Intra-patient Arrhythmia Heartbeat Modeling by Gibbs Sampling - Ramírez-Robles, Ethery; Jara-Maldonado, Miguel Angel; Etcheverry, Gibran (Pattern Recognition, 2019)
Heartbeat modeling allows to detect anomalies that reflect the functioning of the heart. Certain approaches face this problem by using Gaussian Mixture Models (GMMs) and other statistical classifiers by extracting the fiducial points provided by the MIT-BIH database. In this work, MIT-BIH database heartbeats are modeled into different heartbeat types from a single subject by using the Gibbs Sampling (GS) algorithm. Firstly, a data pre-processing step is performed; this step involves several tasks such as filtering the raw signals from the MIT-BIH database and reducing the heartbeat types to five. Secondly, the GS is applied to the resulting signals of one subject. Thirdly, the Euclidean distance between each heartbeat type is calculated, and lastly, the Bhattacharyya distance is used to classify heartbeats. The results obtained by the GS algorithm were also compared to results obtained by applying the Expectation Maximization (EM) algorithm to the same data-set. Results allow to conclude that GS is a proper solution for separating each heartbeat type; by providing a significant difference between each heartbeat type which can be used for classification.