Discrimination of productive and non-productive cough by sound analysis

Changes in Cough Airflow and Acoustics After Injection Laryngoplasty

OBJECTIVE/HYPOTHESIS

We explored the following hypotheses in a cohort of patients undergoing injection laryngoplasty: (1) glottic insufficiency affects voluntary cough airflow dynamics and restoring glottic competence may improve parameters of cough strength, (2) cough strength can be inferred from cough acoustic signal, and (3) glottic competence changes cough sounds and correlates with spectrogram morphology.

STUDY TYPE/DESIGN

Prospective interventional study.

METHODS

Subjects with glottic insufficiency secondary to unilateral vocal fold paresis, paralysis, or atrophy, and scheduled for injection laryngoplasty completed an instrumental assessment of voluntary cough airflow using a pneumotachometer and a protocolized voluntary cough sound recording. A Wilcoxon signed-rank test was used to compare the differences between pre- and post-injection laryngoplasty in airflow and acoustic measures. A Spearman rank-order correlation was used to evaluate the association between airflow and acoustic cough measures.

RESULTS

Twenty-five patients (13F:12M, mean age 68.8) completed voluntary cough airflow measurements and 22 completed cough sound recordings. Following injection laryngoplasty, patients had a statistically significant decreased peak expiratory flow rise time (PEFRT) (mean change: -0.03 s, SD: 0.06, p = 0.04) and increased cough volume acceleration (mean change: 13.1 L/s2 , SD: 33.9, p = 0.03), suggesting improved cough effectiveness. Correlation of cough acoustic measures with airflow measures showed a weak relationship between PEFRT and acoustic energy (coefficient: -0.31, p = 0.04) and peak power density (coefficient: -0.35, p = 0.02).

CONCLUSIONS

Our study thus indicates that injection laryngoplasty may help avert aspiration in patients with glottic insufficiency by improving cough effectiveness and that improved cough airflow measures may be tracked with cough sounds.

LEVEL OF EVIDENCE

3 Laryngoscope, 133:S1-S14, 2023.

An evolutionary ensemble learning for diagnosing COVID-19 via cough signals

Objective The spread of the COVID-19 disease has caused great concern around the world and detecting the positive cases is crucial in curbing the pandemic. One of the symptoms of the disease is the dry cough it causes. It has previously been shown that cough signals can be used to identify a variety of diseases including tuberculosis, asthma, etc. In this paper, we proposed an algorithm to diagnose via cough signals the COVID-19 disease. Methods The proposed algorithm is an ensemble scheme that consists of a number of base learners, where each base learner uses a different feature extractor method, including statistical approaches and convolutional neural networks (CNN) for automatic feature extraction. Features are extracted from the raw signal and some transforms performed it, including Fourier, wavelet, Hilbert-Huang, and short-term Fourier transforms. The outputs of these base-learners are aggregated via a weighted voting scheme, with the weights optimised via an evolutionary paradigm. This paper also proposes a memetic algorithm for training the CNNs in the base-learners, which combines the speed of gradient descent (GD) algorithms and global search space coverage of the evolutionary algorithms. Results Experiments were performed on the proposed algorithm and different rival algorithms which included a number of CNN architectures in the literature and generic machine learning algorithms. The results suggested that the proposed algorithm achieves better performance compared to the existing algorithms in diagnosing COVID-19 via cough signals. Conclusion This research showed that COVID-19 could be diagnosed via cough signals and CNNs could be employed to process these signals and it may be further improved by the optimization of CNN architecture.

