ABSTRACT
Automatic classification of cough data can play a vital role in early detection of Covid-19. Lots of Covid-19 symptoms are somehow related to the human respiratory system, which affect sound production organs. As a result, anomalies in cough sound is expected to be discovered in Covid-19 patients as a sign of infection. This drives the research towards detection of potential Covid-19 cases with inspecting cough sound. While there are several well-performing deep networks, which are capable of classifying sound with a high accuracy, they are not suitable for using in early detection of Covid-19 as they are huge and power/memory hungry. Actually, cough recognition algorithms need to be implemented in hand-held or wearable devices in order to generate early Covid-19 warning without the need to refer individuals to health centers. Therefore, accurate and at the same time lightweight classifiers are needed, in practice. So, there is a need to either compress the complicated models or design light-weight models from the beginning which are suitable for implementation on embedded devices. In this paper, we follow the second approach. We investigate a new lightweight deep learning model to distinguish Covid and Non-Covid cough data. This model not only achieves the state of the art on the well-known and publicly available Virufy dataset, but also is shown to be a good candidate for implementation in low-power devices suitable for hand-held applications.
ABSTRACT
BACKGROUND: Industrialization and urbanization had a devastating impact on public health and caused an increase in health related morbidity and mortality. In fact, asthma is a chronic condition which is considered as one of the significant challenges of public health. In this study, we investigated the association of air pollution and weather conditions with excess emergency ward admissions of asthmatic patients in Kermanshah hospitals. METHODS: This was an ecological study. The total number of hospital admissions to emergency wards from all related and major hospitals of Kermanshah was collected from September 2008 through August 2009. In addition, data on air pollution as well as meteorological data were collected from the Environmental Protection Agency and Meteorological Organization of Kermanshah. To determine the association between the number of hospitalization due to asthma with those parameters, Poisson regression was used. RESULTS: The results of Poisson regression revealed a significant association between carbon monoxide, ozone, nitrogen dioxide and temperature with emergency room visits due to asthma in Kermanshah. No associations were found for sulfur dioxide or for particulate matter. CONCLUSION: This study provides further evidence for the significant effect of monoxide carbon on asthma; and it suggests that temperature may have a role in the exacerbation of asthma. However, due to the multi-factorial nature of asthma, other factors also play a major role in the development and exacerbation of this illness.
