Using Mobiles to Monitor Respiratory Diseases

Abstract

In this work, a mobile application is developed to assist patients suffering from chronic obstructive pulmonary disease (COPD) or Asthma that will reduce the dependency on hospital and clinic based tests and enable users to better manage their disease through increased self-involvement. Due to the pervasiveness of smartphones, it is proposed to make use of their built-in sensors and ever increasing computational capabilities to provide patients with a mobile-based spirometer capable of diagnosing COPD or asthma in a reliable and cost effective manner. Data collected using an experimental setup consisting of an airflow source, an anemometer, and a smartphone is used to develop a mathematical model that relates exhalation frequency to air flow rate. This model allows for the computation of two key parameters known as forced vital capacity (FVC) and forced expiratory volume in one second (FEV1) that are used in the diagnosis of respiratory diseases. The developed platform has been validated using data collected from 25 subjects with various conditions. Results show that an excellent match is achieved between the FVC and FEV1 values computed using a clinical spirometer and those returned by the model embedded in the mobile application.

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

“…Deperlioglu et al [80] (46) presented the use of the IoHT, evidencing the need for a safe process that should be included in the model due to the use of the Internet. They used cardiac sounds of pascal B-Training and Physiobank-PhysioNet A-Training 9 for model training and obtained an accuracy rate greater than 90%. The model proposed by the authors uses the digital stethoscope architecture with a bluetooth connection by beacons to the server with cloud access, with CNN for diagnosis.…”

“…Twelve studies approached the context of remote monitoring (IDs = 5, 9, 10, 17,27,36,38,40,43,44,51,55). In addition, IoT increases resource management efficiency 9 https://physionet.org/ through flexibility and mobility using intelligent solutions. However, this requires a transition from clinic-centered treatment to patient-focused medical care so that the hospital, patients, and services are connected.…”

Machine Learning and IoT Applied to Cardiovascular Diseases Identification Through Heart Sounds: A Literature Review

“…The model used the Arduino, connected by Bluetooth, to the notebook for processing and analysis of the signal by the MatLab software and Android smartphone. MatLab software was used in 24 works (IDs = 2, 3, 7, 8, 9,11,16,17,18,24,26,27,30,35,37,39,42,46,48,54,55,56,57,58).…”

“…Health Informatics (HI) has occupied a strategic role in society, generating relevant impacts on economic and human aspects. Recent research in this regard considers physical and mental health involving topics such as prevention and care of noncommunicable diseases [5][6][7], use of mobile and context-aware systems [8,9] and assistance in the treatment of stress, anxiety and depression [4,10,11]. HI research also considers specific diseases such as Alzheimer [12] and specific technology issues as strategies for recording vital signs [13], application of machine learning to identify medication-associated acute kidney injury [14] and use of visual analytics for handling of Electronic Health Records (EHR) [15].…”

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

“…The model used the Arduino, connected by Bluetooth, to the notebook for processing and analysis of the signal by the MatLab software and Android smartphone. MatLab software was used in 24 works (IDs = 2, 3, 7, 8, 9,11,16,17,18,24,26,27,30,35,37,39,42,46,48,54,55,56,57,58).…”