Cough is an essential defensive reflex of the respiratory tract to clear material from the airway, characterized by the precise coordination of laryngeal, abdominal and chest wall muscles, under the control of medullary and higher cortical regions of the brain.^1,2 Cough is associated with a characteristic sound, reflecting a specific pattern of airway vibration carrying critical information on the condition of the respiratory system.^3,4 Cough sounds are unique and recognizable acoustic events. They have a timbre associated with particular disease states, which have been used by clinicians for diagnostic purposes for centuries. However these characteristic acoustic biomarkers have not yet been translated into applied screening or diagnostic technology and are currently limited to diagnostic clues observed by clinicians.⁵ Objective measures and automated analyses of cough sounds could transform the screening, diagnosis, and management of many diseases treated by otolaryngologists, pulmonologists, and other health professionals.

The current ubiquitous availability of audio recording hardware via cellular technology and exponential advances in data analytics via deep learning have created unprecedented opportunities for collecting and analyzing cough sounds. Several promising results have been published in the distinction of normal and abnormal cough sounds,⁶ screening of disease such as COVID-19,^7–9 and prediction of disease severity in common respiratory conditions like asthma,¹⁰ chronic bronchitis,¹¹ and croup.^12,13 Diagnosis of respiratory conditions in low-resource regions is fraught with difficulties due to the lack of field-deployable imaging and laboratory facilities, as well as the scarcity of trained community healthcare workers, and cough sound biomarkers could help improve screening and diagnostics in such settings.

However, there is currently no consensus on protocols for cough sounds recording and analysis to guide research teams and aid clinical translation. This scoping review is presented to overview the existing literature and propose priorities for future research, including data collection standards. The primary objective is to survey the bioacoustics literature on cough sounds to highlight data collection protocols, acoustic analysis approaches, analytical accuracy, and limitations. The secondary objectives are to determine which disciplines and regions are active in cough sound analytics, which diagnoses and populations are a focus, and whether existing cough sound databases are open-access and represent a diverse population. Of note, we included animal studies, as use of cough sounds for livestock respiratory illness monitoring is a rich field of study in veterinary sciences with little reference in the medical literature, despite significant scientific overlap.

The protocol was executed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist.¹⁴ The review protocol, including the objectives, inclusion criteria, and methods were specified in advance and made available on September 3rd, 2021 under the Open Science Framework platform (https://osf.io/gh9t2/).

The literature search yielded 433 unique results after deduplication. Following title and abstract screening, 183 studies were excluded. Ultimately, 108 studies underwent a full-text review. The reviewer agreement was moderate for title and abstract screening (Cohen’s kappa=0.53) and high for full-text review (Cohen’s kappa=0.95). The PRISMA flow diagram outlining the study selection process is delineated in Figure 1. Citations for all included studies is available in Supporting Information, Appendix 3. A summary of all included studies can be found in Table S1.

This scoping review aimed to examine the utilization of cough sounds in screening and diagnostics. The number of publications analyzing cough acoustics over the past decade has been rapidly increasing, which possibly reflects technological advances in recording capabilities – especially with the ubiquitous availability of smartphone recording technology and improved aptitude for analysis of complex data with the advent of machine learning. Our study is the first scoping review of the cough sound literature that is agnostic to the analytic methodology. Other unique features of our scoping review is the incorporation of adult, pediatric, and animal studies, surveying the field broadly across disciplines. This is in contrast with Serrurier et al.,¹⁸ who performed a comparative literature review of the cough sound literature without standardized methodology. In addition, our publication includes studies from veterinary science, in which many cough analysis advances have been made in recent years that could be relevant to human health. We also specifically categorized the human literature into adult and pediatric studies.

The authors found the primary objective of the plurality of studies was diagnostic classification of respiratory illnesses such as COVID-19, COPD, asthma and tuberculosis in the adult study population, and asthma, pneumonia, pertussis and croup in the pediatric population. The number of studies focused on cough detection followed closely. Less commonly, some studies focused on classifying cough sounds based on character and severity.

Recording protocols were highly variable and had several limitations. For instance, though most studies relied on reflexive cough sounds, among those that collected voluntary cough sounds, only a fraction of studies described the specific instructions for subjects to produce cough. Though the presence of background noise may affect the quality of cough sound collection, only a minority of studies utilized a completely noise-free room and nearly a quarter of the studies (23%) did not specify the recording environment.

With respect to hardware for cough sound acquisition, we found the majority of studies used an external microphone. The type of external microphones varied with the aim of the study. For example, cough counting studies often used lapel microphones to ensure comfort of human subjects during continuous data collection. In contrast, studies focused on the quality of the cough sounds were more likely to use contact microphones to reduce noise by deriving audio signals directly from mechanical vibrations transmitted from the point of physical contact with the subject. Contact microphones may pick up otherwise inaudible acoustic data, potentially improving differentiation of cough sound types.

