  Premature ventricular contraction(PVC) is a type of premature ectopic beat
originating from the ventricles. Automatic method for accurate and robust
detection of PVC is highly clinically desired.Currently, most of these methods
are developed and tested using the same database divided into training and
testing set and their generalization performance across databases has not been
fully validated. In this paper, a method based on densely connected
convolutional neural network and spatial pyramid pooling is proposed for PVC
detection which can take arbitrarily-sized QRS complexes as input both in
training and testing. With a much less complicated and more straightforward
architecture,the proposed network achieves comparable results to current
state-of-the-art deep learning based method with regard to accuracy,sensitivity
and specificity by training and testing using the MIT-BIH arrhythmia database
as benchmark.Besides the benchmark database,QRS complexes are extracted from
four more open databases namely the St-Petersburg Institute of Cardiological
Technics 12-lead Arrhythmia Database,The MIT-BIH Normal Sinus Rhythm
Database,The MIT-BIH Long Term Database and European ST-T Database. The
extracted QRS complexes are different in length and sampling rate among the
five databases.Cross-database training and testing is also experimented.The
performance of the network shows an improvement on the benchmark database
according to the result demonstrating the advantage of using multiple databases
for training over using only a single database.The network also achieves
satisfactory scores on the other four databases showing good generalization
capability.
