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A mobile Based Platform for Monitoring Respiratory Diseases

Zubaydi, Fatma Khalil
Description
A Master of Science thesis in Computer Engineering by Fatma Khalil Zubaydi entitled, "A mobile Based Platform for Monitoring Respiratory Diseases" submitted in September 2016. Thesis advisor is Dr. Assim Sagahyroon and thesis co-advisors are Dr. Fadi Aloul and Dr. Hasan Mir. Soft and hard copy available.
Abstract
Chronic respiratory diseases are diseases of the airways and other structures of the lung, usually resulting in difficulty in breathing and other symptoms. Chronic obstructive pulmonary disease (COPD) and Asthma are considered to be the most common of respiratory diseases. By taking into consideration the possibility of disease worsening over time and the negative impact on patient’s daily activities, the continuous monitoring and managing of these diseases has become a necessity. Currently, spirometry remains the recommended test for monitoring and diagnosing both, COPD and Asthma. A patient suffering from COPD or Asthma should be able to monitor his disease in order to avoid a worsening condition over time or exacerbation of the disease in severe cases. Proper monitoring requires regular visits to medical centers for spirometry checks, or else the purchase and use of a portable spirometer; both options are costly in terms of money and time. In this work and due to the pervasiveness and advancement of smartphones, we attempt to make use of their built-in sensors and ever increasing computational capabilities to provide patients with a mobile-based spirometer capable of diagnosing and managing COPD and Asthma in a reliable and cost effective manner. We developed a model that allows the computation of two critical lung parameters: FVC and FEV1 by establishing a relationship between the frequency response of human exhalation recorded by mobile microphone, and the actual flow rate. These two parameters and the FEV1/FVC ratio are critical in assessing the progress and status of the diseases. We designed a Pretest Activity that together with these computed lung parameters is used in the diagnosis phase. Sample data used to test the system is collected from patients at both Oriana, and Al Zahra hospitals in Sharjah, United Arab Emirates (UAE), under the supervision of consultant pulmonologists. Results and the medical diagnosis of the implemented system proved to be in very close proximity with those produced by clinical spirometers. Our work is an attempt among many to confirm the notion that mobile Health (m-Health) can and will play an important role within the healthcare industry in the near future.
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