[{"content":"","externalUrl":null,"permalink":"/roar/authors/","section":"Authors","summary":"","title":"Authors","type":"authors"},{"content":"","externalUrl":null,"permalink":"/roar/categories/","section":"Categories","summary":"","title":"Categories","type":"categories"},{"content":" Cough definition # In order to be able to count cough events, the research community first needs a clear definition of how to identify a cough event. In this work, we define a cough as a forceful burst of air through the lungs and throat. The physiological mechanism of the cough results in both an inward motion of the chest required to propel the air and mucus out of the lungs, as well as a sound due to the vibration of the large airways and laryngeal structures during turbulent flow in expiration.\nThe figure below depicts 9 cough events measured through a chest-worn device containing two microphones and an inertial measurement unit. Each individual cough can be identified through large peaks in the audio signals coinciding with peaks in the accelerometer z signal, which depicts the motion in and out of the chest.\nAudio and kinematic mechanisms of a series of cough events Phases of a cough # Cough events can be divided into a series of phases, described by Chang et al.:\n(occasional) Inspiratory - Air is inhaled to enable lengthening of the expiratory muscles. One inhalation can be followed by one or multiple cough events. Compressive - The glottis is closed to enable building of intrathoracic pressure. It typically lasts approximately 200 ms. Cough spike - Opening of the glottis, followed by supramaximal expiratory flow, which causes the loudest part of the cough sound. This typically lasts 30-50 ms. Expiratory - Air and debris exit the lungs through expiration lasting 200-500 ms. (optional) Voiced - Some coughs are accompanied by a sound eminating from the vocal chords, often causing another peak in the audio signal. The various cough phases can be visualized in the audio and kinematic signals below.\nDelineated cough phases Furthermore, the figure below depicts the various cough phases in two series of coughs, also known as cough bursts, separated by a breath.\nDelineated cough phases in multiple coughs Data annotation guidelines # Determining how well an algorithm counts coughs requires an extensive, fine-grained annotation of the cough recordings that marks the start and endpoints of each individual cough sound. To this end, we propose the following data annotation procedure that takes into account the aforementioned cough phase physiology, as well as the dual modalities of cough signal artefacts.\nThe first step of the annotation involves extracting relevant fiducial points of the microphone and accelerometer z-direction signals. First, the starts and ends of each audio event are visually identified as the start of the largest peak in the audio signal to the end of the gradual decrease of the signal down to its noise floor. This aims to identify the combined cough spike, expiratory, and voiced phase of each cough.\nHowever, the audio signal alone cannot accurately delineate coughs, as it may pick up high-amplitude noises as well as bystander coughs. This is why it is also advisable to delineate the peaks and valleys of the accelerometer z signal to determine whether each sound burst was accompanied by a chest acceleration. Mathematically, peaks and valleys can defined as local minima and maxima of the second derivative of the signal, respectively. The final cough location can then be determined as the start and end locations of audio events, provided that they are accompanied by a peak in the chesta accelerometer signal.\nAs an illustrative example, let us consider the recording in the figure below, which shows two bystander coughs followed by four of the subject’s coughs, then one more bystander cough. We can see from the fiducial points that the audio signal thresholding mistakenly identifies the bystander coughs, while the IMU signal exhibits a peak at every true cough but does not adequately mark the onset of the cough. In this example recording, the final annotation is selected as the regions between audio burst starts and IMU valleys containing an IMU peak in-between.\nCough labeling procedure ","externalUrl":null,"permalink":"/roar/definition/","section":"Respiratory Open Access Research (ROAR)","summary":"","title":"Cough Definition","type":"page"},{"content":"To advance the field of automatic cough counting and enable a fair comparison of different algorithms, we contribute the first publicly accessible, finely-labeled cough biosignal dataset. The public dataset contains 227 minutes of biosignals and nearly 4,300 annotated cough events.\nData access # The dataset can be downloaded from this Zenodo database.\nData collection # Signals # The dataset contains biosignals collected using a nocel lightweight, battery-powered device containing the following sensors:\nAcoustic - two microphones - one facing toward the body and one facing away from it - each sampled at 16 kHz Kinematic - an interial measurement unit - containing accelerometer and gyroscope signals - sampled at 100 Hz Data collection device Subjects # Recordings were collected from 15 healthy subjects (10 male, 10 female; age 26.5 ± 6.5 years; body mass index (BMI) 22.6 ± 4.5 kilograms per square meter). Institutional review board approval was obtained (HREC No.: 085-2022) and all participants signed an informed consent prior to data acquisition.\nNon-cough sounds # In addition to coughs, the subjects produced the following sounds that could possibly be confused with coughing:\nLaughing Throat clearing Deep breathing Environmental noise # To assess how well classifiers perform under real-life noise conditions, several noise factors were intentionally added to the experimental setup. These noises came in the form of audio and kinematic noise.