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Background: Although crackles on chest auscultation represent a fundamental component of the diagnostic suspect for fibrotic interstitial lung disease (ILD), their reliability has not been properly studied. We assessed the agreement among respiratory physicians on the presence and changes over time of audible crackles collected in a prospective longitudinal cohort of patients with fibrotic ILD. Methods: Lung sounds were digitally recorded at baseline and after 12 months at eight anatomical sites. Nine respiratory physicians blindly assessed randomized couples of recordings obtained from the same anatomical site at different timepoints. The physicians indicated the presence of crackles in individual recordings and which recording from each couple eventually had more intense crackles. Fleiss' kappa coefficient was used to measure inter- and intra-rater agreement. Results: Fifty-two patients, mostly with a diagnosis of IPF (n = 40, 76.9%) were prospectively enrolled between October 2019 and May 2021. The final acoustic dataset included 702 single recordings, corresponding to 351 couples of recordings from baseline and 12-months timepoints. Kappa coefficient was 0.57 (95% CI 0.55–0.58) for the presence of crackles and 0.42 (95% CI 0.41–0.43) for acoustic change. Intra-rater agreement, measured for three respiratory physicians on three repeated assessments, ranged from good to excellent for the presence of crackles (κ = 0.87, κ = 0.86, κ = 0.79), and from moderate to good for acoustic change (κ = 0.75, κ = 0.76, κ = 0.57). Conclusions: Agreement between respiratory physicians for the presence of crackles and acoustic change was acceptable, suggesting that crackles represent a reliable acoustic finding in patients with fibrotic ILD. Their role as a lung-derived indicator of disease progression merits further studies. [ABSTRACT FROM AUTHOR]

Abstract Background Although crackles on chest auscultation represent a fundamental component of the diagnostic suspect for fibrotic interstitial lung disease (ILD), their reliability has not been properly studied. We assessed the agreement among respiratory physicians on the presence and changes over time of audible crackles collected in a prospective longitudinal cohort of patients with fibrotic ILD. Methods Lung sounds were digitally recorded at baseline and after 12 months at eight anatomical sites. Nine respiratory physicians blindly assessed randomized couples of recordings obtained from the same anatomical site at different timepoints. The physicians indicated the presence of crackles in individual recordings and which recording from each couple eventually had more intense crackles. Fleiss’ kappa coefficient was used to measure inter- and intra-rater agreement. Results Fifty-two patients, mostly with a diagnosis of IPF (n = 40, 76.9%) were prospectively enrolled between October 2019 and May 2021. The final acoustic dataset included 702 single recordings, corresponding to 351 couples of recordings from baseline and 12-months timepoints. Kappa coefficient was 0.57 (95% CI 0.55–0.58) for the presence of crackles and 0.42 (95% CI 0.41–0.43) for acoustic change. Intra-rater agreement, measured for three respiratory physicians on three repeated assessments, ranged from good to excellent for the presence of crackles (κ = 0.87, κ = 0.86, κ = 0.79), and from moderate to good for acoustic change (κ = 0.75, κ = 0.76, κ = 0.57). Conclusions Agreement between respiratory physicians for the presence of crackles and acoustic change was acceptable, suggesting that crackles represent a reliable acoustic finding in patients with fibrotic ILD. Their role as a lung-derived indicator of disease progression merits further studies.

