Biomedical Arrhythmia Heart Diseases Classification Based on Artificial Neural Network and Machine Learning Approach

Authors

  • Stalin Subbiah
  • Suresh Subramanian

DOI:

https://doi.org/10.14419/ijet.v7i3.27.17642

Published:

2018-08-15

Keywords:

Electrocardiogram, extreme learning machine, support vector machine, back prorogation neural network, MIT-BIH arrhythmia database.

Abstract

In present day, several types of developments are carried toward the medical application.  There has been increased improvement in the processing of ECG signals. The accurate detection of ECG signals with the help of detection of P, Q, R and S waveform. However these waveforms are suffered from some disturbances like noise.  Initially denoising the ECG signal using filters and detect the PQRS waveforms. Four filters are carried out to remove the ECG noises that are Median, Gaussian, FIR and Butterworth filter. ECG signal is analyzed or classify using Extreme Learning Machine (ELM) and it compared with Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN). The paper classifies the ECG signal into two classes, Normal and Abnormal. ECG waveform is detected and analyzed using the 48 records of the MIT-BIH arrhythmia database. Denoising results are evaluated using MSE, RMSE, PSNR, NAE and NCC. The classifier performance is measured in terms of Sensitivity (Se), Positive Predictivity (PP) and Specificity (SP).

 

 

 

References

[1] Maglaveras N, Stamkopoulos T, Diamantaras K, Pappas C & Strintzis M, “ECG pattern recognition and classification using non-linear transformations and neural networks: a reviewâ€, International journal of medical informatics, Vol.52, No.1-3, (1998), pp.191-208.

[2] De Chazal P, Celler BG & Reilly RB, “Using wavelet coefficients for the classification of the electrocardiogramâ€, IEEE 22nd Annual International Conference of the Engineering in Medicine and Biology Society, (2000), pp.64-67.

[3] Srinivasan N, Ge DF & Krishnan SM, “Autoregressive modeling and classification of cardiac arrhythmiasâ€, IEEE 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society, (2002), pp.1405-1406.

[4] Hussain H & Fatt LL, “Efficient ECG signal classification using sparsely connected radial basis function neural networkâ€, 6th WSEAS International Conference on Circuits, Systems, Electronics, Control and Signal Processing, (2007), pp.412-416.

[5] Risk MR, Sobh JF & Saul JP, “Beat detection and classification of ECG using self organizing mapsâ€, IEEE 19th Annual International Conference of the Engineering in Medicine and Biology Society, (1997), pp.89-91.

[6] Özbay Y, Ceylan R & Karlik B, “Integration of type-2 fuzzy clustering and wavelet transform in a neural network based ECG classifierâ€, Expert Systems with Applications, Vol.38, No.1, (2011), pp.1004-1010.

[7] Saxena SC, Kumar V & Hamde ST, “Feature extraction from ECG signals using wavelet transforms for disease diagnosticsâ€, International Journal of Systems Science, Vol.33, No.13, (2002), pp.1073-1085.

[8] Foo SY, Stuart G, Harvey B & Meyer-Baese A, “Neural network-based EKG pattern recognitionâ€, Engineering Applications of Artificial Intelligence, Vol.15, No.3-4, (2002), pp.253-260.

[9] Sternickel K, “Automatic pattern recognition in ECG time seriesâ€, Computer methods and programs in biomedicine, Vol.68, No.2, (2002), pp.109-115.

[10] Güler Ä° & Ãœbeylı ED, “ECG beat classifier designed by combined neural network modelâ€, Pattern recognition, Vol.38, No.2, (2005), pp.199-208.

[11] Ãœbeyli ED, “Combining recurrent neural networks with eigenvector methods for classification of ECG beatsâ€, Digital Signal Processing, Vol.19, No.2, (2009), pp.320-329.

[12] Korürek M & DoÄŸan B, “ECG beat classification using particle swarm optimization and radial basis function neural networkâ€, Expert systems with Applications, Vol.37, No.12, (2010), pp.7563-7569.

[13] De Gaetano A, Panunzi S, Rinaldi F, Risi A & Sciandrone M, “A patient adaptable ECG beat classifier based on neural networksâ€, Applied Mathematics and Computation, Vol.213, No.1, (2009), pp.243-249.

[14] Choi S, Adnane M, Lee GJ, Jang H, Jiang Z & Park HK, “Development of ECG beat segmentation method by combining low pass filter and irregular R–R interval checkup strategyâ€, Expert Systems with Applications, Vol.37, No.7, (2010), pp.5208-5218.

[15] Ning X & Selesnick IW, “ECG enhancement and QRS detection based on sparse derivativesâ€, Biomedical Signal Processing and Control, Vol.8, No.6, (2013), pp.713-723.

[16] Poungponsri S & Yu XH, “An adaptive filtering approach for electrocardiogram (ECG) signal noise reduction using neural networksâ€, Neuro computing, Vol.117, (2013), pp.206-213.

[17] Yeh YC, Chiou CW & Lin HJ, “Analyzing ECG for cardiac arrhythmia using cluster analysisâ€, Expert Systems with Applications, Vol.39, No.1, (2012), pp.1000-1010.

[18] Saini I, Singh D & Khosla A, “QRS detection using K-Nearest Neighbor algorithm (KNN) and evaluation on standard ECG databasesâ€, Journal of advanced research, Vol.4, No.4, (2013), pp.331-344.

[19] Sadhukhan D & Mitra M, “R-peak detection algorithm for ECG using double difference and RR interval processingâ€, Procedia Technology, Vol.4, (2012), pp.873-877.

[20] Rai HM, Trivedi A & Shukla S, “ECG signal processing for abnormalities detection using multi-resolution wavelet transform and Artificial Neural Network classifierâ€, Measurement, Vol.46, No.9, (2013), pp.3238-3246.

[21] Mark R & Moody G, “MIT-BIH arrhythmia database directoryâ€, Cambridge: Massachusetts Institute of Technology, (1988).

[22] Stallin S, Rajkumar P & Rajendran K, “Reduction of Noises in ECG Signal by Various Filtersâ€, International Journal of Engineering Research & Technology, Vol.3, No.1, (2014).

[23] Burges CJC, “A tutorial on support vector machines for pattern recognitionâ€, Data mining and knowledge discovery, Vol.2, (1998), pp.955–971.

[24] A Mukanbetkaliyev, S Amandykova, Y Zhambayev, Z Duskaziyeva, A Alimbetova (2018). The aspects of legal regulation on staffing of procuratorial authorities of the Russian Federation and the Republic of Kazakhstan Opción, Año 33. 187-216.

[25] Villalobos Antúnez, JV (2017). Karl R. Popper, Heráclito y la invención del logos. Un contexto para la Filosofía de las Ciencias Sociales. Opción Vol. 33, Núm. 84. 5-11

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