The Experts below are selected from a list of 4356 Experts worldwide ranked by ideXlab platform
F Sattar - One of the best experts on this subject based on the ideXlab platform.
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new approaches for spectro temporal feature extraction with applications to Respiratory Sound classification
Neurocomputing, 2014Co-Authors: F Jin, F Sattar, D Y T GohAbstract:Auscultation based diagnosis of pulmonary disorders relies on the presence of adventitious Sounds. In this paper, we propose a new set of features based on temporal characteristics of filtered narrowband signal to classify Respiratory Sounds (RSs) into normal and continuous adventitious types. RS signals are first decomposed in the time-frequency domain and features are extracted over selected frequency bins containing distinct signal characteristics based on auto-regressive averaging, the recursively measured instantaneous kurtosis, and the sample entropy histograms distortion. The presented features are compared with existing features using a modified clustering index with different distance metrics. Mean classification accuracies of 97.7% and 98.8% for inspiratory and expiratory segments respectively have been achieved using Support Vector Machine on real recordings.
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signal feature extraction by multi scale pca and its application to Respiratory Sound classification
Medical & Biological Engineering & Computing, 2012Co-Authors: Shengkun Xie, Feng Jin, Sridhar Krishnan, F SattarAbstract:Respiratory Sound (RS) signals carry significant information about the underlying functioning of the pulmonary system by the presence of adventitious Sounds. Although many studies have addressed the problem of pathological RS classification, only a limited number of scientific works have focused in multi-scale analysis. This paper proposes a new signal classification scheme for various types of RS based on multi-scale principal component analysis as a signal enhancement and feature extraction method to capture major variability of Fourier power spectra of the signal. Since we classify RS signals in a high dimensional feature subspace, a new classification method, called empirical classification, is developed for further signal dimension reduction in the classification step and has been shown to be more robust and outperform other simple classifiers. An overall accuracy of 98.34 % for the classification of 689 real RS recording segments shows the promising performance of the presented method.
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log frequency spectrogram for Respiratory Sound monitoring
International Conference on Acoustics Speech and Signal Processing, 2012Co-Authors: Feng Jin, F Sattar, Sridhar KrishnanAbstract:Computerized patient monitoring provides valuable information on clinical disorders in medical practice, and it triggers the need to simplify the extent of resources required to describe large set of complex biomedical signals. In this paper, we present a new signal quantification method based on block-wise similarity measurement between the neighboring regions in the optimized log-frequency spectrogram of audio signals. Low dimensional cepstral feature set for signal quantification is then formed from the reconstructed similarity matrix using 2D principal component analysis. The effectiveness of the method is verified with real Respiratory Sound (RS) signals for the purpose of abnormal RS detection towards RS monitoring. Unlike conventional pathological RS detection methods which extract features from well-segmented inspiratory/ expiratory phase segments, the proposed scheme is able to perform fast detection of various types of abnormality for unsegmented signals.
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automatic Respiratory Sound classification using temporal spectral dominance
International Conference on Multimedia and Expo, 2011Co-Authors: Feng Jin, F Sattar, S KrishnanAbstract:Respiratory Sound (RS) signals carry significant information about the underlying functioning of the pulmonary system. Auscultation based diagnosis of pulmonary disorders relies on the presence of adventitious Sounds. This paper proposes a new method for automatic RS classification based on instantaneous frequency (IF) analysis with the aim to identify various types of pathological RS. The presented method produces a high definition representation of RS signals in the time-frequency (TF) plane. The discarded phase information in spectrogram has been adopted here for the computation of IF and the subsequent temporal-spectral dominance. A new set of features have been extracted to quantify the shapes of the obtained individual TF contour and therefore strongly enhances the identification of multi-components signals such as polyphonic wheezes. An overall accuracy of 92.7 ± 2.9% on real RS recordings shows the promising performance by the presented method.
