CovNet: A Transfer Learning Framework for Automatic COVID-19 Detection From Crowd-Sourced Cough Sounds

Since the COronaVIrus Disease 2019 (COVID-19) outbreak, developing a digital diagnostic tool to detect COVID-19 from respiratory sounds with computer audition has become an essential topic due to its advantages of being swift, low-cost, and eco-friendly. However, prior studies mainly focused on small-scale COVID-19 datasets. To build a robust model, the large-scale multi-sound FluSense dataset is utilised to help detect COVID-19 from cough sounds in this study. Due to the gap between FluSense and the COVID-19-related datasets consisting of cough only, the transfer learning framework (namely CovNet) is proposed and applied rather than simply augmenting the training data with FluSense. The CovNet contains (i) a parameter transferring strategy and (ii) an embedding incorporation strategy. Specifically, to validate the CovNet's effectiveness, it is used to transfer knowledge from FluSense to COUGHVID, a large-scale cough sound database of COVID-19 negative and COVID-19 positive individuals. The trained model on FluSense and COUGHVID is further applied under the CovNet to another two small-scale cough datasets for COVID-19 detection, the COVID-19 cough sub-challenge (CCS) database in the INTERSPEECH Computational Paralinguistics challengE (ComParE) challenge and the DiCOVA Track-1 database. By training four simple convolutional neural networks (CNNs) in the transfer learning framework, our approach achieves an absolute improvement of 3.57% over the baseline of DiCOVA Track-1 validation of the area under the receiver operating characteristic curve (ROC AUC) and an absolute improvement of 1.73% over the baseline of ComParE CCS test unweighted average recall (UAR).

Spectral features and optimal Hierarchical Attention Networks for Pulmonary abnormality detection from the Respiratory Sound signals

The most important concern in the medical field is to consider the analysis of data and perform accurate diagnosis. However, the analysis of pulmonary abnormalities may depend on the diagnostic experience and the medical skills of the physicians, and is a time-consuming practice. In order to solve such issues, an efficient Water Cycle Swarm Optimizer-based Hierarchical Attention Network (WCSO-based HAN) is developed for detecting the pulmonary abnormalities from the respiratory sounds signals. However, the developed optimization technique named WCSO is devised by incorporating the Water Cycle Algorithm (WCA) with Competitive Swarm Optimizer (CSO). Here, the pre-processing is performed using the Hanning window and Spectral gating-based noise reduction method in order to remove the falsifications or noises from the signal. Thereafter, the process of feature extraction is carried out to extract the significant features, such as Bark frequency Cepstral coefficient (BFCC) and the short term features, such asspectral flux and spectral centroid. Once the significant features are extracted, classification is performed using HAN where the training procedure of HAN is carried out using WCSO. Furthermore, the developed WCSO-based HAN obtained efficient performance using True Positive Rate (TPR), True Negative Rate (TNR) and accuracy with the values of 0.943, 0.913, and 0.923 using dataset 1, respectively.

Orthogonality-Constrained CNMF-Based Noise Reduction with Reduced Degradation of Biological Sound

The number of deaths due to cardiovascular and respiratory diseases is increasing annually. Cardiovascular diseases with high mortality rates, such as strokes, are frequently caused by atrial fibrillation without subjective symptoms. Chronic obstructive pulmonary disease is another condition in which early detection is difficult owing to the slow progression of the disease. Hence, a device that enables the early diagnosis of both diseases is necessary. In our previous study, a sensor for monitoring biological sounds such as vascular and respiratory sounds was developed and a noise reduction method based on semi-supervised convolutive non-negative matrix factorization (SCNMF) was proposed for the noisy environments of users. However, SCNMF attenuated part of the biological sound in addition to the noise. Therefore, this paper proposes a novel noise reduction method that achieves less distortion by imposing orthogonality constraints on the SCNMF. The effectiveness of the proposed method was verified experimentally using the biological sounds of 21 subjects. The experimental results showed an average improvement of 1.4 dB in the signal-to-noise ratio and 2.1 dB in the signal-to-distortion ratio over the conventional method. These results demonstrate the capability of the proposed approach to measure biological sounds even in noisy environments.

Unnecessary Chest Tube for a Hemodialytic Patient with Multiple Trauma: A Case Report

Background Pneumothorax (PTX) is a life-threatening condition that overdiagnosis could result in increases in mortality and morbidity of patients, this overdiagnosis would be increased if physicians do not manage the patient classically and do not pay attention to the physical exam and history of the patient. Case presentation: A-71-year old man was admitted to the emergency department due to multiple trauma. His vital signs were stable and in examination, there were two lacerations on his scalp with venous bleeding source and galea transaction; there were also some abrasions all over his body including his thorax. In the physical exam, there was no sucking lesion, decreases in respiratory sounds in auscultation or chest deformity but he had little right hemithorax rib tenderness. In more evaluations, there was a suspected visceral line of pleura in his CXR and no plural sliding movement was seen in E-FAST by the operator. Due to the inconsistency in physical exam and radiologic findings we decided to take a chest CT-scan before the insertion of the chest tube that indicated no PTX for him and the suspected visceral line in CXR was skin fold of a permacath for hemodialysis. Conclusions Several conditions could mimic findings of PTX in CXR that every physician should know and pay attention to them besides special attention to the history taking and physical examination to reduce the mortality and morbidity of patients.

