Detection of COVID-19 from deep breathing sounds using sound spectrum with image augmentation and deep learning techniques

The COVID-19 pandemic is one of the most disruptive outbreaks of the 21st century considering its impacts on our freedoms and social lifestyle. Several methods have been used to monitor and diagnose this virus, which includes the use of RT-PCR test and chest CT/CXR scans. Recent studies have employed various crowdsourced sound data types such as coughing, breathing, sneezing, etc., for the detection of COVID-19. However, the application of artificial intelligence methods and machine learning algorithms on these sound datasets still suffer some limitations such as the poor performance of the test results due to increase of misclassified data, limited datasets resulting in the overfitting of deep learning methods, the high computational cost of some augmentation models, and varying quality feature-extracted images resulting in poor reliability. We propose a simple yet effective deep learning model, called DeepShufNet, for COVID-19 detection. A data augmentation method based on the color transformation and noise addition was used for generating synthetic image datasets from sound data. The efficiencies of the synthetic dataset were evaluated using two feature extraction approaches, namely Mel spectrogram and GFCC. The performance of the proposed DeepShufNet model was evaluated using a deep breathing COSWARA dataset, which shows improved performance with a lower misclassification rate of the minority class. The proposed model achieved an accuracy, precision, recall, specificity, and f-score of 90.1%, 77.1%, 62.7%, 95.98%, and 69.1%, respectively, for positive COVID-19 detection using the Mel COCOA-2 augmented training datasets. The proposed model showed an improved performance compared to some of the state-of-the-art-methods

Respiratory Sound Analysis for the Evidence of Lung Health

Significant changes have been made on audio-based technologies over years in several different fields along with healthcare industry. Analysis of Lung sounds is a potential source of noninvasive, quantitative information along with additional objective on the status of the pulmonary system. To do that medical professionals listen to sounds heard over the chest wall at different positions with a stethoscope which is known as auscultation and is important in diagnosing respiratory diseases. At times, possibility of inaccurate interpretation of respiratory sounds happens because of clinician’s lack of considerable expertise or sometimes trainees such as interns and residents misidentify respiratory sounds. We have built a tool to distinguish healthy respiratory sound from non-healthy ones that come from respiratory infection carrying patients. The audio clips were characterized using Linear Predictive Cepstral Coefficient (LPCC)-based features and the highest possible accuracy of 99.22% was obtained with a Multi-Layer Perceptron (MLP)- based classifier on the publicly available ICBHI17 respiratory sounds dataset [1] of size 6800+ clips. The system also outperformed established works in literature and other machine learning techniques. In future we will try to use larger dataset with other acoustic techniques along with deep learning-based approaches and try to identify the nature and severity of infection using respiratory sounds

Classification of Normal and Crackles Respiratory Sounds into Healthy and Lung Cancer Groups

Lung cancer is the most common cancer worldwide and the third most common cancer in Malaysia. Due to its high prevalence worldwide and in Malaysia, it is an utmost importance to have the disease detected at an early stage which would result in a higher chance of cure and possibly better survival. The current methods used for lung cancer screening might not be simple, inexpensive and safe and not readily accessible in outpatient clinics. In this paper, we present the classification of normal and crackles sounds acquired from 20 healthy and 23 lung cancer patients, respectively using Artificial Neural Network. Firstly, the sounds signals were decomposed into seven different frequency bands using Discrete Wavelet Transform (DWT) based on two different mother wavelets namely Daubechies 7 (db7) and Haar. Secondly, mean, standard deviation and maximum PSD of the detail coefficients for five frequency bands (D3, D4, D5, D6, and D7) were calculated as features. Fifteen features were used as input to the ANN classifier. The results of classification show that db7 based performed better than Haar with perfect 100% sensitivity, specificity and accuracy for testing and validation stages when using 15 nodes at the hidden layer. While for Haar, only testing stage shows the perfect 100% for sensitivity, specificity, and accuracy when using 10 nodes at the hidden layer

Robust Deep Learning Frameworks for Acoustic Scene and Respiratory Sound Classification