Challenges and Opportunities of Deep Learning for Cough-Based COVID-19 Diagnosis: A Scoping Review

In the past two years, medical researchers and data scientists worldwide have focused their efforts on containing the pandemic of coronavirus disease 2019 (COVID-19). Deep learning models have been proven to be capable of efficient medical diagnosis and prognosis in cancer, common lung diseases, and COVID-19. On the other hand, artificial neural networks have demonstrated their potential in pattern recognition and classification in various domains, including healthcare. This literature review aims to report the state of research on developing neural network models to diagnose COVID-19 from cough sounds to create a cost-efficient and accessible testing tool in the fight against the pandemic. A total of 35 papers were included in this review following a screening of the 161 outputs of the literature search. We extracted information from articles on data resources, model structures, and evaluation metrics and then explored the scope of experimental studies and methodologies and analyzed their outcomes and limitations. We found that cough is a biomarker, and its associated information can determine an individual's health status. Convolutional neural networks were predominantly used, suggesting they are particularly suitable for feature extraction and classification. The reported accuracy values ranged from 73.1% to 98.5%. Moreover, the dataset sizes ranged from 16 to over 30,000 cough audio samples. Although deep learning is a promising prospect in identifying COVID-19, we identified a gap in the literature on research conducted over large and diversified data sets.

The Acoustic Dissection of Cough: Diving Into Machine Listening-based COVID-19 Analysis and Detection

OBJECTIVES

The coronavirus disease 2019 (COVID-19) has caused a crisis worldwide. Amounts of efforts have been made to prevent and control COVID-19's transmission, from early screenings to vaccinations and treatments. Recently, due to the spring up of many automatic disease recognition applications based on machine listening techniques, it would be fast and cheap to detect COVID-19 from recordings of cough, a key symptom of COVID-19. To date, knowledge of the acoustic characteristics of COVID-19 cough sounds is limited but would be essential for structuring effective and robust machine learning models. The present study aims to explore acoustic features for distinguishing COVID-19 positive individuals from COVID-19 negative ones based on their cough sounds.

METHODS

By applying conventional inferential statistics, we analyze the acoustic correlates of COVID-19 cough sounds based on the ComParE feature set, i.e., a standardized set of 6,373 acoustic higher-level features. Furthermore, we train automatic COVID-19 detection models with machine learning methods and explore the latent features by evaluating the contribution of all features to the COVID-19 status predictions.

RESULTS

The experimental results demonstrate that a set of acoustic parameters of cough sounds, e.g., statistical functionals of the root mean square energy and Mel-frequency cepstral coefficients, bear essential acoustic information in terms of effect sizes for the differentiation between COVID-19 positive and COVID-19 negative cough samples. Our general automatic COVID-19 detection model performs significantly above chance level, i.e., at an unweighted average recall (UAR) of 0.632, on a data set consisting of 1,411 cough samples (COVID-19 positive/negative: 210/1,201).

CONCLUSIONS

Based on the acoustic correlates analysis on the ComParE feature set and the feature analysis in the effective COVID-19 detection approach, we find that several acoustic features that show higher effects in conventional group difference testing are also higher weighted in the machine learning models.

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.

Diagnosis of Pneumonia by Cough Sounds Analyzed with Statistical Features and AI

Pneumonia is a serious disease often accompanied by complications, sometimes leading to death. Unfortunately, diagnosis of pneumonia is frequently delayed until physical and radiologic examinations are performed. Diagnosing pneumonia with cough sounds would be advantageous as a non-invasive test that could be performed outside a hospital. We aimed to develop an artificial intelligence (AI)-based pneumonia diagnostic algorithm. We collected cough sounds from thirty adult patients with pneumonia or the other causative diseases of cough. To quantify the cough sounds, loudness and energy ratio were used to represent the level and its spectral variations. These two features were used for constructing the diagnostic algorithm. To estimate the performance of developed algorithm, we assessed the diagnostic accuracy by comparing with the diagnosis by pulmonologists based on cough sound alone. The algorithm showed 90.0% sensitivity, 78.6% specificity and 84.9% overall accuracy for the 70 cases of cough sound in pneumonia group and 56 cases in non-pneumonia group. For same cases, pulmonologists correctly diagnosed the cough sounds with 56.4% accuracy. These findings showed that the proposed AI algorithm has value as an effective assistant technology to diagnose adult pneumonia patients with significant reliability.

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.

