Breath Sounds Research Articles

Approaches for Respiratory Sound Analysis in Identification of Respiratory Diseases

Purpose: Medical professionals throughout the world prefer to use conventional stethoscopes to listen to respiratory sounds. Listening to respiratory sounds through stethoscopes is a subjective matter, and proper diagnosis of the disease depends on the skills and ability of the doctor. Computerized analysis of respiratory sounds can help doctors and researchers to characterize different abnormal respiratory patterns and make informed decisions.
 Materials and Methods: This study includes previously reported work in different normal and abnormal respiratory sounds. The IEEE, PubMed, Google Scholar and Elsevier databases were searched and studies with the keywords of lung sound analysis, respiratory sound analysis, and respiratory sound classification were included. Detailed characteristics of normal and abnormal respiratory sounds are mentioned. In addition, Time-amplitude characteristics of different respiratory sound plots are obtained using MATLAB and ICBHI database. This study systematically discusses different approaches for respiratory sound analysis like visual analysis of the time-amplitude signals, frequency analysis, and spectral analysis using fast Fourier transform, statistical analysis, and machine learning approach. A list of relevant datasets is mentioned that can help researchers to do further analysis in this domain.
 Results: The careful observations and analysis show the possibility of predicting respiratory diseases by extracting suitable parameters such as the frequency response and spectral characteristics of the signal. Power spectral density can help us to calculate the maximum, median frequency over an extended period. Using machine learning we can estimate the energy, entropy, spectral features, and wavelets of the signals.
 Conclusion: Computer-based respiratory sound analysis can help medical professionals in making informed decisions. This will help in early diagnosis and devise effective treatment plans for the patients.

Automatic breathing phase identification based on the second derivative of the recorded lung sounds

This paper presents a new method for automatic identification of the inspiratory and expiratory breathing phases in lung sound recordings. Adventitious lung sounds (wheezes and crackles), superimposed on the breath sounds, are generally an early indication of the disease, and their timing in the breathing cycle (early/mid/late inspiratory or expiratory) has clinical significance for monitoring or diagnosing disease. Therefore, the identification of the phases of the breathing cycle is an essential step for clinical interpretation of pulmonary auscultation. The proposed algorithm is designed to be robust in the presence of adventitious lung sounds or where the breath sounds may be noisy compared to healthy lung sounds. The algorithm uses the Savitzky & Golay (SG) filter to estimate the second derivative of the lung sound signal then calculates its normalized absolute value. A threshold value is used to clip any large amplitude peaks and, following low-pas filtering, the breathing phases are visible in the plotted signal. A rule-based approach to locating peaks and troughs is then used to identify inspirations and expirations. The performance of the method is evaluated using four different datasets: (a) a longitudinal dataset recorded from 19 subjects with a diagnosis of idiopathic pulmonary fibrosis (IPF), (b) a cross-sectional dataset recorded from 55 subjects who were referred for high-resolution computed tomography (HRCT) scan of the chest for various clinical indications, (c) a longitudinal dataset recorded from 10 healthy subjects, and (d) an open access lung sounds dataset containing recordings from 41 subjects with wheeze (9 with chronic obstructive pulmonary fibrosis and 32 with asthma). On average for inspiratory phase identification the algorithm had a sensitivity of (mean (standard deviation)) 92.84 (12.88)%, positive predictive value of 90.64(15.96)%, and F1-score of 90.67 (12.53)%. For identification of the expiratory phase, the algorithm had an average sensitivity of 92.19 (13.44)%, an average positive predictive value of 91.55 (15.23)%, and an average F1-score of 90.89 (12.45)%. The method shows good potential for automatic identification of breathing phases in recorded lung sounds.

