Cough Sounds Research Articles

Feasibility of snapshot testing using wearable sensors to detect cardiorespiratory illness (COVID infection in India).

The COVID-19 pandemic has challenged the current paradigm of clinical and community-based disease detection. We present a multimodal wearable sensor system paired with a two-minute, movement-based activity sequence that successfully captures a snapshot of physiological data (including cardiac, respiratory, temperature, and percent oxygen saturation). We conducted a large, multi-site trial of this technology across India from June 2021 to April 2022 amidst the COVID-19 pandemic (Clinical trial registry name: International Validation of Wearable Sensor to Monitor COVID-19 Like Signs and Symptoms; NCT05334680; initial release: 04/15/2022). An Extreme Gradient Boosting algorithm was trained to discriminate between COVID-19 infected individuals (n = 295) and COVID-19 negative healthy controls (n = 172) and achieved an F1-Score of 0.80 (95% CI = [0.79, 0.81]). SHAP values were mapped to visualize feature importance and directionality, yielding engineered features from core temperature, cough, and lung sounds as highly important. The results demonstrated potential for data-driven wearable sensor technology for remote preliminary screening, highlighting a fundamental pivot from continuous to snapshot monitoring of cardiorespiratory illnesses.

A dataset of Solicited Cough Sound for Tuberculosis Triage Testing

Cough is a common and commonly ignored symptom of lung disease. Cough is often perceived as difficult to quantify, frequently self-limiting, and non-specific. However, cough has a central role in the clinical detection of many lung diseases including tuberculosis (TB), which remains the leading infectious disease killer worldwide. TB screening currently relies on self-reported cough which fails to meet the World Health Organization (WHO) accuracy targets for a TB triage test. Artificial intelligence (AI) models based on cough sound have been developed for several respiratory conditions, with limited work being done in TB. To support the development of an accurate, point-of-care cough-based triage tool for TB, we have compiled a large multi-country database of cough sounds from individuals being evaluated for TB. The dataset includes more than 700,000 cough sounds from 2,143 individuals with detailed demographic, clinical and microbiologic diagnostic information. We aim to empower researchers in the development of cough sound analysis models to improve TB diagnosis, where innovative approaches are critically needed to end this long-standing pandemic.

Fused Audio Instance and Representation for Respiratory Disease Detection

Audio-based classification techniques for body sounds have long been studied to aid in the diagnosis of respiratory diseases. While most research is centered on the use of coughs as the main acoustic biomarker, other body sounds also have the potential to detect respiratory diseases. Recent studies on the coronavirus disease 2019 (COVID-19) have suggested that breath and speech sounds, in addition to cough, correlate with the disease. Our study proposes fused audio instance and representation (FAIR) as a method for respiratory disease detection. FAIR relies on constructing a joint feature vector from various body sounds represented in waveform and spectrogram form. We conduct experiments on the use case of COVID-19 detection by combining waveform and spectrogram representation of body sounds. Our findings show that the use of self-attention to combine extracted features from cough, breath, and speech sounds leads to the best performance with an area under the receiver operating characteristic curve (AUC) score of 0.8658, a sensitivity of 0.8057, and a specificity of 0.7958. Compared to models trained solely on spectrograms or waveforms, the use of both representations results in an improved AUC score, demonstrating that combining spectrogram and waveform representation helps to enrich the extracted features and outperforms the models that use only one representation. While this study focuses on COVID-19, FAIR’s flexibility allows it to combine various multi-modal and multi-instance features in many other diagnostic applications, potentially leading to more accurate diagnoses across a wider range of diseases.

