An IoMT enabled deep learning framework for automatic detection of fetal QRS: A solution to remote prenatal care

Abstract

The amalgamation of the Internet of medical things with artificial intelligence shows tremendous benefits in health care. Accurate detection of the fetal QRS complex is highly demanded in fetal heart rate monitoring. Detecting fetal heart rate using electrophysiological signals obtained from abdominal electrodes seems a promising alternative approach. The challenges in determining fetal heart rate from abdominal ECG (AECG) require eliminating maternal components and other noises from the signal at higher accuracy. We propose a novel approach using an IoT-based deep learning architecture to detect fetal QRS complex without eliminating the maternal components in the abdominal ECG. The novelty of the proposed algorithm is twofold: (1) The method uses the time–frequency image (TFI) of abdominal signals as input to the deep neural network and hence promotes the availability of rich features and improves the accurate detection of the fetal QRS complex. (2) The algorithm adapts pre-trained models based on transfer learning for the classification task and thus improves the fetal QRS detection. Two time–frequency approaches, namely Hilbert Huang Transform (HHT) and Stockwell transform (ST), are used to represent input AECG signals as two-dimensional images. The 2013 challenge database is used to evaluate the performance of the proposed approach. The TFI representations of training data using HHT and ST are independently used to train the pre-trained models MobileNet and ResNet18. A comparative analysis is provided in the results between the TFI and deep network architecture. The proposed solution can be suitable for an IoT environment enabling remote fetal monitoring.