Performance evaluation of lung sounds classification using deep learning under variable parameters

It is desired to apply deep learning models (DLMs) to assist physicians in distinguishing abnormal/normal lung sounds as quickly as possible. The performance of DLMs depends on feature-related and model-related parameters heavily. In this paper, the relationship between performance and feature-related parameters of a DLM, i.e., convolutional neural network (CNN) is analyzed through experiments. ICBHI 2017 is selected as the lung sounds dataset. The sensitivity analysis of classification performance of the DLM on three parameters, i.e., the length of lung sounds frame, overlap percentage (OP) of successive frames and feature type, is performed. An augmented and balanced dataset is acquired by the way of white noise addition, time stretching and pitch shifting. The spectrogram and mel frequency cepstrum coefficients of lung sounds are used as features to the CNN, respectively. The results of training and test show that there exists significant difference on performance among various parameter combinations. The parameter OP is performance sensitive. The higher OP, the better performance. It is concluded that for fixed sampling frequency 8 kHz, frame size 128, OP 75% and spectrogram feature is optimum under which the performance is relatively better and no extra computation or storage resources are required.

Time to Clinical Stability in Children With Community-Acquired Pneumonia.

Time to clinical stability (TCS) is a commonly used outcome in adults with community-acquired pneumonia (CAP), yet few studies have evaluated TCS in children. Our objective was to determine the association between TCS and disease severity in children with suspected CAP, as well as factors associated with reaching early stability. This is a prospective cohort study of children (aged 3 months to 18 years) hospitalized with suspected CAP. TCS parameters included temperature, heart rate, respiratory rate, and hypoxemia with the use of supplemental oxygen. TCS was defined as time from admission to parameter normalization. The association of TCS with severity and clinical factors associated with earlier TCS were evaluated. Of 571 children, 187 (32.7%) had at least 1 abnormal parameter at discharge, and none had ≥3 abnormal discharge parameters. A greater proportion of infants (90 [93%]) had all 4 parameters stable at discharge compared with 12- to 18-year-old youths (21 [49%]). The median TCS for each parameter was <24 hours. Younger age, absence of vomiting, diffusely decreased breath sounds, and normal capillary refill were associated with earlier TCS. Children who did not reach stability were not more likely to revisit after discharge. A TCS outcome consisting of physiologic variables may be useful for objectively assessing disease recovery and clinical readiness for discharge among children hospitalized with CAP. TCS may decrease length of stay if implemented to guide discharge decisions. Clinicians can consider factors associated with earlier TCS for management decisions.

A Case of Metapneumovirus Pneumonia-Related Acute Respiratory Distress Syndrome in a Young Adult Patient

Human metapneumovirus (hMPV) infections commonly present as mild upper respiratory tract infections in healthy adults, although severe respiratory complications have been observed, particularly in elderly and immunocompromised patients. We report a case in whom pneumonia caused by hMPV progressed to acute respiratory distress syndrome (ARDS) in a healthy adult without underlying diseases. A 31-year-old female presented with fever and dyspnea, prompting transfer to our hospital for mechanical ventilation 3 days after symptom onset. Auscultation revealed coarse breath sounds and crackles in both lung fields, and chest X-ray showed non-specific infiltrative nodules with poorly defined borders throughout both lungs. ARDS caused by community-acquired pneumonia was diagnosed. hMPV was identified via rapid testing of respiratory samples for genes that encode pneumonia pathogens and drug resistance markers; we employed reverse transcription polymerase chain reactions to these ends. Six days later, the patient was weaned off the mechanical ventilator, and discharged from the hospital in good clinical condition.

