1 / 55

Prof. Dr. Bingli Jiao Wireless Communications Lab Peking University Oct. 13, 2010

Wireless ECG System. Prof. Dr. Bingli Jiao Wireless Communications Lab Peking University Oct. 13, 2010. Outline. 1. Necessarity of E-healthcare 2. Development of Wireless Healthcare in China 3. Wireless ECG System in PKU 4. HHT Algorithm for ECG Signal Diagnosis.

waiello
Download Presentation

Prof. Dr. Bingli Jiao Wireless Communications Lab Peking University Oct. 13, 2010

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Wireless ECG System Prof. Dr. Bingli Jiao Wireless Communications Lab Peking University Oct. 13, 2010

  2. Outline 1. Necessarity of E-healthcare 2. Development of Wireless Healthcare in China 3. Wireless ECG System in PKU 4. HHT Algorithm for ECG Signal Diagnosis

  3. 1. Necessarity of E-healthcare Population of elder persons   In last century, the rate of growth of the elderly population (persons 65 years old and over) has greatly exceeded the growth rate of the population of the country as a whole.   About 1 in 8 Americans were elderly in 1994, but about 1 in 5 would be elderly by the year 2030. The oldest old (persons 85 years old and over) are a small but rapidly growing group, comprising just over 1 percent of the American population in 1994. This population comprised 3.5 million persons in 1994, 28 times larger than in 1900. From 1960 to 1994, this group increased 274 percent. Overall, the oldest old are projected to be the fastest growing part of the elderly population. Bingli Jiao @ Peking University

  4. Heart disease is the leading killer of the elderly. In 1980, 3 of 4 elderly deaths were due to heart disease, cancer, or stroke. These three major causes of death still were responsible for 7 of every 10 elderly deaths in 1991. Among major disease groups, heart disease is the leading cause of death within the elderly population. The total number of deaths due to heart disease in 1991 was about the same as in 1980, at just 600,000. The need for personal assistance with everyday activities increases with age. At older ages, the proportion requiring personal assistance ranged from 9 percent for those 65 to 69 years old, to 50 percent for those 85 years old and over Bingli Jiao @ Peking University

  5. Most of International Companies have Branches in China for Preparing Wireless Healthcare Products and Marketing Bingli Jiao @ Peking University

  6. Forrester says “$34 B Market for Healthcare Unbound Technologies by 2015”80% is Chronic Care Market prospects $US (billions) ADL/elder --- Activity daily life / elder care Chronic ---- Chronic disease management Acute ---- post-hospital monitoring According to Forrester Research company Bingli Jiao @ Peking University

  7. The Investments in China The Forecast of the Investment to the Chinese Health Occupations in 2010, According to CCW Research Company (计世资讯) Bingli Jiao @ Peking University

  8. Potentials of wireless health care in China • Wireless Environments • The Number of mobile users are more than 0.7 billion in China (reported by Ministry of Industry and Information Technology on Sept. 2, 2009). • 3G and wireless LAN networks cover the most area of country and the cities, respectively. • Needs of eHealth Service in China • Information transferring between hospitals: • Only 5% hospitals are ranked as the top level, but they occupy 64% resources, such as experts and equipments. • b) Individual needs • 250 persons per doctor per in China (there are 0.278 million hospitals, and 6.169 million doctors including nurses in China, reported by Ministry of Health on Sept. 8, 2009) Bingli Jiao @ Peking University

  9. Application Cases in China (1) Ocamar Company Bingli Jiao @ Peking University

  10. Ocamar claims that they provide total solution for wireless healthcare system, which supports multi service set identifier (SSID). The networks are divided into two; (1) hospital network and (2) non-hospital network. Access to hospital network needs to pass the Wireless Network Controller (WNC) with “SSID=secure”, while access the non-hospital network with “SSID=guest” Bingli Jiao @ Peking University

  11. Application Cases in China (2) Feya Company Bingli Jiao @ Peking University

  12. Application Cases in China (3) Shenzhen New Element Company Bingli Jiao @ Peking University

  13. Commercial Cases Summary Marketing: still premature International Company: using funding to feed marketing for future, e.g., Microsoft, IBM, Motorola, developing healthcare information management software, devices, system Domestic Companies: getting into marketing for some field tests, most from startup companies, e.g., Ocamar Some of international companies doing business with medical authorities in Hong Kong, e.g., Vital Aire Company has 1000 patients for home health monitoring, and collects data for hospitals Bingli Jiao @ Peking University

  14. Outline Development of Wireless Healthcare in China Wireless ECG System in PKU HHT Algorithm for ECG Signal Diagnosis Bingli Jiao @ Peking University

  15. PKU: Wireless ECG System ECG: electrocardiogram PSDN: packet switched data network BS: base station Bingli Jiao @ Peking University

