WheezeD: Respiration Phase Based Wheeze Detection Using Acoustic Data From Pulmonary Patients Under Attack

Chatterjee, Sudeshna; Rahman, Mahbubur; Nemanti, Ebrahim; Kuang, Jilong

doi:10.4108/eai.20-5-2019.2283516

Cited by 9 publications

(2 citation statements)

References 14 publications

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“…WheezeD is a computer model developed by Chatterjee et al to detect wheezing from audio recordings of respiration [37]. The model first used an algorithm to recognize respiratory phases (inhalation and exhalation) from one-dimensional acoustic data.…”

Section: Literature Reviewmentioning

confidence: 99%

Respiration-Based COPD Detection Using UWB Radar Incorporation with Machine Learning

et al. 2022

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COPD is a progressive disease that may lead to death if not diagnosed and treated at an early stage. The examination of vital signs such as respiration rate is a promising approach for the detection of COPD. However, simultaneous consideration of the demographic and medical characteristics of patients is very important for better results. The objective of this research is to investigate the capability of UWB radar as a non-invasive approach to discriminate COPD patients from healthy subjects. The non-invasive approach is beneficial in pandemics such as the ongoing COVID-19 pandemic, where a safe distance between people needs to be maintained. The raw data are collected in a real environment (a hospital) non-invasively from a distance of 1.5 m. Respiration data are then extracted from the collected raw data using signal processing techniques. It was observed that the respiration rate of COPD patients alone is not enough for COPD patient detection. However, incorporating additional features such as age, gender, and smoking history with the respiration rate lead to robust performance. Different machine-learning classifiers, including Naïve Bayes, support vector machine, random forest, k nearest neighbor (KNN), Adaboost, and two deep-learning models—a convolutional neural network and a long short-term memory (LSTM) network—were utilized for COPD detection. Experimental results indicate that LSTM outperforms all employed models and obtained 93% accuracy. Performance comparison with existing studies corroborates the superior performance of the proposed approach.

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Section: Literature Reviewmentioning

confidence: 99%

Respiration-Based COPD Detection Using UWB Radar Incorporation with Machine Learning

et al. 2022

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“…However, precision falls when respiration rate exceeds 30 respirations/min. Meanwhile, Chatterjee et al [10] develop an algorithm to detect respiratory phases from audio data. They transform the signals into spectral-temporal images and train a wheezing detection model based on convolutional neural networks (CNN), achieving a precision of 96.99%, specificity of 97.96% and a sensibility of 96.08%.…”

Section: Introductionmentioning

confidence: 99%

An algorithm for obtaining the frequency and the times of respiratory phases from nasal and oral acoustic signals

Kemper

Oshita²,

Parra³

et al. 2023

IJECE

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<span lang="EN-US">This work proposes a computational algorithm which extracts the frequency, timings and signal segments corresponding to respiratory phases, through buccal and nasal acoustic signal processing. The proposal offers a computational solution for medical applications which require on-site or remote patient monitoring and evaluation of pulmonary pathologies, such as coronavirus disease 19 (COVID-19). The state of the art presents a few respiratory evaluation proposals through buccal and nasal acoustic signals. Most proposals focus on respiratory signals acquired by a medical professional, using stethoscopes and electrodes located on the thorax. In this case the signal acquisition process is carried out through the use of a low cost and easy to use mask, which is equipped with strategically positioned and connected electret microphones, to maximize the proposed algorithm’s performance. The algorithm employs signal processing techniques such as signal envelope detection, decimation, fast Fourier transform (FFT) and detection of peaks and time intervals via estimation of local maxima and minima in a signal’s envelope. For the validation process a database of 32 signals of different respiratory modes and frequencies was used. Results show a maximum average error of 2.23% for breathing rate, 2.81% for expiration time and 3.47% for inspiration time.</span>

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Cough Detection Using Mobile Phone Accelerometer and Machine Learning Techniques

Ali

Khan²,

Zia³

2022

Springer Series on Bio- And Neurosystems

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WheezeD: Respiration Phase Based Wheeze Detection Using Acoustic Data From Pulmonary Patients Under Attack

Cited by 9 publications

References 14 publications

Respiration-Based COPD Detection Using UWB Radar Incorporation with Machine Learning

Respiration-Based COPD Detection Using UWB Radar Incorporation with Machine Learning

An algorithm for obtaining the frequency and the times of respiratory phases from nasal and oral acoustic signals

Cough Detection Using Mobile Phone Accelerometer and Machine Learning Techniques

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