Acoustic thoracic image of crackle sounds using linear and nonlinear processing techniques

Methods for Adventitious Respiratory Sound Analyzing Applications Based on Smartphones: A Survey

Detection and classification of adventitious acoustic lung sounds plays an important role in diagnosing, monitoring, controlling and, caring the patients with lung diseases. Such systems can be presented as different platforms like medical devices, standalone software or smartphone application. Ubiquity of smartphones and widespread use of the corresponding applications make such a device an attractive platform for hosting the detection and classification systems for adventitious lung sounds. In this paper, the smartphone-based systems for automatic detection and classification of the adventitious lung sounds are surveyed. Such adventitious sounds include cough, wheeze, crackle and, snore. Relevant sounds related to abnormal respiratory activities are considered as well. The methods are shortly described and the analyzing algorithms are explained. The analysis includes detection and/or classification of the sound events. A summary of the main surveyed methods together with the classification parameters and used features for the sake of comparison is given. Existing challenges, open issues and future trends will be discussed as well.

Low Cost Interconnected Architecture for the Hardware Spiking Neural Networks

A novel low cost interconnected architecture (LCIA) is proposed in this paper, which is an efficient solution for the neuron interconnections for the hardware spiking neural networks (SNNs). It is based on an all-to-all connection that takes each paired input and output nodes of multi-layer SNNs as the source and destination of connections. The aim is to maintain an efficient routing performance under low hardware overhead. A Networks-on-Chip (NoC) router is proposed as the fundamental component of the LCIA, where an effective scheduler is designed to address the traffic challenge due to irregular spikes. The router can find requests rapidly, make the arbitration decision promptly, and provide equal services to different network traffic requests. Experimental results show that the LCIA can manage the intercommunication of the multi-layer neural networks efficiently and have a low hardware overhead which can maintain the scalability of hardware SNNs.

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.

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.

Linear and Nonlinear Analysis of Base Lung Sound in Extrinsic Allergic Alveolitis Patients in Comparison to Healthy Subjects

Objective: Pulmonary disorders are frequently characterized by the presence of adventitious sounds added to the breathing or base lung sound (BLS). The aim of this work was to assess the features of BLS in extrinsic allergic alveolitis (EAA) patients in comparison to healthy subjects, applying linear and nonlinear analysis techniques.

Methods: We investigated the multichannel lung sounds on the posterior chest of 16 females, 8 healthy and 8 EAA patients, when breathing at 1.5 L/s. BLS linear features were obtained from the power spectral density (PSD) while nonlinear features were extracted by the concepts of irregularity and complexity, i.e., spectral, sample and multi-scale entropy.

Results: The results demonstrated that spectral percentiles of BLS were lower in EAA patients than in healthy subjects but statistical significance (p<0.05) was obtained only for expiration at the left apical and both basal regions. Also, the maximum amplitude of the PSD in patients reached statistical significance ( p < 0.05) for the expiratory phase at basal regions. In the case of nonlinear techniques, significant lower values ( p < 0.05) were obtained for EAA patients during both respiratory phases at left apical and both basal regions.

Conclusion: In conclusion, we found that BLS in chronic EAA patients is characterized by lower spectral percentiles, lower irregularity and lower complexity than in healthy subjects suggesting the feasibility of its clinical usefulness by screening its temporal alteration.

Acoustic evaluation of pirfenidone on patients with combined pulmonary fibrosis emphysema syndrome

