ECG Arrhythmia Classification Using STFT-Based Spectrogram and Convolutional Neural Network

The classification of electrocardiogram (ECG) signals is very important for the automatic diagnosis of heart disease. Traditionally, it is divided into two steps, including the step of feature extraction and the step of pattern classification. Owing to recent advances in artificial intelligence, it has been demonstrated that deep neural network, which trained on a huge amount of data, can carry out the task of feature extraction directly from the data and recognize cardiac arrhythmias better than professional cardiologists. This paper proposes an ECG arrhythmia classification method using two-dimensional (2D) deep convolutional neural network (CNN). The time domain signals of ECG, belonging to five heart beat…

• NN
  National Natural Science Foundation of China

  Awards: 2016J01012, 51605403
• NS
  Natural Science Foundation of Fujian Province

  Award: 2016J01012
• NS
  Natural Science Foundation of Guangdong Province

  Award: 2015A030310010