Lung Sound Recordings Research Articles

Evaluation of Digital Auscultation to Diagnose Pneumonia in Children 2 to 35 Months of Age in a Clinical Setting in Kathmandu, Nepal: A Prospective Case–Control Study

Objective The objective of this study was to determine the diagnostic validity of digital chest auscultation to improve the differentiation of chest sounds associated with pneumonia in children. Methods This is a prospective case–control study at two hospitals in Nepal. Cases had World Health Organization-defined pneumonia and were classified as radiologically confirmed or nonconfirmed based on radiographic findings. Controls had no respiratory complaints. The presence of crepitations in recorded lung sounds defined pneumonia. Radiologically confirmed pneumonia was the reference standard. Results Sensitivity and specificity of digital auscultation were 56% (95% confidence interval [CI], 40–70%) and 73% (95% CI, 70–76%), respectively. Conclusion Digital auscultation in conjunction with standardized grading of digital lung sounds has the potential to improve the specificity of pneumonia diagnosis, but further development of objective interpretation of lung sounds is needed.

Unidirectional Microphone based Wireless Recorder for the Respiration Sound

The wireless recorder for heart and lung sounds is intuitively convenient to the medical doctors and the patients for its portability which makes the auscultation be possible in a certain range. Such a recorder facilitates increased patient movement (postures and exercises) during diagnosis. Therefore, a wireless communication system for lung sound recording has been developed in this study. Two unidirectional microphones (SM35 and Beta 98H/C, Shure), a wireless body pack transmitter (PGXD1, Shure), and a wireless receiver (PGXD4, Shure) were used in our recording system. The detected sound was transmitted to a mixing console (MG06, Yamaha) for amplification. The system characteristics are as follows: (1) offers excellent low-frequency response, (2) has unidirectional microphone sensing, (3) blocks noise from the environment, (4) records sounds of moving and exercising people, and (5) records other physiological sounds. All of SNREN>35 in tests under the environmental noises in the range of 25 to 40 dB encouraged us to neglect the influences of the environmental noise. The findings strongly recommend that the proposed recording system be adopted by groups researching lung sounds because it reduces the number of complex devices and algorithms necessary for noise reduction, offers a high SNR, and facilitates device use in various environments. Furthermore, the developed system can be used to record lung sounds because of its excellent low-frequency response, especially, using the instrumentation microphone.

Towards the standardisation of lung sound nomenclature.

Auscultation of the lung remains an essential part of physical examination even though its limitations, particularly with regard to communicating subjective findings, are well recognised. The European Respiratory Society (ERS) Task Force on Respiratory Sounds was established to build a reference collection of audiovisual recordings of lung sounds that should aid in the standardisation of nomenclature. Five centres contributed recordings from paediatric and adult subjects. Based on pre-defined quality criteria, 20 of these recordings were selected to form the initial reference collection. All recordings were assessed by six observers and their agreement on classification, using currently recommended nomenclature, was noted for each case. Acoustical analysis was added as supplementary information. The audiovisual recordings and related data can be accessed online in the ERS e-learning resources. The Task Force also investigated the current nomenclature to describe lung sounds in 29 languages in 33 European countries. Recommendations for terminology in this report take into account the results from this survey.

The detection of crackles based on mathematical morphology in spectrogram analysis.

Crackles are very common abnormal breath sounds in the lung and can be used to diagnose pulmonary diseases. In this study, a method is proposed for the detection of adventitious transient sounds from normal breath sounds. This method automatically recognizes crackles based on the extraction and analysis of spectral information from digitally recorded lung sounds. Various mathematical morphology feature sets were extracted through wavelet spectrogram analysis on pulmonary signals. In order to evaluate the effects of different wavelets types on crackle detection, different wavelets were tested. The results showed that the proposed method achieved an 86% accuracy in the detection of crackles. The spectrograms of the crackles in the lung exhibit irregular ellipse image features. For lung sound analysis, this is a useful feature that can be used for the immediate recognition and analysis of crackles.

