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An automated pre-term prediction system using EHG signal with the aid of deep learning technique

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Abstract

Prematurity is the leading cause of infant morbidity and mortality around the world. Surface uterine electromyogram (sEMG) is a non-invasive uterine electromyogram. One of most hopeful biophysical tests is the electro hysterogram (EHG). As a result, EHG prove to be a biomarker for detecting preterm birth may allow us to detect a preterm birth before labour begins. The goal of this article is to use a deep learning technique called Spiking Neural Network with Seagull Optimization Technique to predict preterm labour using EHG signals (SNN-SOA). This work pre-processes the EHG signal and extracts metrics such as sample frequency peak, median frequency, RMSE, and entropy. In this study, signal is pre-processed with filter Wiener for improving the quality of signal. The features stripped from the pre-processed signal is to define the separate-class. Furthermore, Ant Lion Optimization based on opposition is utilised to choose the training methods and multi-kernel-support-vector-machine as forecasting preterm delivery. The suggested method is tested for classification accuracy using MATLAB software. As per the experimental study results, the research framework enhanced accuracy of classification by 3–19percent when matched to earlier task.

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Data Availability

The dataset used in this article is available at https://physionet.org/content/tpehgt/1.0.0/.

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  1. Department of Computer Science and Engineering, Dr. Ambedkar Institute of Technology, Bengaluru - 560056, Karnataka, India

    A. Veena & S. Gowrishankar

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  1. A. Veena
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  2. S. Gowrishankar
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Correspondence to A. Veena.

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A. Veena is a Research Scholar and S. Gowrishankar is a Professor at Dr. Ambedkar Institute of Technology. Dr. Ambedkar Institute of Technology is affiliated to Visvesvaraya Technological University, Belagavi - 590018, India.

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Veena, A., Gowrishankar, S. An automated pre-term prediction system using EHG signal with the aid of deep learning technique. Multimed Tools Appl 83, 4093–4113 (2024). https://doi.org/10.1007/s11042-023-15665-7

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