Adopting Artificial Intelligence Algorithms for Remote Fetal Heart Rate Monitoring and Classification using Wearable Fetal Phonocardiography

This work utilizes a wearable phonocardiogram belt for acquiring signals from remote areas. Four sequential methodologies are undergone in the proposed work for resilience, and effectiveness. Intelligent, and accurate FHR monitoring. At first, the noise and artifacts are removed by performing two levels pre-processing methodology method. The low frequency noises are removed by Chebyshev II High Pass Filter and high frequency noises are removed by a hybrid of EC2EMDAN- PS-MODWT filters respectively. In the next step, the denoised signals are segmented for reducing the complexity in which the segmentation is performed using Multi Agent Deep Q-Learning (MA-DQL) algorithm based on several constraints. Further, the segmented signal is provided for reducing the redundancies in cardiac cycles using Artificial Humming Bird Optimization (AHBO) algorithm. The segmented and non-redundant signals are converted into 3D spectrograms using a machine learning algorithm named Variational Auto Encoder-General Adversarial Networks (VAE-GAN) for analyzing the signals with better visual interpretation in 3D space. Finally, utilizing the 3D spectrogram analysis the feature extraction and classification is taken place by adopting a hybrid of Bidirectional Gated Recurrent Unit (BiGRU) and Multi Boosted Capsule Network (MBCapsNet) into three classes such as normal (110-160 BPM), abnormal (above 180 BPM), and Suspicious (fluctuates between normal and abnormal). The proposed work is implemented and simulated in the MATLAB R2020a tool and the performance is validated by adopting effective validation metrics. The outcomes demonstrate that the suggested work performs better than the current works.