Detection and Classification of Abnormal Respiratory Sounds on a Resource-constraint Mobile Device

Abnormal breath sounds like wheezes, crackles and stridor at times manifest similar morphologies and pathological features of lung airways obstruction. This may pose problems to proper diagnosis and evaluation of the underlying respiratory condition by human auscultation. In this study, the authors experimented with Time-Frequency threshold-dependent (TFTD) algorithm for detection and classification of breath sounds based on Smartphone. The TFTD algorithm computes important and distinct features of each breath sound using spectro-temporal analysis of recorded lung sounds which can enhance qualitative measurement and quantitative indexing of different respiratory sounds. Several algorithms which run exclusively on desktop computers have been developed for detecting and analyzing specific lung sounds such as wheezes. However, few attempts have been made to perform such analysis on portable devices like mobile phones due to computational complexities and high power consumption associated with the analyses. Our experimental results demonstrate that recent smartphones with improved computational capacity are able to provide comparative performance on analysis of respiratory signals. Furthermore, these phones can serve as convenient tools for measuring and detecting early signs of pulmonary disorders particularly at home and during ambulatory care services where conventional and specialized medical devices may not be accessible.

Evaluation of airflow limitation using a new modality of lung sound analysis in asthmatic children

BackgroundReliable assessment of not only symptoms but also lung function is essential in asthma management. We developed a new technology for analyzing lung sounds and assessed its clinical usefulness in asthmatic children. MethodsForty-four children underwent lung sound recording with simultaneous airflow measurement using a sensor on the upper right anterior chest. We calculated a sound parameter index from the amplitude of inspiratory lung sounds at 700 Hz (ic700). ic700 were compared depending on flow and body size. In addition, 184 asthmatic children and 16 non-asthmatic children underwent lung sound analysis and lung function test in an asymptomatic state. In the asthma group, 135 children received treatment continually. The untreated asthma group included 28 children who had never received treatment continually and 21 children who had not been treated for at least 1 year. The asthmatic children were divided into four classes according to asthma severity. ic700 were compared depending on spirometric parameters and asthma severity classification. ResultsThe influences of flow and body size were negligible for ic700. ic700 correlated with FEV1%, MMF and FEF50 (r = −0.436, −0.339 and −0.302, respectively). There was a significant difference of ic700 between asthmatic and non-asthmatic children (p < 0.001), and ic700 correlated with the classification of asthma severity (p < 0.001). The ic700 scores of the severe group were higher than those of the intermittent group and non-asthmatic children. ConclusionsIt was possible to evaluate airway dysfunction of asthma using ic700, which was calculated non-invasively by analyzing lung sounds alone, without measuring body size and airflow.

Vibration response imaging: protocol for a systematic review

BackgroundThe concept of lung sounds conveying information regarding lung physiology has been used extensively in clinical practice, particularly with physical auscultation using a stethoscope. Advances in computer technology have facilitated the construction of dynamic visual images derived from recorded lung sounds. Arguably, the most significant progress in this field was the development of the commercially available vibration response imaging (VRI) (Deep Breeze Ltd, Or-Akiva, Israel). This device provides a non-invasive, dynamic image of both lungs constructed from sounds detected from the lungs using surface sensors. In the literature, VRI has been utilized in a multitude of clinical and research settings. This systematic review aims to address three study questions relating to whether VRI can be used as an evaluative device, whether the images generated can be characterized, and which tools and measures have been used to assess these images.Methods/DesignThis systematic review will involve implementing search strategies in five online journal databases in order to extract articles relating to the application of VRI. Appropriate articles will be identified against a set of pre-determined eligibility criteria and assessed for methodological quality using a standardized scale. Included articles will have data extracted by the reviewers using a standardized evidence table. A narrative synthesis based on a standardized framework will be conducted, clustering evidence into three main groups; one for each of the study questions. A meta-analysis will be conducted if two or more research articles meet pre-determined criteria that allow quantitative synthesis to take place.DiscussionThis systematic review aims to provide a complete overview of the scope of VRI in the clinical and research settings, as well as to discuss methods to interpret the data obtained from VRI. The systematic review intends to help clinicians to make informed decisions on the clinical applicability of the device, to allow researchers to identify further potential avenues of investigation, and to provide methods for the evaluation and interpretation of dynamic and static images. The publication and registration of this review with PROSPERO provides transparency and accountability, and facilitates the appraisal of the proposed systematic review against the original design.Trial registrationPROSPERO registration number: CRD42013003751

A new method of lung sounds filtering using modulated least mean square—Adaptive noise cancellation