Asuhan Keperawatan pada Ny. S dengan Gangguan Sistem Pernapasan : Bronkopneumonia di Ruang Dahlia Rsud Dr. Soeselo Kabupaten Tegal

Bronchopneumonia is an inflammation of the lung parenchyma that extends to the bronchioles, which can be understood as inflammation of the lung tissue with direct spread through the respiratory tract to the bronchi (Hapsari et al., 2022). The purpose of this writing is to understand the nursing care process for Mrs. S with respiratory system disorders: bronchopneumonia in Dahlia Ward, RSUD dr. Soeselo Tegal Regency. This writing method uses a descriptive approach with a nursing process approach. The results of the nursing care found that the patient had shortness of breath, productive cough, and ronchi sounds; the patient and family were unaware of the disease. The nursing diagnoses identified were ineffective airway clearance and knowledge deficit. Interventions were carried out based on SIKI (Indonesian Standard for Nursing Care) and implementation was based on the designed interventions. Evaluation of the two diagnoses revealed that one diagnosis, the knowledge deficit, was resolved, while the issue of ineffective airway clearance was partially resolved. The results of the nursing care were consistent with the theory. This writing is expected to improve skills in providing nursing care.

Wearable bracelet and machine learning for remote diagnosis and pandemic infection detection

The COVID-19 pandemic has highlighted that effective early infection detection methods are essential, as they play a critical role in controlling the epidemic spread. In this work, we investigate the use of wearable sensors in conjunction with machine learning (ML) techniques for pandemic infection detection. We work on designing a wristband that measures various vital parameters such as temperature, heart rate, and SPO2, and transmits them to a mobile application using Bluetooth Low Energy. The accuracy of the wristband measurements is shown to be within 10% of the readings of existing commercial products. The measured data can be used and analyzed for various purposes. To benefit from the existing online datasets related to COVID-19, we use this pandemic as an example in our work. Hence, we also develop ML-based models that use the measured vital parameters along with cough sounds in order to determine whether a case is COVID-19 positive or not. The proposed models are shown to achieve remarkable results, exceeding 90% accuracy. One of our proposed models exceeds 96% performance in terms of accuracy, precision, recall, and F1-Score. The system lends itself reasonably for amendment to deal with future pandemics by considering their specific features and designing the ML models accordingly. Furthermore, we design and develop a mobile application that shows the data collected from the wristband, records cough sounds, runs the ML model, and provides feedback to the user about their health status in a user-friendly, intuitive manner. A successful deployment of such an approach would decrease the load on hospitals and prevent infection from overcrowded spaces inside the hospital.

SCMs: Systematic Conglomerated Models for Audio Cough Signal Classification

A common and natural physiological response of the human body is cough, which tries to push air and other wastage thoroughly from the airways. Due to environmental factors, allergic responses, pollution or some diseases, cough occurs. A cough can be either dry or wet depending on the amount of mucus produced. A characteristic feature of the cough is the sound, which is a quacking sound mostly. Human cough sounds can be monitored continuously, and so, cough sound classification has attracted a lot of interest in the research community in the last decade. In this research, three systematic conglomerated models (SCMs) are proposed for audio cough signal classification. The first conglomerated technique utilizes the concept of robust models like the Cross-Correlation Function (CCF) and Partial Cross-Correlation Function (PCCF) model, Least Absolute Shrinkage and Selection Operator (LASSO) model, elastic net regularization model with Gabor dictionary analysis and efficient ensemble machine learning techniques, the second technique utilizes the concept of stacked conditional autoencoders (SAEs) and the third technique utilizes the concept of using some efficient feature extraction schemes like Tunable Q Wavelet Transform (TQWT), sparse TQWT, Maximal Information Coefficient (MIC), Distance Correlation Coefficient (DCC) and some feature selection techniques like the Binary Tunicate Swarm Algorithm (BTSA), aggregation functions (AFs), factor analysis (FA), explanatory factor analysis (EFA) classified with machine learning classifiers, kernel extreme learning machine (KELM), arc-cosine ELM, Rat Swarm Optimization (RSO)-based KELM, etc. The techniques are utilized on publicly available datasets, and the results show that the highest classification accuracy of 98.99% was obtained when sparse TQWT with AF was implemented with an arc-cosine ELM classifier.

