Classification of abdominal ECG recordings for the identification of fetal risk using random forest and optimal feature selection

Fabian Torres; Boris Escalante-Ramírez; Jorge Perez-Gonzales; Román Anselmo Mora-Gutierrrez; Antonin Ponsich; Scarlet Prieto Rodriguez; Lisbeth Camargo Marin; Mario Guzmán Huerta

doi:10.1117/12.2511562

21 December 2018 Classification of abdominal ECG recordings for the identification of fetal risk using random forest and optimal feature selection

Fabian Torres, Boris Escalante-Ramírez, Jorge Perez-Gonzales, Román Anselmo Mora-Gutierrrez, Antonin Ponsich, Scarlet Prieto Rodriguez, Lisbeth Camargo Marin, Mario Guzmán Huerta

Author Affiliations +

Proceedings Volume 10975, 14th International Symposium on Medical Information Processing and Analysis; 109750B (2018) https://doi.org/10.1117/12.2511562
Event: 14th International Symposium on Medical Information Processing and Analysis, 2018, Mazatlán, Mexico

Abstract

Abdominal electrocardiography (AECG) is an indirect method for obtaining a continuous reading of fetal heart rate and is widely used during pregnancy as a method for assessing fetal well-being. Information obtained by AECG is used for early identification of fetal risk and may help in the anticipation of future complications; however, improper interpretation of the AECG recordings, related with inter- and intra-individual variability, may lead to inadequate treatments that can cause the death of the fetus. A set of 33 indices (4 maternal, 5 temporals, 23 time-frequency and 1 non-linear), extracted from AECG recordings and maternal information, were tested with a Random Forest (RF) classification method for the identification of normal fetuses and fetuses with intrauterine growth restriction. Because RFs may perform poorly when confronted with a high number of features compared to the number of training data available, a Genetic Algorithm (GA) was used to select the minimum set of features that improves the outcome of the RF. The accuracy of the RF method using the 33 indices was of 60%. After a run of the GA, the best individual in the last generation had an accuracy value of 85% and reduced the number of used indices from 33 to 11.

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Fabian Torres, Boris Escalante-Ramírez, Jorge Perez-Gonzales, Román Anselmo Mora-Gutierrrez, Antonin Ponsich, Scarlet Prieto Rodriguez, Lisbeth Camargo Marin, and Mario Guzmán Huerta "Classification of abdominal ECG recordings for the identification of fetal risk using random forest and optimal feature selection", Proc. SPIE 10975, 14th International Symposium on Medical Information Processing and Analysis, 109750B (21 December 2018); https://doi.org/10.1117/12.2511562

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