Advanced processing of lung sound (LS) recording is a significant means to separate heart sounds (HS) and combined low frequency noise from instruments (NI), with saving its characteristics. This paper proposes a new method of LS filtering which separates HS and NI simultaneously. It focuses on the application of least mean squares (LMS) algorithm with adaptive noise cancelling (ANC) technique. The second step of the new method is to modulate the reference input r1(n) of LMS-ANC to acquiesce combining HS and NI signals. The obtained signal is removed from primary signal (original lung sound recording-LS). The original signal is recorded from subjects and derived HS from it and it is modified by a band pass filter. NI is simulated by generating approximately periodic white gaussian noise (WGN) signal. The LMS-ANC designed algorithm is controlled in order to determine the optimum values of the order L and the coefficient convergence μ. The output results are measured using power special density (PSD), which has shown the effectiveness of our suggested method. The result also has shown visual difference PSD (to) normal and abnormal LS recording. The results show that the method is a good technique for heart sound and noise reduction from lung sounds recordings simultaneously with saving LS characteristics.

Computerized lung sound analysis as diagnostic aid for the detection of abnormal lung sounds: A systematic review and meta-analysis

The standardized use of a stethoscope for chest auscultation in clinical research is limited by its inherent inter-listener variability. Electronic auscultation and automated classification of recorded lung sounds may help prevent some of these shortcomings. We sought to perform a systematic review and meta-analysis of studies implementing computerized lung sound analysis (CLSA) to aid in the detection of abnormal lung sounds for specific respiratory disorders. We searched for articles on CLSA in MEDLINE, EMBASE, Cochrane Library and ISI Web of Knowledge through July 31, 2010. Following qualitative review, we conducted a meta-analysis to estimate the sensitivity and specificity of CLSA for the detection of abnormal lung sounds. Of 208 articles identified, we selected eight studies for review. Most studies employed either electret microphones or piezoelectric sensors for auscultation, and Fourier Transform and Neural Network algorithms for analysis and automated classification of lung sounds. Overall sensitivity for the detection of wheezes or crackles using CLSA was 80% (95% CI 72-86%) and specificity was 85% (95% CI 78-91%). While quality data on CLSA are relatively limited, analysis of existing information suggests that CLSA can provide a relatively high specificity for detecting abnormal lung sounds such as crackles and wheezes. Further research and product development could promote the value of CLSA in research studies or its diagnostic utility in clinical settings.

Clinical recording of lung sounds: a new phonopneumograph for both real time and non-time-limited tracing (author's transl)

Clinical recording of lung sounds: a new phonopneumograph for both real time and non-time-limited tracing (author's transl)

The reliability of lung crackle characteristics in cystic fibrosis and bronchiectasis patients in a clinical setting

Lung sounds provide useful information for assessing and monitoring respiratory patients, but standard auscultation is subjective. Computer aided lung sound analysis (CALSA) enables the quantification and characterisation of added lung sounds (e.g. crackles). At present, little is known about the reliability of these sound characteristics. Therefore, the aim of this study was to explore the reliability of crackle initial deflection width (IDW) and two-cycle deflection (2CD) in a clinical population. Fifty-four subjects (37 bronchiectasis, 17 cystic fibrosis) were recruited from out-patient clinics. Three repeated lung sound recordings were taken at seven anatomical sites with a digital stethoscope connected to a laptop computer. The intra-subject reliability of crackle IDW and 2CD was found to be ‘good’ to ‘excellent’, estimated by the analysis of variance, intraclass correlation coefficient (IDW 0.76;0.85, 2CD 0.83;0.94), Bland and Altman 95% limits of agreement (IDW −0.50;0.47 ms, 2CD −2.12;1.87 ms) and smallest real difference (IDW 0.30;0.66 ms, 2CD 1.57;2.42 ms). Crackle 2CD was found to be more reliable than IDW. It is concluded that crackle IDW and 2CD characterized by CALSA have good test–retest reliability. This technique requires further evaluation since CALSA has potential to diagnose or monitor respiratory conditions, and provide an objective physiological measure for respiratory interventions.