경량화 MobileNet을 활용한 축산 데이터 음성 분석

Pigs express their reactions to their environment and health status through a variety of sounds, such as grunting, coughing, and screaming. Given the significance of pig vocalizations, their study has recently become a vital source of data for livestock industry workers. To facilitate this, we propose a lightweight deep learning model based on MobileNet that analyzes pig vocal patterns to distinguish pig voices from farm noise and differentiate between vocal sounds and coughing. This model was able to accurately identify pig vocalizations amidst a variety of background noises and cough sounds within the pigsty. Test results demonstrated that this model achieved a high accuracy of 98.2%. Based on these results, future research is expected to address issues such as analyzing pig emotions and identifying stress levels.

An Exploration of Clinically Meaningful Definitions of Cough Bouts

RationaleThe measurement of cough frequency is widely used in clinical trials, typically expressed as the number of explosive cough sounds per hour. However, this measure does not capture the clustering of coughs into bouts. Coughing bouts contribute to perceived cough severity and the physical complications of coughing, but an agreed standard definition of cough bouts is lacking.ObjectivesTo explore the impact of different definitions of cough bouts on the parameters generated, their relationships with reported cough severity and influence of age and gender in refractory chronic cough (RCC).MethodsWe analysed 24 h acoustic recordings and concurrent cough severity visual analogue scales (VAS) from 91 RCC patients [62% female, median (IQR) age 60.0(54–67.0) years]. A custom-built algorithm calculated cough bouts, defined by the intervals between explosive cough sounds. Bouts defined by inter-cough intervals from ≤0.5 to ≤10 s (0.5 s increments) were explored and parameters including number of bouts, median/maximum bout length, and total bout duration calculated.Measurements and Main ResultsUsing inter-cough intervals of >3 s to define cough bouts made little difference to cough bout parameters. Correlations between cough severity and bout parameters were weak but most likely to be significant when single coughs were removed. Cough free time/total time spent coughing tended to have more influence on cough severity than the average cough bout length, irrespective of the interval used.ConclusionThese analyses favour definitions of cough bouts utilising inter-cough intervals of ≤3 s and the exclusion of single coughs from cough bout analysis.

Automated asthma detection in a 1326-subject cohort using a one-dimensional attractive-and-repulsive center-symmetric local binary pattern technique with cough sounds

Asthma is a common disease. The clinical diagnosis is usually confirmed on a pulmonary function test, which is not always readily accessible. We aimed to develop a computationally lightweight handcrafted machine learning model for asthma detection based on cough sounds recorded using mobile phones. Toward this aim, we proposed a novel feature extractor based on a one-dimensional version of the published attractive-and-repulsive center-symmetric local binary pattern (1D-ARCSLBP), which we tested on a new cough sound dataset. We prospectively recorded cough sounds from 511 asthmatics and 815 non-asthmatic subjects (comprising mostly healthy volunteers), which yielded 1875 one-second cough sound segments for analysis. Our model comprised four steps: (i) preprocessing, in which speech signals and stop times (silent zones between coughs) were removed, leaving behind analyzable cough sound segments; (ii) feature extraction, in which tunable q-factor wavelet transformation was used to perform multilevel signal decomposition into wavelet subbands, allowing 1D-ARCSLBP to extract local low- and high-level features; (iii) feature selection, in which neighborhood component analysis was used to select the most discriminative features; and (iv) classification, in which a standard shallow cubic support vector machine was deployed to calculate binary classification results (asthma versus non-asthma) using tenfold and leave-one-subject-out cross-validations. Our model attained 98.24% and 96.91% accuracy rates with tenfold and leave-one-subject-out cross-validation strategies, respectively, and obtained a low-time complexity. The excellent results confirmed the feature extraction capability of 1D-ARCSLBP and the feasibility of the model being developed into a real-world application for asthma screening.

Feature fusion method for pulmonary tuberculosis patient detection based on cough sound.