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heart Sound localization from Respiratory Sound using a robust wavelet based approach
International Conference on Multimedia and Expo, 2008Co-Authors: F Jin, F Sattar, S G Razul, D Y T GohAbstract:This paper addresses the problem of heart Sounds (HS) localization from single channel Respiratory Sounds (RS) recordings by applying wavelet-based localization scheme. After a wavelet-based multiscale decomposition of the noisy signal, HS contaminated segments are localized in the noisy RS signal based on the cumulative sums of likelihood ratios capturing the dynamic behaviour of the signal. Quantitative evaluation of the localized HS segments for various types of simulated data has been performed. The comparisons between the estimated boundaries of the localized HS segments and the actual segment boundaries of the synchronized pure HS signals show the proposed method is able to localize the HS segments accurately in an automatic way. Also, the test results on real RS recordings in terms of the detection accuracy show the promising performance by the proposed method.
Yasemin P. Kahya - One of the best experts on this subject based on the ideXlab platform.
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An open access database for the evaluation of Respiratory Sound classification algorithms.
Physiological measurement, 2019Co-Authors: Bruno Rocha, Yasemin P. Kahya, D. Filos, Luis Mendes, Gorkem Serbes, Sezer Ulukaya, Niksa Jakovljevic, Tatjana Loncar Turukalo, Ioannis M Vogiatzis, Eleni PerantoniAbstract:OBJECTIVE Over the last few decades, there has been significant interest in the automatic analysis of Respiratory Sounds. However, currently there are no publicly available large databases with which new algorithms can be evaluated and compared. Further developments in the field are dependent on the creation of such databases. APPROACH This paper describes a public Respiratory Sound database, which was compiled for an international competition, the first scientific challenge of the IFMBE's International Conference on Biomedical and Health Informatics. The database includes 920 recordings acquired from 126 participants and two sets of annotations. One set contains 6898 annotated Respiratory cycles, some including crackles, wheezes, or a combination of both, and some with no adventitious Respiratory Sounds. In the other set, precise locations of 10 775 events of crackles and wheezes were annotated. MAIN RESULTS The best system that participated in the challenge achieved an average score of 52.5% with the Respiratory cycle annotations and an average score of 91.2% with the event annotations. SIGNIFICANCE The creation and public release of this database will be useful to the research community and could bring attention to the Respiratory Sound classification problem.
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overcomplete discrete wavelet transform based Respiratory Sound discrimination with feature and decision level fusion
Biomedical Signal Processing and Control, 2017Co-Authors: Sezer Ulukaya, Gorkem Serbes, Yasemin P. KahyaAbstract:Abstract Background and objective Crackle, wheeze and normal lung Sound discrimination is vital in diagnosing pulmonary diseases. Previous works suffer from limited frequency resolution and lack of the ability to deal with oscillatory signals (wheezes). The main objective of this study is to propose a novel wavelet based lung Sound classification system that is capable of adaptively representing crackle, wheeze and normal lung Sound signal time-frequency properties. Methods A method which is based on rational dilation wavelet transform is proposed to classify lung Sounds into three main categories, namely, normal, wheeze and crackle. Six different feature extraction methods were used with five different classifiers all of which were compared with the proposed method on 600 lung Sound episodes in a cross validation scheme. Six statistical subset features were extracted from raw features and fed into classifiers. After comparative evaluation of the proposed method, an ensemble learning scheme was built to increase the performance of the proposed method. Results It has been shown that performance of the proposed method was superior to previous methods in terms of accuracy. Moreover, its computational time was far less than its nearest competitor (S transform). It has also been shown that the proposed method was able to cope with oscillatory type signals as well as transient Sounds performing 95.17% average accuracy for energy subset and 97.38% ensemble average accuracy showing a promising time-frequency tool for biological signals. Conclusions The proposed method has shown better performance even using only one subset of extracted features. It provides better time-frequency resolution for all types of signals of interest and is less redundant than continuous wavelet transform and significantly faster than its nearest competitor.
