ECG Beat Classification using statistical features and optimally selected classifiers

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The aim of the project is to detect whether the person has heart
disease or not by using his ECG wave.

This paper proposes a technique for ECG Arrhythmia classification
by using 6 recognised machine learning models like SVM,KNN,RF,DT,
DA and NB in order to obtain the optimal classifier and its parameters.

The proposed techniques uses 7 statistical features namely Mean,
Variance,Standard Deviation,Skewness,Kurtosis,energy,entropy
rom the QRS complex.

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  1. TECH YOU CAN
  2. artificial intelligence
  3. machine
  4. learning
  5. AI
  6. Deep learning
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