End-to-End AI-Based Point-of-Care Diagnosis System for Classifying Respiratory Illnesses and Early Detection of COVID-19: A Theoretical Framework

Respiratory symptoms can be caused by different underlying conditions, and are often caused by viral infections, such as Influenza-like illnesses or other emerging viruses like the Coronavirus. These respiratory viruses, often, have common symptoms: coughing, high temperature, congested nose, and difficulty breathing. However, early diagnosis of the type of the virus, can be crucial, especially in cases, such as the COVID-19 pandemic. Among the factors that contributed to the spread of the COVID-19 pandemic were the late diagnosis or misinterpretation of COVID-19 symptoms as regular flu-like symptoms. Research has shown that one of the possible differentiators of the underlying causes of different respiratory diseases could be the cough sound, which comes in different types and forms. A reliable lab-free tool for early and accurate diagnosis, which can differentiate between different respiratory diseases is therefore very much needed, particularly during the current pandemic. This concept paper discusses a medical hypothesis of an end-to-end portable system that can record data from patients with symptoms, including coughs (voluntary or involuntary) and translate them into health data for diagnosis, and with the aid of machine learning, classify them into different respiratory illnesses, including COVID-19. With the ongoing efforts to stop the spread of the COVID-19 disease everywhere today, and against similar diseases in the future, our proposed low cost and user-friendly theoretical solution could play an important part in the early diagnosis.

Noncontact Sensing of Contagion

The World Health Organization (WHO) has declared COVID-19 a pandemic. We review and reduce the clinical literature on diagnosis of COVID-19 through symptoms that might be remotely detected as of early May 2020. Vital signs associated with respiratory distress and fever, coughing, and visible infections have been reported. Fever screening by temperature monitoring is currently popular. However, improved noncontact detection is sought. Vital signs including heart rate and respiratory rate are affected by the condition. Cough, fatigue, and visible infections are also reported as common symptoms. There are non-contact methods for measuring vital signs remotely that have been shown to have acceptable accuracy, reliability, and practicality in some settings. Each has its pros and cons and may perform well in some challenges but be inadequate in others. Our review shows that visible spectrum and thermal spectrum cameras offer the best options for truly noncontact sensing of those studied to date, thermal cameras due to their potential to measure all likely symptoms on a single camera, especially temperature, and video cameras due to their availability, cost, adaptability, and compatibility. Substantial supply chain disruptions during the pandemic and the widespread nature of the problem means that cost-effectiveness and availability are important considerations.

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.

Global Physiology and Pathophysiology of Cough: Part 1: Cough Phenomenology - CHEST Guideline and Expert Panel Report

The purpose of this state-of-the-art review is to update the American College of Chest Physicians 2006 guideline on global physiology and pathophysiology of cough. A review of the literature was conducted using PubMed and MEDLINE databases from 1951 to 2019 and using prespecified search terms. We describe the basic phenomenology of cough patterns, behaviors, and morphological features. We update the understanding of mechanical and physiological characteristics of cough, adding a contemporary view of the types of cough and their associated behaviors and sensations. New information about acoustic characteristics is presented, and recent insights into cough triggers and the patient cough hypersensitivity phenotype are explored. Lastly, because the clinical assessment of patients largely focuses on the duration rather than morphological features of cough, we review the morphological features of cough that can be measured in the clinic. This is the first of a two-part update to the American College of Chest Physicians 2006 cough guideline; it provides a more global consideration of cough phenomenology, beyond simply the mechanical aspects of a cough. A greater understanding of the typical features of cough, and their variations, may allow a more informed interpretation of cough measurements and the clinical relevance for patients.

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.

Asthmatic versus healthy child classification based on cough and vocalised /ɑ:/ sounds

Cough is a common symptom presenting in asthmatic children. In this investigation, an audio-based classification model is presented that can differentiate between healthy and asthmatic children, based on the combination of cough and vocalised /ɑ:/ sounds. A Gaussian mixture model using mel-frequency cepstral coefficients and constant-Q cepstral coefficients was trained. When comparing the predicted labels with the clinician's diagnosis, this cough sound model reaches an overall accuracy of 95.3%. The vocalised /ɑ:/ model reaches an accuracy of 72.2%, which is still significant because the dataset contains only 333 /ɑ:/ sounds versus 2029 cough sounds.

