This document discusses the Chronious project, which uses soft computing techniques on smart devices to monitor chronic diseases. Specifically, it monitors elderly patients over 60 affected by chronic obstructive pulmonary disease (COPD) and chronic kidney disease (CKD). The system combines remote sensors, a smart device, and a central decision support system using an ontology, literature search, and rule-based inference engine. It discusses the communication framework, algorithms run on a PDA device, and classification systems used, including rule-based and supervised learning approaches. Issues addressed include resource consumption on the PDA and difficulties updating training algorithms.

1. Using Soft Computer Techniques on Smart Devices for Monitoring Chronic Diseases: the CHRONIOUS case

2. Who am I? Piero Giacomelli [email_address] piero.giacomellitesan Piero Giacomelli

3. Where I live?

4. Where I live?

5. Where I live?

6. What did I study?

7. Where did I study? Galileo studied here with different results…

8. Where did I work?

9. Where did I work?

10. Where did I work?

11. Where did I work?

12. Where did I work?

13. Where do I work now?

14. Which is part of

15. ITALTBS numbers Presence in 12 countries Including: Austria Belgium France Germany India Italy Netherlands Portugal South Arabia Serbia Spain

16. What do I do in TeSAN 1? R&D IT MANAGER (BASICALLY)

17. What do I do 2? I solve (software) problems

18. What I’m here to present ? Soft computing techniques + Smart devices + Chronic diseases in

19. Why Chronious COPD? COPD: by 2020 3.5 million deaths in the world (at least) USA 2000: 10 million adults reported COPD = 8 million physician office and hospital outpatients visit + 1.5 million emergency visit + 726,000 hospitalizations + 119,000 deaths

20. Why Chronious COPD? First cause for COPD

21. Why Chronious COPD? Second cause for COPD

22. Why Chronious COPD? Another cause of COPD in development countries

23. Free advice? Want to reduce risk of contracting COPD? DON’T SMOKE! DON’T DRIVE CARS! DON’T DO BARBECUE!

24. Why Chronious CKD? In the US, 9.6% of non-institutionalized adults are estimated to have CKD Reducing the mortality rates associated to the CKD could save 10% of the loss extimated in 8 bilion USD only in development countries

25. Why Chronious CKD? CKD is difficult to treat because of comorbidities The renal functionality becoming worse at every exacerbation We choose to go to the moon in this decade and do the other things, not because they are easy, but because they are hard (J.F.Kennedy)

27. THE CHRONIOUS SCHEMA BTW: www.chronious.eu

29. The PDA WINDOWS MOBILE 6.5 SQL SERVER CE 2005 .NET FRAMEWORK 3.5

30. Algorithms on the PDA

31. PREPROCESSING ALGORITHMS ECG LINEAR FILTERING + POLINOMYAL FITTING = REMOVAL BASE LINE WANDERING

32. PREPROCESSING ALGORITHMS ECG Daubechies (DB4) wavelets = REMOVAL HIGH FREQUENCY NOISE

33. PREPROCESSING ALGORITHMS ECG Filtering ECG with CWT and FWT + Pan-Tompkins wavelets = QRS DETECTION

34. PREPROCESSING ALGORITHMS RR Reference inspiration signal + STFT (windows size 60s) = CALCULATE REFERENCE RESPIRATION SIGNAL

35. CLASSIFICATION SYSTEM Light rule based expert system Supervised classification system

36. Light rule based Lifesaving rules extracted from the CDSS Weight increase by 2% in the last 24 hours

37. Supervised classification system SVM

38. Training set SVM

39. CLASSIFICATION SYSTEM The rules extracted have been validated by clinician

40. Controlling the stress index Bayesian network Use 9 attributes for evaluating a stress index that can be used to understand if the patient condirion can worse.

41. Problems with the PDA Heavy resource consumption -> bottleneck during preprocessing phase in case of alerting situation Difficulties on updating the training algorithms

42. Last and least