A review of classification techniques for arrhythmia patterns using convolutional neural networks and internet of things (iot) devices

HIGHLIGHTS

• who: MICHAEL OPOKU AGYEMAN and colleagues from the Yildirim et_al [17] worked on a special approach presenting an advanced DL methodology for cardiac arrhythmia detection focused on long-duration electrocardiography analysis (Figure, )A convolutional network was implemented for the distribution signals according to classes. In the first layer, a one-dimension convolution was performed using a vector of , ×, . The activation outputs were normalized with a normalization function. In the D of the max-pooling layer, a new feature map was created extracting the maximum values given in the previous layer. The responsibility of the maxpooling was to . . .
  

   

  

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