Introduction: Auscultation remains a pivotal diagnostic modality for various respiratory pathologies. To augment its clinical relevance, the continuous expansion of our understanding of pulmonary acoustics, coupled with the advancement of auscultation recording and analysis methodologies, is imperative. Material and methods: We investigated how the bimodal presentation of auscultatory signals (sound and visual cue perception) influences the subjective efficacy of pathological respiratory sound detection, which is a critical step in the development of a new auscultation tool. Recordings of pediatric breath sounds were presented in three different forms - audio only, visual representation only (spectrogram) or audiovisual (both together). The F1-score, sensitivity and specificity parameters were calculated and compared to the gold standard (GS). Subsequent to the detection experiment, participants completed a survey to subjectively assess the usability of spectrograms in the procedure. Results: Over 60% of all responders ranked the spectrogram as important (40.8%) or very important (21.1%). Moreover, 11.3% of all participants found this new form of presentation of auscultation results to be more useful than the evaluation of sound only. The addition of visual information did not statistically significantly change the evaluation of the sounds themselves, an observable trend implies that enhancing audio recordings with visualizations can enhance detection performance. This is evident in the 4 p.p. and 2 p.p. sensitivity increments for physicians and students, respectively, even without specialized visual training. Conclusions: Our research findings indicate that the integration of spectrograms with conventional auditory assessment, albeit based on observed trends and survey responses, presents a promising avenue for improving the precision and quality of medical education, as well as enhancing diagnosis and monitoring processes. [ABSTRACT FROM AUTHOR]

Abstract Background Standard thoracic auscultation suffers from limitations, and no systematic analysis of breath sounds in asthmatic horses exists. Objectives First, characterize breath sounds in horses recorded using a novel digital auscultation device (DAD). Second, use DAD to compare breath variables and occurrence of adventitious sounds in healthy and asthmatic horses. Animals Twelve healthy control horses (ctl), 12 horses with mild to moderate asthma (mEA), 10 horses with severe asthma (sEA) (5 in remission [sEA−], and 5 in exacerbation [sEA+]). Methods Prospective multicenter case‐control study. Horses were categorized based on the horse owner‐assessed respiratory signs index. Each horse was digitally auscultated in 11 locations simultaneously for 1 hour. One‐hundred breaths per recording were randomly selected, blindly categorized, and statistically analyzed. Results Digital auscultation allowed breath sound characterization and scoring in horses. Wheezes, crackles, rattles, and breath intensity were significantly more frequent, higher (P

Background: Standard thoracic auscultation suffers from limitations, and no systematic analysis of breath sounds in asthmatic horses exists. Objectives: First, characterize breath sounds in horses recorded using a novel digital auscultation device (DAD). Second, use DAD to compare breath variables and occurrence of adventitious sounds in healthy and asthmatic horses. Animals: Twelve healthy control horses (ctl), 12 horses with mild to moderate asthma (mEA), 10 horses with severe asthma (sEA) (5 in remission [sEA−], and 5 in exacerbation [sEA+]). Methods: Prospective multicenter case‐control study. Horses were categorized based on the horse owner‐assessed respiratory signs index. Each horse was digitally auscultated in 11 locations simultaneously for 1 hour. One‐hundred breaths per recording were randomly selected, blindly categorized, and statistically analyzed. Results: Digital auscultation allowed breath sound characterization and scoring in horses. Wheezes, crackles, rattles, and breath intensity were significantly more frequent, higher (P <.001, P <.01, P =.01, P <.01, respectively) in sEA+ (68.6%, 66.1%, 17.7%, 97.9%, respectively), but not in sEA− (0%, 0.7%, 1.3%, 5.6%) or mEA (0%, 1.0%, 2.4%, 1.7%) horses, compared to ctl (0%, 0.6%, 1.8%, −9.4%, respectively). Regression analysis suggested breath duration and intensity as explanatory variables for groups, wheezes for tracheal mucus score, and breath intensity and wheezes for the 23‐point weighted clinical score (WCS23). Conclusions and Clinical Importance.: The DAD permitted characterization and quantification of breath variables, which demonstrated increased adventitious sounds in sEA+. Analysis of a larger sample is needed to determine differences among ctl, mEA, and sEA− horses. [ABSTRACT FROM AUTHOR]

Auscultation is a standard method of physical examination used by physicians and is widely accepted by doctors and patients for its simplicity, repeatability and non-invasiveness. Artificial intelligence is the 'new integrated frontier' of the thoracic examination, yet there are still concordance discrepancies in obstructive pulmonary diseases; on the contrary, for fibrotic diseases, the degree of concordance increases significantly, as shown by previous clinical studies conducted mainly in children. However, there are data in the literature that appear to be very discordant on certain types of lung noises, such as wet crackles and dry noises; therefore, the application of these devices in daily use in outpatient and hospital settings needs to be further expanded. The integrated data allowed us to make the right diagnosis, also avoiding costs for the national health system and possible invasive procedures such as bronchoscopy, which today remains the “gold standard” for the histological diagnosis of sarcoidosis with lung localisation. Integrated technology could improve the diagnostic capacity in restrictive lung diseases, as shown in this clinical case. Several randomised controlled trials are still needed to increase the significance of this initial integrated research work performed

