Automatic optimization of multichannel electrode configurations for robust fetal heart rate detection by Blind Source Separation

• Published in: IEEE transactions on biomedical engineering Vol. 70; no. 4; pp. 1 - 12
• Main Authors: Galli, A., Peri, E., Rabotti, C., Ouzounov, S., Mischi, M.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; automatic quality assessment; Blind source separation; Cardiotocography - methods; Configuration management; EKG; Electrocardiography - methods; electrode configuration; electrode placement; Electrodes; Electrophysiological recording; Estimation; Feature extraction; Female; Fetal heart rate; Fetal monitoring; Fetal Monitoring - methods; fetal position; Fetuses; Guidelines; Heart rate; Heart Rate, Fetal; Humans; Monitoring; multi-channel measurements; Noise; Optimization; prediction; Pregnancy; Pregnancy complications; Regression models; Signal to noise ratio; Support vector machines; Support Vector Regression
• DOI: 10.1109/TBME.2022.3212587
• ISSN: 0018-9294; 1558-2531

Objective. Fetal heart rate (fHR) evaluation is fundamental to guarantee timely medical intervention in case of pregnancy complications. Due to the limitations of traditional cardiotocography, multichannel electrophysiological recording was proposed as a viable alternative, which requires Blind Source Separation (BSS) techniques. Yet effective and reliable separation of the fetal ECG remains challenging due to multiple noise sources and the effects of varying fetal position. In this work, we demonstrate that the adopted electrode configuration plays a key role in the effectiveness of BSS and propose guidelines for optimal electrode positioning. Moreover, a model is proposed to automatically predict the most suited configuration for accurate BSS-based fHR estimation with a minimal number of leads, to facilitate practical implementation. Methods. We compared fHR estimation accuracy with different electrode configurations on in-silico data, identifying the optimal configuration for a recent BSS method. Based on features extracted from raw signals, we proposed a support vector regression model to automatically identify the best electrode configuration in terms of fHR estimation accuracy and to dynamically adjust it to varying fetal presentation. Evaluation was performed on real and synthetic data. Results. Guidelines for the optimal electrode configuration are proposed by using 4 leads. Prediction of configuration quality shows 80.9% accuracy; the optimal configuration is recognized in 92.2% of the subjects. Conclusion. The proposed method successfully predicts the quality of the configurations, demonstrating the impact of the electrode configuration on the BSS performance. Significance. The method holds potential for long-term fetal monitoring, by dynamically choosing the optimal configuration.