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resonance based Respiratory Sound decomposition aiming at localization of crackles in noisy measurements
International Conference of the IEEE Engineering in Medicine and Biology Society, 2016Co-Authors: Sezer Ulukaya, Gorkem Serbes, Yasemin P. KahyaAbstract:In this work, resonance based decomposition of lung Sounds that aims to separate wheeze, crackle and vesicular Sounds into three individual channels while automatically localizing crackles for both synthetic and real data is presented. Previous works focus on stationary-non stationary discrimination to separate crackles and vesicular Sounds disregarding wheezes which are stationary as compared to crackles. However, wheeze Sounds include important cues about the underlying pathology. Using two different threshold methods and synthetic Sound generation scenarios in the presence of wheezes, resonance based decomposition performs 89.5 % crackle localization recall rate for white Gaussian noise and 98.6 % crackle localization recall rate for healthy vesicular Sound treated as noise at low signal-to-noise ratios. Besides, an adaptive threshold determination which is independent from the channel at which it will be applied is used and is found to be robust to noise.
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Respiratory Sound classification using perceptual linear prediction features for healthy pathological diagnosis
National Biomedical Engineering Meeting, 2014Co-Authors: Sezer Ulukaya, Yasemin P. KahyaAbstract:This study proposes a new model and feature extraction method for the classification of multi-channel Respiratory Sound data with the final aim of building a diagnosis aid tool for the medical doctor. Fourteen-channel data are processed separately and combined at feature level and fed to the support vector machines with radial basis kernel. Healthy-pathological subject based binary classification is employed where the recall rates for the healthy class and pathological class are 95 percent and 80 percent, respectively. The minimum precision rate is 80 percent. The method, when supported by additional features (adventitious Sound frequency, type, etc.), may be employed in clinical practice as an aiding decision maker.
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acoustic mapping of the lung based on source localization of adventitious Respiratory Sound components
International Conference of the IEEE Engineering in Medicine and Biology Society, 2010Co-Authors: Ipek Sen, Murat Saraclar, Yasemin P. KahyaAbstract:The aim of this study is to devise a methodology to estimate and depict the source locations of Respiratory adventitious Sound components in the lungs, particularly crackles, associated with certain pulmonary diseases. Using the multichannel Respiratory Sound signals recorded on the chest wall, we have tried to locate the sources of crackling Sounds. The source localization is performed using basic independent component analysis (basic ICA) followed by an evaluation of the mixing coefficients in a center of weights approach, where after the ICA, by taking the relevant mixing matrix coefficients and assuming them to be placed on the microphone locations, the estimated Sound source location is calculated as the center of those weights. In order to select both the proper data segments prior to the ICA, and the relevant independent component (IC) among the source signal estimates of the ICA subsequently, a Bayesian classifier (under the assumption of Gaussian likelihoods) has been trained, using the data of the same subject yet a different acquisition session from the one intended for source localization. The outcome of the algorithm is a map of estimated source locations of crackles with respect to the microphone locations, which is presented together with the error performances (both validation and test) of the classifier. This approach for the estimation and mapping of the adventitious Sound source locations in the lungs using the acoustic data may be a promising imaging alternative, which is practical, non-expensive and harmless.
D Y T Goh - One of the best experts on this subject based on the ideXlab platform.
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new approaches for spectro temporal feature extraction with applications to Respiratory Sound classification
Neurocomputing, 2014Co-Authors: F Jin, F Sattar, D Y T GohAbstract:Auscultation based diagnosis of pulmonary disorders relies on the presence of adventitious Sounds. In this paper, we propose a new set of features based on temporal characteristics of filtered narrowband signal to classify Respiratory Sounds (RSs) into normal and continuous adventitious types. RS signals are first decomposed in the time-frequency domain and features are extracted over selected frequency bins containing distinct signal characteristics based on auto-regressive averaging, the recursively measured instantaneous kurtosis, and the sample entropy histograms distortion. The presented features are compared with existing features using a modified clustering index with different distance metrics. Mean classification accuracies of 97.7% and 98.8% for inspiratory and expiratory segments respectively have been achieved using Support Vector Machine on real recordings.