Method for automatic detection of wheezing in lung sounds

The present report describes the development of a technique for automatic wheezing recognition in digitally recorded lung sounds. This method is based on the extraction and processing of spectral information from the respiratory cycle and the use of these data for user feedback and automatic recognition. The respiratory cycle is first pre-processed, in order to normalize its spectral information, and its spectrogram is then computed. After this procedure, the spectrogram image is processed by a two-dimensional convolution filter and a half-threshold in order to increase the contrast and isolate its highest amplitude components, respectively. Thus, in order to generate more compressed data to automatic recognition, the spectral projection from the processed spectrogram is computed and stored as an array. The higher magnitude values of the array and its respective spectral values are then located and used as inputs to a multi-layer perceptron artificial neural network, which results an automatic indication about the presence of wheezes. For validation of the methodology, lung sounds recorded from three different repositories were used. The results show that the proposed technique achieves 84.82% accuracy in the detection of wheezing for an isolated respiratory cycle and 92.86% accuracy for the detection of wheezes when detection is carried out using groups of respiratory cycles obtained from the same person. Also, the system presents the original recorded sound and the post-processed spectrogram image for the user to draw his own conclusions from the data.

Sequential Blind Source Extraction For Quasi-Periodic Signals With Time-Varying Period

A novel second-order-statistics-based sequential blind extraction algorithm for blind extraction of quasi-periodic signals, with time-varying period, is introduced in this paper. Source extraction is performed by sequentially converging to a solution that effectively diagonalizes autocorrelation matrices at lags corresponding to the time-varying period, which thereby explicitly exploits a key statistical nonstationary characteristic of the desired source. The algorithm is shown to have fast convergence and yields significant improvement in signal-to-interference ratio as compared to when the algorithm assumes a fixed period. The algorithm is further evaluated on the problem of separation of a heart sound signal from real-world lung sound recordings. Separation results confirm the utility of the introduced approach, and listening tests are employed to further corroborate the results.

Evaluation of Obstructive Lung Disease with Vibration Response Imaging

As optimal treatment and prognosis differ between asthma and COPD, a new diagnostic approach to differentiating between the two disorders would be clinically desirable. We evaluated the utility of vibration response imaging in differentiating between asthma and COPD. Sixty-six subjects with asthma or COPD were recorded, before and after the administration of a short-acting bronchodilator, using a computerized lung sound analysis device. Gray-scale images of breath sound distribution in the lungs, quantitative data in breath sound graphs (timing, amplitude) and automatic crackle and wheeze detection programs were used to differentiate between asthma and COPD subjects. Imaging data were compared with the clinical diagnosis, made by the standard methods (medical history, physical examination, and spirometric indices). Blinded evaluation of images demonstrated a significantly higher rate of improvement in image dynamics, shape and overall improvement following bronchodilator in subjects with asthma compared with those with COPD. Quantitative data showed distinct patterns in timing and amplitude for these two pathologies. Combined analyses based on qualitative image evaluation and quantitative data demonstrated an overall 85% accuracy (84% for asthma, 86% for COPD) in differentiating between asthma and COPD. Combined qualitative and quantitative evaluations of lung sounds are quite sensitive in distinguishing between lung sound recordings of COPD and asthma individuals. Lung sound features of synchronization in timing and intensity provide objective data that may further differentiate these two airway disorders.

Heart sound cancellation from lung sound recordings using time-frequency filtering

During lung sound recordings, heart sounds (HS) interfere with clinical interpretation of lung sounds over the low frequency components which is significant especially at low flow rates. Hence, it is desirable to cancel the effect of HS on lung sound records. In this paper, a novel HS cancellation method is presented. This method first localizes HS segments using multiresolution decomposition of the wavelet transform coefficients, then removes those segments from the original lung sound record and estimates the missing data via a 2D interpolation in the time-frequency (TF) domain. Finally, the signal is reconstructed into the time domain. To evaluate the efficiency of the TF filtering, the average power spectral density (PSD) of the original lung sound segments with and without HS over four frequency bands from 20 to 300 Hz were calculated and compared with the average PSD of the filtered signals. Statistical tests show that there is no significant difference between the average PSD of the HS-free original lung sounds and the TF-filtered signal for all frequency bands at both low and medium flow rates. It was found that the proposed method successfully removes HS from lung sound signals while preserving the original fundamental components of the lung sounds.

