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• We proposed a novel method for noise reduction of multichannel heart sound signals, which was based on FastICA algorithm and CNN model. • The method performs classification and consistency analysis of the independent components to extract reliable independent components of heart sounds. • The method improves the SNRs of each channel signal up to 20 dB, which is superior to the Wiener filtering and singular spectrum analysis (SSA). Various noises could contaminate heart sounds and yield inaccurate diagnosis. This paper proposed a framework of heart sound noise reduction using multi-channel structure. Multi-channel heart sound (MCHS) signals were preprocessed, and then decomposed into independent components (ICs) using FastICA algorithm. The ICs were evaluated for consistency using correlation analysis, and reliable ICs were identified. The reliable ICs were classified based on a convolutional neural network (CNN) into two groups: one is of ICs for heart sounds and the other is of ICs for noise, respectively. Consequently, the MCHS signals were reconstructed from the ICs group of heart sounds. To validate the proposed framework, the MCHS signals of 15 subjects were acquired and mixed with breath sounds under different signal-to-noise ratios (SNRs). the simulation results showed that the framework had effective noise reduction performances in suppressing the breath sounds. The SNR of the denoised MCHS could be improved to 20 dB. the proposed framework was better than traditional methods, such as Wiener filter and singular spectrum analysis (SSA). The practical applications proved that the framework is possible to reconstruct the heart sound signals in low signal quality situation. [ABSTRACT FROM AUTHOR]

In this study, a new method is proposed to assess heart and lung signal quality objectively and automatically on a 5-level scale in real-time, and to assess the effect of signal quality on vital sign estimation. A total of 207 10 s long chest sounds were taken from 119 preterm and full-term babies. Thirty of the recordings from ten subjects were obtained with synchronous vital signs from the Neonatal Intensive Care Unit (NICU). As a reference, seven annotators independently assessed the signal quality. For automatic quality classification, 400 features were extracted from the chest sounds. After feature ranking and selection, class balancing, and hyperparameter optimization, a variety of multi-class and ordinal classification and regression algorithms were trained. Then, heart rate and breathing rate were automatically estimated from the chest sounds. For the deep learning model, YAMNet, a deep convolutional neural network pre-trained on the AudioSet-Youtube corpus for sound classification was used. After modification of the final output layers of the neural network and class balancing, transfer learning was applied to YAMNet for heart and lung signal quality classification. The results of subject-wise leave-one-out cross-validation show that the best-performing models had a balanced accuracy of 56.8% and 51.2% for heart and lung qualities, respectively. The best-performing models for real-time analysis (< 200 ms) had a balanced accuracy of 56.7% and 46.3%, respectively. Our experimental results underscore that increasing the signal quality leads to a reduction in vital sign error.

The work reported in the paper analyses the adventitious stridor breath sound (ST) and the normal bronchial breath sound (BR) using spectral, fractal, and nonlinear signal processing methods. The sixty breath sound signals are subjected to power spectral density (PSD) and wavelet analyses to understand the temporal evolution of the frequency components. The energy envelope of the PSD plot of ST shows three peaks labeled as A (256 Hz), B (369 Hz), and C (540 Hz), of which A alone is present in BR at 265 Hz. The appearance of B and C in the PSD plot of ST is due to the obstructions in the trachea and upper airways caused by lesions. The phase portrait analysis of the time series data of ST and BR gives information about the dynamical system's randomness and sample entropy. The study reveals that the fractal dimension and sample entropy values are higher for BR, which may be due to the musical ordered behavior of ST. The machine learning techniques based on the features extracted from the PSD data and phase portrait parameters offer good predictability, besides the classification of BR and ST, thereby revealing its potential in pulmonary auscultation. [ABSTRACT FROM AUTHOR]

