Energy Efficient UAV Assisted Communication With Spectrum Optimization

Description

* Sale Price for only Code / simulation – For Hardware / more Details contact : 8925533488

Objective:

??????????? 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.

ABSTRACT

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.

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 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.

Customer Reviews

There are no reviews yet.

Be the first to review “Energy Efficient UAV Assisted Communication With Spectrum Optimization”

Your email address will not be published. Required fields are marked *

Identification and classification of pedestrian in videos with LBP based background subtraction and HOG descriptor

Download / View