A Robust Method for Heart Sounds Localization Using Lung Sounds Entropy

Heart sounds are the main unavoidable interference in lung sound recording and analysis. Hence, several techniques have been developed to reduce or cancel heart sounds (HS) from lung sound records. The first step in most HS cancellation techniques is to detect the segments including HS. This paper proposes a novel method for HS localization using entropy of the lung sounds. We investigated both Shannon and Renyi entropies and the results of the method using Shannon entropy were superior. Another HS localization method based on multiresolution product of lung sounds wavelet coefficients adopted from was also implemented for comparison. The methods were tested on data from 6 healthy subjects recorded at low (7.5 ml/s/kg) and medium 115 ml/s/kg) flow rates. The error of entropy-based method using Shannon entropy was found to be 0.1 +/- 0.4% and 1.0 +/- 0.7% at low and medium flow rates, respectively, which is significantly lower than that of multiresolution product method and those of other methods reported in previous studies. The proposed method is fully automated and detects HS included segments in a completely unsupervised manner.

Heart sound cancellation from lung sound recordings using recurrence time statistics and nonlinear prediction

Heart sounds (HS) obscure the interpretation of lung sounds (LS). This letter presents a new method to detect and remove this undesired disturbance. The HS detection algorithm is based on a recurrence time statistic that is sensitive to changes in a reconstructed state space. Signal segments that are found to contain HS are removed, and the arising missing parts are replaced with predicted LS using a nonlinear prediction scheme. The prediction operates in the reconstructed state space and uses an iterated integrated nearest trajectory algorithm. The HS detection algorithm detects HS with an error rate of 4% false positives and 8% false negatives. The spectral difference between the reconstructed LS signal and an LS signal with removed HS was 0.34/spl plusmn/0.25, 0.50/spl plusmn/0.33, 0.46/spl plusmn/0.35, and 0.94/spl plusmn/0.64 dB/Hz in the frequency bands 20-40, 40-70, 70-150, and 150-300 Hz, respectively. The cross-correlation index was found to be 99.7%, indicating excellent similarity between actual LS and predicted LS. Listening tests performed by a skilled physician showed high-quality auditory results.

Qualitative and Quantitative Evaluation of Heart Sound Reduction From Lung Sound Recordings

Recursive least squares (RLS) adaptive noise cancellation (ANC) and wavelet transform (WT) ANC have been applied and compared for heart sound (HS) reduction from lung sounds (LS) recordings. Novel processes for quantitative and qualitative evaluation of any method for HS reduction from LS have also been proposed.

Dependence of nocturnal bronchial obstruction on sleep position

Patients with bronchial obstructions often have problems to stay asleep at night. The interaction between sleep position and bronchial obstructions has not been investigated until now. A total of 20 patients was included in this study. All patients were recorded one night in our sleep laboratory with a parallel recording of lung sounds using a commercial Pulmotrack 1010 system. The bronchial obstructions were lower in lateral position than in supine position for both tracheal and chest sounds (p = 0,083 and p = 0,036; n.s.). This effect seemed to be especially high in patients with many obstruction episodes. From our results we can conclude that there is a small dependence of bronchial obstructions from sleep position. Further investigations are needed to verify this result.

Detection of bronchial breathing caused by pneumonia.

The classic auscultation with stethoscope is the established clinical method for the detection of lung diseases. The interpretation of the sounds depends on the experience of the investigating physician. Therefore, a new computer-based method has been developed to classify breath sounds from digital lung sound recordings. Lung sounds of 11 patients with one-sided pneumonia and bronchial breathing were recorded on both the pneumonia side and on contralateral healthy side simultaneously using two microphones. The spectral power for the 300-600 Hz frequency band was computed for four respiratory cycles and normalized. For each breath, the ratio R between the time-segments (duration = 0.1 s) with the highest inspiratory and highest expiratory flow was calculated and averaged. We found significant differences in R between the pneumonia side (R = 1.4 +/- 1.3) and the healthy side (R = 0.5 +/- 0.5; p = 0.003 Wilcoxon-test) of lung. In 218 healthy volunteers we found R = 0.3 +/- 0.2 as a reference-value. The differences of ratio R (delta R) between the pneumonia side and the healthy side (delta R = 1.0 +/- 0.9) were significantly higher compared to follow-up studies after recovery (delta R = 0.0 +/- 0.1, p = 0.005 Wilcoxon-test). The computer based detection of bronchial breathing can be considered useful as part of a quantitative monitoring of patients at risk to develop pneumonia.