Since the COVID-19, cough sounds have been widely used for screening purposes. Intelligent analysis techniques have proven to be effective in detecting respiratory diseases. In 2021, there were up to 10 million TB-infected patients worldwide, with an annual growth rate of 4.5%. Most of the patients were from economically underdeveloped regions and countries. The PPD test, a common screening method in the community, has a sensitivity of as low as 77%. Although IGRA and Xpert MTB/RIF offer high specificity and sensitivity, their cost makes them less accessible. In this study, we proposed a feature fusion model-based cough sound classification method for primary TB screening in communities. Data were collected from hospitals using smart phones, including 230 cough sounds from 70 patients with TB and 226 cough sounds from 74 healthy subjects. We employed Bi-LSTM and Bi-GRU recurrent neural networks to analyze five traditional feature sets including the Mel frequency cepstrum coefficient (MFCC), zero-crossing rate (ZCR), short-time energy, root mean square, and chroma_cens. The incorporation of features extracted from the speech spectrogram by 2D convolution training into the Bi-LSTM model enhanced the classification results. With traditional futures, the best TB patient detection result was achieved with the Bi-LSTM model, with 93.99% accuracy, 93.93% specificity, and 92.39% sensitivity. When combined with a speech spectrogram, the classification results showed 96.33% accuracy, 94.99% specificity, and 98.13% sensitivity. Our findings underscore that traditional features and deep features have good complementarity when fused using Bi LSTM modelling, which outperforms existing PPD detection methods in terms of both efficiency and accuracy.

Assessment of Lung Health Status by Analyzing Cough Sound Using Swaasa Artificial Intelligence Technology

Introduction: The crucial function of the respiratory system is the facilitation of blood gas exchange process. Spirometry is the diagnostic tool to detect and differentiate obstructive and restrictive respiratory diseases that impair this vital function. The need for a clinical setup, technical expertise, and patient compliance are certain limitations for utilizing spirometry in remote areas. Swaasa® artificial intelligence (AI) platform, which has a Class B manufacturing license from India’s Central Drugs Standard Control Organization, is a validated “software as a medical device,” which aids in screening and diagnosis of respiratory diseases, unbounded by location, equipment and technical expertise. Objectives: The objectives of the study were to determine the “lung health index” and the “pattern of lung health” conditions among nursing students. Materials and Methods: A cross-sectional study was conducted utilizing the Swaasa® AI platform among students of a nursing college in Central Karnataka in June 2023. Based on audiometric analysis of cough sounds, parameters such as underlying respiratory condition, cough count values, and respiratory symptoms experienced, ‘Swaasa’ AI platform derives pattern of respiratory condition (normal/obstructive/restrictive/mixed), ‘Lung Health Index,’ and presence or absence of lung health risk. Data collected in the Swaasa app were downloaded and analyzed using SPSS.v. 20. Results: Lung health risk was present in 58.2% of participants. Abnormal lung pattern was noted in 21.3% of participants (obstructive: 14.8%, restrictive: 2.5%, and mixed pattern: 4.1%). Conclusion: The Swaasa AI platform was utilized in this study to self-test lung health in real time without the need for a clinical setting. A high lung health index was found among 18% of participants. Studies adopting such cough sound analysis involving a larger population from wider geographic areas have to be conducted to detect and monitor respiratory diseases to increase its usage among the medical community in everyday clinical practice and also in remote areas.

COVID-19 Detection from Cough Sounds Using XGBoost and LSTM Networks

COVID-19 Detection from Cough Sounds Using XGBoost and LSTM Networks

Leveraging AI and Machine Learning to Develop and Evaluate a Contextualized User-Friendly Cough Audio Classifier for Detecting Respiratory Diseases: Protocol for a Diagnostic Study in Rural Tanzania.

