Robust Detection of Audio-Cough Events Using Local Hu Moments

Enhanced fingerprint classification through modified PCA with SVD and invariant moments

This research introduces a novel MOMENTS-SVD vector for fingerprint identification, combining invariant moments and SVD (Singular Value Decomposition), enhanced by a modified PCA (Principal Component Analysis). Our method extracts unique fingerprint features using SVD and invariant moments, followed by classification with Euclidean distance and neural networks. The MOMENTS-SVD vector reduces computational complexity by outperforming current models. Using the Equal Error Rate (EER) and ROC curve, a comparative study across databases (CASIA V5, FVC 2002, 2004, 2006) assesses our method against ResNet, VGG19, Neuro Fuzzy, DCT Features, and Invariant Moments, proving enhanced accuracy and robustness.

Feature evaluation of accelerometry signals for cough detection

Cough is a common symptom of multiple respiratory diseases, such as asthma and chronic obstructive pulmonary disorder. Various research works targeted cough detection as a means for continuous monitoring of these respiratory health conditions. This has been mainly achieved using sophisticated machine learning or deep learning algorithms fed with audio recordings. In this work, we explore the use of an alternative detection method, since audio can generate privacy and security concerns related to the use of always-on microphones. This study proposes the use of a non-contact tri-axial accelerometer for motion detection to differentiate between cough and non-cough events/movements. A total of 43 time-domain features were extracted from the acquired tri-axial accelerometry signals. These features were evaluated and ranked for their importance using six methods with adjustable conditions, resulting in a total of 11 feature rankings. The ranking methods included model-based feature importance algorithms, first principal component, leave-one-out, permutation, and recursive features elimination (RFE). The ranking results were further used in the feature selection of the top 10, 20, and 30 for use in cough detection. A total of 68 classification models using a simple logistic regression classifier are reported, using two approaches for data splitting: subject-record-split and leave-one-subject-out (LOSO). The best-performing model out of the 34 using subject-record-split obtained an accuracy of 92.20%, sensitivity of 90.87%, specificity of 93.52%, and F1 score of 92.09% using only 20 features selected by the RFE method. The best-performing model out of the 34 using LOSO obtained an accuracy of 89.57%, sensitivity of 85.71%, specificity of 93.43%, and F1 score of 88.72% using only 10 features selected by the RFE method. These results demonstrate the ability for future implementation of a motion-based wearable cough detector.

Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review

Respiratory diseases represent a significant global burden, necessitating efficient diagnostic methods for timely intervention. Digital biomarkers based on audio, acoustics, and sound from the upper and lower respiratory system, as well as the voice, have emerged as valuable indicators of respiratory functionality. Recent advancements in machine learning (ML) algorithms offer promising avenues for the identification and diagnosis of respiratory diseases through the analysis and processing of such audio-based biomarkers. An ever-increasing number of studies employ ML techniques to extract meaningful information from audio biomarkers. Beyond disease identification, these studies explore diverse aspects such as the recognition of cough sounds amidst environmental noise, the analysis of respiratory sounds to detect respiratory symptoms like wheezes and crackles, as well as the analysis of the voice/speech for the evaluation of human voice abnormalities. To provide a more in-depth analysis, this review examines 75 relevant audio analysis studies across three distinct areas of concern based on respiratory diseases' symptoms: (a) cough detection, (b) lower respiratory symptoms identification, and (c) diagnostics from the voice and speech. Furthermore, publicly available datasets commonly utilized in this domain are presented. It is observed that research trends are influenced by the pandemic, with a surge in studies on COVID-19 diagnosis, mobile data acquisition, and remote diagnosis systems.

Environment Knowledge-Driven Generic Models to Detect Coughs From Audio Recordings

Goal: Millions of people are dying due to respiratory diseases, such as COVID-19 and asthma, which are often characterized by some common symptoms, including coughing. Therefore, objective reporting of cough symptoms utilizing environment-adaptive machine-learning models with microphone sensing can directly contribute to respiratory disease diagnosis and patient care. Methods: In this work, we present three generic modeling approaches - unguided, semi-guided, and guided approaches considering three potential scenarios, i.e., when a user has no prior knowledge, some knowledge, and detailed knowledge about the environments, respectively. Results: From detailed analysis with three datasets, we find that guided models are up to 28% more accurate than the unguided models. We find reasonable performance when assessing the applicability of our models using three additional datasets, including two open-sourced cough datasets. Conclusions: Though guided models outperform other models, they require a better understanding of the environment.