The association between breathing sounds and respiratory health or disease has been exceptionally useful in the practice of medicine since the advent of the stethoscope. Remote patient monitoring technology and artificial intelligence offer the potential to develop practical means of assessing respiratory function or dysfunction through continuous assessment of breathing sounds when patients are at home, at work, or even asleep. Automated reports such as cough counts or the percentage of the breathing cycles containing wheezes can be delivered to a practitioner via secure electronic means or returned to the clinical office at the first opportunity. This has not previously been possible. The four respiratory sounds that most lend themselves to this technology are wheezes, to detect breakthrough asthma at night and even occupational asthma when a patient is at work; snoring as an indicator of OSA or adequacy of CPAP settings; cough in which long-term recording can objectively assess treatment adequacy; and crackles, which, although subtle and often overlooked, can contain important clinical information when appearing in a home recording. In recent years, a flurry of publications in the engineering literature described construction, usage, and testing outcomes of such devices. Little of this has appeared in the medical literature. The potential value of this technology for pulmonary medicine is compelling. We expect that these tiny, smart devices soon will allow us to address clinical questions that occur away from the clinic. [ABSTRACT FROM AUTHOR]

Background: Computerized adventitious respiratory sounds (ARS), such as crackles and wheezes, have been poorly explored in bronchiectasis, especially their measurement properties. This study aimed to test the reliability and validity of ARS in bronchiectasis. Methods: Respiratory sounds were recorded twice at 4 chest locations on 2 assessment sessions (7 days apart) in people with bronchiectasis and daily sputum expectoration. The total number of crackles, number of wheezes and wheeze occupation rate (%) were the parameters extracted. Results: 28 participants (9 men; 62 ± 12 y) were included. Total number of crackles and wheezes showed moderate within-day (ICC 0.87, 95% CI 0.74–0.94; ICC 0.86, 95% CI 0.71–0.93) and between-day reliability (ICC 0.70, 95% CI 0.43–0.86; ICC 0.78, 95% CI 0.56–0.90) considering all chest locations and both respiratory phases; wheeze occupation rate showed moderate within-day reliability (ICC 0.86, 95% CI 0.71–0.93), but poor between-day reliability (ICC 0.71, 95% CI 0.33–0.87). Bland–Altman plots revealed no systematic bias, but wide limits of agreement, particularly in the between-days analysis. All ARS parameters correlated moderately with the amount of daily sputum expectoration (r > 0.4; p < 0.05). No other significant correlations were observed. Conclusion: ARS presented moderate reliability and were correlated with the daily sputum expectoration in bronchiectasis. The use of sequential measurements may be an option to achieve greater accuracy when ARS are used to monitor or assess the effects of physiotherapy interventions in this population. [ABSTRACT FROM AUTHOR]

Computational methods for lung sound analysis are beneficial for computer-aided diagnosis support, storage and monitoring in critical care. In this paper, we use pre-trained ResNet models as backbone architectures for classification of adventitious lung sounds and respiratory diseases. The learned representation of the pre-trained model is transferred by using vanilla fine-tuning, co-tuning, stochastic normalization and the combination of the co-tuning and stochastic normalization techniques. Furthermore, data augmentation in both time domain and time-frequency domain is used to account for the class imbalance of the ICBHI and our multi-channel lung sound dataset. Additionally, we introduce spectrum correction to account for the variations of the recording device properties on the ICBHI dataset. Empirically, our proposed systems mostly outperform all state-of-the-art lung sound classification systems for the adventitious lung sounds and respiratory diseases of both datasets. [ABSTRACT FROM AUTHOR]