Respiratory diseases, including active tuberculosis (TB), asthma, and chronic obstructive pulmonary disease (COPD), constitute substantial global health challenges, necessitating timely and accurate diagnosis for effective treatment and management. This research seeks to develop and evaluate a noninvasive user-friendly artificial intelligence (AI)-powered cough audio classifier for detecting these respiratory conditions in rural Tanzania. This is a nonexperimental cross-sectional research with the primary objective of collection and analysis of cough sounds from patients with active TB, asthma, and COPD in outpatient clinics to generate and evaluate a noninvasive cough audio classifier. Specialized cough sound recording devices, designed to be nonintrusive and user-friendly, will facilitate the collection of diverse cough sound samples from patients attending outpatient clinics in 20 health care facilities in the Shinyanga region. The collected cough sound data will undergo rigorous analysis, using advanced AI signal processing and machine learning techniques. By comparing acoustic features and patterns associated with TB, asthma, and COPD, a robust algorithm capable of automated disease discrimination will be generated facilitating the development of a smartphone-based cough sound classifier. The classifier will be evaluated against the calculated reference standards including clinical assessments, sputum smear, GeneXpert, chest x-ray, culture and sensitivity, spirometry and peak expiratory flow, and sensitivity and predictive values. This research represents a vital step toward enhancing the diagnostic capabilities available in outpatient clinics, with the potential to revolutionize the field of respiratory disease diagnosis. Findings from the 4 phases of the study will be presented as descriptions supported by relevant images, tables, and figures. The anticipated outcome of this research is the creation of a reliable, noninvasive diagnostic cough classifier that empowers health care professionals and patients themselves to identify and differentiate these respiratory diseases based on cough sound patterns. Cough sound classifiers use advanced technology for early detection and management of respiratory conditions, offering a less invasive and more efficient alternative to traditional diagnostics. This technology promises to ease public health burdens, improve patient outcomes, and enhance health care access in under-resourced areas, potentially transforming respiratory disease management globally. PRR1-10.2196/54388.

The role of spectral analysis of cough sounds in the diagnosis of COVID-19

To evaluate the possibility of using spectral analysis of cough sounds in the diagnosis of a new coronavirus infection COVID-19. Spectral toussophonobarography was performed in 218 patients with COVID-19 [48.56% men, 51.44% women, average age 40.2 (32.4; 51.0)], in 60 healthy individuals [50% men, 50% women, average age 41.7 (32.2; 53.0)] with induced cough (by inhalation of citric acid solution at a concentration of 20 g/l through a nebulizer). The recording was made using a contact microphone located on a special tripod at a distance of 15-20 cm from the face of the subject. The resulting recordings were processed in a computer program, after which spectral analysis of cough sounds was performed using Fourier transform algorithms. The following parameters of cough sounds were evaluated: the duration of the cough act (ms), the ratio of the energy of low frequencies (60-600 Hz) to the energy of high frequencies (600-6000 Hz), the frequency of the maximum energy of the cough sound (Hz). After statistical processing, it was found out that the parameters of the cough sound of COVID-19 patients differ from the cough of healthy individuals. The obtained data were substituted into the developed regression equation. Rounded to integers, the resulting number had the following interpretation: "0" - there is no COVID-19, "1" - there is COVID-19. The technique showed high levels of sensitivity and specificity. In addition, the method is characterized by sufficient ease of use and does not require expensive equipment, therefore it can be used in practice for timely diagnosis of COVID-19.

Predicting COVID-19 Cough Sounds Using Spectrogram Analysis Across Multiple Classes

The COVID-19 pandemic has underscored the need for effective diagnostic tools. One promising avenue involves analyzing cough sounds to glean insights into respiratory health. This study presents a new method for predicting COVID-19 cough sounds using spectrogram analysis across various classes. We leverage advanced deep learning models such as DenseNet121, VGG16, ResNet50, and Inception Net, alongside our novel CNN architecture, to extract pertinent features from cough sound spectrograms. We use a diverse dataset encompassing cough sounds from COVID-19 positive and negative cases, as well as other respiratory conditions, for model training and assessment. Our results demonstrate the effectiveness of our approach in accurately categorizing COVID-19 cough sounds, outperforming existing models. This methodology shows promise as a non-invasive, scalable, and economical tool for early COVID-19 detection and monitoring, aiding public health efforts during the pandemic.