Grading the Severity of Damage to the Perijunctional Actomyosin Ring and Zonula Occludens-1 of the Corneal Endothelium by Ensemble Learning Methods

Purpose: In many epithelia, including the corneal endothelium, intracellular/extracellular stresses break down the perijunctional actomyosin ring (PAMR) and zonula occludens-1 (ZO-1) at the apical junctions. This study aims to grade the severity of damage to PAMR and ZO-1 through machine learning. Methods: Immunocytochemical images of PAMR and ZO-1 were drawn from recent studies on the corneal endothelium subjected to hypothermia and oxidative stress. The images were analyzed for their morphological (e.g., Hu moments) and textural features (based on gray-level co-occurrence matrix [GLCM] and Gabor filters). The extracted features were ranked by SHapley analysis and analysis of variance. Then top features were used to grade the severity of damage using a suite of ensemble classifiers, including random forest, bagging classifier (BC), AdaBoost, extreme gradient boosting, and stacking classifier. Results: A partial set of features from GLCM, along with Hu moments and the number of hexagons, enabled the classification of damage to PAMR into Control, Mild, Moderate, and Severe with the area under the receiver operating characteristics curve (AUC) = 0.92 and F1 score = 0.77 with BC. In contrast, a bank of Gabor filters provided a partial set of features that could be combined with Hu moments, branch length, and sharpness for the classification of ZO-1 images into four levels with AUC = 0.95 and F1 score of 0.8 with BC. Conclusions: We have developed a workflow that enables the stratification of damage to PAMR and ZO-1. The approach can be applied to similar data during drug discovery or pathophysiological studies of epithelia.

Nighttime Continuous Contactless Smartphone-Based Cough Monitoring for the Ward: Validation Study

BACKGROUND

Clinical deterioration can go unnoticed in hospital wards for hours. Mobile technologies such as wearables and smartphones enable automated, continuous, noninvasive ward monitoring and allow the detection of subtle changes in vital signs. Cough can be effectively monitored through mobile technologies in the ward, as it is not only a symptom of prevalent respiratory diseases such as asthma, lung cancer, and COVID-19 but also a predictor of acute health deterioration. In past decades, many efforts have been made to develop an automatic cough counting tool. To date, however, there is neither a standardized, sufficiently validated method nor a scalable cough monitor that can be deployed on a consumer-centric device that reports cough counts continuously. These shortcomings limit the tracking of coughing and, consequently, hinder the monitoring of disease progression in prevalent respiratory diseases such as asthma, chronic obstructive pulmonary disease, and COVID-19 in the ward.

OBJECTIVE

This exploratory study involved the validation of an automated smartphone-based monitoring system for continuous cough counting in 2 different modes in the ward. Unlike previous studies that focused on evaluating cough detection models on unseen data, the focus of this work is to validate a holistic smartphone-based cough detection system operating in near real time.

METHODS

Automated cough counts were measured consistently on devices and on computers and compared with cough and noncough sounds counted manually over 8-hour long nocturnal recordings in 9 patients with pneumonia in the ward. The proposed cough detection system consists primarily of an Android app running on a smartphone that detects coughs and records sounds and secondarily of a backend that continuously receives the cough detection information and displays the hourly cough counts. Cough detection is based on an ensemble convolutional neural network developed and trained on asthmatic cough data.

RESULTS

In this validation study, a total of 72 hours of recordings from 9 participants with pneumonia, 4 of whom were infected with SARS-CoV-2, were analyzed. All the recordings were subjected to manual analysis by 2 blinded raters. The proposed system yielded a sensitivity and specificity of 72% and 99% on the device and 82% and 99% on the computer, respectively, for detecting coughs. The mean differences between the automated and human rater cough counts were -1.0 (95% CI -12.3 to 10.2) and -0.9 (95% CI -6.5 to 4.8) coughs per hour within subject for the on-device and on-computer modes, respectively.

CONCLUSIONS

The proposed system thus represents a smartphone cough counter that can be used for continuous hourly assessment of cough frequency in the ward.