This paper presents and explores a robust deep learning framework for auscultation analysis. This aims to classify anomalies in respiratory cycles and detect diseases, from respiratory sound recordings. The framework begins with front-end feature extraction that transforms input sound into a spectrogram representation. Then, a back-end deep learning network is used to classify the spectrogram features into categories of respiratory anomaly cycles or diseases. Experiments, conducted over the ICBHI benchmark dataset of respiratory sounds, confirm three main contributions towards respiratory-sound analysis. Firstly, we carry out an extensive exploration of the effect of spectrogram types, spectral-time resolution, overlapping/non-overlapping windows, and data augmentation on final prediction accuracy. This leads us to propose a novel deep learning system, built on the proposed framework, which outperforms current state-of-the-art methods. Finally, we apply a Teacher-Student scheme to achieve a trade-off between model performance and model complexity which holds promise for building real-time applications. [ABSTRACT FROM AUTHOR]

Abstract Background Wheezes and crackles are well-known signs of lung diseases, but can also be heard in apparently healthy adults. However, their prevalence in a general population has been sparsely described. The objective of this study was to determine the prevalence of wheezes and crackles in a large general adult population and explore associations with self-reported disease, smoking status and lung function. Methods We recorded lung sounds in 4033 individuals 40 years or older and collected information on self-reported disease. Pulse oximetry and spirometry were carried out. We estimated age-standardized prevalence of wheezes and crackles and associations between wheezes and crackles and variables of interest were analyzed with univariable and multivariable logistic regressions. Results Twenty-eight percent of individuals had wheezes or crackles. The age-standardized prevalence of wheezes was 18.6% in women and 15.3% in men, and of crackles, 10.8 and 9.4%, respectively. Wheezes were mostly found during expiration and crackles during inspiration. Significant predictors of expiratory wheezes in multivariable analyses were age (10 years increase - OR 1.18, 95%CI 1.09–1.30), female gender (1.45, 1.2–1.8), self-reported asthma (1.36, 1.00–1.83), and current smoking (1.70, 1.28–2.23). The most important predictors of inspiratory crackles were age (1.76, 1.57–1.99), current smoking, (1.94, 1.40–2.69), mMRC ≥2 (1.79, 1.18–2.65), SpO2 (0.88, 0.81–0.96), and FEV1 Z-score (0.86, 0.77–0.95). Conclusions Nearly over a quarter of adults present adventitious lung sounds on auscultation. Age was the most important predictor of adventitious sounds, particularly crackles. The adventitious sounds were also associated with self-reported disease, current smoking and measures of lung function. The presence of findings in two or more auscultation sites was associated with a higher risk of decreased lung function than solitary findings.

The possibility of a normal distribution indicates that few particles are in the same phase during a breath and their reflections can be observed on the chest wall, then a few explosive waves with relatively large power occurr occasionally. Therefore, the one-cycle sine wave which is simulated as a single burst of the explosive effect phenomenon penetrates through the chest wall and was analysed to explore the reason of the crackle sounds. The results explain the differences between the definitions of crackle proposed by Sovijärvi et al. (2000a). The crackles in the lungs were synthesised by a computer simulation. When the coarse crackles occur, the results indicate that higher burst frequency carriers (greater than 100 Hz) directly penetrate the bandpass filter to simulate the chest wall. The simulated coarse crackle sounds were low pitched, with a high amplitude and long duration. The total duration was greater than 10 ms. However, for a lower frequency carrier (approximately 50 Hz), the fundamental frequency component was filtered out. Therefore, the second harmonic component of the lower frequency carrier, i.e., the fine crackle, penetrated the chest wall. Consequently, it is very possible that the normal lung sounds may contain many crackle-shaped waves with very small amplitudes because of the filtering effects of the chest wall, environment noises, electric devices, stethoscopes, and human ears, the small crackles disappear in the auscultations. In addition, our study pointed out that some unknown crackles of the very low frequency under the bandwidth of the human ears cannot penetrate the airways and be detected by medical doctors. Therefore, it might be necessary to focus advanced electronic instrumentation on them in order to analyse their possible characteristics for diagnosis and treatment of the respiration system. [ABSTRACT FROM AUTHOR]

