Cough Detection Using Hidden Markov Models

Abstract

Respiratory infections and chronic respiratory diseases impose a heavy health burden worldwide. Coughing is one of the most common symptoms of many such infections, and can be indicative of flare-ups of chronic respiratory diseases. Whether at a clinical or public health level, the capacity to identify bouts of coughing can aid understanding of population and individual health status. Developing health monitoring models in the context of respiratory diseases and also seasonal diseases with symptoms such as cough has the potential to improve quality of life, help clinicians and public health authorities with their decisions and decrease the cost of health services. In this paper, we investigated the ability to which a simple machine learning approach in the form of Hidden Markov Models (HMMs) could be used to classify different states of coughing using univariate (with a single energy band as the input feature) and multivariate (with a multiple energy band as the input features) binned time series using both of cough data. We further used the model to distinguish cough events from other events and environmental noise. Our Hidden Markov algorithm achieved 92% AUR (Area Under Receiver Operating Characteristic Curve) in classifying coughing events in noisy environments. Moreover, comparison of univariate with multivariate HMMs suggest a high accuracy of multivariate HMMs for cough event classifications.

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

“…Point-of-care or semicontinuous methods for quantifying coughing or other vocal activities rely on electromyography, respiratory inductive plethysmography, accelerometry, or auditory recordings captured with one or several sensors, sometimes with other exploratory approaches (e.g., the nasal thermistor or the electrocardiography) (36)(37)(38)(39)(40)(41). Digital signal processing followed by machine learning algorithms often serves as the basis for classification (42)(43)(44)(45)(46)(47)(48)(49)(50)(51)(52)(53). Microphone-based methods prevail due to their widespread availability and their alignment with large crowd-sourced datasets (e.g., COUGHVID, HealthMode, DetectNow, VoiceMed).…”

EarCough: Enabling Continuous Subject Cough Event Detection on Hearables

“…Recently, the research community has widely explored automatic cough event detection methods, most of which are audiobased [5,9,14,24,26,29,32], owing to the valuable characteristic spectral signature contained in cough sounds [2]. For instance, Wang et al [29] proposed HearCough, enabling state-of-the-art continuous cough event detection based on the audio signals from commodity hearables.…”

Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities

“…It utilized ML to calculate the statistical analysis for the disease possibility and it achieved 90% accuracy, specificity, and sensitivity in all respiratory diseases except asthma which achieved 84% accuracy. Also, Teyhouee and Osgood [68] proposed a machine learning model by utilizing multiadaptive HMM to classify pulmonary cough sounds from other environmental noisy sounds based on energy band and time series. Their method achieved 92% AUC.…”