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heart Sound localization from Respiratory Sound using a robust wavelet based approach
International Conference on Multimedia and Expo, 2008Co-Authors: F Jin, F Sattar, S G Razul, D Y T GohAbstract:This paper addresses the problem of heart Sounds (HS) localization from single channel Respiratory Sounds (RS) recordings by applying wavelet-based localization scheme. After a wavelet-based multiscale decomposition of the noisy signal, HS contaminated segments are localized in the noisy RS signal based on the cumulative sums of likelihood ratios capturing the dynamic behaviour of the signal. Quantitative evaluation of the localized HS segments for various types of simulated data has been performed. The comparisons between the estimated boundaries of the localized HS segments and the actual segment boundaries of the synchronized pure HS signals show the proposed method is able to localize the HS segments accurately in an automatic way. Also, the test results on real RS recordings in terms of the detection accuracy show the promising performance by the proposed method.
Yan Shi - One of the best experts on this subject based on the ideXlab platform.
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Detection of Respiratory Sounds Based on Wavelet Coefficients and Machine Learning
IEEE Access, 2020Co-Authors: Fei Meng, Maolin Cai, Yan Shi, Zujing LuoAbstract: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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A kind of integrated serial algorithms for noise reduction and characteristics expanding in Respiratory Sound.
International journal of biological sciences, 2019Co-Authors: Fei Meng, Yan Shi, Yixuan Wang, Hongmei ZhaoAbstract:The computer‑based lung Respiratory Sound analysis, which can provide more information about the condition of lung station, has achieved a great development in recent years. However, the external noise in Respiratory Sound signal is a large restriction to the further promotion of this technique. In this paper, a kind of serial integrated de-noising algorithms which consist of a FIR band-pass filter and a modified wavelet filter and an adaptive filter, is proposed to suppress the noise in Respiratory Sound signals. The design of this kind of filter and its practical application are studied. The practical application in de-noise of the lung Sound shows that this filter has a good de-noising effect and a good performance in outstanding the acoustic characteristics.
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the identification of sputum situation based on the Sound from the Respiratory tract
International Conference on Advanced Intelligent Mechatronics, 2018Co-Authors: Jinglong Niu, Yan Shi, Yixuan Wang, Dongkai Shen, Maolin CaiAbstract:In ICU (Intensive Care Unit), the trachea of patients with ventilator should be supervised all time to avoid the sputum depositing. The sputum situation identification by using traditional lung auscultation is time-consuming and related skill is difficult to acquire. Therefore it needs the medical staff to have a good training and experience. In this paper, an automatic sputum situation detection method is proposed. A system which is used to acquire Respiratory Sound was also developed. 46 features were extracted from the Respiratory Sounds based upon Empirical Mode Decomposition (EMD). And then Random Forest classifier is used as the classifier for recognition of sputum situation. In the experiment, 803 Respiratory Sound samples were collected from 14 patients, with each sample corresponding to one Respiratory cycles. The classification results shows that this algorithm can achieve the accuracy of 92.02%.
