An Effective Respiratory Sound Endpoint Detection for Electronic Stethoscope Based on Machine Learning

The aim of the study was to inspect the acoustic properties and sleep characteristics of a preapneic snoring sound. The feasibility of forecasting upcoming respiratory events by snoring sound was also investigated. Participants with habitual snoring or a heavy breathing sound during sleep were recruited consecutively. Polysomnography was conducted, and snoring-related breathing sound was recorded simultaneously. Acoustic features and sleep features were extracted from 30-second samples, and a machine learning algorithm was used to establish 2 prediction models. A total of 74 eligible participants were included. Model 1, tested by 5-fold cross-validation, achieved an accuracy of 0.92 and an area under the curve of 0.94 for respiratory event prediction. Model 2, with acoustic features and sleep information tested by Leave-One-Out cross-validation, had an accuracy of 0.78 and an area under the curve of 0.80. Sleep position was found to be the most important among all sleep features contributing to the performance of the 2 models. Preapneic sound presented unique acoustic characteristics, and snoring-related breathing sound could be deployed as a real-time apneic event predictor. The models, combined with sleep information, serve as a promising tool for an early warning system to forecast apneic events. Wang B, Yi X, Gao J, etal. Real-time prediction of upcoming respiratory events via machine learning using snoring sound signal. J Clin Sleep Med. 2021;17(9):1777-1784.

We designed a computer-based respiratory sound analysis system to identify pediatric normal lung sound. To verify the validity of the computer-based respiratory sound analysis system. First we downloaded the standard lung sounds from the network database (website: http: //www.easyauscultation.com/lung-sounds-reference-guide) and recorded 3 samples of abnormal loud sound (rhonchi, wheeze and crackles) from three patients of The Department of Pediatrics, the First Affiliated Hospital of Xiamen University. We regarded such lung sounds as"reference lung sounds". The"test lung sounds"were recorded from 29 children form Kindergarten of Xiamen University. we recorded lung sound by portable electronic stethoscope and valid lung sounds were selected by manual identification. We introduced Mel-frequency cepstral coefficient (MFCC) to extract lung sound features and dynamic time warping (DTW) for signal classification. We had 39 standard lung sounds, recorded 58 test lung sounds. This computer-based respiratory sound analysis system was carried out in 58 lung sound recognition, correct identification of 52 times, error identification 6 times. Accuracy was 89.7%. Based on MFCC and DTW, our computer-based respiratory sound analysis system can effectively identify healthy lung sounds of children (accuracy can reach 89.7%), fully embodies the reliability of the lung sounds analysis system.

A major worldwide health concern is chronic respiratory diseases (CRDs), which include disorders including asthma, pulmonary hypertension, occupational lung diseases, and chronic obstructive pulmonary disease (COPD). Improving clinical results and treatment efficacy requires an early and precise diagnosis. In order to classify respiratory sounds, this study presents a novel framework that incorporates auditory-inspired characteristics, such as Mel-Frequency Cepstral Coefficients (MFCCs), Mel Spectrograms, and Cochleograms, into a CNN-LSTM architecture. The framework uses sophisticated feature extraction techniques in conjunction with strong data augmentation approaches to address the issue of class imbalance and guarantee a thorough representation of a variety of respiratory sound patterns. Using the Respiratory Sound Database, the suggested model was assessed and showed remarkable performance, obtaining an F1 score of 98.94%, accuracy of 98.90%, specificity of 99.80%, sensitivity of 98.90%, and an ICBHI score of 99.40%. These findings demonstrate the model’s potential as a reliable and efficient tool for the early identification and evaluation of CRDs, which would significantly improve patient care and the management of respiratory illnesses. The outstanding performance further emphasizes the importance in clinical settings, enabling improved management and early identification of chronic respiratory conditions.