The combined pulmonary fibrosis emphysema syndrome (CPFES) overall has a poor prognosis with a 5-year survival of 35-80%. Consequently, to evaluate possible positive effects on patients of novel agents as pirfenidone is relevant. However, the efficacy of pirfenidone in CPFES patients is still not well-known. In this study we propose an alternative to evaluate the effects of pirfenidone treatment on CPFES patients via acoustic information. Quantitative analysis of discontinuous adventitious lung sounds (DLS), known as crackles, has been promising to detect and characterize diverse pulmonary pathologies. The present study combines independent components (ICs) analysis of LS and the automated selection of ICs associated with DLS. ICs's features as fractal dimension, entropy and sparsity produce several clusters by kmeans. Those clusters containing ICs of DLS are exclusively considered to finally estimate the number of DLS per ICs by a time-variant AR modeling. For the evaluation of the effects of pirfenidone, the 2D DLS-ICs spatial distribution in conjunction with the estimated number of DLS events are shown. The methodology is applied to two real cases of CPFES with 6 and 12 months of treatment. The acoustical evaluation indicates that pirfenidone treatment may not be satisfactory for CPFES patients but further evaluation has to be performed.

Detection of different types of noise in lung sounds

Lung sound signal processing has proven to be a great improvement to the traditional acoustic interpretation of lung sounds. However, that analysis can be seriously hindered by the presence of different types of noise originated in the acquisition environment or caused by physiological processes. Consequently, the diagnostic accuracy of pulmonary diseases can be severely affected, especially if the implementation of telemonitoring systems is considered. The present study is focused on the implementation of an algorithm able to identify noisy periods, either voluntarily (vocalizations, chest movement and background voices) or involuntarily produced during acquisitions of lung sounds. The developed approach also had to deal with the presence of simulated cough events, that carry important diagnostic information regarding several pulmonary diseases. Features such as Katz fractal dimension, Teager-Kaiser energy operator and normalized mutual information, were extracted from the time domain of healthy and a pathological lung signals. Noise detection was the result of a good discrimination between uncontaminated lung sounds and both cough and noise episodes and a slightly worse classification of cough events. In fact, detection of cough periods carrying diagnostic information was influenced by the presence of two other types of noise having similar signal characteristics.

Automated clustering of independent components for discontinuous sounds thoracic imaging

Discontinuous lung sounds (DLS), also known as crackles, are abnormal sounds produced by different pulmonary pathologies (PP) whose thoracic spatial distribution and prevalence are relevant for diagnosis purpose. Recently, DLS imaging has been proposed to help diagnose and follow-up PP where automated recognition of DLS is meaningful. The present study focuses on the automated selection of independent components (ICs) associated with DLS. Extraction of ICs information for clustering by k-means is achieved in two ways: (1) forming features vectors (FVs) containing the kurtosis, entropy and sparsity of each IC or (2) by applying mutual information (MI) or Euclidean distance (ED) to all ICs. Next, silhouette index is computed to estimate the number of necessary clusters (C). Afterward, to detect just the clusters containing ICs of DLS a selection index is proposed. Finally, to estimate the number of DLS per ICs in each selected cluster a time-variant AR modeling is applied; the estimated number is shown in conjunction with the 2D-ICs spatial distribution. The methodology is applied to simulated and real cases; DLS imaging results are also compared against clinical auscultation. The results showed that the automated selection via FVs is promising to imaging DLS.

Chaos to randomness: distinguishing between healthy and non-healthy lung sound behaviour

Lung abnormalities and respiratory diseases increase as side effects of urban life and development. Therefore, understanding lung dynamics and its changes during the presence of abnormalities are critical in order to design more reliable tools for the early diagnosis and screening of lung pathology. The goal of this paper is to indicate the chaotic nature of normal lung sound and its transition to randomness in the presence of lung disease. The latter characteristic could serve as an indicator for evaluating the recovery process for patients suffering from lung disease. To verify this idea, we compared group of healthy and non-healthy subjects and also group of non-healthy subjects before and after treatments. Chaotic and randomness indices applied to lung sound signals which captured by multichannel data acquisition system. Results show that the normal lung displays chaotic dynamics. However, with the increase in lung abnormality, moves toward more random behaviour and away from its original chaotic state. Also, chaotic and randomness indices indicate their abilities to classify healthy and non-healthy lung sounds.

