Energy Efficient UAV Assisted Communication With Spectrum Optimization

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Description

Energy Efficient UAV Assisted Communication With Spectrum Optimization

Objective:

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


Energy Efficient UAV Assisted Communication With Spectrum Optimization

ABSTRACT

Cardiac arrhythmia indicates the abnormal electrical activity of the heart that can be a great threat to humans. 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 signals and predicting heart abnormalities has 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 previously extracted features. The Neural Network trained feed-forward neural network has been selected for this research. Here, data used for the analysis of ECG signals are from the database. Energy Efficient UAV Assisted Communication With Spectrum Optimization


Energy Efficient UAV Assisted Communication With Spectrum Optimization

EXISTING SYSTEM:

  • SVM classifier
  • K means clustering

DRAWBACKS:

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

PROPOSED SYSTEM:

  • Noise removal
  • Neural network

APPLICATIONS:

  • Medical applications

SOFTWARE REQUIREMENTS:

  • PYTHON 3.0
  • PYTHON IDLE

Reference:

[1] K. Thygesen et al., Third universal definition of myocardial infarction,? Circulation, vol. 126, no. 16, pp. 2020? 2035, 2012.

[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 eigenspace 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 multi-lead ECG for classification of myocardial infarction,? Biomedical Signal Processing and Control, vol. 31, pp. 71-78, January 2017.

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