Fetal Hypoxia Detection Based on Deep Convolutional Neural Network with Transfer Learning Approach


16 May 2018 Wednesday

Electronic fetal monitoring (EFM) device which is used to record Fetal Heart Rate (FHR) and Uterine Contraction (UC) signals simultaneously is one of the significant tools in terms of the present obstetric clinical applications. In clinical practice, EFM traces are routinely evaluated with visual inspection by observers. For this reason, such a subjective interpretation has been caused various conflicts among observers to arise. Although the existing of international guidelines for ensuring more consistent assessment, the automated FHR analysis has been adopted as the most promising solution. In this study, an innovative approach based on deep convolutional neural network (DCNN) is proposed to classify FHR signals as normal and abnormal. The proposed method composes of three stages. FHR signals are passed through a set of preprocessing procedures in order to ensure more meaningful input images, firstly. Then, a visual representation of time-frequency information, spectrograms are obtained with the help of the Short Time Fourier Transform (STFT). Finally, DCNN method is utilized to classify FHR signals. To this end, the colored spectrograms images are used to train the network. In order to evaluate the proposed model, we conducted extensive experiments on the open CTU-UHB database considering the area under the receiver operating characteristic curve and other several performance metrics derived from the confusion matrix. Consequently, we achieved encouraging results.

Electronic fetal monitoring (EFM) device which is used to record Fetal Heart Rate (FHR) and Uterine Contraction (UC) signals simultaneously is one of the significant tools in terms of the present obstetric clinical applications. In clinical practice, EFM traces are routinely evaluated with visual inspection by observers. For this reason, such a subjective interpretation has been caused various conflicts among observers to arise. Although the existing of international guidelines for ensuring more consistent assessment, the automated FHR analysis has been adopted as the most promising solution. In this study, an innovative approach based on deep convolutional neural network (DCNN) is proposed to classify FHR signals as normal and abnormal. The proposed method composes of three stages. FHR signals are passed through a set of preprocessing procedures in order to ensure more meaningful input images, firstly. Then, a visual representation of time-frequency information, spectrograms are obtained with the help of the Short Time Fourier Transform (STFT). Finally, DCNN method is utilized to classify FHR signals. To this end, the colored spectrograms images are used to train the network. In order to evaluate the proposed model, we conducted extensive experiments on the open CTU-UHB database considering the area under the receiver operating characteristic curve and other several performance metrics derived from the confusion matrix. Consequently, we achieved encouraging results.

 

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