Computerized analysis of fetal heart rate signals as the predictor of neonatal acidemia
Cardiotocography is the primary method for biophysical assessment of fetal state, which is mainly based on the recording and analysis of fetal heart rate (FHR) signal. Computerized systems for fetal monitoring provide a quantitative analysis of FHR signals, however the effective methods of qualitative assessment that could support the process of medical diagnosis are still needed. The measurements of hydronium ions concentration (pH) in neonatal cord blood are an objective indicator of the fetal outcome. Improper pH level is a symptom of acidemia being the result of fetal hypoxia. The paper proposes a two-step analysis of fetal heart rate recordings that allows for effective prediction of the acidemia risk. The first step consists in fuzzy classification of FHR signals. Fuzzy inference corresponds to the clinical interpretation of signals based on the FIGO guidelines. The goal of inference is to eliminate recordings indicating the fetal wellbeing from the further classification process. In the second step, the remained recordings are nonlinearly classified using multilayer perceptron and Lagrangian Support Vector Machines (LSVM). The proposed procedures are evaluated using data collected with computerized fetal surveillance system. The assessment performance is evaluated with the number of correct classifications (CC) and quality index (QI) defined as the geometric mean of sensitivity and specificity. The highest CC=92.0% and QI=88.2% were achieved for the Weighted Fuzzy Scoring System combined with the LSVM algorithm. The obtained results confirm the efficacy of the proposed methods of computerized analysis of FHR signals in the evaluation of the risk of neonatal acidemia.
support vector machines fetal heart rate monitoring fuzzy systems signal classification