Computerized diagnosis of respiratory disorders. SVM based classification of VAR model parameters of respiratory sounds

INTRODUCTION

This article is part of the Focus Theme of Methods of Information in Medicine on "Biosignal Interpretation: Advanced METHODS for Studying Cardiovascular and Respiratory Systems".

OBJECTIVES

This work proposes an algorithm for diagnostic classification of multi-channel respiratory sounds.

METHODS

14-channel respiratory sounds are modeled assuming a 250-point second order vector autoregressive (VAR) process, and the estimated model parameters are used to feed a support vector machine (SVM) classifier. Both a three-class classifier (healthy, bronchiectasis and interstitial pulmonary disease) and a binary classifier (healthy versus pathological) are considered.

RESULTS

In the binary scheme, the sensitivity and specificity for both classes are 85% ± 8.2%. In the three-class classification scheme, the healthy recall (95% ± 5%) and the interstitial pulmonary disease recall and precision (100% ± 0% both) are rather high. However, bronchiectasis recall is very low (30% ± 15.3%), resulting in poor healthy and bronchiectasis precision rates (76% ± 8.7% and 75% ± 25%, respectively). The main reason behind these poor rates is that the bronchiectasis is confused with the healthy case.

CONCLUSIONS

The proposed method is promising, nevertheless, it should be improved such that other mathematical models, additional features, and/or other classifiers are to be experimented in future studies.

Assessment of ICA algorithms for the analysis of crackles sounds

Blind source separation by independent component analysis has been applied extensively in the biomedical field for extracting different contributing sources in a signal. Regarding lung sounds analysis to isolate the adventitious sounds from normal breathing sound is relevant. In this work the performance of FastICA, Infomax, JADE and TDSEP algorithms was assessed using different scenarios including simulated fine and coarse crackles embedded in recorded normal breathing sounds. Our results pointed out that Infomax obtained the minimum Amari index (0.10037) and the maximum signal to interference ratio (1.4578e+009). Afterwards, Infomax was applied to 25 channels of recorded normal breathing sound where simulated fine and coarse crackles were added including acoustic propagation effects. A robust blind crackle separation could improve previous results in generating an adventitious acoustic thoracic imaging.

Exploring an optimal vector autoregressive model for multi-channel pulmonary sound data

The purpose of this study is to find a useful mathematical model for multi-channel pulmonary sound data. Vector auto-regressive (VAR) model schema is adopted and the best set of arguments, namely, the order and sample size of the model and the sampling rate of the data, is aimed to be determined. Both conventional prediction error criteria and a set of three new criteria which are derived specifically for pulmonary sound signals are used to evaluate the success of the model. In terms of these criteria, the second order 250-point model is selected to be the most descriptive VAR model for 14-channel pulmonary sound data. The preferred sampling rate is the original data acquisition rate, which is 9600 samples per second. The effect of normalization of the data with respect to the air flow is also examined. Six normalization schemes are implemented on the data prior to the model fit, and the resulting model parameters are examined in terms of the proposed criterion measures. It is concluded that normalization with respect to flow is not necessary prior to the VAR modeling of pulmonary sound data.

Adventitious lung sounds imaging by ICA-TVAR scheme

Adventitious lung sounds (ALS) as crackles and wheezes are present in different lung alterations and their automated characterization and recognition have become relevant. In fact, recently their 2D spatial distribution (SD) imaging has been proposed to help diagnose of pulmonary diseases. In this work, independent component analysis (ICA) by infomax was used to find crackles sources and from them to apply a time variant autoregressive model (TVAR) to count and imaging the ALS. The proposed methodology was assessed on multichannel LS recordings by embedding simulated fine crackles with known SD in recorded normal breathing sounds. Afterwards, the adventitious image of two patients with fibrosis and emphysema were obtained and contrasted with the classical pulmonary auscultation provided by a pneumologist. The results showed that combining ICA and TVAR leads to a robust methodology to imaging ALS.