1D-FHRNet: Automatic Diagnosis of Fetal Acidosis from Fetal Heart Rate Signals

1D-FHRNet: Automatic Diagnosis of Fetal Acidosis from Fetal Heart Rate Signals

en

Fetal heart rate (FHR) is used to monitor the fetal state by obstetricians as a screening tool. Common guidelines for visual interpretation of FHR signals results in significant subjective variability due to the fetal physiological dynamics complexity. Automated diagnostic technology can assist obstetricians in medical decisions based on artificial intelligence and also can be an automatic diagnostic tool for primary health care centres and remote areas. This work presents a machine learning-based automated diagnostic tool for classification and diagnosis of Fetal Acidosis using FHR. A 1D-CNN model has been proposed because of its ability to automatically diagnose Fetal Acidosis into healthy or pathological conditions with high accuracy. To make the method robust and to improve accuracy with the artefacts present in the signal, the signal pre-processing is performed before training and classification. The accuracy was evaluated on a comprehensive dataset and achieved 99.09% for the diagnosis of Fetal Acidosis. Low-cost electronic hardware integrated with the proposed methodology can perform in real-time and can achieve high accuracy and reliability. This method can be used to support the expert decision and as an automatic stand-alone diagnostic tool that can assist the obstetricians in the early diagnosis of fetal acidosis.

Fetal heart rate (FHR) is used to monitor the fetal state by obstetricians as a screening tool. Common guidelines for visual interpretation of FHR signals results in significant subjective variability due to the fetal physiological dynamics complexity. Automated diagnostic technology can assist obstetricians in medical decisions based on artificial intelligence and also can be an automatic diagnostic tool for primary health care centres and remote areas. This work presents a machine learning-based automated diagnostic tool for classification and diagnosis of Fetal Acidosis using FHR. A 1D-CNN model has been proposed because of its ability to automatically diagnose Fetal Acidosis into healthy or pathological conditions with high accuracy. To make the method robust and to improve accuracy with the artefacts present in the signal, the signal pre-processing is performed before training and classification. The accuracy was evaluated on a comprehensive dataset and achieved 99.09% for the diagnosis of Fetal Acidosis. Low-cost electronic hardware integrated with the proposed methodology can perform in real-time and can achieve high accuracy and reliability. This method can be used to support the expert decision and as an automatic stand-alone diagnostic tool that can assist the obstetricians in the early diagnosis of fetal acidosis.