ECG Signal Recognition Based on Lookup Table and Neural Networks


  • Muzhir Shaban Al-Ani University of Human Development, College of Science and Technology, Department of Information Technology, Sulaymaniyah, KRG, Iraq



Electrocardiograph signals, P-QRS, Healthcare, Heart Diseases


Electrocardiograph (ECG) signals are very important part in diagnosis healthcare the heart diseases. The implemented ECG signals recognition system consists hardware devices, software algorithm and network connection. An ECG is a non-invasive way to help diagnose many common heart problems. A health-care provider can use an ECG to recognize irregular heartbeats, blocked or narrowed arteries in the heart, whether you have ever had a heart attack, and the quality of certain heart disease treatments. The main part of the software algorithm including the recognition of ECG signals parameters such as P-QRST. Since the voltages at which handheld ECG equipment operate are shrinking, signal processing has become an important challenge. The implemented ECG signal recognition approach based on both lookup table and neural networks techniques. In this approach, the extracted ECG features are compared with the stored features to recognize the heart diseases of the received ECG features. The introduction of neural network technology added new benefits to the system implementing the learning and training process.


T. Gandhi, B. K. Panigrahi, M. Bhatia and S. Anand. (2010) “Expert model for detection of epileptic activity in EEG signature”. Expert Systems with Applications, vol. 37, pp. 3513-3520, 2010.

S. Sanei and J. A. Chambers. “EEG Signal Processing”. John Wiley & Sons Ltd., Chichester, 2013.

K. Polat and S. Günes. “Classification of epileptic form EEG using a hybrid system based on decision treeclassifier and fast Fourier transform”. Applied Mathematics and Computation, vol. 187, pp. 1017-1026, 2007.

G. Ouyang, X. Li, C. Dang and D. A. Richards. “Using recurrence plot for determinism analysis of EEG recordings in genetic absence epilepsy rats”. Clinical Neurophysiology, vol. 119, pp. 1747-1755, 2008.

M. Ahmadlou, H. Adeli and A. Adeli. “New diagnostic EEG markers of the Alzheimer’s disease using visibility graph”. Journal of Neural Transmission, vol. 117, no. 9, pp. 1099-1109, 2010.

N. Kannathal, U. R. Acharya, C. M. Lim, Q. Weiming, M. Hidayat and P. K. Sadasivan. “Characterization of EEG: A comparative study”. Computer Methods and Programs in Biomedicine, vol. 80, no. 1, pp. 17-23, 2005.

N. W. Willingenburg, A. Daffertshofer, I. Kingma and J. H. Van Dieen. “Removing ECG contamination from EMG recordings: A comparison of ICA-based and other filtering procedures”. Journal of Electromyography and Kinesiology, vol. 22, no. 3, pp. 485:493, 2010.

C. Marque, C. Bisch, R. Dantas, S. Elayoubi, V. Brosse and C. Perot. “Adaptive filtering for ECG rejection from surface EMG recordings”. Journal of Electromyography and Kinesiology, vol. 15, no. 3, pp. 310-315, 2005.

S. Abbaspour, M. Linden and H. Gholamhosseini. “ECG artifact removal from surface EMG signal using an automated method based on wavelet-ICA”. Studies in Health Technology and Informatics, vol. 211(pHealth), pp. 91-97, 2015.

A. L. Hoff. “A simple method to remove ECG artifacts from trunk muscle EMG signals”. Journal of Electromyography and Kinesiology, vol. 19, no. 6, pp. 554-555, 2009.

P. E. McSharry, G. Clifford, L. Tarassenko and L. A. Smith. “A dynamical model for generating synthetic electrocardiogram signals”. IEEE Transactions on Biomedical Engineering, vol. 50, no. 3, pp. 289-294, 2003.

M. S. AL-Ani and A. A. Rawi. “ECG beat diagnosis approach for ECG printout based on expert system”. International Journal of Emerging Technology and Advanced Engineering, vol. 3, no. 4, pp. 797-807, 2013.

M. S. AL-Ani and A. A. Rawi. “Rule-based expert system for automated ECG diagnosis. International Journal of Advances in Engineering and Technology, vol. 6, no. 4, pp. 1480-1493, 2013.