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Detection of sputum by interpreting the time-frequency distribution of Respiratory Sound signal using image processing techniques
Bioinformatics, 2018Co-Authors: Jinglong Niu, Maolin Cai, Zhixin Cao, Zhaozhi Zhang, Dandan Wang, Yan Shi, Xiaohua Douglas ZhangAbstract:Sputum in the trachea is hard to expectorate and detect directly for the patients who are unconscious, especially those in Intensive Care Unit. Medical staff should always check the condition of sputum in the trachea. This is time-consuming and the necessary skills are difficult to acquire. Currently, there are few automatic approaches to serve as alternatives to this manual approach. We develop an automatic approach to diagnose the condition of the sputum. Our approach utilizes a system involving a medical device and quantitative analytic methods. In this approach, the time-frequency distribution of Respiratory Sound signals, determined from the spectrum, is treated as an image. The sputum detection is performed by interpreting the patterns in the image through the procedure of preprocessing and feature extraction. In this study, 272 Respiratory Sound samples (145 sputum Sound and 127 non-sputum Sound samples) are collected from 12 patients. We apply the method of leave-one out cross-validation to the 12 patients to assess the performance of our approach. That is, out of the 12 patients, 11 are randomly selected and their Sound samples are used to predict the Sound samples in the remaining one patient. The results show that our automatic approach can classify the sputum condition at an accuracy rate of 83.5%. The matlab codes and examples of datasets explored in this work are available at Bioinformatics online. yesoyou@gmail.com or douglaszhang@umac.mo. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com
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detection of sputum by interpreting the time frequency distribution of Respiratory Sound signal using image processing techniques
Bioinformatics, 2018Co-Authors: Jinglong Niu, Maolin Cai, Zhixin Cao, Zhaozhi Zhang, Dandan Wang, Yan Shi, Xiaohua Douglas ZhangAbstract:Motivation Sputum in the trachea is hard to expectorate and detect directly for the patients who are unconscious, especially those in Intensive Care Unit. Medical staff should always check the condition of sputum in the trachea. This is time-consuming and the necessary skills are difficult to acquire. Currently, there are few automatic approaches to serve as alternatives to this manual approach. Results We develop an automatic approach to diagnose the condition of the sputum. Our approach utilizes a system involving a medical device and quantitative analytic methods. In this approach, the time-frequency distribution of Respiratory Sound signals, determined from the spectrum, is treated as an image. The sputum detection is performed by interpreting the patterns in the image through the procedure of preprocessing and feature extraction. In this study, 272 Respiratory Sound samples (145 sputum Sound and 127 non-sputum Sound samples) are collected from 12 patients. We apply the method of leave-one out cross-validation to the 12 patients to assess the performance of our approach. That is, out of the 12 patients, 11 are randomly selected and their Sound samples are used to predict the Sound samples in the remaining one patient. The results show that our automatic approach can classify the sputum condition at an accuracy rate of 83.5%. Availability and implementation The matlab codes and examples of datasets explored in this work are available at Bioinformatics online. Contact yesoyou@gmail.com or douglaszhang@umac.mo. Supplementary information Supplementary data are available at Bioinformatics online.
F Jin - One of the best experts on this subject based on the ideXlab platform.
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new approaches for spectro temporal feature extraction with applications to Respiratory Sound classification
Neurocomputing, 2014Co-Authors: F Jin, F Sattar, D Y T GohAbstract:Auscultation based diagnosis of pulmonary disorders relies on the presence of adventitious Sounds. In this paper, we propose a new set of features based on temporal characteristics of filtered narrowband signal to classify Respiratory Sounds (RSs) into normal and continuous adventitious types. RS signals are first decomposed in the time-frequency domain and features are extracted over selected frequency bins containing distinct signal characteristics based on auto-regressive averaging, the recursively measured instantaneous kurtosis, and the sample entropy histograms distortion. The presented features are compared with existing features using a modified clustering index with different distance metrics. Mean classification accuracies of 97.7% and 98.8% for inspiratory and expiratory segments respectively have been achieved using Support Vector Machine on real recordings.
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heart Sound localization from Respiratory Sound using a robust wavelet based approach
International Conference on Multimedia and Expo, 2008Co-Authors: F Jin, F Sattar, S G Razul, D Y T GohAbstract:This paper addresses the problem of heart Sounds (HS) localization from single channel Respiratory Sounds (RS) recordings by applying wavelet-based localization scheme. After a wavelet-based multiscale decomposition of the noisy signal, HS contaminated segments are localized in the noisy RS signal based on the cumulative sums of likelihood ratios capturing the dynamic behaviour of the signal. Quantitative evaluation of the localized HS segments for various types of simulated data has been performed. The comparisons between the estimated boundaries of the localized HS segments and the actual segment boundaries of the synchronized pure HS signals show the proposed method is able to localize the HS segments accurately in an automatic way. Also, the test results on real RS recordings in terms of the detection accuracy show the promising performance by the proposed method.