Hybrid shallow and deep learned feature mixture model for arrhythmia classification

@article{Paul2018HybridSA,
  title={Hybrid shallow and deep learned feature mixture model for arrhythmia classification},
  author={Tanmay Paul and Arnab Chakraborty and Subhrajit Kundu},
  journal={2018 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT)},
  year={2018},
  pages={1-4}
}
For acquiring the abnormality of the signal, the Electrocardiography (ECG) signals are classified into two classes. Usually, ECG is acquired by placing an electrode on the skin and recording the ECG signals. Then, these signals are used by the physician to analyze the situation of the patients and report their health. In this paper, four features are extracted from the ECG signal in order to classify the signal. The MIT-BIH arrhythmia dataset, which contains an enormous number of signals… CONTINUE READING

References

Publications referenced by this paper.
SHOWING 1-10 OF 24 REFERENCES

et.al., A knowledge-based real time embedded platform for arrhythmia beat classification

S. Raj
  • 2015
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Recurrence quantification & ARIMA based forecasting of rainfall-temperature dynamics

  • 2016 International Conference on Signal Processing and Communication (ICSC)
  • 2016
VIEW 1 EXCERPT

et.al., ”ECG signal analysis using wavelet coherence and s-transform for classification of cardiovascular diseases.

S. Agarwal
  • Advances in Computing, Communications and Informatics (ICACCI),
  • 2016
VIEW 1 EXCERPT

Investigation and classification of ECG beat using Input Output Additional Weighted Feed Forward Neural Network

  • 2013 International Conference on Signal Processing , Image Processing & Pattern Recognition
  • 2013

Similar Papers

Loading similar papers…