This work addresses the problem of lung sound classification, in particular, the problem of distinguishing between wheeze and normal sounds. Wheezing sound detection is an important step to associate lung sounds with an abnormal state of the respiratory system, usually associated with tuberculosis or another chronic obstructive pulmonary diseases (COPD). The paper presents an approach for automatic lung sound classification, which uses different state-of-the-art sound features in combination with a C-weighted support vector machine (SVM) classifier that works better for unbalanced data. Feature extraction methods used here are commonly applied in speech recognition and related problems thanks to the fact that they capture the most informative spectral content from the original signals. The evaluated methods were: Fourier transform (FT), wavelet decomposition using Wavelet Packet Transform bank of filters (WPT) and Mel Frequency Cepstral Coefficients (MFCC). For comparison, we evaluated and contrasted the proposed approach against previous works using different combination of features and/or classifiers. The different methods were evaluated on a set of lung sounds including normal and wheezing sounds. A leave-two-out per-case cross-validation approach was used, which, in each fold, chooses as validation set a couple of cases, one including normal sounds and the other including wheezing sounds. Experimental results were reported in terms of traditional classification performance measures: sensitivity, specificity and balanced accuracy. Our best results using the suggested approach, C-weighted SVM and MFCC, achieve a 82.1% of balanced accuracy obtaining the best result for this problem until now. These results suggest that supervised classifiers based on kernel methods are able to learn better models for this challenging classification problem even using the same feature extraction methods.

Lung sound classification using cepstral-based statistical features

In the last 2 years, medical researchers and clinical scientists have paid close attention to the problem of respiratory sound classification to classify COVID-19 disease symptoms. In the physical world, very few AI-based (Artificial Intelligence) techniques are often used to detect COVID-19/SARS-CoV-2 respiratory disease symptoms from the human respiratory system-generated acoustic sounds such as acoustic voice sound, breathing (inhale and exhale) sounds, and cough sound. We propose a light-weight Convolutional Neural Network (CNN) with Modified-Mel-frequency Cepstral Coefficient (M-MFCC) using different depths and kernel sizes to classify COVID-19 and other respiratory sound disease symptoms such as Asthma, Pertussis, and Bronchitis. The proposed network outperforms conventional feature extraction models and existing Deep Learning (DL) models for COVID-19/SARS-CoV-2 classification accuracy in the range of 4–10%. The model’s performance is compared with the COVID-19 crowdsourced benchmark dataset and gives a competitive performance. We applied different receptive fields and depths in the proposed model to get different contextual information that should aid in classification. And our experiments suggested 1 times 12 receptive fields and a depth of 5-Layer for the light-weight CNN to extract and identify the features from respiratory sound data. The model is also trained and tested with different modalities of data to showcase its effectiveness in classification.

Targeted Deletion of Jun/AP-1 in Alveolar Epithelial Cells Causes Progressive Emphysema and Worsens Cigarette Smoke–Induced Lung Inflammation

Classification of respiratory sounds between normal and abnormal is very crucial for screening and diagnosis purposes. Lung associated diseases can be detected through this technique. With the advancement of computerized auscultation technology, the adventitious sounds such as crackles can be detected and therefore diagnostic test can be performed earlier. In this paper, Mel-frequency Cepstral Coefficient (MFCC) is used to extract features from normal and crackles respiratory sounds. By using statistical computation such as mean and standard deviation (SD) of cepstral based coefficients it can differentiate between crackles and normal sounds. The result shows that the first three statistical values of SD of coefficients provide distinctive feature between normal and crackles respiratory sounds. Hence, MFCCs can be used as feature extraction method of respiratory sounds to classify between normal and crackles as screening and diagnostic tool.