Detection of Neurogenic Voice Disorders Using the Fisher Vector Representation of Cepstral Features

Neurogenic voice disorders (NVDs) are caused by damage or malfunction of the central or peripheral nervous system that controls vocal fold movement. In this paper, we investigate the potential of the Fisher vector (FV) encoding in automatic detection of people with NVDs. FVs are used to convert features from frame level (local descriptors) to utterance level (global descriptors). At the frame level, we extract two popular cepstral representations, namely, Mel-frequency cepstral coefficients (MFCCs) and perceptual linear prediction cepstral coefficients (PLPCCs), from acoustic voice signals. In addition, the MFCC features are also extracted from every frame of the glottal source signal computed using a glottal inverse filtering (GIF) technique. The global descriptors derived from the local descriptors are used to train a support vector machine (SVM) classifier. Experiments are conducted using voice signals from 80 healthy speakers and 80 patients with NVDs (40 with spasmodic dysphonia (SD) and 40 with recurrent laryngeal nerve palsy (RLNP)) taken from the Saarbruecken voice disorder (SVD) database. The overall results indicate that the use of the FV encoding leads to better identification of people with NVDs, compared to the defacto temporal encoding. Furthermore, the SVM trained using the combination of FVs derived from the cepstral and glottal features provides the overall best detection performance.

Past and Trends in Cough Sound Acquisition, Automatic Detection and Automatic Classification: A Comparative Review

Cough is a very common symptom and the most frequent reason for seeking medical advice. Optimized care goes inevitably through an adapted recording of this symptom and automatic processing. This study provides an updated exhaustive quantitative review of the field of cough sound acquisition, automatic detection in longer audio sequences and automatic classification of the nature or disease. Related studies were analyzed and metrics extracted and processed to create a quantitative characterization of the state-of-the-art and trends. A list of objective criteria was established to select a subset of the most complete detection studies in the perspective of deployment in clinical practice. One hundred and forty-four studies were short-listed, and a picture of the state-of-the-art technology is drawn. The trend shows an increasing number of classification studies, an increase of the dataset size, in part from crowdsourcing, a rapid increase of COVID-19 studies, the prevalence of smartphones and wearable sensors for the acquisition, and a rapid expansion of deep learning. Finally, a subset of 12 detection studies is identified as the most complete ones. An unequaled quantitative overview is presented. The field shows a remarkable dynamic, boosted by the research on COVID-19 diagnosis, and a perfect adaptation to mobile health.

Development and technical validation of a smartphone-based pediatric cough detection algorithm

INTRODUCTION

Coughing is a common symptom in pediatric lung disease and cough frequency has been shown to be correlated to disease activity in several conditions. Automated cough detection could provide a noninvasive digital biomarker for pediatric clinical trials or care. The aim of this study was to develop a smartphone-based algorithm that objectively and automatically counts cough sounds of children.

METHODS

The training set was composed of 3228 pediatric cough sounds and 480,780 noncough sounds from various publicly available sources and continuous sound recordings of 7 patients admitted due to respiratory disease. A Gradient Boost Classifier was fitted on the training data, which was subsequently validated on recordings from 14 additional patients aged 0-14 admitted to the pediatric ward due to respiratory disease. The robustness of the algorithm was investigated by repeatedly classifying a recording with the smartphone-based algorithm during various conditions.

RESULTS

The final algorithm obtained an accuracy of 99.7%, sensitivity of 47.6%, specificity of 99.96%, positive predictive value of 82.2% and negative predictive value 99.8% in the validation dataset. The correlation coefficient between manual- and automated cough counts in the validation dataset was 0.97 (p < .001). The intra- and interdevice reliability of the algorithm was adequate, and the algorithm performed best at an unobstructed distance of 0.5-1 m from the audio source.

CONCLUSION

This novel smartphone-based pediatric cough detection application can be used for longitudinal follow-up in clinical care or as digital endpoint in clinical trials.

Automatic cough detection from realistic audio recordings using C-BiLSTM with boundary regression

Automatic cough detection in the patients' realistic audio recordings is of great significance to diagnose and monitor respiratory diseases, such as COVID-19. Many detection methods have been developed so far, but they are still unable to meet the practical requirements. In this paper, we present a deep convolutional bidirectional long short-term memory (C-BiLSTM) model with boundary regression for cough detection, where cough and non-cough parts need to be classified and located. We added convolutional layers before the LSTM to enhance the cough features and preserve the temporal information of the audio data. Considering the importance of the cough event integrity for subsequent analysis, the novel model includes an embedded boundary regression on the last feature map for both higher detection accuracy and more accurate boundaries. We delicately designed, collected and labelled a realistic audio dataset containing recordings of patients with respiratory diseases, named the Corp Dataset. 168 h of recordings with 9969 coughs from 42 different patients are included. The dataset is published online on the MARI Lab website (https://mari.tongji.edu.cn/info/1012/1030.htm). The results show that the system achieves a sensitivity of 84.13%, a specificity of 99.82% and an intersection-over-union (IoU) of 0.89, which is significantly superior to other related models. With the proposed method, all the criteria on cough detection significantly increased. The open source Corp Dataset provides useful material and a benchmark for researchers investigating cough detection. We propose the state-of-the-art system with boundary regression, laying the foundation for identifying cough sounds in real-world audio data.

