Detection of Premature Ventricular Complexes using Semisupervised Autoencoders and Random Forests.

In this paper, we propose a technique for detection of premature ventricular complexes (PVC) based on information obtained from single-lead electrocardiogram (ECG) signals. A combination of semisupervised autoencoders and Random Forests models are used for feature extraction and PVC detection. The ECG signal is first denoised using Stationary Wavelet Transforms and denoising convolutional autoencoders. Following this, PVC classification is performed. Individual ECG beat segments along with features derived from three consecutive beats are used to train a hybrid autoencoder network to learn class-specific beat encodings. These encodings, along with the beat-triplet features, are then input to a Random Forests classifier for final PVC classification. Results: The performance of our algorithm was evaluated on ECG records in the MIT-BIH Arrhythmia Database (MITDB) and the St. Petersburg INCART Database (INCARTDB). Our algorithm achieves a sensitivity of 92.67% and a PPV of 95.58% on the MITDB database. Similarly, a sensitivity of 88.08% and a PPV of 94.76% are achieved on the INCARTDB database.