IDENTIFICATION OF CARDIAC ARRHYTHMIA USING ARTIFICIAL NEURAL NETWORK: A PRACTICAL APPROACH

1. IDENTIFICATION OF CARDIAC ARRHYTHMIA USING ARTIFICIAL NEURAL NETWORK: A PRACTICAL APPROACH

2. What you should know? • ECG stands for Electrocardiogram which is the recording of the electrical activity of the heart. • It is the only biological signal that has a explainable morphology and is periodic. • Very important in the medical diagnosis and easily recordable.

3. How to interpret ECG tracing

4. Cardiac Arrhythmia: The loss of periodic or normal rhythm of the heart. There are 24 kinds. • Ventricular premature beats: The early rise of QRS wave after the P wave. Premature ventricular depolarisation. Represents the case of ventricular fibrillation, tachycardia. • Fusion beats: Arise due to ectopic beats (beats that originate at location other than SA node) and coincide or overlap with the normal rhythm. May indicate towards abnormal activity and serious hormonal disturbances

5. Problem Definition • More than 35 million ECG’s are taken worldwide • Used for detection and diagnosis of different cases of cardiac problems and other physiological parameter disturbances. • All are interpreted by doctors and result completely depends upon their personal experience. Medication follows

6. EMA Journal Report, University of Auckland • 48 senior trainees and 74 other trainees that included 53 basic ones. • Accuracy for senior trainees was found to be 67.5% . • In life threatening cases such as ventricular tachycardia and ventricular fibrillation it was 43.8% and 73.8% respectively

7. Need of solution • Misinterpretation may lead faulty diagnosis and treatment. • May pose threat to the life of the patient. • Doctors expert in interpretation are not available in the rural areas. • Increased complexity of ailments in patients increases the problem more during diagnosis

8. Alas! Solution Exists… • An approach can be through the artificial neural networks (ANN). • ANN is the simulation of the biological neuron for flexible computing capabilities and in advanced stage known as ARTIFICIAL INTELLIGENCE (AI)

9. Eureka! : The IDEA • 84 neural networks designed • Three architectures were chosen: Backpropagation Feedforward, Elman Network and Cascade Forward network • They were modified using the network parameters like, number of hidden neurons, transfer function, number of inputs for training etc.

10. Continued… • Model designed was stochastic in nature and a lot of patient inputs were used to train it properly and upto a satisfactory condition • The data was taken from MIT-BIH Arrhythmia database using Cygwin, an open source GNU software. Thanks to Dr. George Moody, who helped me a lot

11. Data Arrangement: Patient Inputs • Our objective was to design an efficient model for the ventricular premature beats, fusion beats and the normal heart beats • Data was taken from patient number 208 of the database and was arranged in a 301x1 matrix where 301 represents different samples of 1 patient

12. shows a sample of a ECG recording from the database. The records contain two leads: in most records, the upper signal is a modified limb lead II (MLII), obtained by placing the electrodes on the chest. The lower signal is usually a modified lead V1 (occasionally V2 or V5, and in one instance V4); as for the upper signal, the electrodes are also placed on the chest.

13. THE DATA WAS ARRANGED IN SUCH A WAY THAT THE 151ST SAMPLE OUT OF 301 SAMPLES, WAS THE R PEAK OF THE QRS COMPLEX OF THE ECG SIGNAL Sample no: 2092 2093 2094 . .. 2242 . . .2390 2391 2392 MLII values: 933 927 922. . . . 1265 . . . 992 991 990 151st bit R peak value

14. Continued… • We made two more considerations: Cases of VPB and FB were taken from all other patients also • All other patient data available was also included as a fourth case of unclassified patients. • networks were trained with total of 2921 patient data of all cases.

15. RESULTS! • Results varied for all kinds of network , best are mentioned here • For Ventricular Premature beats: Accuracy 98.3% with feedforward model • For fusion beats: 92% accuracy with Feedforward network model • For normal beats: 99.3% accuracy with cascade forward model

17. MATLAB code ECG ANALYSIS ANN HARDWARE IDENTIFIED ABNORMALITY ECG machine SIGNAL PROCESSOR SYNCHRONISED The Practical Approach !

18. ECG hardware • Will consist of Modified Limb Two system • MLII system is most acceptable for rough conditions where possibilities of high muscle artifacts are present. • The data taken will be sent to the Signal Processor hardware

19. Signal Processor • MSP 430 signal processor. High reliability and practical for rough conditions • Designed specially for signal processing and has desirable features. • The data taken from ECG will be converted using 11 bit ADC or 16bit for more resolution

20. Continued… • Converted data will be then sent to a programmed window • Taking a fiducial point 5ms after the signal has been received, the next highest value in 30ms duration will be considered as the R peak • When R peak has been detected once, then the hardware takes 150 samples before R peak and 150 samples after R peak. Thus making 301 sample one patient data.

21. ANN HARDWARE An ARM7 based controller integrates itself with the DSP hardware and to receive the 301 sample input and the code in its flash that deals with the whole neuronal calculations

22. TxD transmitted patient data • DSP hardware • Addition of results and bias • Transferred values are multiplied consecutively to weights and added to previous product • Tansigmoid Function tansig(n)=[2/(1+exp(-2n)]-1 • W2 multiplication Internal RAM • Addition and Tansigmoid multiplication • Threshold Ɵ>=0.5 • Transmit to LCD : if yes POSITIVE • if no NEGATIVE

23. Why use ARM7 controllers? • Need of large RAM for complex calculations • Faster processing of mathematical calculations • Reduction in size, though an ASIC based chip will reduce the size further • Reliable USART system and low power consumption • ISP feature more reliable and enhanced

24. What so unique about this concept? FEATURES & TECHNICAL PROBLEMS

25. FEATURES • Simple technology and efficient • Low cost hardware • ASIC capabilities present • Small size and portability: Mobile application • Gives standard for acceptable prediction of ECG • Eliminates higher chances of misinterpretation • Integration capabilities with the normal ECG machine with some modifications

26. TECHNICAL CONSTRAINTS • Moderation in testing for practical situations • Synchronization care: Single bit sensitivity and calculations • Professionalized testing process and hardware design: Data Redundancy Considerations • 3 stage process more prone to technical faults • Need of ASIC increases R&D costs

27. What else it can do actually? POSSIBILITIES!!

28. Inclusion of all cases of cardiac arrhythmia along with the HRV factor to include MI prediction capabilities to the machine • Decrease in size through ASIC technology to make it portable • ASIC when applied will help to integrate it with the telemetry system and remote monitoring of patients under intensive care or special care can be done anywhere in the world. • With ASIC technology it is also possible to integrate the same with pacemakers and keep local hospital alert in case of emergency.

29. What is the social relevance of this work? Social paraphernalia

30. Healthcare scenario • The healthcare facilities in rural India are extremely poor and unevenly distributed. • Government schemes are not sufficient to meet the challenges • It avoids taking innovative steps at large scale due to some reasons. • Doctors are poorly informed and less experienced.

31. Cardiovascular patients are on a rise and hence better treatment facilities are important

32. More than just research paper publications, and landing rovers on Moon and Mars, we need to solve some basic problems with simple solutions!

33. THANKING YOU Project Guide: Dr. Shahanaz Ayub Reader, Department of E&C, BIET Jhansi Presented By: Ashutosh Kumar Pandey B.Tech Biomedical Engineering Bundelkhand University