Classification of normal and abnormal lung sounds using neural network and support vector machines

Abbasi, Samira; Derakhshanfar, Roya; Abbasi, Ataollah; Sarbaz, Yashar

doi:10.1109/iraniancee.2013.6599555

Cited by 26 publications

(13 citation statements)

References 12 publications

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“…It is worth noting that a relatively small body of literature is concerned with rhonchi, stridor, squawks, pleural rub, and egophony analysis, which illustrates that additional research is needed. 54,58,64,67,80,83,84,87,97,102 Although auscultation with a stethoscope has several disadvantages 141 and limitations, 32 it is the technique that is most frequently used because it is easy to use. 142,143 Nowadays, digital techniques including electronic stethoscopes and microphones are generally used to collect data for analysis.…”

Section: Discussionmentioning

confidence: 99%

Lung and Heart Sounds Analysis: State-of-the-Art and Future Trends

Padilla-Ortiz

Ibarra

2018

Crit Rev Biomed Eng

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Lung sounds, which include all sounds that are produced during the mechanism of respiration, may be classified into normal breath sounds and adventitious sounds. Normal breath sounds occur when no respiratory problems exist, whereas adventitious lung sounds (wheeze, rhonchi, crackle, etc.) are usually associated with certain pulmonary pathologies. Heart and lung sounds that are heard using a stethoscope are the result of mechanical interactions that indicate operation of cardiac and respiratory systems, respectively. In this article, we review the research conducted during the last six years on lung and heart sounds, instrumentation and data sources (sensors and databases), technological advances, and perspectives in processing and data analysis. Our review suggests that chronic obstructive pulmonary disease (COPD) and asthma are the most common respiratory diseases reported on in the literature; related diseases that are less analyzed include chronic bronchitis, idiopathic pulmonary fibrosis, congestive heart failure, and parenchymal pathology. Some new findings regarding the methodologies associated with advances in the electronic stethoscope have been presented for the auscultatory heart sound signaling process, including analysis and clarification of resulting sounds to create a diagnosis based on a quantifiable medical assessment. The availability of automatic interpretation of high precision of heart and lung sounds opens interesting possibilities for cardiovascular diagnosis as well as potential for intelligent diagnosis of heart and lung diseases.

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Section: Discussionmentioning

confidence: 99%

Lung and Heart Sounds Analysis: State-of-the-Art and Future Trends

Padilla-Ortiz

Ibarra

2018

Crit Rev Biomed Eng

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“…The window size and overlap were selected based on the results of studies on tracheal sound. [ 12 17 ] Spectrogram images were obtained using STFT and applying hamming window to each segment. [ 4 ] Since the minimum period valuable for the detection of respiratory depression is 10 s, the spectrogram images are obtained in each 10 s. Then, images were labeled as positive or negative.…”

Section: Methodsmentioning

confidence: 99%

Tracheal sound analysis for automatic detection of respiratory depression in adult patients during cataract surgery under sedation

et al. 2018

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Background:Tracheal sound analysis is a simple way to study the abnormalities of upper airway like airway obstruction. Hence, it may be an effective method for detection of alveolar hypoventilation and respiratory depression. This study was designed to investigate the importance of tracheal sound analysis to detect respiratory depression during cataract surgery under sedation. Methods: After Institutional Ethical Committee approval and informed patients’ consent, we studied thirty adults American Society of Anesthesiologists I and II patients scheduled for cataract surgery under sedation anesthesia. Recording of tracheal sounds started 1 min before administration of sedative drugs using a microphone. Recorded sounds were examined by the anesthesiologist to detect periods of respiratory depression longer than 10 s. Then, tracheal sound signals converted to spectrogram images, and image processing was done to detect respiratory depression. Finally, depression periods detected from tracheal sound analysis were compared to the depression periods detected by the anesthesiologist.Results:We extracted five features from spectrogram images of tracheal sounds for the detection of respiratory depression. Then, decision tree and support vector machine (SVM) with Radial Basis Function (RBF) kernel were used to classify the data using these features, where the designed decision tree outperforms the SVM with a sensitivity of 89% and specificity of 97%.Conclusions:The results of this study show that morphological processing of spectrogram images of tracheal sound signals from a microphone placed over suprasternal notch may reliably provide an early warning of respiratory depression and the onset of airway obstruction in patients under sedation.

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“…CAS is often called the wheezes is continuous, high pitch, rather sighing sound that it is usually heard at expiration and sometimes on inspiration. It occurs when the flow of air through the narrowed airways due to secretions, foreign body or injury that prevents air flow (Abbasi et al, 2013). Wheeze can occur in the inspiration phase, expiratory phase or both.…”

Section: Wheezementioning

confidence: 99%

“…Hashemi et al (2011) added skewness and kurtosis calculations on each sub-band of Kandaswamy's method. Abbasi et al (2013) only change ANN with SVM to test Kandaswamy's method. SVM has better performance compared with ANN on daubechies8 wavelet decomposition.…”

Section: Wavelet Domain Signal Processingmentioning

confidence: 99%

Signal Domain in Respiratory Sound Analysis: Methods, Application and Future Development

Rizal¹,

Hidayat²,

Nugroho³

2015

Journal of Computer Science

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The development of digital signal processing technology encourages researchers to develop better methods for automatic lungs sound recognition system than the existing ones. Lung sounds were originally assessed manually according to doctor's expertise. Signal processing techniques are intended to reduce subjectivity factor. Signal processing techniques for lung sound recognition are developed by researchers based on their point of view to the lung sounds. Several researchers developed signal processing methods in a time domain. Meanwhile, other researchers developed signal processing techniques in a frequency domain or combined some signal domains. This paper describes the sensor used, the dataset used and the characteristics of extraction techniques as well as the classifier in the system developed by the previous researchers. In the final section, we describe some possible development of the future potential application of lung sound analysis.

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Classification of normal and abnormal lung sounds using neural network and support vector machines

Cited by 26 publications

References 12 publications

Lung and Heart Sounds Analysis: State-of-the-Art and Future Trends

Lung and Heart Sounds Analysis: State-of-the-Art and Future Trends

Tracheal sound analysis for automatic detection of respiratory depression in adult patients during cataract surgery under sedation

Signal Domain in Respiratory Sound Analysis: Methods, Application and Future Development

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