J. E. Madias, R. Bazaz, H. Agarwal, M. Win and L. Medepalli. “Anasarca-mediated attenuation of the amplitude of electrocardiogram complexes: A description of a heretofore unrecognized phenomenon”. Journal of the American College of Cardiology, vol. 38, no. 3, pp. 756-764, 2001.

U. R. Acharya, V. K. Sudarshan, H. Adeli, J. Santhosh, J. E. W. Koh, S. D. Puthankatti and A. Adeli A. “A novel depression diagnosis index using nonlinear features in EEG signals”. European Neurology, vol. 74, no. 79-83, 2015.

K. N. Khan, K. M. Goode, J. G. F. Cleland, A. S. Rigby, N. Freemantle, J. Eastaugh, A. L. Clark, R. de Silva, M. J. Calvert, K. Swedberg, M. Komajda, V. Mareev, F. Follath and EuroHeart Failure Survey Investigators. “Prevalence of ECG abnormalities in an international survey of patients with suspected or confirmed heart failure at death or discharge. European Journal of Heart Failure, vol. 9, pp. 491-501, 2007.

K. Y. K. Liao, C. C. Chiu and S. J. Yeh. “A novel approach for classification of congestive heart failure using relatively short-term ECG waveforms and SVM classifier. In: Proceedings of the International Multi-Conference of Engineers and Computer Scientists, IMECS March 2015, Hong Kong, pp. 47-50, 2015.

R. J. Martis, U. R. Acharya and C. M. Lim. “ECG beat classification using PCA, LDA, ICA and discrete wavelet transform”. Biomedical Signal Processing and Control, vol. 8, no. 5, pp. 437-448, 2013.

U. Orhan. “Real-time CHF detection from ECG signals using a novel discretization method”. Computers in Biology and Medicine, vol. 43, pp. 1556-1562, 2013.

J. Pan and W. J. Tompkins. “A real time QRS detection algorithm”. IEEE Transactions on Biomedical Engineering, vol. 32, no. 3, 1985.

M. Sadaka, A. Aboelela, S. Arab and M. Nawar. Electrocardiogram as prognostic and diagnostic parameter in follow up of patients with heart failure. Alexandria Journal of Medicine, vol. 49, pp. 145- 152, 2013.

K. Senen, H. Turhan, A. R. Erbay, N. Basar, A. S. Yasar, O. Sahin and E. Yetkin. “P wave duration and P wave dispersion in patients with dilated cardiomyopathy”. European Journal of Heart Failure, vol. 6, pp. 567-569, 2004.

R. A. Thuraisingham. “A classification system to detect congestive heart failure using second-order difference plot of RR intervals”. Cardiology Research and Practice, vol. 2009, p. ID807379, 2009.

E. D. Ubeyli. “Feature extraction for analysis of ECG signals”. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Milano, Italy, pp. 1080-1083, 2008.

R. Rodríguez, A. Mexicano, J. Bila, S. Cervantes and R. Ponce. “Feature extraction of electrocardiogram signals by applying adaptive threshold and principal component analysis”. Journal of Applied Research and Technology, vol. 13, pp. 261-269, 2015.

H. Gothwal, S. Kedawat and R. Kumar. “Cardiac arrhythmias detection in an ECG beat signal using fast Fourier transform and artificial neural network”. Journal of Biomedical Science and Engineering, vol. 4, pp. 289-296, 2011.

S. A. Chouakri, F. Bereksi-Reguig, A. T. Ahmed. “QRS complex detection based on multi Wavelet packet decomposition”. Applied Mathematics and Computation, vol. 217, pp. 9508-9525, 2011.

D. S. Benitez, P. A. Gaydecki, A. Zaidi and A. P. Fitzpatrick. “A new QRS detection algorithm based on the Hilbert Transform”. Computers in Cardiology, vol. 2000, pp. 379-382, 2000.

G. Vijaya, V. Kumar and H. K. Verma. “ANN-based QRS-complex analysis of ECG”. Journal of Medical Engineering and Technology, vol. 22, pp. 160-167, 1998.

M. Ayat, M. B. Shamsollahi, B. Mozaffari and S. Kharabian. “ECG denoising using modulus maxima of wavelet transform”. In: Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine EMBC, pp. 416-419, 2009.

F. Chiarugi, V. Sakkalis, D. Emmanouilidou, T. Krontiris, M. Varanini and I. Tollis. “Adaptive threshold QRS detector with best channel selection based on a noise rating system”. Computers in Cardiology, vol. 2007, pp. 157-160, 2007.