Cough detection using a non-contact microphone: A nocturnal cough study

An automatic non-contact cough detector designed especially for night audio recordings that can distinguish coughs from snores and other sounds is presented. Two different classifiers were implemented and tested: a Gaussian Mixture Model (GMM) and a Deep Neural Network (DNN). The detected coughs were analyzed and compared in different sleep stages and in terms of severity of Obstructive Sleep Apnea (OSA), along with age, Body Mass Index (BMI), and gender. The database was composed of nocturnal audio signals from 89 subjects recorded during a polysomnography study. The DNN-based system outperformed the GMM-based system, at 99.8% accuracy, with a sensitivity and specificity of 86.1% and 99.9%, respectively (Positive Predictive Value (PPV) of 78.4%). Cough events were significantly more frequent during wakefulness than in the sleep stages (p < 0.0001) and were significantly less frequent during deep sleep than in other sleep stages (p < 0.0001). A positive correlation was found between BMI and the number of nocturnal coughs (R = 0.232, p < 0.05), and between the number of nocturnal coughs and OSA severity in men (R = 0.278, p < 0.05). This non-contact cough detection system may thus be implemented to track the progression of respiratory illnesses and test reactions to different medications even at night when a contact sensor is uncomfortable or infeasible.

Automatic cough classification for tuberculosis screening in a real-world environment

Objective.The automatic discrimination between the coughing sounds produced by patients with tuberculosis (TB) and those produced by patients with other lung ailments.Approach.We present experiments based on a dataset of 1358 forced cough recordings obtained in a developing-world clinic from 16 patients with confirmed active pulmonary TB and 35 patients suffering from respiratory conditions suggestive of TB but confirmed to be TB negative. Using nested cross-validation, we have trained and evaluated five machine learning classifiers: logistic regression (LR), support vector machines, k-nearest neighbour, multilayer perceptrons and convolutional neural networks.Main Results.Although classification is possible in all cases, the best performance is achieved using LR. In combination with feature selection by sequential forward selection, our best LR system achieves an area under the ROC curve (AUC) of 0.94 using 23 features selected from a set of 78 high-resolution mel-frequency cepstral coefficients. This system achieves a sensitivity of 93% at a specificity of 95% and thus exceeds the 90% sensitivity at 70% specificity specification considered by the World Health Organisation (WHO) as a minimal requirement for a community-based TB triage test.Significance.The automatic classification of cough audio sounds, when applied to symptomatic patients requiring investigation for TB, can meet the WHO triage specifications for the identification of patients who should undergo expensive molecular downstream testing. This makes it a promising and viable means of low cost, easily deployable frontline screening for TB, which can benefit especially developing countries with a heavy TB burden.

Novel COVID-19 Screening Using Cough Recordings of A Mobile Patient Monitoring System

Since the COVID-19 pandemic began, research has shown promises in building COVID-19 screening tools using cough recordings as a convenient and inexpensive alternative to current testing techniques. In this paper, we present a novel and fully automated algorithm framework for cough extraction and COVID-19 detection using a combination of signal processing and machine learning techniques. It involves extracting cough episodes from audios of a diverse real-world noisy conditions and then screening for the COVID-19 infection based on the cough characteristics. The proposed algorithm was developed and evaluated using self-recorded cough audios collected from COVID-19 patients monitored by Biovitals^® Sentinel remote patient management platform and publicly available datasets of various sound recordings. The proposed algorithm achieves a duration Area Under Receiver Operating Characteristic curve (AUROC) of 98.6% in the cough extraction task and a mean cross-validation AUROC of 98.1% in the COVID-19 classification task. These results demonstrate high accuracy and robustness of the proposed algorithm as a fast and easily accessible COVID-19 screening tool and its potential to be used for other cough analysis applications.