Comparison of Silence Removal Methods for the Identification of Audio Cough Events

Sensing technologies are embedded in our everyday lives. Smart homes typically use an Audio Virtual Assistant (AVA) (e.g. Alexa, Siri, and Google Home) interface that collects sensor information, which can provide security, assist in everyday activities and monitor health related information. One such measure is cough, changes of which can be a marker of worsening conditions for many respiratory diseases. Creating a reliable monitoring system utilizing technology that may already be present in the home (i.e. AVA) may provide an opportunity for early intervention and reductions in the number of long-term hospitalizations. This paper focuses on the optimization of the silence removal and segmentation step in an at home setting with low to moderate background noise to identify cough events. Three commonly used methods (Standard deviation (SD), Short-term Energy (SE), Zero-crossing rate (ZCR)) were compared to manual segmentations. Each method was applied to 209 audio files that were manually verified to contain at least one cough event and the average segmentation accuracy, over segmentation and under segmentation results were compared. The ZCR method had the highest accuracy (89%); however, it completely failed under moderate noise conditions. The SD method had the best combination of accuracy (86%), ability to perform under noisy conditions and low prevalence of over and under segmentation (22% and 15% respectively). Therefore, we recommend using an adaptive approach to silence removal among cough events based on the level of background noise (i.e use the ZCR method when the background noise is low and the SD method when it is higher) prior to implementation of a cough classification system.

Continuous Sound Collection Using Smartphones and Machine Learning to Measure Cough

Background

Despite the efforts of research groups to develop and implement at least partial automation, cough counting remains impractical. Analysis of 24-h cough frequency is an established regulatory endpoint which, if addressed in an automated manner, has the potential to ease cough symptom evaluation over multiple 24-h periods in a patient-centric way, supporting the development of novel treatments for chronic cough, an unmet clinical need.

Objectives

In light of recent technological advancements, we propose a system based on the use of smartphones for objective continuous sound collection, suitable for automated cough detection and analysis. Two capabilities were identified as necessary for naturalistic cough assessment: (1) recording sound in a continuous manner (sound collection), and (2) detection of coughs from the recorded sound (cough detection).

Methods

This work did not involve any human subject testing or trials. For sound collection, we designed, built, and verified technical parameters of a smartphone application for sound collection. Our cough detection work describes the development of a mathematical model for sound analysis and cough identification. Performance of the model was compared to previously published results of commercially available solutions and to human raters. The compared solutions use the following methods to automatically or semi-automatically assess cough: 24-h sound recording with an ambulatory device with multiple microphones, automatic silence removal, and manual recording review for cough count.

Results

Sound collection: the application demonstrated the ability to continuously record sounds using the phone's internal microphone; the technical verification informed the configuration of the technical and user experience parameters. Cough detection: our cough recognition sensitivity to cough as determined by human listeners was 90 at 99.5% specificity preset and 75 at 99.9% specificity preset for a dataset created from publicly available data.

Conclusions

Sound collection: the application reliably collects sound data and uploads them securely to a remote server for subsequent analysis; the developed sound data collection application is a critical first step toward future incorporation in clinical trials. Cough detection: initial experiments with cough detection techniques yielded encouraging results for application to patient-collected data from future studies.

Development of Machine Learning for Asthmatic and Healthy Voluntary Cough Sounds: A Proof of Concept Study

(1) Background: Cough is a major presentation in childhood asthma. Here, we aim to develop a machine-learning based cough sound classifier for asthmatic and healthy children. (2) Methods: Children less than 16 years old were randomly recruited in a Children’s Hospital, from February 2017 to April 2018, and were divided into 2 cohorts—healthy children and children with acute asthma presenting with cough. Children with other concurrent respiratory conditions were excluded in the asthmatic cohort. Demographic data, duration of cough, and history of respiratory status were obtained. Children were instructed to produce voluntary cough sounds. These clinically labeled cough sounds were randomly divided into training and testing sets. Audio features such as Mel-Frequency Cepstral Coefficients and Constant-Q Cepstral Coefficients were extracted. Using a training set, a classification model was developed with Gaussian Mixture Model–Universal Background Model (GMM-UBM). Its predictive performance was tested using the test set against the physicians’ labels. (3) Results: Asthmatic cough sounds from 89 children (totaling 1192 cough sounds) and healthy coughs from 89 children (totaling 1140 cough sounds) were analyzed. The sensitivity and specificity of the audio-based classification model was 82.81% and 84.76%, respectively, when differentiating coughs from asthmatic children versus coughs from ‘healthy’ children. (4) Conclusion: Audio-based classification using machine learning is a potentially useful technique in assisting the differentiation of asthmatic cough sounds from healthy voluntary cough sounds in children.