IJCM_83A: Primary Ciliary Dyskinesia in a child

Background: PCD is rare genetic disorder with incidence of 1:10,000. We present a 10-year-boy who came with recurrent respiratory-infections with Dextrocardia and bilateral Bronchiectasis for interesting clinical presentation. Objective: PCD is rare inherited disease with mutations which impair normal cilia function, characterized by impaired muco-ciliary clearance, causing recurrent-respiratory infections leading to bronchiectasis. Treatment includes preventing and managing complications. Methodology: 10-year-old child came cough-cold since 2 days, and hurried breathing since 1 day. Child had multiple episodes of respiratory- infections which was treated with antibiotics and nebulization on OP basis. Developmental delay also noted, currently in speech domain, can speak 2-3 words. On examination, open mouth breathing and clubbing(grade 3) present. On auscultation bilateral bronchial breath sounds with coarse crepitations. Heart sounds heard louder on right side, but no murmur was heard. Chest X-ray and 2D-ECHO showed Situs-Solitus with Dextrocardia. Child required non-invasive ventilation and IV antibiotics for treatment. Child was weaned off to room air by day 4. CT Thorax done showed bronchiectatic changes in bilateral lung with volume loss of right hemithorax with mediastinal shift to right. CT Nasopharynx and OMC done for open-mouth breathing was normal, and advised steroid nasal spray for nasal blockage. Physiotherapist counselled parents about postural drainage. Genetic testing for PCD was sent, report awaited. He is on regular follow up and doing well. Results: Primary defect in PCD is structure and/or function of cilia leading to accumulation of secretions. Diagnostic criteria includes recurrent chest infections, bronchitis, and rhinitis, with situs-inversus, infertility, decreased mucociliary clearance and cilia showing ultrastructural defect on electron microscopy. We suspected PCD with clinical features and imaging studies. Conclusion: Patients are frequently troubled by repeated infections for which they need to seek medical attention which is reason for morbidity. As there is no specific treatment, early diagnosis and prevention of complication is key to management.

Laryngeal and oropharyngeal adenocarcinoma with pulmonary metastases in a common raven (Corvus corax).

A captive 15-year-old male common raven (Corvus corax) was presented for post-mortem examination. It had been previously presented to a local veterinarian due to a 3-4 weeks long history of abnormal respiratory sounds. Upon admission, the bird demonstrated severe dyspnea and a massive amount of mucous in the oropharynx. After symptomatic treatment, dyspnea deteriorated dramatically, and euthanasia was elicited because of poor prognosis. The necropsy revealed a 2.65 x 2.15 x 2.18 cm expansile and poorly delineated cauliflower-shaped mass around the glottis and extending inside the tracheal lumen. Additionally, a dilated salivary gland in the adjacent tissue and multifocal reddish-fleshy areas in the lung parenchyma were detected. Histopathological examination identified the mass as moderately differentiated, tubular adenocarcinoma with invasive growth and moderate to marked cellular atypia and numerous mitoses. The presumptive origin of the neoplasia was one of the salivary glands. Multiple metastases were identified in the lung both macroscopically and histologically. Bacterial culture and molecular testing for West Nile and Usutu viruses were negative. To the authors' knowledge, this is the first report of metastatic laryngeal and oropharyngeal adenocarcinoma in a common raven.

Clinical predictors of hypoxic pneumonia in children from the Eastern Highlands Province, Papua New Guinea: secondary analysis of two prospective observational studies

BackgroundPneumonia is the leading cause of death in young children globally and is prevalent in the Papua New Guinea highlands. We investigated clinical predictors of hypoxic pneumonia to inform local treatment guidelines in this resource-limited setting. MethodsBetween 2013 and 2020, two consecutive prospective observational studies were undertaken enrolling children 0-4 years presenting with pneumonia to health-care facilities in Goroka Town, Eastern Highlands Province. Logistic regression models were developed to identify clinical predictors of hypoxic pneumonia (oxygen saturation <90% on presentation). Model performance was compared against established criteria to identify severe pneumonia. FindingsThere were 2067 cases of pneumonia; hypoxaemia was detected in 36·1%. The strongest independent predictors of hypoxic pneumonia were central cyanosis on examination (adjusted odds ratio [aOR] 5·14; 95%CI 3·47-7·60), reduced breath sounds (aOR 2·92; 95%CI 2·30-3·71), and nasal flaring or grunting (aOR 2·34; 95%CI 1·62-3·38). While the model developed to predict hypoxic pneumonia outperformed established pneumonia severity criteria, it was not sensitive enough to be clinically useful at this time. InterpretationGiven signs and symptoms are unable to accurately detect hypoxia, all health care facilities should be equipped with pulse oximeters. However, for the health care worker without access to pulse oximetry, consideration of central cyanosis, reduced breath sounds, nasal flaring or grunting, age-specific tachycardia, wheezing, parent-reported drowsiness, or bronchial breathing as suggestive of hypoxaemic pneumonia, and thus severe disease, may prove useful in guiding management, hospital referral and use of oxygen therapy.