A cognitive IoT-based framework for effective diagnosis of COVID-19 using multimodal data

The COVID-19 emerged at the end of 2019 and has become a global pandemic. There are many methods for COVID-19 prediction using a single modality. However, none of them predicts with 100% accuracy, as each individual exhibits varied symptoms for the disease. To decrease the rate of misdiagnosis, multiple modalities can be used for prediction. Besides, there is also a need for a self-diagnosis system to narrow down the risk of virus spread in testing centres. Therefore, we propose a robust IoT and deep learning-based multi-modal data classification method for the accurate prediction of COVID-19. Generally, highly accurate models require deep architectures. In this work, we introduce two lightweight models, namely CovParaNet for audio (cough, speech, breathing) classification and CovTinyNet for image (X-rays, CT scans) classification. These two models were identified as the best unimodal models after comparative analysis with the existing benchmark models. Finally, the obtained results of the five independently trained unimodal models are integrated by a novel dynamic multimodal Random Forest classifier. The lightweight CovParaNet and CovTinyNet models attain a maximum accuracy of 97.45% and 99.19% respectively even with a small dataset. The proposed dynamic multimodal fusion model predicts the final result with 100% accuracy, precision, and recall, and the online retraining mechanism enables it to extend its support even in a noisy environment. Furthermore, the computational complexity of all the unimodal models is minimized tremendously and the system functions effectively with 100% reliability even in the absence of any one of the input modalities during testing.

Deep Neural Network-Based Respiratory Pathology Classification Using Cough Sounds

Intelligent systems are transforming the world, as well as our healthcare system. We propose a deep learning-based cough sound classification model that can distinguish between children with healthy versus pathological coughs such as asthma, upper respiratory tract infection (URTI), and lower respiratory tract infection (LRTI). To train a deep neural network model, we collected a new dataset of cough sounds, labelled with a clinician's diagnosis. The chosen model is a bidirectional long-short-term memory network (BiLSTM) based on Mel-Frequency Cepstral Coefficients (MFCCs) features. The resulting trained model when trained for classifying two classes of coughs-healthy or pathology (in general or belonging to a specific respiratory pathology)-reaches accuracy exceeding 84% when classifying the cough to the label provided by the physicians' diagnosis. To classify the subject's respiratory pathology condition, results of multiple cough epochs per subject were combined. The resulting prediction accuracy exceeds 91% for all three respiratory pathologies. However, when the model is trained to classify and discriminate among four classes of coughs, overall accuracy dropped: one class of pathological coughs is often misclassified as the other. However, if one considers the healthy cough classified as healthy and pathological cough classified to have some kind of pathology, then the overall accuracy of the four-class model is above 84%. A longitudinal study of MFCC feature space when comparing pathological and recovered coughs collected from the same subjects revealed the fact that pathological coughs, irrespective of the underlying conditions, occupy the same feature space making it harder to differentiate only using MFCC features.

COVID-19 cough classification using machine learning and global smartphone recordings

We present a machine learning based COVID-19 cough classifier which can discriminate COVID-19 positive coughs from both COVID-19 negative and healthy coughs recorded on a smartphone. This type of screening is non-contact, easy to apply, and can reduce the workload in testing centres as well as limit transmission by recommending early self-isolation to those who have a cough suggestive of COVID-19. The datasets used in this study include subjects from all six continents and contain both forced and natural coughs, indicating that the approach is widely applicable. The publicly available Coswara dataset contains 92 COVID-19 positive and 1079 healthy subjects, while the second smaller dataset was collected mostly in South Africa and contains 18 COVID-19 positive and 26 COVID-19 negative subjects who have undergone a SARS-CoV laboratory test. Both datasets indicate that COVID-19 positive coughs are 15%–20% shorter than non-COVID coughs. Dataset skew was addressed by applying the synthetic minority oversampling technique (SMOTE). A leave-p-out cross-validation scheme was used to train and evaluate seven machine learning classifiers: logistic regression (LR), k-nearest neighbour (KNN), support vector machine (SVM), multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM) and a residual-based neural network architecture (Resnet50). Our results show that although all classifiers were able to identify COVID-19 coughs, the best performance was exhibited by the Resnet50 classifier, which was best able to discriminate between the COVID-19 positive and the healthy coughs with an area under the ROC curve (AUC) of 0.98. An LSTM classifier was best able to discriminate between the COVID-19 positive and COVID-19 negative coughs, with an AUC of 0.94 after selecting the best 13 features from a sequential forward selection (SFS). Since this type of cough audio classification is cost-effective and easy to deploy, it is potentially a useful and viable means of non-contact COVID-19 screening.

