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.

Objective: The study was conducted to know the impact of COVID-19 vaccination on menstrual cycle patterns, pre- and post-menstrual symptoms in women aged 18-45 years. Design & Setting: Multicentric observational study conducted in six institutes of national importance in different states of India over one year. Population: A total of 5709 female participants fulfilling inclusion criteria were enrolled. Methods: Data about impact of vaccines (COVISHIELD and COVAXIN) and prior COVID-19 infection on menstrual cycle and its associated symptoms were obtained using an online and offline survey. Main Outcome: COVID-19 vaccination with COVISHILED/COVAXIN resulted in menstrual cycle disturbances. Results: Of 5709 participants, 78.2% received COVISHIELD, 21.8% COVAXIN. Of all, 333(5.8%) developed post-vaccination menstrual disturbances with 32.7% frequent cycles, 63.7% prolonged cycles, and 3.6% inter-menstrual bleed. 301 participants, noticed changes in the amount of bleeding, with 50.2% excessive, 48.8% scanty, and 0.99% amenorrhea followed by heavy bleeding. Furthermore, the irregularities of menstrual cycle (p=0.011) and length (0.001) were significantly higher in the COVAXIN group (7.2%) as compared to COVISHIELD (5.3%). A total of 721 participants complained of newly developed/worsening pre- and post-menstrual symptoms. These symptoms were significantly higher in COVISHIELD group (p=0.031) with generalized weakness and body pains as main complaints (p=0.001). No significant difference was observed in COVID-19 infection incidence with these vaccines. When comparing menstrual abnormalities among those with COVID-19 infection, no significant associations were observed (p >0.05). Conclusions: COVISHILED and COVAXIN resulted in menstrual cycle disturbances and pre-and post-menstrual symptoms. The menstrual irregularities were significantly higher with COVAXIN vaccine.