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.

Detection of atrial fibrillation using discrete-state Markov models and Random Forests.

In this paper, we present a fully automated technique for robust detection of Atrial Fibrillation (AF) episodes in single-lead electrocardiogram (ECG) signals using discrete-state Markov models and Random Forests. METHODS: The ECG signal is first preprocessed using Stationary Wavelet Transforms (SWT) for noise suppression, signal quality assessment and subsequent R-peak detection. Discrete-state Markov probabilities modelling transitions between successive RR intervals along with other statistical quantities derived from the RR-interval series constitute the feature set to perform AF classification using Random Forests. Further enhancement in AF detection is achieved by using a post-processing false positive suppression algorithm based on autocorrelation analysis of the RR-interval series. Datasets: The AF classifier was trained using the Physionet/Computing in Cardiology 2017 AF Challenge dataset and the Atrial Fibrillation Termination Database (AFTDB). The test datasets consist of the MIT-BIH Atrial Fibrillation Database (AFDB) and the MIT-BIH Arrhythmia Database (MITDB). RESULTS: Our algorithms achieved sensitivity, specificity and F-score values of 97.4%, 98.6% and 97.7% respectively on the AFDB dataset and 96.3%, 97.0% and 85.6% respectively on the MITDB dataset. It was also observed that inclusion of the false positive suppression step resulted in a 1.1% increase in specificity and a 4.0% increase in F-score for the MITDB dataset without any decrease in sensitivity. CONCLUSION: The proposed method of AF detection, combining Markov models and Random Forests, achieves high accuracy across multiple databases and demonstrates comparable or superior performance to several other state-of-the-art algorithms.