A system for recording high fidelity cough sound and airflow characteristics

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.

A Passive Smart Face Mask for Wireless Cough Monitoring: A Harmonic Detection Scheme With Clutter Rejection

Cough detection has aroused great interest because the assessment of cough frequency may improve diagnosis accuracy for dealing with several diseases, such as chronic obstructive pulmonary disease (COPD) and the recent COVID-19 global pandemic crisis. Here, we propose and experimentally demonstrate a wireless smart face mask based on a passive harmonic tag for real-time cough monitoring and alert. Our results show that the cough events can be successfully monitored through non-contact track of the received signal strength indicator (RSSI) at the harmonic frequency. Owing to the frequency orthogonality between the launched and backscattered radio-frequency (RF) signals, the harmonic tag-based smart mask can well suppress the electromagnetic interferences, such as clutters and crosstalks in noisy environments. We envision that this zero-power and lightweight wireless wearable device may be beneficial for cough monitoring and the public health condition in terms of tracking potential contagious person and virus-transmissive events.

Sound: a non-invasive measure of cough intensity

Introduction

Cough intensity is an important determinant of cough severity reported by patients. Cough sound analysis has been widely validated for the measurement of cough frequency but few studies have validated its use in the assessment of cough strength. We investigated the relationship between cough sound and physiological measures of cough strength.

Methods

32 patients with chronic cough and controls underwent contemporaneous measurements of voluntary cough sound, flow and oesophageal pressure. Sound power, peak energy, rise-time, duration, peak-frequency, bandwidth and centroid-frequency were assessed and compared with physiological measures. The relationship between sound and subjective cough strength Visual Analogue Score (VAS), the repeatability of cough sounds and the effect of microphone position were also assessed.

Results

Sound power and energy correlated strongly with cough flow (median Spearman’s r=0.87–0.88) and oesophageal pressure (median Spearman’s r=0.89). Sound power and energy correlated strongly with cough strength VAS (median Spearman’s r=0.84–0.86) and were highly repeatable (intraclass correlation coefficient=0.93–0.94) but both were affected by change in microphone position.

Conclusions

Cough sound power and energy correlate strongly with physiological measures and subjective perception of cough strength. Power and energy are highly repeatable measures but the microphone position should be standardised. Our findings support the use of cough sound as an index of cough strength.

Classification of voluntary cough airflow patterns for prediction of abnormal spirometry

Measurement of partial expiratory flow-volume curves has become an important technique in diagnosing lung disease, particularly in children and in the elderly. The objective of this study was to investigate the feasibility of predicting abnormal spirometry using the partial flow-volume curve generated during a voluntary cough. Here, abnormal spirometry is defined as less than the lower limit of normal (LLN) predicted by standard reference equations [1]. Cough airflow signals of 107 subjects (56 male, 51 female) were previously collected [2] from patients performing spirometry in a pulmonary function clinic. A variety of features were extracted from the airflow signal. A support vector machine (SVM) classifier was developed to predict abnormal spirometry. Airflow signal features and SVM parameters were selected using a genetic algorithm. The ability of the classifier to distinguish between normal and abnormal spirometry based on cough flow was evaluated by comparing the classifiers decisions with the LLN for the given subject's spirometry, including forced expiratory volume in one second (FEV1), forced vital capacity (FV C), and their ratio (FEV1=FV C%). Findings indicated that it was possible to classify patients whose spirometry results were less than the LLN with an overall accuracy of 76% for FEV1, 65% for FV C, and 76% for the ratio FEV1=FV C%. Accuracies were determined by repeated double cross-validation [3]. This study demonstrates the potential of using airflow measured during voluntary coughing to identify test subjects with abnormal spirometry.

Monitoring chronic cough: current and future techniques

Coughing produces a characteristic sound that is readily recognized by the human ear and provides the opportunity to objectively quantify coughing through acoustic recordings. The development of digital recording technologies has facilitated such recordings over the extended time periods needed to capture symptom episodes. However, the manual counting of coughs by listening to long recordings is time-consuming, laborious and restricts usage to research studies. This article outlines the challenges in recording, analyzing and quantifying cough sounds and describes the systems under development. Progress is being made towards automated algorithms to identify and count cough sounds; however, current systems have only been tested over short time periods and in limited patient groups. Further work is required to achieve broadly applicable accurate cough monitoring systems.

Classification of voluntary cough sound and airflow patterns for detecting abnormal pulmonary function

Background

Involuntary cough is a classic symptom of many respiratory diseases. The act of coughing serves a variety of functions such as clearing the airways in response to respiratory irritants or aspiration of foreign materials. It has been pointed out that a cough results in substantial stresses on the body which makes voluntary cough a useful tool in physical diagnosis.

Methods

In the present study, fifty-two normal subjects and sixty subjects with either obstructive or restrictive lung disorders were asked to perform three individual voluntary coughs. The objective of the study was to evaluate if the airflow and sound characteristics of a voluntary cough could be used to distinguish between normal subjects and subjects with lung disease. This was done by extracting a variety of features from both the cough airflow and acoustic characteristics and then using a classifier that applied a reconstruction algorithm based on principal component analysis.

Results

Results showed that the proposed method for analyzing voluntary coughs was capable of achieving an overall classification performance of 94% and 97% for identifying abnormal lung physiology in female and male subjects, respectively. An ROC analysis showed that the sensitivity and specificity of the cough parameter analysis methods were equal at 98% and 98% respectively, for the same groups of subjects.

Conclusion

A novel system for classifying coughs has been developed. This automated classification system is capable of accurately detecting abnormal lung function based on the combination of the airflow and acoustic properties of voluntary cough.