M. Elgendi. “Fast QRS detection with an optimized knowledge-based method: Evaluation on 11 standard ECG databases”. PLoS One, vol. 8, p. e73557, 2013.

A. Rehman, M. Mustafa, I. Israr and M. Yaqoob. “Survey of wearable sensors with comparative study of noise reduction ECG filters”. International Journal of Computing and Network Technology, vol. 1, pp. 61-81, 2013.

M. Elgendi, B. Eskofier and D. Abbott. “Fast T wave detection calibrated by clinical knowledge with annotation of P and T waves”. Sensors (Basel), vol. 15, pp. 17693-17714, 2015.

M. Rahimpour, B. M. Asl. “P wave detection in ECG signals using an extended Kalman filter: An evaluation in different arrhythmia contexts”. Physiological Measurement, vol. 37, pp. 1089-1104, 2016.

A. Z. Mohammed, A. F. Al-Ajlouni, M. A. Sabah and R. J. Schilling. “A new algorithm for the compression of ECG signals based on mother wavelet parameterization and best-threshold levels selection”. Digital Signal Processing, vol. 23, pp. 1002-1011, 2013.

B. M. Reza, R. Kaamran and K. Sridhar. “Robust ultra-low-power algorithm for normal and abnormal ECG signals based on compressed sensing theory”. Procedia Computer Science, vol. 19, pp. 206-213, 2013.

M. E. Ann and M. A. Andrés. “Discriminant analysis of multivariate time series: Application to diagnosis based on ECG signals”. Computational Statistics and Data Analysis, vol. 70, pp. 67-87, 2014.

M. H. Vafaie, M. Ataei and H. R. Koofigar. “Heart diseases prediction based on ECG signals’ classification using a genetic fuzzy system and dynamical model of ECG signals”. Biomedical Signal Processing and Control, vol. 14, pp. 291-296, 2014.

A. Kamal and A. Nader. “Design and implementation of a multiband digital filter using FPGA to extract the ECG signal in the presence of different interference signals”. Computers in Biology and Medicine, vol. 62, pp. 1-13, 2015.

E. Farideh, S. Seyed-Kamaledin and N. Homer. “Automatic sleep staging by simultaneous analysis of ECG and respiratory signals in long epochs”. Biomedical Signal Processing and Control, vol. 18, pp. 69-79, 2015.

S. H. Shirin and M. Behbood. “A new personalized ECG signal classification algorithm using Block-based Neural Network and Particle Swarm Optimization”. Biomedical Signal Processing and Control, vol. 25, pp. 12-23, 2016.

Y. Om Prakash and R. Shashwati. “Smoothening and Segmentation of ECG signals using total variation denoising, minimization, majorization and bottom-up approach”. Procedia Computer Science, vol. 85, pp. 483-489, 2016.

M. Aleksandar and G. Marjan. “Improve d pipeline d wavelet implementation for filtering ECG signals”. Pattern Recognition Letters, vol. 95, pp. 85-90, 2017.

M. Kumar, R. B. Pachori and U. R. Acharya. “Characterization of coronary artery disease using flexible analytic wavelet transform applied on ECG signals”. Biomedical Signal Processing and Control, vol. 31, pp. 301-308, 2017.

M. S. Al-Ani. “Electrocardiogram waveform classification based on P-QRS-T Wave recognition.” UHD Journal of Science and Technology, vol. 2, no. 2, pp. 7-14, 2018.

J. J. Nallikuzhy and S. Dandapat. “Spatial enhancement of ECG using multiple joint dictionary learning”. Biomedical Signal Processing and Control, vol. 54, p. 101598, 2019.

C. Han and L. Shi. “ML–ResNet: A novel network to detect and locate myocardial infarction using 12 leads ECG.” Computer Methods and Programs in Biomedicine, vol. 185, p. 105138, 2020.

L. A. Abdulla and M. S. Al-Ani. “A review study for electrocardiogram signal classification”. UHD Journal of Science and Technology (UHDJST), vol. 4, no. 1, 2020.

L. A. Abdullah and M. S. Al-Ani. “CNN-LSTM based model for ECG arrhythmias and myocardial infarction classification”. Advances in Science Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 601-606, 2020.






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