HRVFrame: Java-Based Framework for Feature Extraction from Cardiac Rhythm

Heart rate variability (HRV) analysis can be successfully applied to automatic classification of cardiac rhythm abnormalities. This paper presents a novel Java-based computer framework for feature extraction from cardiac rhythms. The framework called HRVFrame implements more than 30 HRV linear time domain, frequency domain, time-frequency domain, and nonlinear features. Output of the framework in the form of .arff files enables easier medical knowledge discovery via platforms such as RapidMiner or Weka. The scope of the framework facilitates comparison of models for different cardiac disorders. Some of the features implemented in the framework can also be applied to other biomedical time-series. The thorough approach to feature extraction pursued in this work is also encouraged for other types of biomedical time-series.

• Type: Journal
• Year: 2011
• Where: AIME
• Authors: Alan Jovic, Nikola Bogunovic
• Keywords: AIME 2011, Artificial Intelligence, Cardiac Rhythm, Cardiac Rhythms, Heart Rate Variability