  16. PKU: Wireless ECG System Service Function Modules Mobile ECG Healthcare System Healthcare Services ECG Data Collection Terminal GPRS Wireless Communication Module On-line Consultation Data Management ECG Diagnosis Data Center and Web Server ECG Monitoring Data Mining Emergency Alarm Bingli Jiao @ Peking University

  17. PKU: Wireless ECG System Bingli Jiao @ Peking University

  18. PKU: Terminal Test Bingli Jiao @ Peking University

  19. PKU: Test Board Bingli Jiao @ Peking University

  20. PKU: Monitoring Server Bingli Jiao @ Peking University

  21. Outline Development of Wireless Healthcare in China Wireless ECG System in PKU HHT Algorithm for ECG Signal Diagnosis Bingli Jiao @ Peking University

  22. HHT Algorithm for ECG Signal Diagnosis In 1998, Hilbert-Huang Transformation (HHT) method was proposed for analyzing non-stationary and nonlinear data[1]. The method can be divided into two-step consisting of empirical mode decomposition (EMD) and Hilbert spectral analysis. X(t) 2 1 S(t) 0 -1 -2 10 20 30 40 50 60 70 80 90 100 110 120 Step 0: obtain the original signal Bingli Jiao @ Peking University

  23. In the EMD step, the algorithm generates Intrinsic Model Functions(IMF), as follows: • (1) Connect all maximum points of x(t) by cubic spline line • (2) Connect all minimum points of x(t) by cubic spline line • Calculate an average line, , and, then, generate a proto mode IMF by • repeat (1), (2) for , we calculate an average line, ,and then another proto mode IMF • . • . • till IMF1 is obtained (with a stopping cretiria) by . Bingli Jiao @ Peking University

  24. Then one calculate IMF2 starting from the residue • . • which will be used as in above processing procedures. • Finally, the input signal, x(t) can be expressed by Bingli Jiao @ Peking University

  25. EMD Process (4) IMF 1; iteration 0 2 1 S(t) 0 -1 -2 10 20 30 40 50 60 70 80 90 100 110 120 Step 1: Find the local maximum points Bingli Jiao @ Peking University

  26. EMD Process (5) IMF 1; iteration 0 2 1 S(t) 0 -1 -2 10 20 30 40 50 60 70 80 90 100 110 120 Step 2: Construct the envelope of local maximum points Bingli Jiao @ Peking University

  27. EMD Process (6) IMF 1; iteration 0 2 1 S(t) 0 -1 -2 10 20 30 40 50 60 70 80 90 100 110 120 Step 3: Find the local minimum points Bingli Jiao @ Peking University

  28. EMD Process (7) IMF 1; iteration 0 2 1 S(t) 0 -1 -2 10 20 30 40 50 60 70 80 90 100 110 120 Step 4: Construct the envelope of local minimum points Bingli Jiao @ Peking University

  29. EMD Process (8) IMF 1; iteration 0 2 1 S(t) 0 -1 -2 10 20 30 40 50 60 70 80 90 100 110 120 Step 5:compute the mean value defined by the local maximum & minimum envelope Bingli Jiao @ Peking University

  30. EMD Process (9) IMF 1; iteration 0 2 1 m1 S(t) 0 -1 -2 10 20 30 40 50 60 70 80 90 100 110 120 residue 1.5 1 0.5 S(t)-m1=h1 h1 0 -0.5 -1 -1.5 10 20 30 40 50 60 70 80 90 100 110 120 Step 6: The difference between the original signal and the mean value is defined as 1st component h1 Bingli Jiao @ Peking University

  31. EMD Process (10) IMF 1; iteration 1 1.5 1 0.5 h1 0 -0.5 -1 -1.5 10 20 30 40 50 60 70 80 90 100 110 120 residue Sifting Purpose: ●remove the carrier waves ●make waveforms much more symmetrical Sifting process must be repeated many times before achieving these purposes! Bingli Jiao @ Peking University

  32. EMD Process (11) IMF 1; iteration 1 1.5 1 0.5 h1 0 -0.5 -1 -1.5 10 20 30 40 50 60 70 80 90 100 110 120 residue Step 1: Find the local maximum points Repeat Iteration 0 ! Bingli Jiao @ Peking University

  33. EMD Process (12) IMF 1; iteration 1 1.5 1 0.5 h1 0 -0.5 -1 -1.5 10 20 30 40 50 60 70 80 90 100 110 120 residue Step 2: Construct the envelope of local maximum points Bingli Jiao @ Peking University

  34. EMD Process (13) IMF 1; iteration 1 1.5 1 0.5 h1 0 -0.5 -1 -1.5 10 20 30 40 50 60 70 80 90 100 110 120 residue Step 3: Find the local minimum points Bingli Jiao @ Peking University

