Discontinuous Adventitious Sounds Research Articles

Comprehensive Analysis System for Automated Respiratory Cycle Segmentation and Crackle Peak Detection.

Digital auscultation is a well-known method for assessing lung sounds, but remains a subjective process in typical practice, relying on the human interpretation. Several methods have been presented for detecting or analyzing crackles but are limited in their real-world application because few have been integrated into comprehensive systems or validated on non-ideal data. This work details a complete signal analysis methodology for analyzing crackles in challenging recordings. The procedure comprises five sequential processing blocks: (1) motion artifact detection, (2) deep learning denoising network, (3) respiratory cycle segmentation, (4) separation of discontinuous adventitious sounds from vesicular sounds, and (5) crackle peak detection. This system uses a collection of new methods and robustness-focused improvements on previous methods to analyze respiratory cycles and crackles therein. To validate the accuracy, the system is tested on a database of 1000 simulated lung sounds with varying levels of motion artifacts, ambient noise, cycle lengths and crackle intensities, in which ground truths are exactly known. The system performs with average F-score of 91.07% for detecting motion artifacts and 94.43% for respiratory cycle extraction, and an overall F-score of 94.08% for detecting the locations of individual crackles. The process also successfully detects healthy recordings. Preliminary validation is also presented on a small set of 20 patient recordings, for which the system performs comparably. These methods provide quantifiable analysis of respiratory sounds to enable clinicians to distinguish between types of crackles, their timing within the respiratory cycle, and the level of occurrence. Crackles are one of the most common abnormal lung sounds, presenting in multiple cardiorespiratory diseases. These features will contribute to a better understanding of disease severity and progression in an objective, simple and non-invasive way.

A Smartphone-Based System for Automated Bedside Detection of Crackle Sounds in Diffuse Interstitial Pneumonia Patients.

In this work, we present a mobile health system for the automated detection of crackle sounds comprised by an acoustical sensor, a smartphone device, and a mobile application (app) implemented in Android. Although pulmonary auscultation with traditional stethoscopes had been used for decades, it has limitations for detecting discontinuous adventitious respiratory sounds (crackles) that commonly occur in respiratory diseases. The proposed app allows the physician to record, store, reproduce, and analyze respiratory sounds directly on the smartphone. Furthermore, the algorithm for crackle detection was based on a time-varying autoregressive modeling. The performance of the automated detector was analyzed using: (1) synthetic fine and coarse crackle sounds randomly inserted to the basal respiratory sounds acquired from healthy subjects with different signal to noise ratios, and (2) real bedside acquired respiratory sounds from patients with interstitial diffuse pneumonia. In simulated scenarios, for fine crackles, an accuracy ranging from 84.86% to 89.16%, a sensitivity ranging from 93.45% to 97.65%, and a specificity ranging from 99.82% to 99.84% were found. The detection of coarse crackles was found to be a more challenging task in the simulated scenarios. In the case of real data, the results show the feasibility of using the developed mobile health system in clinical no controlled environment to help the expert in evaluating the pulmonary state of a subject.

Ensemble EMD based Time-Frequency Analysis of Continuous Adventitious Signal Processing

The importance of lung sound analyses is increasing day by day very rapidly. In this paper, we present a new method for analysis of two classes of lung signals namely wheezes and crackles. The procedure used in this article is based on improved Empirical Mode Decomposition (EMD) called Ensemble Empirical Mode Decomposition (EEMD) to analyze and compare continuous and discontinuous adventitious sounds with EMD. These two proposed procedures decompose the lung signals into a set of instantaneous frequency components. Function (IMF). The continuous and discontinuous adventitious sounds are present in an asthmatic patient, produces a non-stationary and nonlinear signal pattern. The empirical mode decomposition (EMD) decomposes such characteristic signals. The instantaneous frequency and spectral analysis related to dual techniques specified above are utilized by IMF to investigate and present the outcome in the time-frequency distribution to investigate the qualities of inbuilt properties of lung sound waves. The Hilbert marginal spectrum has been used to represent total amplitude and energy contribution from every frequency value. Finally, the resultant EEMD analysis is better for wheezes that solves mode mixing issues and improvisation is seen over the EMD method.

Detection of Respiratory Crackle Sounds via an Android Smartphone-based System.

