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