AsthmaGuide: an asthma monitoring and advice ecosystem

Recently, there has been an increased use of wireless sensor networks and embedded systems in the medical sector. Healthcare providers are now attempting to use these devices to monitor patients in a more accurate and automated way. This would permit healthcare providers to have up-todate patient information without physical interaction, allowing for more accurate diagnoses and better treatment. One group of patients that can greatly benefit from this kind of daily monitoring is asthma patients. Healthcare providers need daily information in order to understand the current risk factors for asthma patients and to provide appropriate advice. It is not only important to monitor patients' lung health, but also to monitor other physiological parameters, environmental factors, medication, and subjective feelings. We develop a smartphone, sensor rich, and cloud based asthma system called AsthmaGuide, in which a smartphone is used as a hub for collecting comprehensive information. The data, including data over time, is then displayed in a cloud web application for both patients and healthcare providers to view. AsthmaGuide also provides an advice and alarm infrastructure based on the collected data and parameters set by healthcare providers. With these components, AsthmaGuide provides a comprehensive ecosystem that allows patients to be involved in their own health and also allows doctors to provide more effective day-today care. Using real asthma patient wheezing sounds, we also develop two different types of classification approaches and show that one is 96% accurate, the second is 98.6% accurate and both outperform the state of art which is 87% accurate at automatically detecting wheezing. AsthmaGuide has both English and Korean language implementations.

[1]  L.J. Hadjileontiadis,et al.  Multimedia database "Marburg Respiratory Sounds (MARS)" , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[2]  Y.P. Kahya,et al.  Comparison of different feature sets for respiratory sound classifiers , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[3]  Rajkumar Palaniappan,et al.  Machine learning in lung sound analysis: a systematic review , 2013 .

[4]  D. Scuse,et al.  A comparison of neural network models for wheeze detection , 1995, IEEE WESCANEX 95. Communications, Power, and Computing. Conference Proceedings.

[5]  P. Gibson,et al.  Monitoring the patient with asthma: an evidence-based approach. , 2000, The Journal of allergy and clinical immunology.

[6]  A. Bohadana,et al.  Fundamentals of lung auscultation. , 2014, The New England journal of medicine.

[7]  Mohammed Bahoura,et al.  Pattern recognition methods applied to respiratory sounds classification into normal and wheeze classes , 2009, Comput. Biol. Medicine.

[8]  N. Malmurugan,et al.  Neural classification of lung sounds using wavelet coefficients , 2004, Comput. Biol. Medicine.

[9]  A. Bohadana,et al.  Fundamentals of lung auscultation. , 2014, The New England journal of medicine.

[10]  Atri Rudra,et al.  iMAP: Indirect measurement of air pollution with cellphones , 2009, 2009 IEEE International Conference on Pervasive Computing and Communications.

[11]  J. Mathew,et al.  Efficacy of an individualized written home‐management plan in the control of moderate persistent asthma: A randomized, controlled trial , 2005, Acta paediatrica.

[12]  Joseph Finkelstein,et al.  Telematic System for Monitoring of Asthma Severity in Patients' Homes , 1998, MedInfo.

[13]  Deborah Estrin,et al.  PEIR, the personal environmental impact report, as a platform for participatory sensing systems research , 2009, MobiSys '09.

[14]  Jen-Chien Chien,et al.  Wheeze Detection Using Cepstral Analysis in Gaussian Mixture Models , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[15]  P Helistö,et al.  A new method for automatic wheeze detection. , 1998, Technology and health care : official journal of the European Society for Engineering and Medicine.

[16]  Raimon Jané,et al.  Detection of wheezing during maximal forced exhalation in patients with obstructed airways. , 2002, Chest.

[17]  R. Jane,et al.  Algorithm for time-frequency detection and analysis of wheezes , 2000, Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No.00CH37143).

[18]  Bor-Shing Lin,et al.  An FPGA-Based Rapid Wheezing Detection System , 2014, International journal of environmental research and public health.

[19]  Ratko Magjarević,et al.  A Novel Approach to Wheeze Detection , 2007 .