Pulmonary auscultation with traditional stethoscope, although useful, has limitations for detecting discontinuous adventitious respiratory sounds (crackles) that commonly occur in respiratory diseases. In this work, we present the development of a mobile health system for the automated detection of crackle sounds, comprised by an acoustical sensor, a smart phone device, and a mobile application (app) implemented in Android. The app allows the physician to record, store, reproduce, and analyze respiratory sounds directly on the smart phone. The algorithm for crackle detection was based on a time-varying autoregressive modeling. Performance of the automated detector was analyzed using synthetic fine and coarse crackle sounds randomly added to the basal respiratory sounds acquired from healthy subjects with different signal to noise ratios. Accuracy and sensitivity were found to range from 90.7% to 94.0% and from 91.2% to 94.2%, respectively. Application of the proposed mobile system to real acquired data from a patient with pulmonary fibrosis is also exemplified.

Detection of patients considering observation frequency of continuous and discontinuous adventitious sounds in lung sounds.

We propose an improved approach for distinguishing between healthy subjects and patients with pulmonary emphysema by the use of one stochastic acoustic model for continuous adventitious sounds and another for discontinuous adventitious sounds. These models are able to represent the spectral features of the adventitious sounds for the detection of abnormal respiration. However, abnormal respiratory sounds with unclassifiable spectral features are present among the continuous and discontinuous adventitious sounds and mixing noises. These sounds cause difficulties in achieving a highly accurate classification. In this study, the difference in occurrence frequencies between two types of adventitious sounds for each considered auscultation point and inspiration/expiration was considered. This difference, in combination with the confusion tendency of the classifier, was formulated as the validity score of each respiratory sound. The conventional spectral likelihood and the newly formulated validity score were combined to perform detection of abnormal respiration and patients. In the classification of healthy subjects and patients, the proposed approach achieved a higher classification rate (87.7%) than the conventional method (85.2%), demonstrating the statistical superiority (5% level) of the former.

A dynamic fuzzy neural filter for separation of discontinuous adventitious sounds from vesicular sounds

This paper presents a recurrent filter that performs real-time separation of discontinuous adventitious sounds from vesicular sounds. The filter uses two Dynamic Fuzzy Neural Networks, operating in parallel, to perform the task of separation of the lung sounds, obtained from patients with pulmonary pathology. Extensive experimental results, including fine/coarse crackles and squawks, are given, and a performance comparison with a series of other models is conducted, underlining the separation capabilities of the proposed filter and its improved performance with respect to its competing rivals.

A recurrent fuzzy filter for the analysis of lung sounds

This paper presents a recurrent fuzzy-neural filter for real-time separation of discontinuous adventitious sounds from vesicular sounds. The filter uses two dynamic neuron-based fuzzy neural networks to perform the task of separation. The networks are generated by the dynamic orthogonal least-squares method and are applied to all kinds of lung sounds. Extensive experimental results are given and a performance comparison with a series of other models is conducted, underlining the effectiveness of the proposed filter and its superior performance over its competing rivals.

An orthogonal least squares-based fuzzy filter for real-time analysis of lung sounds

Pathological discontinuous adventitious sounds (DAS) are strongly related with the pulmonary dysfunction. Its clinical use for the interpretation of respiratory malfunction depends on their efficient and objective separation from vesicular sounds (VS). In this paper, an automated approach to the isolation of DAS from VS, based on their nonstationarity, is presented. The proposed scheme uses two fuzzy inference systems (FISs), operating in parallel, to perform the task of adaptive separation, resulting in the orthogonal least squares-based fuzzy filter (OLS-FF). By applying the OLS-FF to fine/coarse crackles and squawks, selected from three lung sound databases, the coherent structure of DAS is revealed and they are efficiently separated from VS. The important time domain DAS features, related to diagnostic information, are preserved and their true location and structural morphology are automatically identified. When compared to previous works, the OLS-FF performs quite similarly, but with significantly lower computational load, resulting in a faster real-time clinical screening of DAS.

Separation of crackles and squawks from vesicular sounds using a wavelet‐based filtering technique

An automated way of revealing the diagnostic character of discontinuous adventitious sounds (DAS), i.e. crackles and squawks, by isolating them from vesicular sounds (VS), based on their nonstationarity, is presented in this paper. The proposed algorithm combines multiresolution analysis with hard thresholding in order to compose a wavelet‐based stationary‐non‐stationary filter (WTST‐NST). Applying the WTST‐NST filter to fine/coarse crackles and squawks, selected from three lung sound databases, the coherent structure of the DAS is revealed and they are separated from VS. When compared to other separation tools, in noiseless case, the WTST‐NST filter performed more accurately, objectively, and with lower computational cost. Owing to its simple implementation it can easily be used in clinical medicine.