\nAudio noise:\nTraffic Music Bystander cough Kinematic noise:\nWalking Dataset structure # The files are arranged in a hierarchical structure as shown in the figure below. For each experimental condition, there are three to four corresponding files: .wav audio files for the body-facing and outward-facing microphones, a .csv file for the IMU data, and in the case of cough recordings, a .json file containing the cough location annotations. The gender and BMI of each subject are recorded in the biodata.json file.\nSubject ID/ ├── Trial 1/ │ ├── Sit/ │ │ ├── No noise | | | ├── Cough │ │ | | ├── body-facing-mic.wav │ │ | | ├── outward-facing-mic.wav │ │ | | ├── imu.csv │ │ | | └── ground-truth.json | | | ├── Laugh │ │ | | ├── ... | | | ├── Deep breathing │ │ | | ├── ... | | | └── Throat clearing │ │ | ├── ... | | ├── Traffic │ │ | ├── ... | | ├── Music │ │ | ├── ... | | ├── Bystander cough │ │ | ├── ... │ ├── Walk │ │ ├── ... | ├── biodata.json | ... ","externalUrl":null,"permalink":"/roar/dataset/","section":"Respiratory Open Access Research (ROAR)","summary":"","title":"Open cough counting dataset","type":"page"},{"content":" The need for automatic respiratory event detection tools # Chronic cough and cough hypersensitivity disorders are globally prevalent conditions that significantly impair patients’ quality of life. These conditions are difficult to treat due to the difficulty in identifying causes, including individual triggers and underlying pulmonary disorders. Current clinical practice assesses severity and treatment efficacy through patient questionnaires, which are only moderately correlated to actual cough counts. Hence, there is significant interest in using smart wearable devices to automatically provide objective daily cough counts as a more accurate, unbiased means of assessment.\nThe problems # While efforts are being made to develop wearables to automatically detect and quantify cough events, such monitoring devices have not yet been incorporated into routine clinical practice due to lack of consistency in their validation resulting in slow progress and lack of trust on reported results. There are three main reasons for this heterogeneity:\nThe clinical definition of different cough events lacks standardization. The data used is typically private or unlabeled. Methodologies to assess the accuracy of event detection are different between research groups and often inappropriate. ROAR mission statement # The goal of the ROAR is to contribute Open Research Data (ORD), community guidelines, and standards to propose a unified framework for validating cough event detection algorithms. The main objective is the development of standards to unify the workflow for the validation of respiratory events detection algorithms to make data adhere to the principles of Findable, Accessible, Interpretable, and Reusable (FAIR) data. This website serves as a central hub and reference for standardizing clinical definitions and methodologies, setting best-practice standards for reasearch and industry teams developing tools for automatic cough detection. Furthermore, we contribute the first publicly accessible cough counting dataset, including real-world noise scenarios, multimodal biosignal recordings, and fine-grained cough event labels following our best practice guidelines.\nWebsite structure # Cough event definition and data annotation guidelines: Information about the physiological mechanism behind cough, which informs our data labeling, annotation, and evaluation standards Open data for cough counting: Description of an open dataset for cough detection, including its content and structure. Algorithm validatio framework: Proposed framework for measuring the cough detection performance of automatic tools. Tools: Tools for implementing the ROAR methodology for cough event detection Credits # If you use the ROAR methodology in one of your scientific studies, please make sure to credit one of the following works.\nOpen dataset:\nOrlandic L, Thevenot J, Teijeiro T, Atienza D. “A Multimodal Dataset for Automatic Edge-AI Cough Detection”. IEEE /nternational Engineering in Medicine and Biology Conference (EMBC). 2023. ROAR annotation and evaluation methodology:\nOrlandic L, Dan J, Thevenot J, Teijeiro T, Sauty A, Atienza D. How to count coughs: an event-based framework for evaluating automatic cough detection algorithm performance”. IEEE International Conference on Body Sensor Networks (BSN).2024 An example of ROAR used to develop a cough detection algorithm:\nAlbini S, Orlandic L, Dan J, Thevenot J, Teijeiro T, Atienza D. “Cough-E: A multimodal, privacy-preserving cough detection algorithm for the edge”. IEEE Journal of Biomedical Health Informatics (JBHI). 2025. ","externalUrl":null,"permalink":"/roar/","section":"Respiratory Open Access Research (ROAR)","summary":"","title":"Respiratory Open Access Research (ROAR)","type":"page"},{"content":" Goals for objective cough monitoring # The guidelines of the European Respiratory Society (ERS) highlight multiple clinically significant endpoints in cough monitoring, including:\nthe number of cough events, seconds containing at least one cough, breaths followed by coughing cough bouts, which are sequences of coughs that are not separated by a breath Studying the pattern of coughing is crucial, as cough bouts correlate more closely than individual coughs with pathology and reported severity, and can indicate different underlying physiological mechanisms, according to Dockry et al. Thus, automated tools should monitor both cough frequency and the temporal distribution of cough patterns to provide insights into the patient’s symptomatology and guide treatment plans.