An advanced recording and analysis system for the differentiation of guinea pig cough responses to citric acid and prostaglandin E2 in real time

Cough number and/or sound have been used to assess cough sensitivity/intensity and to discriminate cough patterns in clinical settings. However, to date, only manual counting of cough number in an offline manner is applied in animal cough studies, which diminishes the efficiency of cough identification and hinders the diagnostic discrimination of cough patterns, especially in animals with pulmonary diseases. This study aims to validate a novel recording/analysis system by which cough numbers are automatically counted and cough patterns are comprehensively differentiated in real time. The experiment was carried out in conscious guinea pigs exposed to aerosolized citric acid (CA, 150 mM) and prostaglandin E2 (PGE2, 0.43 mM). Animal body posture (video), respiratory flow, and cough acoustics (audio) were simultaneously monitored and recorded. Cough number was counted automatically, and cough sound parameters including waveform, duration, power spectral density, spectrogram, and intensity, were analyzed in real time. Our results showed that CA- and PGE2-evoked coughs had the same cough numbers but completely different patterns [individual coughs vs. bout(s) of coughs]. Compared to CA-evoked coughs, PGE2-evoked coughs possess a longer latency, higher cough rate (coughs/min), shorter cough sound duration, lower cough sound intensity, and distinct cough waveforms and spectrograms. A few mucus- and wheeze-like coughs were noted in response to CA but not to PGE2. In conclusion, our recording/analysis system is capable of automatically counting the cough number and successfully differentiating the cough pattern by using valuable cough sound indexes in real time. Our system enhances the objectivity, accuracy, and efficiency of cough identification and count, improves the intensity evaluation, and offers ability for pattern discrimination compared to traditional types of cough identification. Importantly, this approach is beneficial for assessing the efficacy of putative antitussive drugs in animals without or with pulmonary diseases, particularly in cases without significant change in cough number.

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.

Automated algorithm for Wet/Dry cough sounds classification

Cough is the most common symptom of several respiratory diseases. It is a defense mechanism of the body to clear the respiratory tract from foreign materials inhaled accidentally or produced internally by infections. The identification of wet and dry cough is an important clinical finding, aiding in the differential diagnosis. Wet coughs are more likely to be associated with bacterial infections. At present, the wet/dry decision is based on the subjective judgment of a physician, during a typical consultation session. It is not available for long term monitoring or in the assessment of treatment efficacy. In this paper we address these issues and develop fully automated technology to classify cough into 'Wet' and 'Dry' categories. We propose novel features and a Logistic regression-based model for the classification of coughs into wet/dry classes. The performance of the method was evaluated on a clinical database of pediatric and adult coughs recorded using a bed-side non-contact microphone. The sensitivity and specificity of the classification were obtained as 79±9% and 72.7±8.7% respectively. These indicate the potential of the method as a useful clinical tool for cough monitoring, especially at home settings.

