Energy Efficient UAV Assisted Communication With Spectrum Optimization


Energy Efficient UAV Assisted Communication With Spectrum Optimization


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


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


  • 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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