Open-access software for analysis of fetal heart rate signals. (August 2018) - Cömert, Zafer; Kocamaz, Adnan Fatih
Highlights: We introduce an open-access software called CTG-OAS. CTG-OAS is equipped with advanced signal processing and machine learning tools for analysis of FHR signals. A novel feature extraction method based on a combination of STFT and GLCM is proposed for detecting fetal hypoxia. ANN, SVM, and k -NN machine learning algorithms are used for classifying FHR signals. We present a web platform (www.ctganalysis.com ) for sharing developments in the software and for distributing it freely. Abstract: Cardiotocography (CTG) comprises fetal heart rate (FHR) and uterine contraction (UC) signals that are simultaneously recorded. In clinical practice, a visual examination is subjectively performed by observers depending on the guidelines to evaluate CTG traces. Owing to this visual assessment, the variability in the interpretation of CTG between inter- and even intra-observers is considerably high. In addition, traditional clinical practice involves different human factors that distort the quantitative quality of the evaluation. Automated CTG analysis is the most promising way to tackle the main shortcomings of CTG by providing reproducibility of the evaluation as well as the quantitative results. In this study, open-access software (CTG-OAS) developed with MATLAB ® is introduced for the analysis of FHR signals. The software contains important processes of the automated CTG analysis, from accessing the database to conducting model evaluations. In addition to traditionally used morphological, linear, nonlinear, and time–frequency features, the developed software introduces an innovative approach called image-based time–frequency features to characterize FHR signals. All functions of the software are well documented, and it is distributed freely for research purposes. In addition, an experimental study on the publicly accessible CTU-UHB database was performed using CTG-OAS to test the reliability of the software. The experimental study obtained results that included an accuracy of 77.81%, sensitivity of 76.83%, specificity of 78.27%, and geometric mean of 77.29%. These fairly promising results indicate that the software can be a valuable tool for the analysis of CTG signals. In addition, the results obtained using CTG-OAS can be easily compared to different algorithms. Moreover, different experimental setups can be designed using the tools provided by the software. Thus, the software can contribute to the development of new algorithms.