In recent years, cellular phones, with their wide availability and rapidly evolving hardware and software sophistication, have quickly replaced individual condenser microphones and associated recorder via non-contact recording. The use of smartphones was first noted in 2011, becoming more predominant from 2016 onwards. Smartphones offer the convenience of built-in microphones, data storage and processing software under one device. Smartphones also stand out as convenient and ubiquitous for mobile data collection at a population level, and hold the potential of continuous monitoring at the individual level. However, continuous audio sensing by smartphone poses complex privacy concerns, which have limited implementation and adoption.¹⁹

Nearly all studies used preprocessing techniques of the cough sound prior to running an acoustic analysis. Most acoustic analysis relied on spectrograms, which represent the spectro-temporal correlations present in the audio signal in the form of an image. Of particular concern for cough sounds, which are explosive acoustic events, is the phenomenon of clipping, i.e. the loss of high amplitude acoustic data when an audio signal is too loud. To avoid clipping in the recording of cough sounds, the distance between the subject’s mouth and the microphone becomes pertinent. Additionally, if the subject is too close to the microphone, gusts of air cause distortion of audio data, compromising its quality.²⁰ Relatively few studies mentioned the exact distance between the subject and the microphone, with the average distance being 50 cm.

Irrespective of their objective, the majority of studies focused on single modality of cough acoustic data in their predictive models. The few studies that used a multi-modality approach combined cough sounds with demographic data or clinical data such as body temperature or respiratory rate. A handful of studies also supplemented their acoustic data with patient reported outcome measures, such as the cough visual analogue scale and the Leicester Cough Questionnaire. A multi-modality approach has been associated with an increase in the precision of disease predictive models based on cough sounds.²¹

Developments in the cough sound literature correlate with technological advancements in audio recording and data analytics. For instance, ambulatory audio recording devices appeared in the 1990s, and enabled continuous monitoring and real-time data capture of cough sounds. In the following decades, the introduction of the Fast Fourier Transform (FFT) underlying spectrograms and Mel-frequency cepstral coefficients (MFCCs) allowed researchers to capture more acoustic data dimensions.²² Additionally, in the past decade, the development of machine learning based on artificial neural networks expanded the ability to analyze vast amounts of complex digitized data, allowing researchers to exploit the potential of cough sounds at a much greater scale and with higher level of predictive precision. Researchers now have programs that simplify and increase the efficiency of data pre-processing, with such capabilities as distinguishing and tagging the contaminant background noises and allowing for drastic increase in the magnitude of data incorporated in predictive models. Data storage has also recently shifted online, mostly to the cloud, making accessibility of data easy and convenient. Availability of open-access large datasets of cough sounds has further contributed to the acceleration of publications in the past decade.

Livestock studies demonstrated that a one-to-one ratio of microphone to subject yields the best results in terms of audio acquisition quality. Factors which influenced hardware choice for animal cough sound recording included body weight and the species of animal involved. Most animal studies focused on field recording, with limitations related to ambient noise and poor discrimination between individual animals. This approach was particularly effective in respiratory infection spread monitoring and localization of sick animals. Interestingly, such field recordings have not been implemented for humans, likely due to challenges related to privacy concerns.

Our scoping review revealed trends of drawbacks amongst the studies reviewed. Included studies were authored by scientists in resource-affluent countries, despite the potential for higher impact in low-resource settings. In addition, study populations were consistently small, and there was a lack of prospective studies. Inclusion of demographic features were inconsistent across studies, and as highlighted in other scoping reviews in bioacoustics, we found a lack of reporting on race/ethnicity, gender, and socio-economic status of participants in human studies.²³

This scoping review attempted to examine all relevant articles to cough sounds in screening and diagnostics in humans and animals published in English, though our selected search terms may not have captured all the relevant literature. The lack of critical appraisal of the included literature is inherent to scoping reviews and limits our ability to report on the quality of the medical evidence in this field of bioacoustics.

This study is also limited by the date range of the literature search, which does not include advances in the literature in the past two years as the field has continued to expand. Notably, diagnosis of COVID-19 from cough sounds using machine learning has remained a frequent focus.^24–26 For instance, a recent study by Davidson et al. sought to classify COVID-19-related pneumonia severity based on cough sounds.²⁷ Kuluozturk et al. used a machine learning model to diagnose COVID-19 as well as heart failure and acute asthma based on cough sounds,²⁸ while Yellapu et al. focused on the diagnosis of pulmonary tuberculosis.²⁹ Novel cough detection algorithms have also continued to be developed,^30,31 and Xu et al. proposed a smartphone-based cough sound analysis as a potential home-based pulmonary function test.³² Importantly, the effort to expand publicly available cough sound datasets has also continued, improving the landscape for better training and testing of future machine learning models.³³

While no cough detection or diagnostic technology has yet achieved FDA approval, multiple cough monitoring devices, including VitaloJak and the Leicester Cough Monitor, have been used in research.³⁴ More recently, several app-based ventures, notably ResApp (for cough sound diagnosis)³⁵ and Hyfe (for cough counting),³⁶ have continued to develop rapidly and are likely to apply for regulatory approval for clinical use in the near future. Should this technology become clinically available, it has the potential to significantly impact the practice of otolaryngologists. For example, cough diagnosis algorithms could play a role in the diagnosis of conditions such as croup, for which cough sounds have diagnostic value. Cough detection and cough counting technology could transform the management of neurogenic cough managed by otolaryngologists, allowing for reliable and objective assessment of response to treatment as measured by cough number and frequency.

Cough sound analysis holds the promise of accessible, non-invasive, and inexpensive diagnostic tools but is limited in practice currently. The majority of current studies hinge on non-standardized data collection protocols and small, non-diverse, private datasets from single institutions. To build a robust model with wide utility and applicability, researchers need a diverse population, in order to capture all possible cough sound variants. The advent of machine learning and the ubiquity of smartphone technology with high quality audio recording capabilities in the past decade has accelerated interest in cough acoustics for screening and diagnosis.