\nHow are current works reporting results? # There are two main gaps between reported algorithm performance metrics and the above-mentioned clinically relevant endpoints. These gaps relate to what counts as a \u0026ldquo;correct\u0026rdquo; cough sample, as well as which metrics are reported to measure algorithm performance.\nTypically, recordings are segmented into windows of a fixed length, which are often seconds-long and therefore can contain multiple coughs. A sample is determined to be cough-positive if one or more coughs appear in the window. The figures below depict two recordings, each segmented into two windows.\nThe problem with sample-based reporting We can see a few problems already. First, even though the second subject has more coughs than the first, the algorithm scores them equally, so the temporal distribution of the cough events is lost information. Second, depending on the window length, the sample can contain a fraction of a cough, a full cough, or multiple coughs, so it is impossible to compare the success of two algorithms that are based on two different segmentation methods.\nThe second problem is that metrics based on True Negative (TN) samples are often reported to evaluate algorithm success. However, TNs may misrepresent practicality as they are heavily influenced by cough frequency and long periods without coughs, and therefore do not contribute useful information in a long-term monitoring scenario. For example, take the case of the image below, which depicts a very basic classifier that never detects a cough.\nThe problem with TN-based metrics In this signal that contains more silence than coughing, the classifier scores quite well: 90% accuracy and 100% specificity. However, this algorithm isn\u0026rsquo;t giving any useful information to doctors who want to know whether a given medication is making their patients cough more or less.\nIt is therefore imparative that we redifine how success is evaluated in the context of cough counting. To do this, we have two propositions.\nProposition 1: Event-based evaluation framework # In order to overcome the issues with evaluating algorithm success based on data windows, we propose an event-based framework to truly evaluate how well algorithms detect each individual cough event. An example of an algorithm performance evaluation is shown below, in which reference cough event lications (REF) are compared to the hypothesized ones (HYP).\nEvent-based scoring example Unlike window-based metrics, these event-based metrics measure the ability of an algorithm to correctly identify the temporal locations of annotated events. True positive events are determined based on the overlap between individual true and predicted cough events, including some tolerance around the ground-truth locations. We suggest using a tolerance value of 0.25 s, which corresponds to the time required for the lungs to compress before a cough, as well as the minimum expiratory phase following a cough. For more information about these the different phases of coughs used to set these thresholds, check out our cough event definition page.\nOverall, we propose an eventbased evaluation framework to identify individual cough onsets and offsets, providing precise information on cough patterns to assess disease severity and treatment efficacy.\nProposition 2: Reporting meaningful algorithm performance metrics # Traditional metrics like Specificity and Accuracy, used in over 60% of studies, are highly sensitive to class imbalance present in the dataset.\nWe strongly recommend using Sensitivity, Precision, F-1 Score, and False Positives per hour for evaluation. These metrics are based on true positive, false negative, and false positive samples, and therefore cannot be saturated by TNs.\n","externalUrl":null,"permalink":"/roar/framework/","section":"Respiratory Open Access Research (ROAR)","summary":"","title":"ROAR framework for validation of cough detection algorithms","type":"page"},{"content":"","externalUrl":null,"permalink":"/roar/series/","section":"Series","summary":"","title":"Series","type":"series"},{"content":"","externalUrl":null,"permalink":"/roar/tags/","section":"Tags","summary":"","title":"Tags","type":"tags"},{"content":"We provide certain tools to encourage reproducibility and consistency of results reported in the field of automated seizure detection algorithm.\ntimescoring # esl-epfl/timescoring Lib for event and sample based performance metrics Python 25 5 We built a library that provides different scoring methodologies to compare a reference time series with binary annotation (ground-truth annotations of the neurologist) to hypothesis binary annotations (provided by a machine learning pipeline). These different scoring methodologies provide a count of correctly identified events (True Positives) as well as missed events (False Negatives) and wrongly marked events (False positions)\nIn more details, we measure performance on the level of:\nSamples : Performance metric that threats every label sample independently. Events (e.g. cough) : Classifies each event in both reference and hypothesis based on overlap of both. Both methods are illustrated in the following figures :\nCough signal manipulation tools # To accompany the cough counting dataset, we developed the following Git repository: https://github.com/esl-epfl/edge-ai-cough-count/\nThe repository contains code for:\nEasily iterating through the file structures of the dataset Data segmentation Data augmentation Applying the event-based framework to test model predictions Cough-E # The Cough-E model is an open-source example of a cough detection model that was trained and evaluated using our cough counting dataset and evaluation framework. The model training code and embedded C implementation can be found here: https://github.com/esl-epfl/Cough-E\n","externalUrl":null,"permalink":"/roar/tools/","section":"Respiratory Open Access Research (ROAR)","summary":"","title":"Tools","type":"page"}]