Respiratory diseases constitute one of the leading causes of death worldwide and directly affect the patient’s quality of life. Early diagnosis and patient monitoring, which conventionally include lung auscultation, are essential for the efficient management of respiratory diseases. Manual lung sound interpretation is a subjective and time-consuming process that requires high medical expertise. The capabilities that deep learning offers could be exploited in order that robust lung sound classification models can be designed. In this paper, we propose a novel hybrid neural model that implements the focal loss (FL) function to deal with training data imbalance. Features initially extracted from short-time Fourier transform (STFT) spectrograms via a convolutional neural network (CNN) are given as input to a long short-term memory (LSTM) network that memorizes the temporal dependencies between data and classifies four types of lung sounds, including normal, crackles, wheezes, and both crackles and wheezes. The model was trained and tested on the ICBHI 2017 Respiratory Sound Database and achieved state-of-the-art results using three different data splitting strategies—namely, sensitivity 47.37%, specificity 82.46%, score 64.92% and accuracy 73.69% for the official 60/40 split, sensitivity 52.78%, specificity 84.26%, score 68.52% and accuracy 76.39% using interpatient 10-fold cross validation, and sensitivity 60.29% and accuracy 74.57% using leave-one-out cross validation.

Background: The use of a smartphone built-in microphone for auscultation is a feasible alternative to the use of a stethoscope, when applied by physicians., Objective: This cross-sectional study aims to assess the feasibility of this technology when used by parents-the real intended end users., Methods: Physicians recruited 46 children (male: n=33, 72%; age: mean 11.3, SD 3.1 y; children with asthma: n=24, 52%) during medical visits in a pediatric department of a tertiary hospital. Smartphone auscultation using an app was performed at 4 locations (trachea, right anterior chest, and right and left lung bases), first by a physician (recordings: n=297) and later by a parent (recordings: n=344). All recordings (N=641) were classified by 3 annotators for quality and the presence of adventitious sounds. Parents completed a questionnaire to provide feedback on the app, using a Likert scale ranging from 1 ("totally disagree") to 5 ("totally agree")., Results: Most recordings had quality (physicians' recordings: 253/297, 85.2%; parents' recordings: 266/346, 76.9%). The proportions of physicians' recordings (34/253, 13.4%) and parents' recordings (31/266, 11.7%) with adventitious sounds were similar. Parents found the app easy to use (questionnaire: median 5, IQR 5-5) and were willing to use it (questionnaire: median 5, IQR 5-5)., Conclusions: Our results show that smartphone auscultation is feasible when performed by parents in the clinical context, but further investigation is needed to test its feasibility in real life., (© Catarina Santos-Silva, Henrique Ferreira-Cardoso, Sónia Silva, Pedro Vieira-Marques, José Carlos Valente, Rute Almeida, João A Fonseca, Cristina Santos, Inês Azevedo, Cristina Jácome. Originally published in JMIR Pediatrics and Parenting (https://pediatrics.jmir.org).)

In this work further research was achieved on the separation by Independent Components (IC) of simulated abnormal breathing sounds (ABS) sources immersed in normal breathing sounds. This study considers only ABS discontinuous sounds, known as crackles, and includes fine and coarse types for both inspiratory and expiratory phases. Additionally, we develop a novel proposal to automated characterization of the IC associated with crackles. We analyzed the efficiency of three independent component analysis algorithms, i.e. FastICA, Infomax and TDSEP, through the Amari index, the signal to interference ratio, and the total relative distortion index. In the simulated multichannel signal scenarios, the performance indexes showed that Infomax is the best algorithm to solve the problem of blind source separation, supporting the results found in previous efforts. Finally, the presence of crackles in the IC obtained by Infomax was determined through their kurtosis and skewness, whereas the type of crackle was found by their characterization via the spectrogram of selected IC. Results indicate that the proposed methodology is able to adequately extract the crackle sources and identify the respiratory cycle phase in which they appear. Also, we managed to estimate the type and number of existing crackles in each source. In conclusion, our methodology can provide quantitative information on the clinical relevance of crackles in respiratory patients. [ABSTRACT FROM AUTHOR]