Although research on Acoustic Scene Classification (ASC) is very close to, or even overshadowed by different popular research areas known as Automatic Speech Recognition (ASR), Speaker Recognition (SR) or Image Processing (IP), this field potentially opens up several distinct and meaningful application areas based on environment context detection. The challenges of ASC mainly come from different noise resources, various sounds in real-world environments, occurring as single sounds, continuous sounds or overlapping sounds. In comparison to speech, sound scenes are more challenging mainly due to their being unstructured in form and closely similar to noise in certain contexts. Although a wide range of publications have focused on ASC recently, they show task-specific ways that either explore certain aspects of an ASC system or are evaluated on limited acoustic scene datasets. Therefore, the aim of this thesis is to contribute to the development of a robust framework to be applied for ASC, evaluated on various recently published datasets, and to achieve competitive performance compared to the state-of-the-art systems. To do this, a baseline model is firstly introduced. Next, extensive experiments on the baseline are conducted to identify key factors affecting final classification accuracy. From the comprehensive analysis, a robust deep learning framework, namely the Encoder-Decoder structure, is proposed to address three main factors that directly affect an ASC system. These factors comprise low-level input features, high-level feature extraction methodologies, and architectures for final classification. Within the proposed framework, three spectrogram transformations, namely Constant Q Transform (CQT), gammatone filter (Gamma), and log-mel, are used to convert recorded audio signals into spectrogram representations that resemble two-dimensional images. These three spectrograms used are referred to as low-level input features. To extract high-level features from spectrograms, a novel Encoder architecture, based on Convolutional Neural Networks, is proposed. In terms of the Decoder, also referred as to the final classifier, various models such as Random Forest Classifier, Deep Neural Network and Mixture of Experts, are evaluated and structured to obtain the best performance. To further improve an ASC system's performance, a scheme of two-level hierarchical classification, replacing the role of Decoder classification recently mentioned, is proposed. This scheme is useful to transform an ASC task over all categories into multiple ASC sub-tasks, each spanning fewer categories, in a divide-and- conquer strategy. At the highest level of the proposed scheme, meta-categories of acoustic scene sounds showing similar characteristics are classified. Next, categories within each meta-category are classified at the second level. Furthermore, an analysis of loss functions applied to different classifiers is conducted. This analysis indicates that a combination of entropy loss and triplet loss is useful to enhance performance, especially with tasks that comprise fewer categories. Further exploring ASC in terms of potential application to the health services, this thesis also explores the 2017 Internal Conference on Biomedical Health Informatics (ICBHI) benchmark dataset of lung sounds. A deep-learning frame- work, based on our novel ASC approaches, is proposed to classify anomaly cycles and predict respiratory diseases. The results obtained from these experiments show exceptional performance. This highlights the potential applications of using advanced ASC frameworks for early detection of auditory signals. In this case, signs of respiratory diseases, which could potentially be highly useful in future in directing treatment and preventing their spread

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

IMPROVING THE QUALITY, ANALYSIS AND INTERPRETATION OF BODY SOUNDS ACQUIRED IN CHALLENGING CLINICAL SETTINGS

Despite advances in medicine and technology, Acute Lower Respiratory Diseases are a leading cause of sickness and mortality worldwide, highly affecting countries where access to appropriate medical technology and expertise is scarce. Chest auscultation provides a low-cost, non-invasive, widely available tool for the examination of pulmonary health. Despite universal adoption, its use is riddled by a number of issues including subjectivity in interpretation and vulnerability to ambient noise, limiting its diagnostic capability. Digital auscultation and computerized methods come as a natural aid towards overcoming such imposed limitations. Focused on the challenges, we address the demanding real-life scenario of pediatric lung auscultation in busy clinical settings. Two major objectives lead to our contributions: 1) Can we improve the quality of the delicate auscultated sounds and reduce unwanted noise contamination; 2) Can we augment the screening capabilities of current stethoscopes using computerized lung sound analysis to capture the presence of abnormal breaths, and can we standardize findings. To address the first objective, we developed an adaptive noise suppression scheme that tackles contamination coming from a variety of sources, including subject-centric and electronic artifacts, and environmental noise. The proposed method was validated using objective and subjective measures including an expert reviewer panel and objective signal quality metrics. Results revealed the ability and superiority of the proposed method to i) suppress unwanted noise when compared to state-of-the-art technology, and ii) faithfully maintain the signature of the delicate body sounds. The second objective was addressed by exploring appropriate feature representations that capture distinct characteristics of body sounds. A biomimetic approach was employed, and the acoustic signal was projected onto high-dimensional spaces spanning time, frequency, temporal dynamics and spectral modulations. Trained classifiers produced localized decisions on these breath content features, indicating lung diseases. Unlike existing literature, our proposed scheme is further able to combine and integrate the localized decisions into individual, patient-level evaluation. A large corpus of annotated patient data was used to validate our approach, demonstrating the superiority of the proposed features and patient evaluation scheme. Overall findings indicate that improved accessible auscultation care is possible, towards creating affordable health care solutions with worldwide impact

Influence of event duration on automatic wheeze classification

Patients with respiratory conditions typically exhibit adventitious respiratory sounds, such as wheezes. Wheeze events have variable duration. In this work we studied the influence of event duration on wheeze classification, namely how the creation of the non-wheeze class affected the classifiers' performance. First, we evaluated several classifiers on an open access respiratory sound database, with the best one reaching sensitivity and specificity values of 98% and 95%, respectively. Then, by changing one parameter in the design of the non-wheeze class, i.e., event duration, the best classifier only reached sensitivity and specificity values of 55% and 76%, respectively. These results demonstrate the importance of experimental design on the assessment of wheeze classification algorithms' performance.publishe