High frequency analysis of cough sounds in pediatric patients with respiratory diseases

Cough is a common symptom in a range of respiratory diseases and is considered a natural defense mechanism of the body. Despite its critical importance in the diagnosis of illness, there are no golden methods to objectively assess cough. In a typical consultation session, a physician may briefly listen to the cough sounds using a stethoscope placed against the chest. The physician may also listen to spontaneous cough sounds via naked ears, as they naturally propagate through air. Cough sounds carry vital information on the state of the respiratory system but the field of cough analysis in clinical medicine is in its infancy. All existing cough analysis approaches are severely handicapped by the limitations of the human hearing range and simplified analysis techniques. In this paper, we address these problems, and explore the use of frequencies covering a range well beyond the human perception (up to 90 kHz) and use wavelet analysis to extract diagnostically important information from coughs. Our data set comes from a pediatric respiratory ward in Indonesia, from subjects diagnosed with asthma, pneumonia and rhinopharyngitis. We analyzed over 90 cough samples from 4 patients and explored if high frequencies carried useful information in separating these disease groups. Multiple regression analysis resulted in coefficients of determination (R(2)) of 77-82% at high frequencies (15 kHz-90 kHz) indicating that they carry useful information. When the high frequencies were combined with frequencies below 15kHz, the R(2) performance increased to 85-90%.

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.

A system for recording high fidelity cough sound and airflow characteristics

Cough is considered an early sign of many respiratory diseases. Recently, there has been increased interest in measuring, analyzing, and characterizing the acoustical properties of a cough. In most cases the main focus of those studies was to distinguish between involuntary coughs and ambient sounds over a specified time period. The objective of this study was to develop a system to measure high fidelity voluntary cough sounds to detect lung diseases. To further augment the analysis capability of the system, a non-invasive flow measurement was also incorporated into the design. One of the main design considerations was to increase the fidelity of the recorded sound characteristics by increasing the signal to noise ratio of cough sounds and to minimize acoustical reflections from the environment. To accomplish this goal, a system was designed with a mouthpiece connected to a cylindrical tube. A microphone was attached near the mouthpiece so that its diaphragm was tangent to the inner surface of the cylinder. A pneumotach at the end of the tube measured the airflow generated by the cough. The system was terminated with an exponential horn to minimize sound reflections. Custom software was developed to read, process, display, record, and analyze cough sound and airflow characteristics. The system was optimized by comparing acoustical reflections and total signal to background noise ratios across different designs. Cough measurements were also collected from volunteer subjects to assess the viability of the system. Results indicate that analysis of cough characteristics has the potential to detect lung disease.

Chronic wet cough: Protracted bronchitis, chronic suppurative lung disease and bronchiectasis

The role of persistent and recurrent bacterial infection of the conducting airways (endobronchial infection) in the causation of chronic respiratory symptoms, particularly chronic wet cough, has received very little attention over recent decades other than in the context of cystic fibrosis (CF). This is probably related (at least in part) to the (a) reduction in non-CF bronchiectasis in affluent countries and, (b) intense focus on asthma. In addition failure to characterize endobronchial infections has led to under-recognition and lack of research. The following article describes our current perspective of inter-related endobronchial infections causing chronic wet cough; persistent bacterial bronchitis (PBB), chronic suppurative lung disease (CSLD) and bronchiectasis. In all three conditions, impaired muco-ciliary clearance seems to be the common risk factor that provides organisms the opportunity to colonize the lower airway. Respiratory infections in early childhood would appear to be the most common initiating event but other conditions (e.g., tracheobronchomalacia, neuromuscular disease) increases the risk of bacterial colonization. Clinically these conditions overlap and the eventual diagnosis is evident only with further investigations and long term follow up. However whether these conditions are different conditions or reflect severity as part of a spectrum is yet to be determined. Also misdiagnosis of asthma is common and the diagnostic process is further complicated by the fact that the co-existence of asthma is not uncommon. The principles of managing PBB, CSLD and bronchiectasis are the same. Further work is required to improve recognition, diagnosis and management of these causes of chronic wet cough in children.