Advancing Cough Classification: Swin Transformer vs. 2D CNN with STFT and Augmentation Techniques

Coughing, a common symptom associated with various respiratory problems, is a crucial indicator for diagnosing and tracking respiratory diseases. Accurate identification and categorization of cough sounds, specially distinguishing between wet and dry coughs, are essential for understanding underlying health conditions. This research focuses on applying the Swin Transformer for classifying wet and dry coughs using short-time Fourier transform (STFT) representations. We conduct a comprehensive evaluation, including a performance comparison with a 2D convolutional neural network (2D CNN) model, and exploration of two distinct image augmentation methods: time mask augmentation and classical image augmentation techniques. Extensive hyperparameter tuning is performed to optimize the Swin Transformer’s performance, considering input size, patch size, embedding size, number of epochs, optimizer type, and regularization technique. Our results demonstrate the Swin Transformer’s superior accuracy, particularly when trained on classically augmented STFT images with optimized settings (320 × 320 input size, RMS optimizer, 8 × 8 patch size, and an embedding size of 128). The approach achieves remarkable testing accuracy (88.37%) and ROC AUC values (94.88%) on the challenging crowdsourced COUGHVID dataset, marking improvements of approximately 2.5% and 11% increases in testing accuracy and ROC AUC values, respectively, compared to previous studies. These findings underscore the efficacy of Swin Transformer architectures in disease detection and healthcare classification problems.

Analysis of Vocal Signatures of COVID-19 in Cough Sounds: A Newer Diagnostic Approach Using Artificial Intelligence.

Artificial intelligence (AI) based models are explored increasingly in the medical field. The highly contagious pandemic of coronavirus disease 2019 (COVID-19) affected the world and availability of diagnostic tools high resolution computed tomography (HRCT) and/or real-time reverse transcriptase-polymerase chain reaction (RTPCR) was very limited, costly and time consuming. Therefore, the use of AI in COVID-19 for diagnosis using cough sounds can be efficacious and cost effective for screening in clinic or hospital and help in early diagnosis and further management of patients. To develop an accurate and fast voice-processing AI software to determine voice-based signatures in discriminating COVID-19 and non-COVID-19 cough sounds for screening of COVID-19. A prospective study involving 117 patients was performed based on online and/or offline voice data collection of cough sounds of COVID-19 patients in isolation ward of a tertiary care teaching hospital and non-COVID-19 participants using a smart phone. A website-based AI software was developed to identify the cough sounds as COVID-19 or non-COVID-19. The data were divided into three segments including training set, validation set and test set. A pre-processing algorithmwas utilized and combined with Short Time Fourier Transform feature representation and Logistic regression model. A precise software was used to identify vocal signatures and K-fold cross validation was carried out. A total of 117 audio recordings of cough sounds were collected through the developed website after inclusion-exclusion criteria out of which 52 have been marked belonging to COVID-19 positive, while 65 were marked as COVID-19 negative/unsure /never had COVID-19, which were assumed to be COVID-19 negative based on RT-PCR test results. The mean and standard error values for the accuracies attained at the end of each experiment in training, validation and testing set were found to be 67.34%±0.22, 58.57%±1.11 and 64.60%±1.79 respectively. The weight values were found to be positive which were contributing towards predicting the samples as COVID-19 positive with large spikes around 7.5 kHz, 7.8 kHz, 8.6 kHz and 11 kHz which can be used for classification. The proposed AI based approach can be a helpful screening tool for COVID-19 using vocal sounds of cough. It can help the health system by reducing the cost burden and improving overall diagnosis and management of the disease.

Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds.