Chronic obstructive pulmonary disease (COPD) is a major public health concern across the world. Since it is an incurable disease, early detection, and accurate diagnosis are very crucial for preventing the progression of the disease. Lung sounds provide reliable and accurate prognoses for identifying respiratory diseases. Recently, Altan et al. recorded 12-channel real-time lung sound dataset, namely RespiratoryDatabase@TR, for five different severity levels of COPD at Antakya State Hospital Turkey, and proposed deep learning frameworks for two-class COPD classification and five-class classification using a deep belief network (DBN) classifier and extreme learning machine (ELM) classifier respectively. A classification accuracy of 95.84% and 94.31% were achieved for two-class and five-class, respectively. In this paper, we have proposed a melspectrogram snippet representation learning framework for both two-class and five-class COPD classification. The proposed framework consists of the following stages: data augmentation and pre-processing, melspectrogram snippet representation generation from lung sound, and fine-tuning of a pre-trained YAMNet. Experimental analysis on the RespiratoryDatabase@TR dataset demonstrates that the proposed framework achieves accuracies of 99.25% and 96.14% for binary and multi-class COPD severity classification, respectively, which are superior to the only existing methods proposed by Altan et al. for severity analysis of COPD using lung sounds.

Pulmonary crackles and plural friction rubs are adventitious lung sounds that provide valuable information on underlying lung diseases. Due to similarities in their time domain characteristics, there exists the need to distinguish these two sounds to help novice medical doctors and to aid in automated diagnosis in telemedicine applications. This paper focuses on analyzing these two sounds using Mel Frequency Cepstral Coefficients (MFCC) speech analysis technique. The MFCC's were calculated for crackles and pleural friction rub lung sound and four basic statistical parameters of MFCC were calculated. The Standard deviation of MFCC shows maximum linear separability among other parameters. Therefore, the standard deviation of MFCC can be used as potential feature for classifying adventitious lung sounds pertaining to pulmonary crackles and pleural friction rubs.

Rationale Multimodal clinical audio data, including speech and breath sounds, play a critical role in assessing respiratory function in disorders affecting the thoracic cage, including neurodegenerative, infectious, and respiratory disorders. Machine learning (ML) has shown promise in identifying patterns to enhance predictive accuracy in these disorders due to its data processing power with chronic respiratory conditions that disrupt airflow, such as Chronic Obstructive Pulmonary Disease (COPD) and Interstitial Lung Disease (ILD), being notable examples. This study investigates the feasibility of using an ML model to predict clinical parameters including pulmonary function, smoking status, and disease presence by analyzing breath and vocal characteristics. Methods Two-hundred patients with various respiratory and thoracic pathologies were recruited via convenience sampling from a university medical center. Data were collected using digital stethoscopes and microphones to record breath sounds at eight anatomical locations along with vocal samples during a variety of vocal tasks (Figure 1). Audio data were converted into Mel-spectrograms and used to train a convolutional neural network (CNN) model to predict patient outcomes, including gender, smoking status, COPD, and ILD. The dataset was divided into a 90:10 training-to-validation ratio. Fig. 1. Representation of study workflow showing data collection, data transformation, and subsequent model training. Results In predicting smoking status, breath recordings achieved 80% accuracy, 95% specificity, 63% sensitivity, and an AUC of 0.82 compared to 65%, 69%, 57%, and 0.58 for the voice-based model. In COPD classification, breath data yielded 75% accuracy, 62% specificity, and 80% sensitivity in contrast to 62.5%, 80%, and 57% for the vocal model. In ILD classification, breath data yielded 65% accuracy, 58% specificity, and 75% sensitivity in contrast to 65%, 77%, and 55% for the vocal model. Additionally, in predicting pulmonary function via Forced Vital Capacity (FVC) and the FEV1/FVC ratio, breath sounds had lower mean squared error (MSE) values relative to voice models (0.7 vs. 10.2, and 111.6 vs. 2308.6), demonstrating superior performance. Conclusion Breath sounds offer significant predictive value in identifying pulmonary conditions and estimating functional metrics, outperforming vocal and cough data in most tasks. Given the low cost of obtaining clinical audio data, uncovering reliable biomarkers in audio patterns via ML methods may assist in diagnosis, risk stratification, and identification of patients in whom preventative care may be beneficial. Future work will require larger datasets to improve model robustness and explore more complex biomarker integration, such as combining breath and voice data, for enhanced clinical prediction.

