Intelligent Heart Disease Recognition using Neural Networks

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Intelligent Heart Disease Recognition using Neural Networks

  1. 1. 24th Annual Multi-topic International Symposium IEEEP Karachi 2009 Intelligent Heart Disease Recognition using Neural Networks Engr. Dileep Kumar Dr. Bhawani Shanker Chowdhry Engr. Attiya Baqai Engr. Bhagwandas Mehran University of Engineering and Technology Jamshoro Pakistan
  2. 2. Intelligent Heart Disease Recognition using Neural Networks
  3. 3. Tele-ECG System
  4. 4. Tele ECG in the World  “A wireless ECG system for continuous event recording and communication to a clinical alarm station” Fensli, R.; Gunnarson, E.; Hejlesen, O Engineering in Medicine and Biology Society, 2004. IEMBS apos;04. 26th Annual International Conference of the IEEE.  Online Tele-ECG System in a remote Brazilian Emergency Room: The evaluation of the time interval from door to discharge of cardiac patients Adolfo L F Sparenberg1,2, Thais Russomano1,2 , Eleonora R Soares2, Tatiane Schaun2, Ricardo B Cardoso1, Robert Timm2,  Scientists of the Bhabha Atomic Research Centre India have developed a compact, low-cost and portable tele-electro cardiogram (ECG) system that could be controlled by mobile phone by means of a bluetooth connection in Feb 2009.
  5. 5. Aim of our research  Provide Automatic cardiac disease Analysis.  Remove the need of a specialized cardiologist  Make expert and accurate judgments based on patient’s past record  Remote diagnosis during absence of medical personnel and experts  A simple, easy and cost effective diagnosis  Provide continuous storage and assessment of patient’s ECG
  6. 6. Main features of Neural Networks  Neural networks learn by experience rather than by remodeling or reprogramming ………Intelligence  They have the ability to generalize  They do not require a prior understanding of the process  They are robust to noisy data  VLSI implementation is very easy
  7. 7. Current and future research  This research is ongoing in various parts of world  There are annual international competitions for getting better results in this research area  Highly accurate results have not been obtained till today
  8. 8. Main Goals of our research  To carry out research into Automated interpretation of ECG signal.  To find the efficient features of the ECG signal  To use Neural Networks for signal classification
  9. 9. Software tools and ECG data used in this research  Simulation based research, in MATLAB  Signal processing toolbox of Matlab SPtool  Neural Network tool box for Matlab NNtool  Availability of real time ECG data of patients at MIT-BIH database
  10. 10. Experimental tasks of our research  Signal pre-processing of ECG for noise removal  Interpretation of ECG  QRS complex and other fiducially obtained point detection  Efficient feature extraction for input to NN  Classification of cardiac problems using Neural Networks Fiducial SIGNAL QRS Feature NN PRE- Point Detection Extraction Classification PROCESSING Detection
  11. 11. Electrocardiogram (ECG) Normal ECG values for healthy persons Wave Duration (s) P 0.08-0.10 QRS 0.06-0.10 T 0.12-0.16 Shape of ECG
  12. 12. ECG interpretation
  13. 13. Cardiac conditions  Normal beat  Right Bundle Branch Block beat (RBBB)  Left Bundle Branch Block beat (LBBB)  Atrial Premature Contraction beat (APC)  Ventricular premature Contraction beat (VPC)  Paced beat
  14. 14. Right Bundle Branch Block Beat Diagnosis of RBBB is mainly based on widened QRS 0.12 seconds or more
  15. 15. Atrial Premature Contraction Beat (APC)  A premature beat, appears early than expected  Decrease in R-R interval
  16. 16. Data for NN Classification Feature Type Source Description 1 M R, S waves R-S interval 2 M P, R waves P-R interval 3 M QRS complex QRS width 4 M Q, T waves Q-T interval 5 M R waves R amplitude 6 M R waves R-R interval (HBR) 7 S QRS complex QRS energy 8 S ECG waves Auto correlation coefficient 9 S ECG waves Mean or expectation vector 10 S Histogram Maximum Amplitude of the signal 11-23 C ECG waveform 13 compressed ECG sample M= Morphological points S=Statistical points C=compressed points
  17. 17. Compressed Signals  ECG contains 360 points for one beat but to reduce complexity we have taken 52 essential points and further the signal is compressed to 4:1