With advances in digital stethoscopes, internet of things, signal processing and machine learning, chest sounds can be easily collected and transmitted to the cloud for remote monitoring and diagnosis. However, low quality of recordings complicates remote monitoring and diagnosis, particularly for neonatal care. This paper proposes a new method to objectively and automatically assess the signal quality to improve the accuracy and reliability of heart rate (HR) and breathing rate (BR) estimation from noisy neonatal chest sounds. A total of 88 10-second long chest sounds were taken from 76 preterm and full-term babies. Six annotators independently assessed the signal quality, number of detectable beats, and breathing periods from these recordings. For quality classification, 187 and 182 features were extracted from heart and lung sounds, respectively. After feature selection, class balancing, and hyperparameter optimization, a dynamic binary classification model was trained. Then HR and BR were automatically estimated from the chest sound and several approaches were compared.The results of subject-wise leave-one-out cross-validation, showed that the model distinguished high and low quality recordings in the test set with 96% specificity, 81% sensitivity and 93% accuracy for heart sounds, and 86% specificity, 69% sensitivity and 82% accuracy for lung sounds. The HR and BR estimated from high quality sounds resulted in significantly less median absolute error (4 bpm and 12 bpm difference, respectively) compared to those from low quality sounds. The methods presented in this work, facilitates automated neonatal chest sound auscultation for future telehealth applications. [ABSTRACT FROM AUTHOR]

In this work, the sleep stages discrimination by breath sounds have been used as characteristic parameters for monitoring sleeping condition. Our objective was to determine and verify a high performance algorithm to correlate the sleep stages with respective stages of breath sound signals. The algorithm was designed for breath sound signals and was tested with several all-night days data acquired with our laboratory-made sleep monitoring system. Furthermore, a commercial system was compared for validating the efficiency of our proposed algorithm. This work is the first study that successfully used breath sounds to classify sleeping conditions. The experiment validates the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Objective: Lung auscultation plays an important role in the diagnosis of pulmonary diseases in children. The objective of this study was to evaluate the use of an artificial intelligence (AI) algorithm for the detection of breath sounds in a real clinical environment among children with pulmonary diseases.Method: The auscultations of breath sounds were collected in the respiratory department of Shanghai Children's Medical Center (SCMC) by using an electronic stethoscope. The discrimination results for all chest locations with respect to a gold standard (GS) established by 2 experienced pediatric pulmonologists from SCMC and 6 general pediatricians were recorded. The accuracy, sensitivity, specificity, precision, and F1-score of the AI algorithm and general pediatricians with respect to the GS were evaluated. Meanwhile, the performance of the AI algorithm for different patient ages and recording locations was evaluated.Result: A total of 112 hospitalized children with pulmonary diseases were recruited for the study from May to December 2019. A total of 672 breath sounds were collected, and 627 (93.3%) breath sounds, including 159 crackles (23.1%), 264 wheeze (38.4%), and 264 normal breath sounds (38.4%), were fully analyzed by the AI algorithm. The accuracy of the detection of adventitious breath sounds by the AI algorithm and general pediatricians with respect to the GS were 77.7% and 59.9% (p < 0.001), respectively. The sensitivity, specificity, and F1-score in the detection of crackles and wheeze from the AI algorithm were higher than those from the general pediatricians (crackles 81.1 vs. 47.8%, 94.1 vs. 77.1%, and 80.9 vs. 42.74%, respectively; wheeze 86.4 vs. 82.2%, 83.0 vs. 72.1%, and 80.9 vs. 72.5%, respectively; p < 0.001). Performance varied according to the age of the patient, with patients younger than 12 months yielding the highest accuracy (81.3%, p < 0.001) among the age groups.Conclusion: In a real clinical environment, children's breath sounds were collected and transmitted remotely by an electronic stethoscope; these breath sounds could be recognized by both pediatricians and an AI algorithm. The ability of the AI algorithm to analyze adventitious breath sounds was better than that of the general pediatricians.

Objectives: Computerized breath sound based diagnostic methods are one of the emerging technologies gaining popularity in terms of detecting respiratory disorders. However, the breath sound signal used in such automated systems used to be too noisy, which affects the quality of the diagnostic interpretations. To address this problem, the proposed work presents the new hybrid approach to reject the noises from breath sound., Methods: In this method, 80 chronic obstructive pulmonary disease (COPD), 75 asthmatics and 80 normal breath sounds were recorded from the participants of a hospital. Each of these breath sound data were decontaminated using hybrid method of Butterworth band-pass filter, transient artifact reduction algorithm and spectral subtraction algorithm. The study examined the algorithms noise rejection potential over each category of breath sound by estimating the noise rejection performance metrics, i.e., mean absolute error (MAE), mean square error (MSE), peak signal to noise ratio (PSNR), and signal to noise ratio (SNR)., Results: Using this algorithm, the study obtained a high value of SNR of 70 dB and that of PSNR of 72 dB., Conclusions: The study could definitely a suitable one to suppress noises and to produce noise free breath sound signal., (© 2024 Walter de Gruyter GmbH, Berlin/Boston.)