Artificial intelligence in the respiratory sounds analysis and computer diagnostics of bronchial asthma

Using a database containing audio files of respiratory sound records of asthmatic patients and healthy patients, a method of computer-aided diagnostics based on the machine learning technique creation of neural networks, has been developed. The database contains 952 records of respiratory sounds of asthma patients at different stages of the disease, aged from several months to 47 years, and 167 records of volunteers. Records were carried out with a quiet breathing at four points: in the oral cavity, above the trachea, on the chest, the second intercostal space on the right side, and at a point on the back. The developed method of computer-aided diagnostics allows diagnosing bronchial asthma with high reliability: sensitivity of 89.3%, specificity of 86%, accuracy of about 88% and Youden index of 0.753. The program learned once makes it possible to diagnose bronchial asthma with high reliability regardless of patient's gender and age, a stage of disease, as well as the point of sound recording. The developed method can be used as an additional screening method for the diagnostics of bronchial asthma and serve as the basis for development of computer control methods, including remote control (telemedicine) of patients condition and the effectiveness of the applied drugs in real time.

FrWCSO-DRN: Fractional Water Cycle Swarm Optimizer-based deep residual network for pulmonary abnormality detection from respiratory sound signal

Respiratory sounds disclose significant information regarding the lungs of patients. Numerous methods are developed for analyzing the lung sounds. However, clinical approaches require qualified pulmonologists to diagnose such kind of signals appropriately and are also time consuming. Hence, an efficient Fractional Water Cycle Swarm Optimizer-based Deep Residual Network (FrWCSO-based DRN) is developed in this research for detecting the pulmonary abnormalities using respiratory sounds signals. The proposed FrWCSO is newly designed by the incorporation of Fractional Calculus (FC) and Water Cycle Swarm Optimizer WCSO. Meanwhile, WCSO is the combination of Water Cycle Algorithm (WCA) with Competitive Swarm Optimizer (CSO). The respiratory input sound signals are pre-processed and the important features needed for the further processing are effectively extracted. With the extracted features, data augmentation is carried out for minimizing the over fitting issues for improving the overall detection performance. Once data augmentation is done, feature selection is performed using proposed FrWCSO algorithm. Finally, pulmonary abnormality detection is performed using DRN where the training procedure of DRN is performed using the developed FrWCSO algorithm. The developed method achieved superior performance by considering the evaluation measures, namely True Positive Rate (TPR), True Negative Rate (TNR) and testing accuracy with the values of 0.963, 0.932, and 0.948, respectively.

Abnormal Respiratory Sounds Classification Using Deep CNN Through Artificial Noise Addition

Respiratory sound (RS) attributes and their analyses structure a fundamental piece of pneumonic pathology, and it gives symptomatic data regarding a patient's lung. A couple of decades back, doctors depended on their hearing to distinguish symptomatic signs in lung audios by utilizing the typical stethoscope, which is usually considered a cheap and secure method for examining the patients. Lung disease is the third most ordinary cause of death worldwide, so; it is essential to classify the RS abnormality accurately to overcome the death rate. In this research, we have applied Fourier analysis for the visual inspection of abnormal respiratory sounds. Spectrum analysis was done through Artificial Noise Addition (ANA) in conjunction with different deep convolutional neural networks (CNN) to classify the seven abnormal respiratory sounds—both continuous (CAS) and discontinuous (DAS). The proposed framework contains an adaptive mechanism of adding a similar type of noise to unhealthy respiratory sounds. ANA makes sound features enough reach to be identified more accurately than the respiratory sounds without ANA. The obtained results using the proposed framework are superior to previous techniques since we simultaneously considered the seven different abnormal respiratory sound classes.

Artificial Intelligence Approach to the Monitoring of Respiratory Sounds in Asthmatic Patients

Background: Effective and reliable monitoring of asthma at home is a relevant factor that may reduce the need to consult a doctor in person.Aim: We analyzed the possibility to determine intensities of pathological breath phenomena based on artificial intelligence (AI) analysis of sounds recorded during standard stethoscope auscultation.Methods: The evaluation set comprising 1,043 auscultation examinations (9,319 recordings) was collected from 899 patients. Examinations were assigned to one of four groups: asthma with and without abnormal sounds (AA and AN, respectively), no-asthma with and without abnormal sounds (NA and NN, respectively). Presence of abnormal sounds was evaluated by a panel of 3 physicians that were blinded to the AI predictions. AI was trained on an independent set of 9,847 recordings to determine intensity scores (indexes) of wheezes, rhonchi, fine and coarse crackles and their combinations: continuous phenomena (wheezes + rhonchi) and all phenomena. The pair-comparison of groups of examinations based on Area Under ROC-Curve (AUC) was used to evaluate the performance of each index in discrimination between groups.Results: Best performance in separation between AA and AN was observed with Continuous Phenomena Index (AUC 0.94) while for NN and NA. All Phenomena Index (AUC 0.91) showed the best performance. AA showed slightly higher prevalence of wheezes compared to NA.Conclusions: The results showed a high efficiency of the AI to discriminate between the asthma patients with normal and abnormal sounds, thus this approach has a great potential and can be used to monitor asthma symptoms at home.