Massive diaphragmatic eventration: plication

Diaphragmatic eventration (DE) is a distinctive condition characterised by the abnormal elevation or displacement of a portion of the diaphragm, often leading to a protrusion of abdominal contents into the thoracic cavity. Unlike diaphragmatic hernias, eventrations involve a congenital or acquired weakness of the diaphragmatic muscle itself rather than a structural defect. Congenital eventrations typically arise from the incomplete development or muscular hypoplasia of the diaphragm, while acquired forms may result from trauma, surgery, or neurological disorders affecting the phrenic nerve. Clinical manifestations vary widely, with some individuals remaining asymptomatic, while others may experience respiratory difficulties, especially when lying down. The case involves a 65-year-old female with a history of breathlessness exacerbated in the lying position, improved when upright. Examination revealed low oxygen saturation, absent breath sounds on the left chest, and bowel sounds in that region. Chest X-ray and HRCT indicated bowel loops in the left chest due to eventration of the left hemidiaphragm. Surgical intervention involved thoracotomy, identifying a lax left hemidiaphragm, mobilising abdominal contents, and performing plication with proline 1-0. Closure was completed with an intercostal drainage (ICD) in the left thoracic cavity. Postoperative period was uneventful. This case emphasises the significance of prompt diagnosis and surgical intervention in managing DE, showcasing the effectiveness of thoracotomy, hemidiaphragm plication, and ICD placement.

Research on lung sound classification model based on dual-channel CNN-LSTM algorithm

ulmonary diseases have a significant impact on human health and life safety, and abnormalities in the lungs are a direct response to lung diseases. Establishing an effective lung sound classification model that can assist in diagnosis is of great significance for electronic auscultation.In addressing the issue of lung sound signal classification, this study introduces a deep learning classification model based on a dual-channel CNN-LSTM algorithm. Initially, Mel-scale Frequency Cepstral Coefficients (MFCC) are employed for feature extraction from the dataset, transforming lung sound signals into Mel spectrograms. On this foundation, a dual-channel algorithm classification model is constructed, with parallel Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) modules. The CNN module is designed to capture spatial dimension features of the input data, while the LSTM module focuses on temporal dimension features. These two feature sets are fused together, enabling the model to classify lung sounds and thereby assisting in diagnosing pulmonary diseases for healthcare practitioners. This experiment used the ICBHI2017 Challenge Lungs dataset and obtained 5054 pieces of data through data augmentation and sampling techniques.The results show that the accuracy, recall, and F1 score of this model reach 99.01%, 99.13%, and 0.9915, respectively, significantly superior to other models, highlighting its practical application value.

Successful resolution of mesenteric tuberculosis and HIV co-infection following 6 months of anti-tuberculosis therapy: a case report

BACKGROUND: Mesenteric tuberculosis (TB), a rare extrapulmonary variant of TB, poses diagnostic and therapeutic challenges in its management. Therefore, discussing a case of mesenteric TB is both intriguing and informative for gaining insights into its clinical presentation and optimal treatment strategies. CASE PRESENTATION: A 50-year-old male presented symptoms of blackish stools three days post-gallstone surgery, accompanied by approximately two months of abdominal discomfort, described as squeezing or stabbing, alleviated temporarily by analgesics, alongside abdominal distension, rigidity, decreased appetite, and afternoon fevers. Physical examination revealed normal vital signs, with pale conjunctiva and asymmetrical chest wall movement, dull percussion, and decreased breath sounds in the lower left hemithorax. Abdominal inspection indicated distension, postsurgical signs, and ascites. Following laboratory investigations, chest and abdominal radiographs, and tissue biopsies, the patient was diagnosed with HIV co-infection along with pulmonary and mesenteric TB. Treatment comprised an intensive phase of four fixed-dose combinations (FDC) of anti-TB drugs, followed by a continuation phase of two FDC tablets, alongside first-line antiretroviral (ARV) therapy and cotrimoxazole prophylaxis. By the sixth month follow-up, clinical improvement was observed, with resolution of symptoms and weight gain to 56 kg. CONCLUSION: This case highlights the effective management of mesenteric TB and HIV co-infection, emphasizing the importance of comprehensive care and collaborative efforts between TB and HIV/AIDS control programs.

