An Automated System towards Diagnosis of Pneumonia using Pulmonary Auscultations

Aziz, Sumair; Khan, Muhammad Umar; Shakeel, M. Saad; Mushtaq, Zohaib; Khan, Aleena Zaman

doi:10.1109/macs48846.2019.9024789

Cited by 33 publications

(10 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These parameters were not further utilized by the researcher to classify the COPD and pneumonia LS [ 20 ]. Nevertheless, the number of extracted features in some existing research works is less than the proposed technique but it can only identify a single pulmonary illness from LS analysis [ 6 , 12 , 18 , 25 , 26 ]. Details are presented in Table 8 .…”

Section: Resultsmentioning

confidence: 99%

“…The thresholding of skewness, kurtosis, and statistical analysis is performed to recognize the pneumonia subjects from cough analysis. The researchers are reluctant to claim the maximum authenticity of the proposed system as a large data set could change the thresholding estimates to differentiate pneumonia and normal subjects [ 17 , 18 , 19 ]. In [ 20 ], the statistical analysis of COPD and pneumonia LS is performed to design a detection system for multiple lung diseases.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

An Automated System for Classification of Chronic Obstructive Pulmonary Disease and Pneumonia Patients Using Lung Sound Analysis

Naqvi

Choudhry

2020

Sensors

View full text Add to dashboard Cite

Chronic obstructive pulmonary disease (COPD) and pneumonia are two of the few fatal lung diseases which share common adventitious lung sounds. Diagnosing the disease from lung sound analysis to design a noninvasive technique for telemedicine is a challenging task. A novel framework is presented to perform a diagnosis of COPD and Pneumonia via application of the signal processing and machine learning approach. This model will help the pulmonologist to accurately detect disease A and B. COPD, normal and pneumonia lung sound (LS) data from the ICBHI respiratory database is used in this research. The performance analysis is evidence of the improved performance of the quadratic discriminate classifier with an accuracy of 99.70% on selected fused features after experimentation. The fusion of time domain, cepstral, and spectral features are employed. Feature selection for fusion is performed through the back-elimination method whereas empirical mode decomposition (EMD) and discrete wavelet transform (DWT)-based techniques are used to denoise and segment the pulmonic signal. Class imbalance is catered with the implementation of the adaptive synthetic (ADASYN) sampling technique.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

An Automated System for Classification of Chronic Obstructive Pulmonary Disease and Pneumonia Patients Using Lung Sound Analysis

Naqvi

Choudhry

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…The primary purpose of EMD is to compute the inherent oscillatory components in every time location from a signal in an iterative manner. 40,41 . Figure 6 illustrates the steps involved in the algorithm of EMD.…”

Section: Proposed Methodologymentioning

confidence: 99%

A novel embedded system design for the detection and classification of cardiac disorders

Riaz

Aziz

Khan

et al. 2021

Computational Intelligence

Self Cite

View full text Add to dashboard Cite

Phonocardiogram (PCG) signals hold significant prognostic and diagnostic information about cardiac health. Numerous PCG or heart sound based automated detection algorithms were previously proposed to assist the disease diagnosis process. Most of the previous studies only focused on algorithmic development. This study presents an intelligent, portable, and low‐cost embedded system for the classification of cardiac disorders associated with heart murmurs. Different stages corresponding to the developed embedded system implementation are summarized as follows: The first stage consists of the acquisition of PCG signals of both normal and patients from various hospitals with a customized and low‐cost stethoscope. The second stage describes the preprocessing, localization of S1 and S2 heart sounds, and the extraction of systole and diastole from a heart signal with an empirical mode decomposition integrated with the self‐developed algorithm. In the third stage, discriminant features are extracted to represent various cardiac classes of PCG signals in a compact manner. In the final stage of the algorithm, the k‐nearest neighbors classifier is trained and tested to distinguish between normal and four cardiac disorders. The proposed algorithm achieved 94% mean accuracy through comprehensive experimentation. The cardiac disorders classification algorithm is implemented on a RP‐based embedded system. Software application with an interactive graphical interface is also designed to assist users. The developed intelligent system is portable, low‐cost, and it enables regular patient‐monitoring. The proposed system has the potential to be employed at remote locations where the availability of doctors remains challenging.

show abstract

“…EMD is prevalently used to kick out irrelevant information in the biomedical signal analysis (Aziz et al, 2020;Aziz, Khan, Shakeel, et al, 2019;Huang et al, 1998;Khan et al, 2020;Khan, Aziz, Sohail, et al, 2019;Soh et al, 2020a). EMD is a data-dependent technique that extracts fundamental frequency components from the signal.…”

Section: Signal Preprocessing: Empirical Mode Decompositionmentioning

confidence: 99%

Pulse plethysmograph signal analysis method for classification of heart diseases using novel local spectral ternary patterns

et al. 2022

Self Cite

View full text Add to dashboard Cite

Cardiac disorders are one of the prime reasons for an increasing global death rate. Reliable and efficient diagnosis procedures are imperative to minimize the risk posed by heart disorders. Computer-aided diagnosis, based on machine learning and biomedical signal analysis, has recently been adopted by researchers to accurately predict cardiac ailments. Multi-channel Electrocardiogram signals are mostly used in scientific literature as an indicator to diagnose cardiac disorders. Recently pulse plethysmograph (PuPG) signal got attention as an evolving biosignal and promising diagnostic tool to detect heart disorders since it has a simple sensor with low cost, non-invasive, reliable, and easy to handle technology. This article proposes a computer-aided diagnosis system to detect Myocardial Infarction, Dilated Cardiomyopathy, and Hypertension from PuPG signals. Raw PuPG signal is first preprocessed through empirical mode decomposition (EMD) by removing the redundant and useless information content. Then, highly discriminative features are extracted from preprocessed PuPG signal through novel local spectral ternary patterns (LSTP). Extracted LSTPs are input to a variety of classification methods such as support vector machines (SVM), K-nearest neighbours, decision tree, and so on. SVM with cubic kernel yielded the best classification performance of 98.4% accuracy, 96.7% sensitivity, and 99.6% specificity with 10-fold cross-validation. The proposed framework was trained and tested on a selfcollected PuPG signals database of heart disorders. A comparison with previous studies and other feature descriptors shows the superiority of the proposed system. This research provides better insights into the contributions of PuPG signals towards reliable detection of heart disorder through low-cost and non-invasive means.

show abstract

An Automated System towards Diagnosis of Pneumonia using Pulmonary Auscultations

Cited by 33 publications

References 21 publications

An Automated System for Classification of Chronic Obstructive Pulmonary Disease and Pneumonia Patients Using Lung Sound Analysis

An Automated System for Classification of Chronic Obstructive Pulmonary Disease and Pneumonia Patients Using Lung Sound Analysis

A novel embedded system design for the detection and classification of cardiac disorders

Pulse plethysmograph signal analysis method for classification of heart diseases using novel local spectral ternary patterns

Contact Info

Product

Resources

About