The effects of cough on sleep and vice-versa are important and have been discussed in recent studies. Sleep disruption is common in patients who cough and is often why they seek medical attention. Sleep is classically divided into five sleep stages: 1) wake, 2) Rapid Eye Movements (REM)–including rapid brain waves and eye movements accompanied by low muscle tone throughout the body, and three stages of Non-REM (NREM): 3) N 1 or drowsiness–a transitional stage between wakefulness and stage N 2, 4) N 2 –a sleep stage that accounts for approximately 45%-55% of the sleep time (N 1 and N 2 are defined as light sleep), and 5) N 3 –also known as slow-wave sleep or deep sleep. Sleep is an essential function of the brain and plays an important role in an individual’s performance, learning ability, and physical movement. (d) Spectrogram of a cough event with the third phase. (c) Spectrogram of a cough event without the third phase. (a) Cough event without the third phase in the time domain, (b) Cough event with a third phase. An example of two recorded cough sounds with and without the third phase is shown in Fig 1, in the time and frequency domains.Īn example of two cough sounds–divided into its three phases: 1) explosive, 2) intermediate, and 3) voiced. The third phase (not always present) is composed of a turbulent airflow that includes a pitch frequency caused by activation of the vocal cords. The second phase is composed of a laminar airflow characterized by smaller amplitudes. The first phase is an initial explosive phase with a very sharp increase in energy while air is released. It is also associated with many pathological conditions, most of which are related to the respiratory system (e.g., Chronic Obstructive Pulmonary Disease (COPD), asthma, and COVID-19), although others are seasonal such as allergies or colds.Ī typical cough sound contains two or three phases. Cough is a respiratory reflex considered to have defensive capabilities aimed at removing mucus and foreign particles from the lower airways. These sleep sounds can be related to physiological characteristics of the recorded individual, such as breathing, snoring, movement, speech, and coughing. This non-contact cough detection system may thus be implemented to track the progression of respiratory illnesses and test reactions to different medications even at night when a contact sensor is uncomfortable or infeasible.ĭuring sleep, different sounds can be recorded and analyzed. A positive correlation was found between BMI and the number of nocturnal coughs (R = 0.232, p < 0.05), and between the number of nocturnal coughs and OSA severity in men (R = 0.278, p < 0.05).

Cough events were significantly more frequent during wakefulness than in the sleep stages ( p < 0.0001) and were significantly less frequent during deep sleep than in other sleep stages ( p < 0.0001). The DNN-based system outperformed the GMM-based system, at 99.8% accuracy, with a sensitivity and specificity of 86.1% and 99.9%, respectively (Positive Predictive Value (PPV) of 78.4%). The database was composed of nocturnal audio signals from 89 subjects recorded during a polysomnography study. The detected coughs were analyzed and compared in different sleep stages and in terms of severity of Obstructive Sleep Apnea (OSA), along with age, Body Mass Index (BMI), and gender. Two different classifiers were implemented and tested: a Gaussian Mixture Model (GMM) and a Deep Neural Network (DNN). An automatic non-contact cough detector designed especially for night audio recordings that can distinguish coughs from snores and other sounds is presented.

Societal impact of nocturnal cough