Hybrid method for noise rejection from breath sound using transient artifact reduction algorithm and spectral subtraction.

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. 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). Using this algorithm, the study obtained a high value of SNR of 70 dB and that of PSNR of 72 dB. The study could definitely a suitable one to suppress noises and to produce noise free breath sound signal.

A novel method in COPD diagnosing using respiratory signal generation based on CycleGAN and machine learning

Objective The main goal of this research is to use distinctive features in respiratory sounds for diagnosing Chronic Obstructive Pulmonary Disease (COPD). This study develops a classification method by utilizing inverse transforms to effectively identify COPD based on unique respiratory features while comparing the classification performance of various optimal algorithms. Method Respiratory sounds are divided into individual breathing cycles. In the data standardization and augmentation phase, the CycleGAN model enhances data diversity. Comprehensive analyses for these segments are then implemented using various Wavelet families and different spectral transformations representing characteristic signals. Advanced convolutional neural networks, including VGG16, ResNet50, and InceptionV3, are used for the classification task. Results The results of this study demonstrate the effectiveness of the mentioned method. Notably, the best-performing method utilizes Wavelet Bior1.3 after standardization in combination with InceptionV3, achieving a remarkable 99.75% F1-score, the gold standard for classification accuracy. Conclusion Inverse transformation techniques combined with deep learning models show significant accuracy in detecting COPD disease. These findings suggest the feasibility of early COPD diagnosis through AI-powered characterization of acoustic features. Motivation and Significance The motivation behind this research stems from the urgent need for early and accurate diagnosis of Chronic Obstructive Pulmonary Disease (COPD). COPD is a respiratory disease that poses many difficulties when detected late, potentially causing severe harm to the patient’s quality of life and increasing the healthcare burden. Timely identification and intervention are crucial to reduce the progression of the disease and improve patient outcomes.

A deep CNN-based acoustic model for the identification of lung diseases utilizing extracted MFCC features from respiratory sounds

A deep CNN-based acoustic model for the identification of lung diseases utilizing extracted MFCC features from respiratory sounds

Analysis of respiratory properties using the low-frequency piezoelectric sensor in patients undergoing intravenous sedation: a prospective observational study

BackgroundPatients who undergo general anesthesia may experience respiratory failure postoperatively because of respiratory depression or upper airway obstruction. AimWe investigated whether a piezoelectric sensor (AYA-P sensor) can be used to detect upper airway obstruction in patients undergoing intravenous sedation. MethodsThis prospective observational study included 26 patients who underwent dental treatment under sedation at a dental clinic. Participants were evaluated using a newly defined airway obstruction score (AOS). The AYA-P sensors were attached to the suprasternal notch and the midpoint between the umbilicus and xiphoid process. The patient's respiratory status was classified into four categories based on thoracic movements, capnography, and breathing sounds from recorded data (S1: no airflow, S2: hypoventilation, S3: normal breathing, and S4: artifact). The data obtained from the AYA-P sensors were processed using a 0.4-Hz low-pass filter, calculated using the moving standard deviation (MSD) method, and further processed using a 0.1-Hz low-pass filter (fMSD). Next, these data were differentiated (D-fMSD). We defined the positive predictive value (PPV) as fMSD or D-fMSD values higher than a threshold α in categories S1 and S2. PPV(αMAX) was defined as the maximum PPV, and the negative predictive value (NPV[αMAX]) was defined using the same αMAX. ResultsWe compared each of the apnea-hypopnea time ratio (AHT%) and AOS with PPV(αMAX) and NPV(αMAX). The AHT% and AOS were correlated with PPV(αMAX) and NPV(αMAX) in the neck and abdomen, respectively. These findings suggest that an AYA-P sensor attached to the neck and abdomen can detect a patient's respiratory status. ConclusionProcessed data obtained from AYA-P sensors can predict upper airway obstruction in patients, and the predicted data may be dependent on individual anatomical differences. Clinical trial registrationThis study was registrated in the University Hospital Medical Information Network Clinical Trials Registry (UMIN-CTR) clinical trial: Unique ID: UMIN000047007.