  18. 18. Data for NN Classification Feature Type Source Description 1 M R, S waves R-S interval 2 M P, R waves P-R interval 3 M QRS complex QRS area 4 M Q, T waves Q-T interval 5 M R waves R amplitude 6 M R waves HBR 7 S QRS complex QRS energy 8 S ECG waves Auto correlation coefficient 9 S ECG waves Mean or expectation vector 10 S Histogram Maximum Amplitude of the signal 11-23 C ECG waveform 13 compressed ECG sample M= Morphological points S=Statistical points C=compressed points
  19. 19. Experimental tasks of our research  Signal pre-processing of ECG for noise removal  Interpretation of ECG  QRS complex and other fiducially obtained point detection  Efficient feature extraction for input to NN  Classification of cardiac problems using Neural Networks Fiducial SIGNAL QRS Feature NN PRE- Point Detection Extraction Classification PROCESSING Detection
  20. 20. Simple architecture interconnections  Architecture of Neural Networks made in our research. This is feed forward back propagation. R-S N P-R A Q-T V R Comp 1 L Comp 2 P 23 element Input Hidden Layer 6 neurons Output Layer Layer
  21. 21. Network used in our research  Training function  Learning function  Number of hidden layers  Number of Neurons in hidden layers  Number of nodes in input and output layer  Time for training  Number of epochs
  22. 22. Results
  23. 23. Experimental tasks of our research  Signal pre-processing of ECG for noise removal  Interpretation of ECG  QRS complex and other fiducially obtained point detection  Efficient feature extraction for input to NN  Classification of cardiac problems using Neural Networks Fiducial SIGNAL QRS Feature NN PRE- Point Detection Extraction Classification PROCESSING Detection
  24. 24. Pre-Processing of ECG signal  Filtering of the ECG signal  Removing High Frequency components  Removing Low Frequency components  Removing Power line interference
  25. 25. Low pass filter
  26. 26. Notch filter
  27. 27. High pass filter
  28. 28. Removing High Frequency components
  29. 29. Removing Base line wandering
  30. 30. Signal Smoothing
  31. 31. Experimental tasks of our research  Signal pre-processing of ECG for noise removal  Interpretation of ECG  QRS complex and other fiducial point detection  Efficient feature extraction for input to NN  Classification of cardiac problems using Neural Networks Fiducial SIGNAL QRS Feature NN PRE- Point Detection Extraction Classification PROCESSING Detection
  32. 32. Detection of QRS points  Steps involved:  Derivate  Thresholding  Matlab programming
  33. 33. Derivative of Normal ECG
  34. 34. Threshold value of Derivative signal
  35. 35. Detection of Q, R and S points
  36. 36. Detection of 5 QRS waves
  37. 37. Detection for 10 QRS waves
  38. 38. Detection of other points
  39. 39. All points detection of Normal signal
  40. 40. QRS points detection of database with Cardiac problem
  41. 41. Compression of the signal
  42. 42. Data for NN Classification Features Type Source Description 1 M R, S waves R-S interval 2 M P, R waves P-R interval 3 M QRS complex QRS area 4 M Q, T waves Q-T interval 5 M R waves R amplitude 6 M R waves HBR 7 S QRS complex QRS energy 8 S ECG waves Auto correlation coefficient 9 S ECG waves Mean or expectation vector 10 S Histogram Maximum Amplitude of the signal 11-23 C ECG waveform 13 compressed ECG sample M= Morphological points S=Statistical points C=compressed points
  43. 43. Training of Network Training performance of Network. Training Samples: 100 Testing Samples: 100 Network Input Output Hidden Epochs Elapsed MSE Layer layer Layer Time (sec) Classifier 23 6 5 12 9.0650 0.00484 Training Performance of Network
  44. 44. Classification Results Training Samples: 100 Testing Samples: 100 Network Input Neurons Output Neurons Heart Problem Recognition rate % Average rate % N 88 A 96 V 91 Classifier 23 6 90.6 R 98 L 84 P 87 N = Normal Beat A = Atrial Premature Beat V = Ventricular Premature Beat Beat R = Right Bundle Branch Block Beat L = Left Bundle Branch Block P = Paced Beat
  45. 45. Conclusion and Future Recommendations  The aim of the research was to find the efficient features which are given to NN for classification.  We have worked on only 6 beats and in future this can be extended to greater number of beats. We used 100 number of Training and testing samples and this can be extended, for greater average rate of recognition.  This is ongoing research and is conducted in different part of world and different competitions are held worldwide for best classification at www.physionet.org
  46. 46. Intelligent Heart Disease Recognition using Neural Networks Thank you

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