BACKGROUND: Stridor is a high-pitched breath sound resulting from turbulent air flow in the narrowing areas of the large airways. A wide variety of differential diagnoses must be considered in the pediatric patient with stridor, including infectious and noninfectious causes, being foreign body aspiration as one of the etiologies to consider. CLINICAL CASE: Previously healthy 11-month-old infant with sudden stridor and 5-dayold refusal of food. He received 2 antimicrobial schemes without improvement, in a fortuitous way it was detected, in the thorax radiography, a rounded object, opaque radio, located in the superior third of the esophagus. A foreign body, similar to a button battery, was removed endoscopically, leaving an ulcerated lesion with areas of necrosis (Zargar IIB). It was treated with broad-spectrum antibiotics, steroids and parenteral nutrition. His progress was satisfactory, without complications. In the control endoscopy at 6 weeks a scar was noticed in the affected area. CONCLUSIONS: Cough and acute-onset stridor, followed by gastrointestinal manifestations such as vomiting and feeding refusal in infants and toddlers without a history of infectious disease, forced to rule out the presence of a foreign body, additional evaluation and endoscopic removal in an appropriate time. [ABSTRACT FROM AUTHOR]

Aim and Objective: The aim of the study was to evaluate the effect of balloon blowing exercise on wheezing patients. Introduction: Wheezing is a dynamic sign, occurs when airflow through narrowed airways, by creating vibrations in the audible frequency. In this highly polluted society, all age groups are affected invariably with respiratory diseases, in which wheezing is the most common condition. To control the severity of wheezing, Balloon blowing exercise has been introduced. It is cheap and can be followed by all age groups. The principle followed in blowing the balloon exercise is to increase the maximum expiratory volume of lungs. Materials and Methods: The sample size of 15 students of the age group 18-20 years was selected. They were given the balloon and asked to blow the balloon for the consecutive 30 days, both in the morning and evening for 15 min. The lung functions such as FVC, FEV1, and PEF were measured using spirometer before and after the exercise. Results and Discussion: There is a significant increase in the forced vital capacity (FVC) of wheezing patient after balloon blowing exercise. Among the respiratory function test parameters, only FVC showed a significant increase of P = 0.056. Since it is a pilot study, it had many limitations. Therefore, future studies need to be conducted with more subjects, measuring the balloon resistance accurately. Conclusions: Blowing the balloon strengthens the respiratory muscles and results in increased of forced vital capacity (FVC), forced expiratory volume in one sec (FEV1), and peak expiratory flow (PEF). This exercise improves pulmonary function and reduces the risk of lung disorder from wheezing. [ABSTRACT FROM AUTHOR]

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.

Stridor is a high-pitched respiratory sound that signals upper airway obstruction. It can be encountered by clinicians in a variety of clinical settings and requires a team-based, interdisciplinary approach. Early recognition is crucial, as the differential diagnosis can be broad, and causes range from benign to life-threatening. This article reviews the most commonly encountered causes of chronic congenital stridor in infants, focusing on the diagnostic approach, pathophysiology, clinical presentation, and management strategies. [ABSTRACT FROM AUTHOR]

In this work, a simple method for monitoring sleeping conditions by all-night breath sound measurement is proposed. Our research target is to develop and validate a high performance method to classify sleeping conditions into several stages based on breath sound. Subjects were recorded in home/group house using bluetooth breath sound sensor. Breath sound-based features extracted from time and spectral domains can accurately discriminate between snore or non-snore and respiratory variance of breath sound, and so on. This breath sound signal analysis method enables detection and analysis of apnea/hypopnea. This work is the first study that successfully used simple method to monitor sleeping conditions. The experiment validate the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