BACKGROUND: For clinical practice and research, it would be easier to auscultate lung sounds without simultaneously measuring air flow. This study evaluated whether the presence of adventitious lung sounds and the characteristics of normal lung sounds differ between spontaneous and standardized breathing in a general population. METHODS: A cross-sectional study was conducted with 116 subjects (53.4% female, mean age 59.2 ± 11.6 y). The subjects reported heart/lung diseases and the degree of dyspnea, and spirometry was carried out. Lung sounds were recorded at 6 chest locations, first during spontaneous breathing and then during breathing with a standardized air flow of 1.5 L/s. Crackles and wheezes were identified by 4 observers. Intensity and frequency of normal lung sounds in the 100-2,000 Hz band were determined. RESULTS: Inspiratory crackles were heard in 19 subjects (16.4%) during spontaneous breathing and in 18 subjects during standardized breathing (15.5%). Only 5 subjects were identified with both methods (kappa = 0.13). Expiratory wheezes were heard in 18 subjects (15.5%) during spontaneous breathing and in 23 subjects during standardized breathing (19.8%). Nine subjects were identified with both methods (kappa = 0.32). The mean intensity and median frequency of normal lung sounds were significantly higher during standardized breathing than during spontaneous breathing, both at inspiration (23.1 dB vs 20.1 dB and 391.6 Hz vs 367.3 Hz) and expiration (20 dB vs17.6 dB and 376.3 Hz vs 355 Hz). Dyspnea was more frequently reported when expiratory wheezes were present, but this association was only statistically significant during standardized breathing (P = .03). During spontaneous breathing, increased mean intensity and median frequency during expiration were associated with an increased reporting of heart/lung diseases (P = .02 and P = .01, respectively). CONCLUSIONS: The mode of breathing had an impact on both adventitious and normal lung sounds. Although adventitious sounds were found with similar frequency between the modes of breathing, less than half of these subjects were identified with both methods. Spontaneous breathing was not inferior to standardized breathing in reflecting lung disease. [ABSTRACT FROM AUTHOR]

Due to the subjectivity involved currently in pulmonary auscultation process and its diagnostic to evaluate the condition of respiratory airways, this work pretends to evaluate the performance of clustering algorithms such as k-means and DBSCAN to perform a computational analysis of lung sounds aiming to visualize a representation of such sounds that highlights the presence of crackles and the energy associated with them. In order to achieve that goal, Wavelet analysis techniques were used in contrast to traditional frequency analysis given the similarity between the typical waveform for a crackle and the wavelet sym4. Once the lung sound signal with isolated crackles is obtained, the clustering process groups crackles in regions of high density and provides visualization that might be useful for the diagnostic made by an expert. Evaluation suggests that k-means groups crackle more effective than DBSCAN in terms of generated clusters.

Auscultation of the lung is an important part of the respiratory examination and is helpful in diagnosing various respiratory disorders. Auscultation assesses airflow through the trachea-bronchial tree. It is important to distinguish normal respiratory sounds from abnormal ones for example crackles, wheezes, and pleural rub in order to make correct diagnosis. It is necessary to understand the underlying pathophysiology of various lung sounds generation for better understanding of disease processes. Bedside teaching should be strengthened in order to avoid erosion in this age old procedure in the era of technological explosion.