New developments in the objective assessment of cough

A variety of different methods are available for measuring cough. In clinical practice and most clinical trials subjective reporting of cough is relied upon, using scoring systems or visual analog scores (VAS). Although these measures give an indication of patients' perceptions of the severity of the symptom, they may be unreliable because they are influenced by other factors such as mood, vigilance, and expectations. An objective measure of cough would therefore be a valuable tool. In the last decade advances in computer technology and the availability of portable digital sound recording devices have resulted in a resurgence of interest in developing ambulatory systems for recording cough. The ultimate goal is an automated detection system of use in the wide variety of conditions that cause cough. Multidisciplinary teams of researchers around the world are applying techniques such as neural networks, voice recognition models, and other signal processing techniques to this problem. The main challenge is achieving high sensitivity with good discrimination of noncough signals. For cough sound detection, this is confounded by both the variability of the acoustics of cough sounds within and between individuals and the amount and variety of speech sounds that must be discriminated. Significant progress is being made and it is likely that accurate automated objective monitoring systems will be available in the near future. These systems have the potential to change the way cough is measured in clinical practice and clinical trials, allowing a better understanding of the effect of existing and novel treatments on this troublesome symptom.

Ambulatory methods for recording cough

Recording cough sounds to objectively quantify coughing was first performed using large reel-to-reel tape recorders more than 40 years ago. Coughs were counted manually, which is an extremely laborious and time-consuming process. Current technologies including digital recording techniques, data compression and improvements in digital storage capacity should make the process of recording and counting coughs suitable for automation; however, to date no accurate, objective cough monitoring device is available. Cough sounds are easily distinguishable from other vocalizations by the human ear and hence it is reasonable to assume that coughs sounds should have characteristic, identifying acoustic properties. However, the acoustic features of spontaneously occurring cough sounds are extremely variable. Furthermore, in even the worst cases of cough, the time spent speaking is an order of magnitude greater than the time spent coughing. It follows that even an algorithm that mistakes only a very small proportion of speech as cough will still have an unacceptable false positive rate. There is a clear need for an objective measure of cough for use in clinical practice, clinical research and trials of novel treatments. In the near future automated ambulatory systems with sufficient accuracy to be of clinical use should be available.

The description of cough sounds by healthcare professionals

BACKGROUND

Little is known of the language healthcare professionals use to describe cough sounds. We aimed to examine how they describe cough sounds and to assess whether these descriptions suggested they appreciate the basic sound qualities (as assessed by acoustic analysis) and the underlying diagnosis of the patient coughing.

METHODS

53 health professionals from two large respiratory tertiary referral centres were recruited; 22 doctors and 31 staff from professions allied to medicine. Participants listened to 9 sequences of spontaneous cough sounds from common respiratory diseases. For each cough they selected patient gender, the most appropriate descriptors and a diagnosis. Cluster analysis was performed to assess which cough sounds attracted similar descriptions.

RESULTS

Gender was correctly identified in 93% of cases. The presence or absence of mucus was correct in 76.1% and wheeze in 39.3% of cases. However, identifying clinical diagnosis from cough was poor at 34.0%. Cluster analysis showed coughs with the same acoustics properties rather than the same diagnoses attracted the same descriptions.

CONCLUSION

These results suggest that healthcare professionals can recognise some of the qualities of cough sounds but are poor at making diagnoses from them. It remains to be seen whether in the future cough sound acoustics will provide useful clinical information and whether their study will lead to the development of useful new outcome measures in cough monitoring.

New non-invasive automatic cough counting program based on 6 types of classified cough sounds

Cough consisting of an initial deep inspiration, glottal closure, and an explosive expiration accompanied by a sound is one of the most common symptoms of respiratory disease. Despite its clinical importance, standard methods for objective cough analysis have yet to be established.

OBJECT

We investigated the characteristics of cough sounds acoustically, designed a program to discriminate cough sounds from other sounds, and finally developed a new objective method of non-invasive cough counting. In addition, we evaluated the clinical efficacy of that program.

SUBJECTS AND METHODS

We recorded cough sounds using a memory stick IC recorder in free-field from 2 patients and analyzed the intensity of 534 recorded coughs acoustically according to time domain. First we squared the sound waveform of recorded cough sounds, which was then smoothed out over a 20 ms window. The 5 parameters and some definitions to discriminate the cough sounds from other noise were identified and the cough sounds were classified into 6 groups. Next, we applied this method to develop a new automatic cough count program. Finally, to evaluate the accuracy and clinical usefulness of this program, we counted cough sounds collected from another 10 patients using our program and conventional manual counting. And the sensitivity, specificity and discriminative rate of the program were analyzed.

