Gender Difference in Chronic Cough: Are Women More Likely to Cough?

Abstract

Chronic cough is a common complaint for patients to seek medical cares all over the world. Worldwide, about two thirds of chronic cough patients are females. However, in some regions of China the prevalence of chronic cough between sexes is roughly the same. Estrogen and progesterone can not only have an effect on transient receptor potential vanilloid 1 channel, eosinophils and mast cells, but also influence laryngeal dysfunction, gastroesophageal reflux disease and obstructive sleep apnea hypopnea syndrome, which may lead to increased cough sensitivity in women. On the other hand, the quality of life was adversely affected more in female patients with chronic cough. Both hormones possibly cause gender difference in chronic cough.

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

“…For each symptom class, the total number of samples and their gender distributions are listed in Table 1. Similar to other COVID-19 related acoustic databases (Muguli et al, 2021; B. W. Schuller, Batliner, et al, 2021), the sample numbers at the two statuses are imbalanced.…”

“…Furthermore, the age distribution is examined across all symptom conditions (see Figure 1). Apart from all three tasks, it is interesting to investigate the performance in these tasks under different genders or age ranges, as previous studies have shown gender and age differences in cough behaviour (Bai et al, 2021;Ioan et al, 2014). As shown in Figure 1, the median of the age ranges is around 30, which is used to split the data into two age groups for each task.…”

“…A promising approach for a screening tool fulfilling these requirements could be based on bioacoustic signals such as speech sounds or cough sounds (Brown et al, 2020; Hecker et al, 2021; B. W. Schuller, Batliner, et al, 2021; B.…”

“…Motivated by acoustic voice peculiarities found for various diseases, machine learning has been increasingly applied to automatically detect medical conditions from voice, such as upper respiratory tract infection (Albes et al, 2020), Parkinson’s disease (Yaman et al, 2020), and depression (Ringeval et al, 2019). Recent studies on the automatic detection of COVID-19 from speech signals achieved promising results through both traditional machine learning (Han et al, 2021; B. W. Schuller, Batliner, et al, 2021; Shimon et al, 2021; Stasak et al, 2021) and deep learning techniques (Hassan et al, 2020; Pinkas et al, 2020; B.…”

“…Recent studies on the automatic detection of COVID-19 from speech signals achieved promising results through both traditional machine learning (Han et al, 2021; B. W. Schuller, Batliner, et al, 2021; Shimon et al, 2021; Stasak et al, 2021) and deep learning techniques (Hassan et al, 2020; Pinkas et al, 2020; B. W. Schuller, Batliner, et al, 2021).…”