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. Intelligent Heart Disease
Recognition using Neural Networks

3. Tele-ECG System

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. 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. 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. 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. 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. 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. 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. 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. ECG interpretation

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. Right Bundle Branch Block Beat
Diagnosis of RBBB is mainly based on widened
QRS 0.12 seconds or more

15. Atrial Premature Contraction Beat (APC)
 A premature beat, appears early than expected
 Decrease in R-R interval

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. 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. 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. 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. 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. 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. Results

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. Pre-Processing of ECG signal
 Filtering of the ECG signal
 Removing High Frequency components
 Removing Low Frequency components
 Removing Power line interference

25. Low pass filter

26. Notch filter

27. High pass filter

28. Removing High Frequency components

29. Removing Base line wandering

30. Signal Smoothing

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. Detection of QRS points
 Steps involved:
 Derivate
 Thresholding
 Matlab programming

33. Derivative of Normal ECG

34. Threshold value of Derivative signal

35. Detection of Q, R and S points

36. Detection of 5 QRS waves

37. Detection for 10 QRS waves

38. Detection of other points

39. All points detection of Normal signal

40. QRS points detection of database with
Cardiac problem

41. Compression of the signal

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. 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. 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. 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. Intelligent Heart Disease
Recognition using Neural Networks
Thank you