Automated, multiparametric monitoring of respiratory biomarkers and vital signs in clinical and home settings for COVID-19 patients

Capabilities in continuous monitoring of key physiological parameters of disease have never been more important than in the context of the global COVID-19 pandemic. Soft, skin-mounted electronics that incorporate high-bandwidth, miniaturized motion sensors enable digital, wireless measurements of mechanoacoustic (MA) signatures of both core vital signs (heart rate, respiratory rate, and temperature) and underexplored biomarkers (coughing count) with high fidelity and immunity to ambient noises. This paper summarizes an effort that integrates such MA sensors with a cloud data infrastructure and a set of analytics approaches based on digital filtering and convolutional neural networks for monitoring of COVID-19 infections in sick and healthy individuals in the hospital and the home. Unique features are in quantitative measurements of coughing and other vocal events, as indicators of both disease and infectiousness. Systematic imaging studies demonstrate correlations between the time and intensity of coughing, speaking, and laughing and the total droplet production, as an approximate indicator of the probability for disease spread. The sensors, deployed on COVID-19 patients along with healthy controls in both inpatient and home settings, record coughing frequency and intensity continuously, along with a collection of other biometrics. The results indicate a decaying trend of coughing frequency and intensity through the course of disease recovery, but with wide variations across patient populations. The methodology creates opportunities to study patterns in biometrics across individuals and among different demographic groups.

Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes: The 2019 Literature Year in Review

Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this article, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (eg, natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope that the studies described in this article help readers: (1) understand the breadth and depth of data science's ability to improve clinical processes and patient outcomes that are relevant to nurses and (2) identify gaps in the literature that are in need of exploration.

Smart homes that detect sneeze, cough, and face touching

Coughing, sneezing, and face touching activities are three primary ways of spreading disease. With the onset of COVID-19 it is paramount to monitor these activities at home and practice good hygiene. To help stop the spread of disease, we have developed a wireless sensing system capable of detecting voluntary coughs, sneezes, and face touching with alert based notifications sent to a mobile application. Our system uses radio frequency technology to capture motion, speed, direction, and range information from human activities. It does this by using a combination of a continuous wave Doppler and frequency modulated continuous wave radar. By observing a set of features related to the sensed motion, we designed a set of fuzzy logic IF-THEN rules that can differentiate each activity from each other with an overall accuracy of 96%. In addition, our system enables smart homes to detect and localize these activities at different distances up to 2.74 m, through walls, and with multiple people. We envision our system helping not only with prevention of COVID-19, but supporting contact tracing efforts by monitoring people's activities at home.

Methods for Adventitious Respiratory Sound Analyzing Applications Based on Smartphones: A Survey

Detection and classification of adventitious acoustic lung sounds plays an important role in diagnosing, monitoring, controlling and, caring the patients with lung diseases. Such systems can be presented as different platforms like medical devices, standalone software or smartphone application. Ubiquity of smartphones and widespread use of the corresponding applications make such a device an attractive platform for hosting the detection and classification systems for adventitious lung sounds. In this paper, the smartphone-based systems for automatic detection and classification of the adventitious lung sounds are surveyed. Such adventitious sounds include cough, wheeze, crackle and, snore. Relevant sounds related to abnormal respiratory activities are considered as well. The methods are shortly described and the analyzing algorithms are explained. The analysis includes detection and/or classification of the sound events. A summary of the main surveyed methods together with the classification parameters and used features for the sake of comparison is given. Existing challenges, open issues and future trends will be discussed as well.

Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic

Coronavirus disease 2019 (COVID-19) has emerged as a pandemic with serious clinical manifestations including death. A pandemic at the large-scale like COVID-19 places extraordinary demands on the world's health systems, dramatically devastates vulnerable populations, and critically threatens the global communities in an unprecedented way. While tremendous efforts at the frontline are placed on detecting the virus, providing treatments and developing vaccines, it is also critically important to examine the technologies and systems for tackling disease emergence, arresting its spread and especially the strategy for diseases prevention. The objective of this article is to review enabling technologies and systems with various application scenarios for handling the COVID-19 crisis. The article will focus specifically on 1) wearable devices suitable for monitoring the populations at risk and those in quarantine, both for evaluating the health status of caregivers and management personnel, and for facilitating triage processes for admission to hospitals; 2) unobtrusive sensing systems for detecting the disease and for monitoring patients with relatively mild symptoms whose clinical situation could suddenly worsen in improvised hospitals; and 3) telehealth technologies for the remote monitoring and diagnosis of COVID-19 and related diseases. Finally, further challenges and opportunities for future directions of development are highlighted.