Lung cancer is the most common cancer worldwide and the third most common cancer in Malaysia. Due to its high prevalence worldwide and in Malaysia, it is an utmost importance to have the disease detected at an early stage which would result in a higher chance of cure and possibly better survival. The current methods used for lung cancer screening might not be simple, inexpensive and safe and not readily accessible in outpatient clinics. In this paper, we present the classification of normal and crackles sounds acquired from 20 healthy and 23 lung cancer patients, respectively using Artificial Neural Network. Firstly, the sounds signals were decomposed into seven different frequency bands using Discrete Wavelet Transform (DWT) based on two different mother wavelets namely Daubechies 7 (db7) and Haar. Secondly, mean, standard deviation and maximum PSD of the detail coefficients for five frequency bands (D3, D4, D5, D6, and D7) were calculated as features. Fifteen features were used as input to the ANN classifier. The results of classification show that db7 based performed better than Haar with perfect 100% sensitivity, specificity and accuracy for testing and validation stages when using 15 nodes at the hidden layer. While for Haar, only testing stage shows the perfect 100% for sensitivity, specificity, and accuracy when using 10 nodes at the hidden layer. Lung cancer is the most common cancer worldwide and the third most common cancer in Malaysia. Due to its high prevalence worldwide and in Malaysia, it is an utmost importance to have the disease detected at an early stage which would result in a higher chance of cure and possibly better survival. The current methods used for lung cancer screening might not be simple, inexpensive and safe and not readily accessible in outpatient clinics. In this paper, we present the classification of normal and crackles sounds acquired from 20 healthy and 23 lung cancer patients, respectively using Artificial Neural Network. Firstly, the sounds signals were decomposed into seven different frequency bands using Discrete Wavelet Transform (DWT) based on two different mother wavelets namely Daubechies 7 (db7) and Haar. Secondly, mean, standard deviation and maximum PSD of the detail coefficients for five frequency bands (D3, D4, D5, D6, and D7) were calculated as features. Fifteen features were used as input to the ANN classifier. The results of classification show that db7 based performed better than Haar with perfect 100% sensitivity, specificity and accuracy for testing and validation stages when using 15 nodes at the hidden layer. While for Haar, only testing stage shows the perfect 100% for sensitivity, specificity, and accuracy when using 10 nodes at the hidden layer. Lung cancer is the most common cancer worldwide and the third most common cancer in Malaysia. Due to its high prevalence worldwide and in Malaysia, it is an utmost importance to have the disease detected at an early stage which would result in a higher chance of cure and possibly better survival. The current methods used for lung cancer screening might not be simple, inexpensive and safe and not readily accessible in outpatient clinics. In this paper, we present the classification of normal and crackles sounds acquired from 20 healthy and 23 lung cancer patients, respectively using Artificial Neural Network. Firstly, the sounds signals were decomposed into seven different frequency bands using Discrete Wavelet Transform (DWT) based on two different mother wavelets namely Daubechies 7 (db7) and Haar. Secondly, mean, standard deviation and maximum PSD of the detail coefficients for five frequency bands (D3, D4, D5, D6, and D7) were calculated as features. Fifteen features were used as input to the ANN classifier. The results of classification show that db7 based performed better than Haar with perfect 100% sensitivity, specificity and accuracy for testing and validation stages when using 15 nodes at the hidden layer. While for Haar, only testing stage shows the perfect 100% for sensitivity, specificity, and accuracy when using 10 nodes at the hidden layer. Lung cancer is the most common cancer worldwide and the third most common cancer in Malaysia. Due to its high prevalence worldwide and in Malaysia, it is an utmost importance to have the disease detected at an early stage which would result in a higher chance of cure and possibly better survival. The current methods used for lung cancer screening might not be simple, inexpensive and safe and not readily accessible in outpatient clinics. In this paper, we present the classification of normal and crackles sounds acquired from 20 healthy and 23 lung cancer patients, respectively using Artificial Neural Network. Firstly, the sounds signals were decomposed into seven different frequency bands using Discrete Wavelet Transform (DWT) based on two different mother wavelets namely Daubechies 7 (db7) and Haar. Secondly, mean, standard deviation and maximum PSD of the detail coefficients for five frequency bands (D3, D4, D5, D6, and D7) were calculated as features. Fifteen features were used as input to the ANN classifier. The results of classification show that db7 based performed better than Haar with perfect 