Scientific evidence shows that acoustic analysis could be an indicator for diagnosing COVID-19. From analyzing recorded breath sounds on smartphones, it is discovered that patients with COVID-19 have different patterns in both the time domain and frequency domain. These patterns are used in this paper to diagnose the infection of COVID-19. Statistics of the sound signals, analysis in the frequency domain, and Mel-Frequency Cepstral Coefficients (MFCCs) are then calculated and applied in two classifiers, k-Nearest Neighbors (kNN) and Convolutional Neural Network (CNN), to diagnose whether a user is contracted with COVID-19 or not. Test results show that, amazingly, an accuracy of over 97% could be achieved with a CNN classifier and more than 85% on kNN with optimized features. Optimization methods for selecting the best features and using various metrics to evaluate the performance are also demonstrated in this paper. Owing to the high accuracy of the CNN model, the CNN model was implemented in an Android app to diagnose COVID-19 with a probability to indicate the confidence level. The initial medical test shows a similar test result between the method proposed in this paper and the lateral flow method, which indicates that the proposed method is feasible and effective. Because of the use of breath sound and tested on the smartphone, this method could be used by everybody regardless of the availability of other medical resources, which could be a powerful tool for society to diagnose COVID-19. [ABSTRACT FROM AUTHOR]

Abstract: Since breath sound (BS) contains important indicators of respiratory health and disease, analysis and detection of BS has become an important topic, with diagnostic and assessment of treatment capabilities. In this paper, the identification and classification of respiratory disorders based on the enhanced perceptual and cepstral feature set (PerCepD) is proposed. The hybrid PerCepD feature can capture the time-frequency characteristics of BS very well. Thus, it is very effective for the exploration and classification of normal and pathological BS related data. The classification models based on support vector machine (SVM) and artificial neural network (ANN) have been adopted to achieve automatic detection from BS data. The high detection accuracy results validate the performance of the proposed feature sets and classification model. The experimental results also demonstrate that the high accuracy of the pathological BS data can provide reliable diagnostic suggestions for breath disorders, such as flu, pneumonia and bronchitis. [Copyright &y& Elsevier]

Abstract: In this paper, the study of lung and heart diagnosis application is investigated based on frequency separation of breath sound frequency. The aim of study is to investigate how to separate and process the breath sound frequency into lung sound frequency and heart sound frequency. Since the lung and the heart are the vital organ of human, therefore the needs of immediate preventive and curative actions are primarily important. Here, the frequency separation of breath sound is carried out by using the modulation filtering method. From the investigation, although the results still need some more improvement, however it shows that the method is implementable for the separation of lung sound and heart sound from the breath sounds frequency. It is then useful for the development of application for lung and heart diagnosis diseases. [Copyright &y& Elsevier]

Background and objective: It is difficult for clinicians to identify changes in breath sounds caused by bronchoconstriction when wheezing is not audible. A breath sound analyser can identify changes in the frequency of breath sounds caused by bronchoconstriction. The present study aimed to identify the changes in the frequency of breath sounds during bronchoconstriction and bronchodilatation using a breath sound analyser. Methods: Thirty-six children (8.2 ± 3.7 years; males : females, 22 : 14) underwent spirometry, methacholine inhalation challenge and breath sound analysis. Methacholine inhalation challenge was performed and baseline respiratory resistance, minimum dose of methacholine (bronchial sensitivity) and speed of bronchoconstriction in response to methacholine (Sm: bronchial reactivity) were calculated. The highest frequency of inspiratory breath sounds (HFI), the highest frequency of expiratory breath sounds (HFE) and the percentage change in HFI and HFE were determined. The HFI and HFE were compared before methacholine inhalation (pre-HFI and pre-HFE), when respiratory resistance reached double the baseline value (max HFI and max HFE), and after bronchodilator inhalation (post-HFI and post-HFE). Results: Breath sounds increased during methacholine-induced bronchoconstriction. Max HFI was significantly greater than pre-HFI ( P < 0.001), and decreased to the basal level after bronchodilator inhalation. Post-HFI was significantly lower than max HFI ( P < 0.001). HFI and HFE were also significantly changed ( P < 0.001). The percentage change in HFI showed a significant correlation with the speed of bronchoconstriction in response to methacholine ( P = 0.007). Conclusions: Methacholine-induced bronchoconstriction significantly increased HFI, and the increase in HFI was correlated with bronchial reactivity. [ABSTRACT FROM AUTHOR]