Artificial intelligence-based framework to identify the abnormalities in the COVID-19 disease and other common respiratory diseases from digital stethoscope data using deep CNN.

The utilization of lung sounds to diagnose lung diseases using respiratory sound features has significantly increased in the past few years. The Digital Stethoscope data has been examined extensively by medical researchers and technical scientists to diagnose the symptoms of respiratory diseases. Artificial intelligence-based approaches are applied in the real universe to distinguish respiratory disease signs from human pulmonary auscultation sounds. The Deep CNN model is implemented with combined multi-feature channels (Modified MFCC, Log Mel, and Soft Mel) to obtain the sound parameters from lung-based Digital Stethoscope data. The model analysis is observed with max-pooling and without max-pool operations using multi-feature channels on respiratory digital stethoscope data. In addition, COVID-19 sound data and enriched data, which are recently acquired data to enhance model performance using a combination of L2 regularization to overcome the risk of overfitting because of less respiratory sound data, are included in the work. The suggested DCNN with Max-Pooling on the improved dataset demonstrates cutting-edge performance employing a multi-feature channels spectrogram. The model has been developed with different convolutional filter sizes (, , , , and ) that helped to test the proposed neural network. According to the experimental findings, the suggested DCNN architecture with a max-pooling function performs better to identify respiratory disease symptoms than DCNN without max-pooling. In order to demonstrate the model's effectiveness in categorization, it is trained and tested with the DCNN model that extract several modalities of respiratory sound data.

DeepRespNet: A deep neural network for classification of respiratory sounds

Respiratory sounds convey significant information about the pulmonary status. This study proposes a deep learning-based framework to create an automatic, non-invasive, diagnostic method of categorizing pulmonary sounds. A labelled database of pulmonary sounds has been collected using an electronic stethoscope and audio recording instrument. Two deep learning architectures, 1D DeepRespNet and 2D DeepRespNet are proposed in this work that were trained and evaluated with normalised 1-D time series and 2-D spectrograms of acoustic signals of six types of lung sounds, respectively. The models were highly optimized to yield superior performance on the considered dataset. Experimental results demonstrate that the 2D DeepRespNet model trained with spectrogram-based representations yields higher accuracy of 95.2% on the test data as compared to the 1D DeepRespNet trained on the time-series data. The proposed model may be deployed on a single board computer or integrated into a smartphone to develop a standalone diagnostic tool to accurately and objectively classify abnormal lung sounds with low time complexity.

A Retrospective Analysis of Needle Thoracostomies at a Tertiary Level 2 Trauma Center.