RESULTS

This program successfully discriminated recorded cough sounds out of 1902 sound events collected from 10 patients at a rate of 93.1%. The sensitivity was 90.2% and the specificity was 96.5%.

CONCLUSION

Our new cough counting program can be sufficiently useful for clinical studies.

Cough quality in children: a comparison of subjective vs. bronchoscopic findings

Background

Cough is the most common symptom presenting to doctors. The quality of cough (productive or wet vs dry) is used clinically as well as in epidemiology and clinical research. There is however no data on the validity of cough quality descriptors. The study aims were to compare (1) cough quality (wet/dry and brassy/non-brassy) to bronchoscopic findings of secretions and tracheomalacia respectively and, (2) parent's vs clinician's evaluation of the cough quality (wet/dry).

Methods

Cough quality of children (without a known underlying respiratory disease) undergoing elective bronchoscopy was independently evaluated by clinicians and parents. A 'blinded' clinician scored the secretions seen at bronchoscopy on pre-determined criteria and graded (1 to 6). Kappa (K) statistics was used for agreement, and inter-rater and intra-rater agreement examined on digitally recorded cough. A receiver operating characteristic (ROC) curve was used to determine if cough quality related to amount of airway secretions present at bronchoscopy.

Results

Median age of the 106 children (62 boys, 44 girls) enrolled was 2.6 years (IQR 5.7). Parent's assessment of cough quality (wet/dry) agreed with clinicians' (K = 0.75, 95%CI 0.58–0.93). When compared to bronchoscopy (bronchoscopic secretion grade 4), clinicians' cough assessment had the highest sensitivity (0.75) and specificity (0.79) and were marginally better than parent(s). The area under the ROC curve was 0.85 (95%CI 0.77–0.92). Intra-observer (K = 1.0) and inter-clinician agreement for wet/dry cough (K = 0.88, 95%CI 0.82–0.94) was very good. Weighted K for inter-rater agreement for bronchoscopic secretion grades was 0.95 (95%CI 0.87–1). Sensitivity and specificity for brassy cough (for tracheomalacia) were 0.57 and 0.81 respectively. K for both intra and inter-observer clinician agreement for brassy cough was 0.79 (95%CI 0.73–0.86).

Conclusions

Dry and wet cough in children, as determined by clinicians and parents has good clinical validity. Clinicians should however be cognisant that children with dry cough may have minimal to mild airway secretions. Brassy cough determined by respiratory physicians is highly specific for tracheomalacia.

Assessing the sound of cough towards vocality

The presented research positively evaluates the vocality of the cough sound by estimating the global cough fundamental frequency or pitch. The fundamental frequency was determined by autocorrelation analysis on both the original time-signal and the linear predicted time-signal. The experimental cough database was registered in the free acoustical field on respectively three pathological and nine healthy non-smoking human subjects and on two pathological and two healthy Belgian Landrace piglets. For both species differences between pitch values for cough-sounds originating from subjects suffering from a respiratory infection and healthy subjects are put forward. The retrieved pitch-difference between respectively healthy and infected subjects indicates the existence of acoustically different cough-classes in accordance with a different cause or physical condition of the respiratory system.

Automated recognition of spontaneous versus voluntary cough

Cough or cough epochs may be an important and persistent symptom in many respiratory diseases requiring both a continuous and objective observation. The research presented in this paper is aimed at assessing a blind data-based classification between 'spontaneous' and 'voluntary' human cough on individual sound samples. Cough sounds were registered in the free acoustic field on 3 pathological and 9 healthy non-smoking subjects, all aged between 20 and 30. Each sound is represented by the normalized power spectral density (PSD). Different transformations of the cough PSD-vector are chosen as input features to the classification algorithm. An experimental error rate comparison between different neural and fuzzy classification networks is performed. All evaluated algorithms used the Euclidean metric. This resulted in a correct class-discrimination between 'spontaneous' and 'voluntary' cough for 96% of the cough database.