Real-time separation of discontinuous adventitious sounds from vesicular sounds using a fuzzy rule-based filter.

The separation of pathological discontinuous adventitious sounds (DAS) from vesicular sounds (VS) is of great importance to the analysis of lung sounds since DAS are related to certain pulmonary pathologies. An automated way of revealing the diagnostic character of DAS, by isolating them from VS, based on their nonstationarity, is presented in this paper. The proposed algorithm uses two adaptive network-based fuzzy inference systems to compose a generalized fuzzy rule-based stationary-nonstationary filter (GFST-NST). The training procedure of the fuzzy inference systems involves the outputs of the wavelet transform-based stationary-nonstationary filter (WTST-NST), proposed by Hadjileontiadis and Panas [1]. The basic idea of the GFST-NST was initially proposed by the authors with the introduction of the fuzzy rule-based stationary-nonstationary filter (FST-NST) [2], tested with the separation of crackles from VS. The main contribution of this paper is the modification of the structure of the FST-NST filter to a serial-type fuzzy filter that, unlike the parallel operation of the FST-NST filter, sends a predicted stationary signal (VS) into the predictor of the nonstationary (DAS). Applying the GFST-NST filter to fine-coarse crackles and squawks, selected from three lung sound databases, the coherent structure of DAS is revealed and they are separated from VS. The separation performance of the GFST-NST filter was evaluated through quantitative and qualitative indexes that proved its efficiency and superiority against the FST-NST filter. When compared to the WTST-NST filter, the GFST-NST filter performed similarly in accuracy and objectiveness, but in a faster way. Thus, the GFST-NST filter combines the separation accuracy of the WTST-NST filter with the real-time implementation of the FST-NST filter, so it can easily be used in clinical medicine as a module of an integrated intelligent patient evaluation system.

Separation of discontinuous adventitious sounds from vesicular sounds using a wavelet-based filter

The separation of pathological discontinuous adventitious sounds (DAS) from vesicular sounds (VS) is of great importance to the analysis of lung sounds, since DAS are related to certain pulmonary pathologies. An automated way of revealing the diagnostic character of DAS by isolating them from VS, based on their nonstationarity, is presented in this paper. The proposed algorithm combines multiresolution analysis with hard thresholding in order to compose a wavelet transform-based stationary-nonstationary filter (WTST-NST). Applying the WTST-NST filter to fine/coarse crackles and squawks, selected from three lung sound databases, the coherent structure of DAS is revealed and they are separated from VS. When compared to other separation tools, the WTST-NST filter performed more accurately, objectively, and with lower computational cost. Due to its simple implementation it can easily be used in clinical medicine.

呼吸音における crackle 分離抽出を目的としたディジタルフィルタシステム

A nonlinear digital filter system is proposed for automatic extraction of crackles which are discontinuous adventitious sounds in lung sounds. This system is composed of two nonlinear digital filters; one is a stationary nonstationary separating filter, and the other is a width-discriminating filter. The former separates nonstationary signals from stationary ones in the lung sounds, using the prediction error to the input lung sound signal. If the prediction error is small enough, the lung sound is considered to be stationary, but if the error is large, a nonstationary signal is considered to occur and the nonstationary part is separated. The latter, the width-discriminating filter, performs signal extraction by considering the signal wave form of the crackles, simply realized by logical algebra. This filter extracts an impulsive signal, which is a small-width wave, and its succeeding waves; such wave form is typical of that of crackles. First, crackles are roughly separated by the stationary-nonstationary separating filter as nonstationary signals, and then are more precisely extracted from the nonstationary output by the width-discriminating filter. Some examples of processing actual lung sound data by this system show its high performance.

Separation of fine crackles from vesicular sounds by a nonlinear digital filter.

Crackles are discontinuous adventitious sounds, and their separation is an important process in the analysis of lung sounds. In order to separate the crackles from vesicular sounds automatically, we used a nonlinear digital filter which was designed to separate nonstationary from stationary signals. We applied this filter to the lung sounds recorded from six patients with pulmonary fibrosis. The separation was satisfactory enough to make this method useful in clinical medicine.