A tension pneumothorax is a condition that results in elevated pressure within the pleural space. The effective management of tension pneumothorax relies on needle decompression, commonly performed at the second intercostal space (ICS) midclavicular line (MCL). However, some literature suggests that catheters placed in the second intercostal space midclavicular line are prone to higher failure rates compared to the fifth intercostal space midaxillary line (MAL) (42.5% versus 16.7%, respectively). In this study, we aim to identify and scrutinize the prevalence of prehospital needle decompression from one tertiary care center over eight years and examine their trends, efficacies, or pitfalls. It is hypothesized that preclinical providers are performing needle decompression prematurely and unnecessarily. A set of 90 patient records obtained using the trauma registry at Saint Francis Hospital, Tulsa, Oklahoma, were retrospectively reviewed to evaluate the management and outcomes of tension pneumothorax, as well as the indications documented for needle decompression. Patient charts were reviewed via Epic Hyperspace (Epic, Madison, WI). The Oklahoma Emergency Medical Service Information System (OKEMSIS) also provided information contributing to the sample population. The most documented indications for needle decompressions included diminished or absent breath sounds (52.70%), hypoxia (15.54%), hypotension, and hemodynamic instability (6.76%). Emergency medical services (EMS) reported improvements in 51 (56.67%) patients after needle thoracostomy. Improvements in vital signs after needle decompression were sporadic. The most common complication was catheter dislodging, which occurred most in the second intercostal space midclavicular line. Only nine patients had an oxygen saturation (SpO2) below 92% and a systolic blood pressure (SBP) below 100 mm Hg prior to receiving needle decompression. Current practices for tension pneumothorax show little improvement in vital signs before and after needle decompression. Vital signs prior to needle decompression often do not indicate tension pneumothorax physiology. Preclinical providers may be inappropriately performing needle decompressions, an invasive procedure with complications.

Clinical characteristics of treatment-seeking youth with misophonia.

Misophonia is a psychiatric condition characterized by strong emotional and/or behavioral responses to auditory stimuli, leading to distress and functional impairment. Despite previous attempts to define and categorize this condition, misophonia is not currently included in the Diagnostic and Statistical Manual of Mental Disorders or International Classification of Diseases. The lack of formal diagnostic consensus presents challenges for research aimed at assessing and treating this clinical presentation. The current study presents clinical characteristics of youth (N = 47) with misophonia in the largest treatment-seeking sample to date. We examined demographic characteristics of the sample, frequency of comorbid disorders, frequency of specific misophonia symptoms (i.e., triggers, emotionaland behavioral responses, and impairments), and caregiver-child symptom agreement. Misophonia symptoms were evaluated using a multimodal assessment including clinician, youth, and caregiver reports on empirically established misophonia measures, and concordance among measures was assessed. Youth seeking treatment for misophonia presented with marked misophonia symptoms and an array of comorbid conditions. Youth and caregivers identified various triggers of misophonia symptoms (e.g., chewing sounds, breathing sounds), as well as a wide range of emotional (e.g., anger, annoyance, disgust) and behavioral (e.g., aggression, avoidance) responses to triggers. Youth and caregivers exhibited high agreement on misophonia triggers but lower agreement on symptom severity and associated impairment. Compared to younger children (aged 8-13), older children (aged 14+) appeared to report symptom severity and associated impairment more reliably. Misophonia is a heterogenous and impairing clinical condition that warrants future investigation and evidence-based treatment development.

Breath Measurement Method for Synchronized Reproduction of Biological Tones in an Augmented Reality Auscultation Training System.

An educational augmented reality auscultation system (EARS) is proposed to enhance the reality of auscultation training using a simulated patient. The conventional EARS cannot accurately reproduce breath sounds according to the breathing of a simulated patient because the system instructs the breathing rhythm. In this study, we propose breath measurement methods that can be integrated into the chest piece of a stethoscope. We investigate methods using the thoracic variations and frequency characteristics of breath sounds. An accelerometer, a magnetic sensor, a gyro sensor, a pressure sensor, and a microphone were selected as the sensors. For measurement with the magnetic sensor, we proposed a method by detecting the breathing waveform in terms of changes in the magnetic field accompanying the surface deformation of the stethoscope based on thoracic variations using a magnet. During breath sound measurement, the frequency spectra of the breath sounds acquired by the built-in microphone were calculated. The breathing waveforms were obtained from the difference in characteristics between the breath sounds during exhalation and inhalation. The result showed the average value of the correlation coefficient with the reference value reached 0.45, indicating the effectiveness of this method as a breath measurement method. And the evaluations suggest more accurate breathing waveforms can be obtained by selecting the measurement method according to breathing method and measurement point.