Energy Efficient UAV Assisted Communication With Spectrum Optimization


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??????????? The main objective of this project is to identify and to classify the heart abnormalities in an ECG signal. Based on the peak amplitude values can perform the classification operation.


Cardiac arrhythmia indicates abnormal electrical activity of heart that can be a great threat to human. So it needs to be identified for clinical diagnosis and treatment. Analysis of ECG signal plays an important role in diagnosing cardiac diseases. An efficient method of analyzing ECG signal and predicting heart abnormalities have been proposed in this paper. In the proposed scheme, at first the QRS components have been extracted from the noisy ECG signal by rejecting the background noise. The final task is to classify the heart abnormalities according to previous extracted features. The Neural Network trained feed-forward neural network has been selected for this research. Here, data used for the analysis of ECG signal are from database.


  • SVM classifier
  • K means clustering


  • High Computational load
  • Poor discriminatory power
  • Less accuracy in classification


  • Noise removal
  • Neural network


  • Medical applications


  • PYTHON 3.0


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[2] L. Schamroth, An Introduction to Electrocardiography, 7th ed. New York, USA: Wiley, 2009.

[3] S. Mitra, M. Mitra and B. B. Chaudhuri, “A Rough-Set-Based Inference Engine for ECG Classification,” IEEE Trans. Instrum. Meas., vol. 55, no. 6, pp. 2198-2206, Dec. 2006. doi: 10.1109/TIM.2006.884279

[4] Fayn, Jocelyne. “A classification tree approach for cardiac ischemia detection using spatiotemporal information from three standard ECG leads.” IEEE Trans. Biomed. Eng., vol. 58, no. 1, pp. 95-102, 2011.

[5] L. Sun et al., ?ECG analysis using multiple instance learning for myocardial infarction detection,? IEEE Trans. Biomed. Eng., vol. 59, no. 12, pp. 3348?3356, Dec. 2012.

[6] P.-C. Chang, J.-J. Lin, J.-C. Hsieh, and J. Weng, ?Myocardial infarction classification using multi-lead ECG using hidden Markov models and Gaussian mixture models,? Appl. Soft Comput., vol. 12, no. 10, pp. 3165?3175, 2012.

[7] L. Sharma, R. Tripathy, and S. Dandapat, ?Multiscale energy and eigen space approach to detection and localization of myocardial infarction,? IEEE Trans.Biomed. Eng., vol. 62, no. 7, pp. 1827-1837, 2015.

[8] S. Padhy, and S. Dandapat, ?Third-order tensor based analysis of multilead ECG for classification of myocardial infarction,? Biomedical Signal Processing and Control, vol. 31, pp. 71-78, January 2017.

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