The present and future of cough counting tools

The widespread use of cough counting tools has, to date, been limited by a reliance on human input to determine cough frequency. However, over the last two decades advances in digital technology and audio capture have reduced this dependence. As a result, cough frequency is increasingly recognised as a measurable parameter of respiratory disease. Cough frequency is now the gold standard primary endpoint for trials of new treatments for chronic cough, has been investigated as a marker of infectiousness in tuberculosis (TB), and used to demonstrate recovery in exacerbations of chronic obstructive pulmonary disease (COPD). This review discusses the principles of automatic cough detection and summarises key currently and recently used cough counting technology in clinical research. It additionally makes some predictions on future directions in the field based on recent developments. It seems likely that newer approaches to signal processing, the adoption of techniques from automatic speech recognition, and the widespread ownership of mobile devices will help drive forward the development of real-time fully automated ambulatory cough frequency monitoring over the coming years. These changes should allow cough counting systems to transition from their current status as a niche research tool in chronic cough to a much more widely applicable method for assessing, investigating and understanding respiratory disease.

Automatic Recognition, Segmentation, and Sex Assignment of Nocturnal Asthmatic Coughs and Cough Epochs in Smartphone Audio Recordings: Observational Field Study

Background

Asthma is one of the most prevalent chronic respiratory diseases. Despite increased investment in treatment, little progress has been made in the early recognition and treatment of asthma exacerbations over the last decade. Nocturnal cough monitoring may provide an opportunity to identify patients at risk for imminent exacerbations. Recently developed approaches enable smartphone-based cough monitoring. These approaches, however, have not undergone longitudinal overnight testing nor have they been specifically evaluated in the context of asthma. Also, the problem of distinguishing partner coughs from patient coughs when two or more people are sleeping in the same room using contact-free audio recordings remains unsolved.

Objective

The objective of this study was to evaluate the automatic recognition and segmentation of nocturnal asthmatic coughs and cough epochs in smartphone-based audio recordings that were collected in the field. We also aimed to distinguish partner coughs from patient coughs in contact-free audio recordings by classifying coughs based on sex.

Methods

We used a convolutional neural network model that we had developed in previous work for automated cough recognition. We further used techniques (such as ensemble learning, minibatch balancing, and thresholding) to address the imbalance in the data set. We evaluated the classifier in a classification task and a segmentation task. The cough-recognition classifier served as the basis for the cough-segmentation classifier from continuous audio recordings. We compared automated cough and cough-epoch counts to human-annotated cough and cough-epoch counts. We employed Gaussian mixture models to build a classifier for cough and cough-epoch signals based on sex.

Results

We recorded audio data from 94 adults with asthma (overall: mean 43 years; SD 16 years; female: 54/94, 57%; male 40/94, 43%). Audio data were recorded by each participant in their everyday environment using a smartphone placed next to their bed; recordings were made over a period of 28 nights. Out of 704,697 sounds, we identified 30,304 sounds as coughs. A total of 26,166 coughs occurred without a 2-second pause between coughs, yielding 8238 cough epochs. The ensemble classifier performed well with a Matthews correlation coefficient of 92% in a pure classification task and achieved comparable cough counts to that of human annotators in the segmentation of coughing. The count difference between automated and human-annotated coughs was a mean –0.1 (95% CI –12.11, 11.91) coughs. The count difference between automated and human-annotated cough epochs was a mean 0.24 (95% CI –3.67, 4.15) cough epochs. The Gaussian mixture model cough epoch–based sex classification performed best yielding an accuracy of 83%.

Conclusions

Our study showed longitudinal nocturnal cough and cough-epoch recognition from nightly recorded smartphone-based audio from adults with asthma. The model distinguishes partner cough from patient cough in contact-free recordings by identifying cough and cough-epoch signals that correspond to the sex of the patient. This research represents a step towards enabling passive and scalable cough monitoring for adults with asthma.

A Comparative Study of Features for Acoustic Cough Detection Using Deep Architectures

Automatic cough detection is key to tracking the condition of patients suffering from tuberculosis. We evaluate various acoustic features for performing cough detection using deep architectures. As most previous studies have adopted features designed for speech recognition, we assess the suitability of these techniques as well as their respective extraction parameters. Short-time Fourier transform (STFT), mel-frequency cepstral coefficients (MFCC) and mel-scaled filter banks (MFB) were evaluated using deep neural networks, convolutional neural networks and long-short term models. We find experimentally that, by regarding each cough sound as a single input feature instead of multiple shorter features, better performance can be achieved. Longer analysis windows also provide enhancement in contrast to the classic 25 ms frame. Although MFCC performance is improved by sinusoidal liftering, STFT and MFB lead to better results. Using MFB and the optimum segment and frame lengths, an improvement exceeding 7% in the area under the receiver operating characteristic curve across all classifiers is achieved.

