A Modified K-NN Algorithm for Holter Waveform Classification Based on Kernel Function
Gang Zheng, Guochao Cao · 2008
Several problems are existed when K-NN (K- nearest neighbor) method is used to classify the Holter waveforms: the data scale is too large; the classification algorithm needs training samples; the K-NN is a linear classification method. Therefore, this paper proposes a new K-NN algorithm; the algorithm is based on kernel function. Through this change, classification is transformed from linear to non-linear. The max-min distance algorithm and k-means clustering algorithm are used to form the training sample set for the modified K-NN algorithm. By this method, Holter waveforms are classified more correctly and automatically.