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Blockchain based automatic diagnosis of the 12-lead ECG using a deep neural network

Blockchain based automatic diagnosis of the 12-lead ECG using a deep neural network

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Blockchain based
automatic diagnosis of
the 12-lead ECG using a
deep neural network
Paper
Title
1
Motivatio
n
15.16%
IN BD
75%
IN BD
110M
MEN
80M
WOMEN
Coronary heart patients
Death rate on heart
attack
1 in 6
Death on heart attack
Death responsible for
heart diseases in Low
and middle income
countries. 2
Problem
Statement
1 Doctor Insufficiency
5.6 per 10,000
2 In Rural Area
Not always available cardiologist,
most are MBBS or Paramedical
doctors.
3 Emergency section
Consultant are available not
specialist.
4 Coronary Diagnosis
ECG is a primary diagnosis,
sometimes doctors find
difficulties to infer proper
result.
5
Extended Diagnosis
Doctors then give ETT and
ECHO test to detect specific
heart condition.
3
Our Research
6
Abnormalities
1st degree AV block (1dAVb)
Right bundle branch block (RBBB)
Sinus tachycardia (ST)
Atrial fibrillation (AF)
Sinus bradycardia (SB)
Left bundle branch block (LBBB)
First Automatically detect Abnormal ECG signal
4
Tools and
technologies
Pytho
n
Jupyter
Noteboo
k
Excel
Solidit
y
5
Methodology
Deep Neural Network (DNN) architecture:
6
Ad

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Blockchain based automatic diagnosis of the 12-lead ECG using a deep neural network

  • 1. Blockchain based automatic diagnosis of the 12-lead ECG using a deep neural network Paper Title 1
  • 2. Motivatio n 15.16% IN BD 75% IN BD 110M MEN 80M WOMEN Coronary heart patients Death rate on heart attack 1 in 6 Death on heart attack Death responsible for heart diseases in Low and middle income countries. 2
  • 3. Problem Statement 1 Doctor Insufficiency 5.6 per 10,000 2 In Rural Area Not always available cardiologist, most are MBBS or Paramedical doctors. 3 Emergency section Consultant are available not specialist. 4 Coronary Diagnosis ECG is a primary diagnosis, sometimes doctors find difficulties to infer proper result. 5 Extended Diagnosis Doctors then give ETT and ECHO test to detect specific heart condition. 3
  • 4. Our Research 6 Abnormalities 1st degree AV block (1dAVb) Right bundle branch block (RBBB) Sinus tachycardia (ST) Atrial fibrillation (AF) Sinus bradycardia (SB) Left bundle branch block (LBBB) First Automatically detect Abnormal ECG signal 4
  • 6. Methodology Deep Neural Network (DNN) architecture: 6
  • 7. 1 2 3 4 5 C u r r e n t P r o g r e s s 17 paper study Dataset Research Building DNN Model Training Detect Abnormal ECG Signal 7
  • 8. 2 3 4 11 Predict specific 6 abnormalities from ECG signal Blockchain Integration Develop DNN Model for specific prediction Hyperparameter tuning & Statistical analysis FUTU RE WORK 8
  • 9. Timeline Read Dataset September October November December January February Pre-Processing & Feature Selection Build DNN Model Train DNN Model & Evaluation Paper Writing Work Review 9
  • 10. References 1] G. A. Roth et. al., “Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: A systematic analysis for the Global Burden of Disease Study 2017,” The Lancet, vol. 392, pp. 1736–1788, Nov. 2018. [2] J. L. Willems, C. Abreu-Lima, P. Arnaud, J. H. van Bemmel, C. Brohet, R. Degani, B. Denis, I. Graham, G. van Herpen, and P. W. Macfarlane, “Testing the performance of ECG computer programs: The CSE diagnostic pilot study,” Journal of Electrocardiology, vol. 20 Suppl, pp. 73–77, Oct. 1987. [3] J. Schl¨apfer and H. J. Wellens, “Computer-Interpreted Electrocardiograms: Benefits and Limitations,” Journal of the American College of Cardiology, vol. 70, p. 1183, Aug. 2017. [4] J. L. Willems, C. Abreu-Lima, P. Arnaud, J. H. van Bemmel, C. Brohet, R. Degani, B. Denis, J. Gehring, I. Graham, and G. van Herpen, “The diagnostic performance of computer programs for the interpretation of electrocardiograms,” The New England Journal of Medicine, vol. 325, pp. 1767–1773, Dec. 1991. [5] A. P. Shah and S. A. Rubin, “Errors in the computerized electrocardiogram interpretation of cardiac rhythm.,” Journal of Electrocardiology, vol. 40, no. 5, pp. 385–390, 2007 Sep-Oct. [6] N. A. M. Estes, “Computerized interpretation of ECGs: Supplement not a substitute,” Circulation. Arrhythmia and Electrophysiology, vol. 6, pp. 2– 4, Feb. 2013. [7] G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury, “Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups,” IEEE Signal Processing Magazine, vol. 29, pp. 82–97, Nov. 2012. 10
  • 11. References [8] G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury, “Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups,” IEEE Signal Processing Magazine, vol. 29, pp. 82–97, Nov. 2012. [9] W. W. Stead, “Clinical implications and challenges of artificial intelligence and deep learning,” JAMA, vol. 320, pp. 1107–1108, Sept. 2018. [10] Naylor C, “On the prospects for a (deep) learning health care system,” JAMA, vol. 320, pp. 1099–1100, Sept. 2018. [11] G. Hinton, “Deep learning—a technology with the potential to transform health care,” JAMA, vol. 320, pp. 1101–1102, Sept. 2018. [12] World Health Organization, Global Status Report on Noncommunicable Diseases 2014: Attaining the Nine Global Noncommunicable Diseases Targets; a Shared Responsibility. Geneva: World Health Organization, 2014. OCLC: 907517003. [13] N. A. M. Estes, “Computerized interpretation of ECGs: Supplement not a substitute,” Circulation. Arrhythmia and Electrophysiology, vol. 6, pp. 2–4, Feb. 2013. [14] G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury, “Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups,” IEEE Signal Processing Magazine, vol. 29, pp. 82–97, Nov. 2012. [15] R. Sassi, R. R. Bond, A. Cairns, D. D. Finlay, D. Guldenring, G. Libretti, L. Isola, M. Vaglio, R. Poeta, M. Campana, C. Cuccia, and F. Badilini, “PDF- ECG in clinical practice: A model for long-term preservation of digital 12-lead ECG data,” Journal of Electrocardiology, vol. 50, no. 6, pp. 776–780, 2017 Nov - Dec 11
  • 12. References [16] G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury, “Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups,” IEEE Signal Processing Magazine, vol. 29, pp. 82–97, Nov. 2012. [17] M. B. Alkmim, R. M. Figueira, M. S. Marcolino, C. S. Cardoso, M. Pena de Abreu, L. R. Cunha, D. F. da Cunha, A. P. Antunes, A. G. d. A. Resende, E. S. Resende, and A. L. P. Ribeiro, “Improving patient access to specialized health care: The Telehealth Network of Minas Gerais, Brazil,” Bulletin of the World Health Organization, vol. 90, pp. 373–378, May 2012. 12