  35. EMD Process (14) IMF 1; iteration 1 1.5 1 0.5 h1 0 -0.5 -1 -1.5 10 20 30 40 50 60 70 80 90 100 110 120 residue Step 4: Construct the envelope of local minimum points Bingli Jiao @ Peking University

  36. EMD Process (15) IMF 1; iteration 1 1.5 1 0.5 h1 0 -0.5 -1 -1.5 10 20 30 40 50 60 70 80 90 100 110 120 residue 1.5 1 0.5 h1-m1=h11 0 -0.5 -1 Step 5:After the second cycle, we get the new 1st component h11 -1.5 10 20 30 40 50 60 70 80 90 100 110 120 Bingli Jiao @ Peking University

  37. EMD Process (16) IMF 1; iteration 8 1 0.5 h1(k-1) m1k 0 -0.5 -1 10 20 30 40 50 60 70 80 90 100 110 120 residue 1 SD<0.1 0.5 h1(k-1) -m1k=h1k IMF1 0 -0.5 -1 10 20 30 40 50 60 70 80 90 100 110 120 Bingli Jiao @ Peking University

  38. EMD Process (17) IMF 2; iteration 5 1 0.5 h24 m2k 0 -0.5 -1 10 20 30 40 50 60 70 80 90 100 110 120 residue 1 SD<0.1 0.5 h2(k-1) –m2k=h2k 0 IMF2 -0.5 -1 10 20 30 40 50 60 70 80 90 100 110 120 Bingli Jiao @ Peking University

  39. EMD Process (18) IMF 3; iteration 12 0.2 0.1 m3k 0 -0.1 -0.2 10 20 30 40 50 60 70 80 90 100 110 120 residue 0.2 h3k 0.1 SD<0.1 0 -0.1 IMF3 -0.2 10 20 30 40 50 60 70 80 90 100 110 120 Bingli Jiao @ Peking University

  40. EMD Process (19) IMF 4; iteration 16 0.15 0.1 0.05 0 -0.05 -0.1 -0.15 10 20 30 40 50 60 70 80 90 100 110 120 residue 0.15 0.1 0.05 0 -0.05 IMF4 -0.1 -0.15 10 20 30 40 50 60 70 80 90 100 110 120 Bingli Jiao @ Peking University

  41. EMD Process (20) IMF 5; iteration 11 0.1 0.05 S(t) 0 -0.05 -0.1 10 20 30 40 50 60 70 80 90 100 110 120 residue 0.1 0.05 0 -0.05 IMF5 -0.1 10 20 30 40 50 60 70 80 90 100 110 120 Bingli Jiao @ Peking University

  42. EMD Result (21) Empirical Mode Decomposition imf1 imf2 imf3 imf4 imf5 imf6 res. 10 20 30 40 50 60 70 80 90 100 110 120 Bingli Jiao @ Peking University

  43. HHT Algorithm for ECG Signal Diagnosis • Additional example • X(t) == IMF === Bingli Jiao @ Peking University

  44. HHT Theory Basis • Hilbert Transform( HT ) • By omitting the residue, one use Hilbert to find instant frequency Bingli Jiao @ Peking University

  45. HHT Advantage Data analysis whether from physical measurements or numerical modeling, most likely will have one or more of the following problems: (a) the total data span is too short; (b) the data are non-stationary; and (c) the data represent nonlinear processes. Fourier spectrum defines uniform harmonic components globally, and it can’t tell us when the exact frequency component occur. But Hilbert spectrum is very useful in regrouping the decomposed data in the time-frequency space; it is a local and adaptive method of analysis. The HHT algorithm has proved to be a powerful procedure for analyzing non-stationary and nonlinear data. Since its introduction, many applications have been found, which include analyzing acoustic, biological, ocean, earthquake, climate and mechanical vibration data. Bingli Jiao @ Peking University

  46. EMD Process (1) The decomposition termination condition of each IMF: Bingli Jiao @ Peking University

  47. Hilbert Spectrum Application (22) (a)The calibration data composed of two different cosine functions (b)The Hilbert Spectrum for the calibration data Bingli Jiao @ Peking University

  48. ECG signals A major issue: How to track and analyze QRS waves? 4 5 5 Bingli Jiao @ Peking University

  49. ECG Signal Diagnosis WaveletTransform Algorithm Hilbert-Huang Transform Algorithm • Data sources from MIT-BIH arrhythmia database • Official link: http://www.physionet.org/physiobank/database/mitdb/ • Use MATLAB to convert the binary data source to decimal Bingli Jiao @ Peking University

  50. 106.dat 3 2.5 2 1.5 1 Voltage /mv 0.5 0 -0.5 -1 0 2 4 6 8 10 Time /s Typic ECG signals Bingli Jiao @ Peking University

More Related