A Wearable RF Sensor for Monitoring Respiratory Patterns

We present a non-invasive approach for continuous monitoring of respiration dynamics using a wearable radio-frequency (RF) sensor based on near-field coherent sensing. A continuous-wave RF signal at 1.8 GHz is generated by a software-defined radio, with both transmitter (Tx) and receiver (Rx) antennas placed close to the xiphoid process. The experimental prototype of the mobile sensor can modulate the internal organ motion in the near-field region of the Tx antenna and is then received by the nearby Rx antenna to be demodulated and sampled. Through peak detection, we have identified inhalation and exhalation peaks of each breath cycle to estimate the breath rate and the lung volume. The extracted respiratory parameters are compared with the conventional chest belts data for various simulated respiratory conditions including voluntary deep, fast-shallow and slow-shallow breathing. We also characterized simulated central sleep apneas, Cheyne-Stokes, Biot's, ataxic and coughing conditions. To accurately identify obstructive apnea, we presented a two-sensor approach that can capture paradoxical movement of thorax and abdomen. The on-line recognition of these respiratory patterns can be employed not only to continuously monitor patients with chronic respiratory disorders but also to provide real-time feedback for future therapeutic purposes.

Recognition of Activities of Daily Living and Environments Using Acoustic Sensors Embedded on Mobile Devices

The identification of Activities of Daily Living (ADL) is intrinsic with the user’s environment recognition. This detection can be executed through standard sensors present in every-day mobile devices. On the one hand, the main proposal is to recognize users’ environment and standing activities. On the other hand, these features are included in a framework for the ADL and environment identification. Therefore, this paper is divided into two parts—firstly, acoustic sensors are used for the collection of data towards the recognition of the environment and, secondly, the information of the environment recognized is fused with the information gathered by motion and magnetic sensors. The environment and ADL recognition are performed by pattern recognition techniques that aim for the development of a system, including data collection, processing, fusion and classification procedures. These classification techniques include distinctive types of Artificial Neural Networks (ANN), analyzing various implementations of ANN and choosing the most suitable for further inclusion in the following different stages of the developed system. The results present 85.89% accuracy using Deep Neural Networks (DNN) with normalized data for the ADL recognition and 86.50% accuracy using Feedforward Neural Networks (FNN) with non-normalized data for environment recognition. Furthermore, the tests conducted present 100% accuracy for standing activities recognition using DNN with normalized data, which is the most suited for the intended purpose.

A Machine Hearing System for Robust Cough Detection Based on a High-Level Representation of Band-Specific Audio Features

Cough is a protective reflex conveying information on the state of the respiratory system. Cough assessment has been limited so far to subjective measurement tools or uncomfortable (i.e., non-wearable) cough monitors. This limits the potential of real-time cough monitoring to improve respiratory care.

OBJECTIVE

This paper presents a machine hearing system for audio-based robust cough segmentation that can be easily deployed in mobile scenarios.

METHODS

Cough detection is performed in two steps. First, a short-term spectral feature set is separately computed in five predefined frequency bands: [0, 0.5), [0.5, 1), [1, 1.5), [1.5, 2), and [2, 5.5125] kHz. Feature selection and combination are then applied to make the short-term feature set robust enough in different noisy scenarios. Second, high-level data representation is achieved by computing the mean and standard deviation of short-term descriptors in 300 ms long-term frames. Finally, cough detection is carried out using a support vector machine trained with data from different noisy scenarios. The system is evaluated using a patient signal database which emulates three real-life scenarios in terms of noise content.

RESULTS

The system achieves 92.71% sensitivity, 88.58% specificity, and 90.69% Area Under Receiver Operating Charcteristic (ROC) curve (AUC), outperforming state-of-the-art methods.

CONCLUSION

Our research outcome paves the way to create a device for cough monitoring in real-life situations.

SIGNIFICANCE

Our proposal is aligned with a more comfortable and less disruptive patient monitoring, with benefits for patients (allows self-monitoring of cough symptoms), practitioners (e.g., assessment of treatments or better clinical understanding of cough patterns), and national health systems (by reducing hospitalizations).