Separating Heart Sound from Lung Sound Using LabVIEW

Khan, T.; Vijayakumar, P.

doi:10.7763/ijcee.2010.v2.188

Cited by 17 publications

(10 citation statements)

References 14 publications

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“…Moreover, clinical respiratory sounds are complicated to get in practice, and the samples of lung sounds are small. Thus, the researchers have normally selected conventional machine learning techniques, such as hidden Markov model (HMM) [29], support vector machine (SVM) [30] artificial neural network (ANN) [28], and k-Nearest Neighbor [21], instead of a deep learning technique for categorizing the lung sounds [2].…”

Section: Introductionmentioning

confidence: 99%

Fr-WCSO- DRN: Fractional Water Cycle Swarm Optimizer-Based Deep Residual Network for Pulmonary Abnormality Detection from Respiratory Sound Signals

Dar¹,

Srivastava²,

Lone

2022

SN COMPUT. SCI.

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Respiratory sounds disclose significant information regarding the lungs of patients. Numerous methods are developed for analyzing the lung sounds. However, clinical approaches require qualified pulmonologists to diagnose such kind of signals appropriately and are also time consuming. Hence, an efficient Fractional Water Cycle Swarm Optimizerbased Deep Residual Network (FrWCSO-based DRN) is developed in this research for detecting the pulmonary abnormalities using respiratory sounds signals. The proposed FrWCSO is newly designed by the incorporation of Fractional Calculus (FC) and Water Cycle Swarm Optimizer WCSO. Meanwhile, WCSO is the combination of Water Cycle Algorithm (WCA) with Competitive Swarm Optimizer (CSO). The respiratory input sound signals are pre-processed and the important features needed for the further processing are effectively extracted. With the extracted features, data augmentation is carried out for minimizing the over fitting issues for improving the overall detection performance. Once data augmentation is done, feature selection is performed using proposed FrWCSO algorithm. Finally, pulmonary abnormality detection is performed using DRN where the training procedure of DRN is performed using the developed FrWCSO algorithm. The developed method achieved superior performance by considering the evaluation measures, namely True Positive Rate (TPR), True Negative Rate (TNR) and testing accuracy with the values of 0.963, 0.932, and 0.948, respectively.

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Section: Introductionmentioning

confidence: 99%

Fr-WCSO- DRN: Fractional Water Cycle Swarm Optimizer-Based Deep Residual Network for Pulmonary Abnormality Detection from Respiratory Sound Signals

Dar¹,

Srivastava²,

Lone

2022

SN COMPUT. SCI.

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

FrWCSO-DRN: Fractional Water Cycle Swarm Optimizer-based deep residual network for pulmonary abnormality detection from respiratory sound signal

Dar¹,

Srivastava

Lone

2021

Preprint

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Respiratory sounds disclose significant information regarding the lungs of patients. Numerous methods are developed for analyzing the lung sounds. However, clinical approaches require qualified pulmonologists to diagnose such kind of signals appropriately and are also time consuming. Hence, an efficient Fractional Water Cycle Swarm Optimizer-based Deep Residual Network (FrWCSO-based DRN) is developed in this research for detecting the pulmonary abnormalities using respiratory sounds signals. The proposed FrWCSO is newly designed by the incorporation of Fractional Calculus (FC) and Water Cycle Swarm Optimizer WCSO. Meanwhile, WCSO is the combination of Water Cycle Algorithm (WCA) with Competitive Swarm Optimizer (CSO). The respiratory input sound signals are pre-processed and the important features needed for the further processing are effectively extracted. With the extracted features, data augmentation is carried out for minimizing the over fitting issues for improving the overall detection performance. Once data augmentation is done, feature selection is performed using proposed FrWCSO algorithm. Finally, pulmonary abnormality detection is performed using DRN where the training procedure of DRN is performed using the developed FrWCSO algorithm. The developed method achieved superior performance by considering the evaluation measures, namely True Positive Rate (TPR), True Negative Rate (TNR) and testing accuracy with the values of 0.963, 0.932, and 0.948, respectively.

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“…However, the auscultation depends greatly on the medical skills and diagnostic experience of the physician, which are difficult to acquire. With the development of computer-based respiratory sounds, automatic lung sound recognition based on machine learning has an important clinical significance for the diagnosis of lung abnormalities [4].…”

Section: Introductionmentioning

confidence: 99%

“…Clinical respiratory sounds are difficult to acquire in practice, and the sample set of lung sounds is often small. Therefore, researchers have generally chosen traditional machine learning methods, such as an artificial neural network (ANN) [9][14], hidden Markov model (HMM) [15][16], support vector machine (SVM) [17][18], or k-Nearest Neighbor [4], instead of a deep learning method for the classification of lung sounds. Chamberlain et al [19] attempted to recognize wheezes and crackles through deep learning conducted on 11,627 sounds recorded from 11 different auscultation locations on 284 patients.…”

Section: Introductionmentioning

confidence: 99%

Detection of Respiratory Sounds Based on Wavelet Coefficients and Machine Learning

et al. 2020

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Respiratory sounds reveal important information of the lungs of patients. However, the analysis of lung sounds depends significantly on the medical skills and diagnostic experience of the physicians and is a time-consuming process. The development of an automatic respiratory sound classification system based on machine learning would, therefore, be beneficial. In this study, 705 respiratory sound signals (240 crackles, 260 rhonchi, and 205 normal respiratory sounds) were acquired from 130 patients. We found that similarities between the original and wavelet decomposed signals reflected the frequency of the signals. The Gaussian kernel function was used to evaluate the wavelet signal similarity. We combined the wavelet signal similarity with the relative wavelet energy and wavelet entropy as the feature vector. A 5-fold cross-validation was applied to assess the performance of the system. The artificial neural network model, which was applied, achieved the classification accuracy and classified the respiratory sound signals with an accuracy of 85.43%.

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Separating Heart Sound from Lung Sound Using LabVIEW

Cited by 17 publications

References 14 publications

Fr-WCSO- DRN: Fractional Water Cycle Swarm Optimizer-Based Deep Residual Network for Pulmonary Abnormality Detection from Respiratory Sound Signals

Fr-WCSO- DRN: Fractional Water Cycle Swarm Optimizer-Based Deep Residual Network for Pulmonary Abnormality Detection from Respiratory Sound Signals

FrWCSO-DRN: Fractional Water Cycle Swarm Optimizer-based deep residual network for pulmonary abnormality detection from respiratory sound signal

Detection of Respiratory Sounds Based on Wavelet Coefficients and Machine Learning

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