. However, some features may not perform well in different environments. They need to be manually designed for a specific application. Thus, data processing is an important step towards producing robust input data for the classification process. This paper presents experimental procedures using the deep learning approach to automatically learn discriminative features and classify the user’s location. Extensive experiments performed in an indoor laboratory environment demonstrate that the approach can achieve 84.2% accuracy compared to the other basic machine learning algorithms.

A telemedicine tool framework for lung sounds classification using ensemble classifier algorithms

Measurement ◽  
2020 ◽  
Vol 162 ◽  
pp. 107883
Author(s):  
Mustafa Musa Jaber ◽  
Sura Khalil Abd ◽  
P.Mohamed Shakeel ◽  
M.A. Burhanuddin ◽  
Mohammed Abdulameer Mohammed ◽  
...  
Keyword(s):  
Ensemble Classifier ◽  
Lung Sounds ◽  
Classifier Algorithms
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