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Classification of fetal heart rate series

We study the problem of accurate automatic classification of fetal heart rate (FHR) signals using three different classification methods. FHR time series data are segmented into short (15s) spans of data, and features are extracted from them. These features include some established metrics of FHR trends such as acceleration and deceleration durations as well as a new set of features derived from the sequence of beat-to-beat percentage changes of the FHR signals. In total, we use 10 different features and demonstrate the feasibility of using them for classifying short segments into one of two suitably defined classes denoted as normal or abnormal. Classification is achieved using three different methods: support vector machine, a parametric Bayesian method and a non-parametric Bayesian method utilizing a neighbour-counting procedure for class-conditional density estimation. The performances of these methods are demonstrated on a database of physician-annotated recordings from which 580 short epochs of FHR patterns were extracted.