In the demand for protecting the increasing aged groups from heart attacks, the improvement of the mobile electrocardiogram (ECG) monitoring systems becomes significant. The limitations of the arrhythmia classification in these systems are the lack of ability to cope with motion state and the low accuracy in new users' data. This paper proposes a system which applies the impulse radio ultra wideband radar data as additional information to assist the arrhythmia classification of ECG recordings in the slight motion state. Besides, this proposed system employs a cascade convolutional neural network to achieve an integrated analysis of ECG recordings and radar data. The experiments are implemented in the Caffe platform and the result reaches an accuracy of 88.89% in the slight motion state. It turns out that this proposed system keeps a stable accuracy of classification for normal and abnormal heartbeats in the slight motion state.