The respiratory sound classification paper focuses on analyzing sounds collected through the stethoscope at various chest positions to understand critical concerns of human breathing. Most doctors used a stethoscope for auscultation of lung sounds, along with heart sound, as a basic checkup and utilized later in predicting chest pathology. Using deep learning-based sound classification can build intelligent digital stethoscopes or understand overall health conditions later used along with other Lung organ analyses. The solution enables affordable and practical tools for remote parts of India, where doctors or specialists are not keen to work. Such solutions can be used as first analysis before further investigation by traveling to far-off specialist hospitals. The lung sound was prepared and hosted by a team from the University of Coimbra, Portugal; the University de Aveiro, Portugal; and the Aristotle University of Thessaloniki, Greece and the collaboration at Kaggle website lung sounds of patients who are Normal lung functioning and with some anomaly collected. The collected lung sounds are labeled as Normal, seven classes - Abnormal. Overall, eight levels are considered to classify using a deep learning model. The paper proposes a multipath convolution neural network (CNN) model for analyzing sound after exploring all approaches, like Recurrent Neural network (RNN), Machine learning models like SVM, and other CNN models as per paper [3]. Lung sound is processed using signal processing methods and converts wav files to Mel-frequency cepstral coefficients (MFCCs), Mel spectrogram, and chromatogram from a waveform or power spectrogram. Instead of using one feature among three, the paper built a Multipath CNN model to feed all three components to model and concatenate feature maps to classify the classes. The model was evaluated with various parameters like accuracy and F1 score. The proposed model gives 96.17% for the dataset used. The result implies data, and a multipath approach helps to fuse features extracted from different images and allows models to classify well.

Automated classification of lung sound signals based on empirical mode decomposition

Deep learning has transformed medical diagnostics, especially in analyzing lung sounds to assess respiratory conditions. Traditional methods like CT scans and X-rays are impractical in resource-limited settings due to radiation exposure and time consumption, while conventional stethoscopes often lead to misdiagnosis due to subjective interpretation and environmental noise. This study evaluates deep learning models for lung sound classification using the International Conference on Biomedical Health Informatics 2017 dataset, comprising 920 annotated samples from 126 subjects. Pre-processing includes down sampling, segmentation, normalization, and audio clipping, with feature extraction techniques like spectrogram and Mel-frequency cepstral coefficients (MFCC). The adopted automatic lung sound diagnosis network (ASLD-Net) model with triple feature input (time domain, spectrogram, and MFCC) achieved the highest accuracy at 97.25%, followed by the dual feature model (spectrogram and MFCC) at 95.65%. Single-input models with spectrogram and MFCC performed well, while the time domain input alone had the lowest accuracy.

Recently, many studies were performed using several techniques to classify and diagnose lung sound, but as a drawback the age category was limited, almost adult only, as well as the insufficient number of samples and this unfortunately leads to an unfair classification of lung sound. While this study deals with different methods to analyze lung sounds and extract distinctive features then classify them to diagnose lung sounds in infant and children to one of the three categories: Normal, Wheeze, or Stridor. Features were extracted using three different techniques in separate ways to compare the effectiveness; these techniques are Discrete Wavelet transform (DWT), Short Time Fourier Transform (STFT) and Mel-Frequency Cepstral Coefficients (MFCCs). After that the sounds are categorized using four different classification techniques which include Artificial Neural Network (ANN), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Naive Bayes (NB). The main aim of this research is to choose the best signal processing technique with the most suitable classifier to diagnose lung sounds by categorizing 300 lung sounds especially in infants and children to Normal, Wheeze, or Stridor. These sounds are collected from Alexandria University Children Hospital (AUCH) – Egypt as a particular environment which is considered one of the main advantages of this research. Moreover, extra 146 wheezes were used to validate the usefulness of the classifiers. The results were very promising.