100% sensitivity, specificity and accuracy for testing and validation stages when using 15 nodes at the hidden layer. While for Haar, only testing stage shows the perfect 100% for sensitivity, specificity, and accuracy when using 10 nodes at the hidden layer. Lung cancer is the most common cancer worldwide and the third most common cancer in Malaysia. Due to its high prevalence worldwide and in Malaysia, it is an utmost importance to have the disease detected at an early stage which would result in a higher chance of cure and possibly better survival. The current methods used for lung cancer screening might not be simple, inexpensive and safe and not readily accessible in outpatient clinics. In this paper, we present the classification of normal and crackles sounds acquired from 20 healthy and 23 lung cancer patients, respectively using Artificial Neural Network. Firstly, the sounds signals were decomposed into seven different frequency bands using Discrete Wavelet Transform (DWT) based on two different mother wavelets namely Daubechies 7 (db7) and Haar. Secondly, mean, standard deviation and maximum PSD of the detail coefficients for five frequency bands (D3, D4, D5, D6, and D7) were calculated as features. Fifteen features were used as input to the ANN classifier. The results of classification show that db7 based performed better than Haar with perfect 100% sensitivity, specificity and accuracy for testing and validation stages when using 15 nodes at the hidden layer. While for Haar, only testing stage shows the perfect 100% for sensitivity, specificity, and accuracy when using 10 nodes at the hidden layer. Lung cancer is the most common cancer worldwide and the third most common cancer in Malaysia. Due to its high prevalence worldwide and in Malaysia, it is an utmost importance to have the disease detected at an early stage which would result in a higher chance of cure and possibly better survival. The current methods used for lung cancer screening might not be simple, inexpensive and safe and not readily accessible in outpatient clinics. In this paper, we present the classification of normal and crackles sounds acquired from 20 healthy and 23 lung cancer patients, respectively using Artificial Neural Network. Firstly, the sounds signals were decomposed into seven different frequency bands using Discrete Wavelet Transform (DWT) based on two different mother wavelets namely Daubechies 7 (db7) and Haar. Secondly, mean, standard deviation and maximum PSD of the detail coefficients for five frequency bands (D3, D4, D5, D6, and D7) were calculated as features. Fifteen features were used as input to the ANN classifier. The results of classification show that db7 based performed better than Haar with perfect 100% sensitivity, specificity and accuracy for testing and validation stages when using 15 nodes at the hidden layer. While for Haar, only testing stage shows the perfect 100% for sensitivity, specificity, and accuracy when using 10 nodes at the hidden layer. Lung cancer is the most common cancer worldwide and the third most common cancer in Malaysia. Due to its high prevalence worldwide and in Malaysia, it is an utmost importance to have the disease detected at an early stage which would result in a higher chance of cure and possibly better survival. The current methods used for lung cancer screening might not be simple, inexpensive and safe and not readily accessible in outpatient clinics. In this paper, we present the classification of normal and crackles sounds acquired from 20 healthy and 23 lung cancer patients, respectively using Artificial Neural Network. Firstly, the sounds signals were decomposed into seven different frequency bands using Discrete Wavelet Transform (DWT) based on two different mother wavelets namely Daubechies 7 (db7) and Haar. Secondly, mean, standard deviation and maximum PSD of the detail coefficients for five frequency bands (D3, D4, D5, D6, and D7) were calculated as features. Fifteen features were used as input to the ANN classifier. The results of classification show that db7 based performed better than Haar with perfect 100% sensitivity, specificity and accuracy for testing and validation stages when using 15 nodes at the hidden layer. While for Haar, only testing stage shows the perfect 100% for sensitivity, specificity, and accuracy when using 10 nodes at the hidden layer. [ABSTRACT FROM AUTHOR]

Objective: This paper describes the state of the art, scientific publications, and ongoing research related to the methods of analysis of respiratory sounds. Methods and Material: Narrative review of the current medical and technological literature using Pubmed and personal experience. Results: We outline the various techniques that are currently being used to collect auscultation sounds and provide a physical description of known pathological sounds for which automatic detection tools have been developed. Modern tools are based on artificial intelligence and techniques such as artificial neural networks, fuzzy systems, and genetic algorithms. Conclusion: The next step will consist of finding new markers to increase the efficiency of decision-aiding algorithms and tools. [ABSTRACT FROM AUTHOR]

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.