Chest disease refers to a wide range of conditions affecting the lungs, such as COVID-19, lung cancer (LC), consolidation lung (COL), and many more. When diagnosing chest disorders medical professionals may be thrown off by the overlapping symptoms (such as fever, cough, sore throat, etc.). Additionally, researchers and medical professionals make use of chest X-rays (CXR), cough sounds, and computed tomography (CT) scans to diagnose chest disorders. The present study aims to classify the nine different conditions of chest disorders, including COVID-19, LC, COL, atelectasis (ATE), tuberculosis (TB), pneumothorax (PNEUTH), edema (EDE), pneumonia (PNEU). Thus, we suggested four novel convolutional neural network (CNN) models that train distinct image-level representations for nine different chest disease classifications by extracting features from images. Furthermore, the proposed CNN employed several new approaches such as a max-pooling layer, batch normalization layers (BANL), dropout, rank-based average pooling (RBAP), and multiple-way data generation (MWDG). The scalogram method is utilized to transform the sounds of coughing into a visual representation. Before beginning to train the model that has been developed, the SMOTE approach is used to calibrate the CXR and CT scans as well as the cough sound images (CSI) of nine different chest disorders. The CXR, CT scan, and CSI used for training and evaluating the proposed model come from 24 publicly available benchmark chest illness datasets. The classification performance of the proposed model is compared with that of seven baseline models, namely Vgg-19, ResNet-101, ResNet-50, DenseNet-121, EfficientNetB0, DenseNet-201, and Inception-V3, in addition to state-of-the-art (SOTA) classifiers. The effectiveness of the proposed model is further demonstrated by the results of the ablation experiments. The proposed model was successful in achieving an accuracy of 99.01%, making it superior to both the baseline models and the SOTA classifiers. As a result, the proposed approach is capable of offering significant support to radiologists and other medical professionals.

COVID-19 Detection From Respiratory Sounds With Hierarchical Spectrogram Transformers.

Monitoring of prevalent airborne diseases such as COVID-19 characteristically involves respiratory assessments. While auscultation is a mainstream method for preliminary screening of disease symptoms, its utility is hampered by the need for dedicated hospital visits. Remote monitoring based on recordings of respiratory sounds on portable devices is a promising alternative, which can assist in early assessment of COVID-19 that primarily affects the lower respiratory tract. In this study, we introduce a novel deep learning approach to distinguish patients with COVID-19 from healthy controls given audio recordings of cough or breathing sounds. The proposed approach leverages a novel hierarchical spectrogram transformer (HST) on spectrogram representations of respiratory sounds. HST embodies self-attention mechanisms over local windows in spectrograms, and window size is progressively grown over model stages to capture local to global context. HST is compared against state-of-the-art conventional and deep-learning baselines. Demonstrations on crowd-sourced multi-national datasets indicate that HST outperforms competing methods, achieving over 90% area under the receiver operating characteristic curve (AUC) in detecting COVID-19 cases.

SUPER-COUGH: A Super Learner-based ensemble machine learning method for detecting disease on cough acoustic signals

Sound classification has obtained considerable attention in recent years due to its wide range of applications in various fields, such as speech recognition, sound surveillance, music analysis, and environmental monitoring. Because of its success, audio classification can also be employed in medical applications. Coughing is the most common disease symptom, and cough sounds might be used to diagnose them. This research focuses on identifying observable features of cough and classifying them into positive, negative, or symptomatic categories. A novel ensemble learning model based on the super learner (SL) is proposed to diagnose the disease using cough sounds utilizing various audio features such as Frequency Distribution, Time Domain Features, Spectral Features, and Time–Frequency Features. The SL method is a cross-validated approach to stacked generalization, and it can select an optimal learner from a set of learners and improve performance by selecting and merging models using cross-validation. The proposed SL model comprises DT, RF, LR, SVM, ET, and k-NN algorithms. We use the public Coughvid dataset, and the proposed model achieves a correct classification rate for symptomatic cases, which was 90.90%, and the positive predictive value for COVID-19 cases was 84.50%. The SL3 model attains 72%, 78%, 73%, 74.4%, and 78.85% precision, recall, f1-score, accuracy, and average AUC values, respectively. The numerical results show that the proposed model might be implemented to diagnose various other diseases that can be determined from respiratory sounds.