Detection of Premature Ventricular Contractions Using Densely Connected Deep Convolutional Neural Network with Spatial Pyramid Pooling Layer
This work addresses the need for accurate and robust automatic PVC detection in clinical settings, though it is incremental as it builds on existing deep learning approaches with architectural modifications.
The paper tackled the problem of detecting premature ventricular contractions (PVCs) in ECG signals by proposing a densely connected convolutional neural network with spatial pyramid pooling, achieving comparable accuracy, sensitivity, and specificity to state-of-the-art deep learning methods on the MIT-BIH arrhythmia database and showing improved performance and good generalization across four additional databases.
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.