Automatic cough detection based on airflow signals for portable spirometry system

Soliński, Mateusz; Łepek, Michał; Kołtowski, Łukasz

doi:10.1016/j.imu.2020.100313

Cited by 31 publications

(24 citation statements)

References 21 publications

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“…A pandemic can be defined as the emergence of a disease that leads to a fast rise in deaths and infection rates on a large-scale [1]. Recently, the COVID-19 pandemic spread rapidly worldwide and caused unprecedented infections and deaths with WHO reporting around 13,616,593 and 585,727 in 216 countries [69], [70]. Several studies proposed the automated diagnosis of COVID-19 using X-ray, CT-Scan, clinical data, vital signs, and cough samples [71], [72], [12], [70], [80].…”

Section: ) Covid-19 Diagnosis Approachesmentioning

confidence: 99%

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

et al. 2021

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Coughing is a common symptom of several respiratory diseases. The sound and type of cough are useful features to consider when diagnosing a disease. Respiratory infections pose a significant risk to human lives worldwide as well as a significant economic downturn, particularly in countries with limited therapeutic resources. In this study we reviewed the latest proposed technologies that were used to control the impact of respiratory diseases. Artificial Intelligence (AI) is a promising technology that aids in data analysis and prediction of results, thereby ensuring people's well-being. We conveyed that the cough symptom can be reliably used by AI algorithms to detect and diagnose different types of known diseases including pneumonia, pulmonary edema, asthma, tuberculosis (TB), COVID19, pertussis, and other respiratory diseases. We also identified different techniques that produced the best results for diagnosing respiratory disease using cough samples. This study presents the most recent challenges, solutions, and opportunities in respiratory disease detection and diagnosis, allowing practitioners and researchers to develop better techniques. INDEX TERMSArtificial intelligence (AI), cough detection, 2019 novel coronavirus disease (Covid-19), respiratory illness diagnosis, cough-based diagnosis.

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Section: ) Covid-19 Diagnosis Approachesmentioning

confidence: 99%

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

et al. 2021

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“…Deep neural networks and convolutional neural networks architectures were used to evaluate the features. Soliński et al (2020) presented a model for automatic cough identification based on airflow signals. The proposed algorithm was trained and validated by a set of data obtained from a web application called AioCare.…”

Section: Related Workmentioning

confidence: 99%

Detection and Diagnostic Approach of COVID-19 Based on Cough Sound Analysis

Al-Ani¹,

Mohammed²,

Sultan³

et al. 2021

Journal of Computer Science

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started at the end of 2019 and then spread out around the world as a pandemic at the beginning of 2020. At that time, researchers began to work on detecting and diagnosing this virus, where many methods have been applied for this reason. This study focuses on how to diagnose coronavirus through patients' cough. Accordingly, real samples were taken from people infected by the coronavirus and others, who are suffering from some respiratory diseases. The cough of a person with coronavirus is characterized by its dryness and differs from other cough sounds through a set of factors that are considered for study and analysis through this study. Among these factors is the sound energy, which is found to be the most effective factor and hence implemented as a key indicator for COVID-19 detection. The discrete wavelet transform is the adopted method to realize the detection process via approximation and the analysis of coefficients details. The obtained results show acceptable detection accuracy for the considered samples. Minor mismatching in the detection process is noticed during the procedure, which is mainly due to some patients being infected with the respiratory diseases that exhibit similar symptoms.

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“…Cough is a predominant symptom of COVID-19 and thus, for possible preliminary screening and diagnosing COVID-19, researchers have utilized human cough by identifying COVID-19 specific cough and differentiating it from other similar sounds such as speech and laughter. They are motivated to analyze human cough for COVID-19 detection because prior studies [123], [124], [125], [126] have shown that cough from distinct respiratory syndromes have distinct latent features which are extracted by applying appropriate signal processing and mathematical transformations to train AI or Deep learning models for diagnostic purposes. In understanding the impact of COVID-19 on healthy speech production and the changes in voice of COVID-19 infected patients, several initiatives have been taken as recently in a study [127], it has shown that approximately 25-32% COVID-19 patients form the sampled populations in the UK and USA have a hoarse glottal voice quality [128].…”

Section: Covid-19 Detection Through Sound Analysismentioning

confidence: 99%

Deep Learning Based COVID-19 Detection: Challenges and Future Directions

Khan¹,

Arshad²,

Azam³

et al. 2021

Preprint

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Coronavirus (COVID-19) is an ecumenical pandemic that has affected the whole world drastically by raising a global calamitous situation. Due to this pernicious disease, millions of people have lost their lives. The scientists are still far from knowing how to tackle the coronavirus due to its multiple mutations found around the globe. Standard testing technique called Polymerase Chain Reaction (PCR) for the clinical diagnosis of COVID-19 is expensive and time consuming. However, to assist specialists and radiologists in COVID-19 detection and diagnosis, deep learning plays an important role. Many research efforts have been done that leverage deep learning techniques and technologies for the identification or categorization of COVID-19 positive patients, and these techniques are proved to be a powerful tool that can automatically detect or diagnose COVID-19 cases. In this paper, we identify significant challenges regarding deep learning-based systems and techniques that use different medical imaging modalities, including Cough and Breadth, Chest X-ray, and Computer Tomography (CT) to combat COVID-19 outbreak. We also pinpoint important research questions for each category of challenges. The challenges highlighted in this paper will call an attention to the noticeable weaknesses and problems in the existing deep learning based COVID-19 detection systems and techniques. Moreover, the research questions for each challenge will guide the researchers to come up with novel solutions in COVID-19 detection.

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Automatic cough detection based on airflow signals for portable spirometry system

Cited by 31 publications

References 21 publications

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

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

Detection and Diagnostic Approach of COVID-19 Based on Cough Sound Analysis

Deep Learning Based COVID-19 Detection: Challenges and Future Directions

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