Capacitive ECG (cECG) can measure the cardiac electrical signal via capacitive coupling between electrodes and skin. This unconstrained measurement is suitable for personal heart monitoring; however, the instability in the quality of the signal hinders a further use of the signal. To use the cECG for heart monitoring, an adapted framework that could automatically classify the signal into clear cECG, arrhythmias and noise signal is a prerequisite. In view of this problem, the conventional quality estimation method using predefined features based on R-peak detection is not suitable for this unconstrained measurement of cECG. In this study, we examine the feasibility of arrhythmias detection from the cECG measurement using a convolutional neural network (CNN) model. The malignant ventricular tachycardia (VT) and ventricular fibrillation (VF) do not have the Q-R-S waveforms and therefore may be easily classified as the noise. Hence, in this study, we used the cECG signals that have 3 classes in quality (C1: clear signal; C2: blurry signal with significant R peak and N: noise) and the arrhythmias signals (VT, VF, and atrial fibrillation) from open databases to train the classification model. 13 subjects were recruited in an experiment for the cECG data collection in the Nara Institute of Science and Technology. As a result, the CNN model could recognize C1 and AF signal with over 0.98 recalls and precisions; whereas the recall and precision of VT and VF are lower scores and the lower scores were caused mainly by the similarity between VT and VF. Given the results of the CNN model, this CNN-based framework can accurately label the C1, AF, and malignant ventricular arrhythmias (VT and VF) signals. Further stratification of the C2, VT, and VF will further enhance the use of the cECG measurement.