Ensemble feature extraction-based prediction of fetal arrhythmia using cardiotocographic signals

Magesh, S.; Rajakumar, P.S.

doi:10.1016/j.measen.2022.100631

Cited by 11 publications

(2 citation statements)

References 18 publications

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“…A third study proposed an Artificial Neural Network (ANN) based system for the diagnosis of cardiac arrhythmia using standard 12-lead ECG signal recordings. In all of these studies, the MITBIH database was used to evaluate performance, and the results were found to be satisfactory [9].…”

Section: Introductionmentioning

confidence: 99%

A Real-time ECG CTG based Ensemble Feature Extraction and Unsupervised Learning based Classification Framework for Multi-class Abnormality Prediction

Aditya¹,

Devi²,

Prasad³

2023

IJACSA

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Cardiovascular diseases (CVDs) are a leading cause of death worldwide. Early detection and diagnosis of these diseases can greatly reduce complications and improve outcomes for high-risk individuals. One method for detecting CVDs is through the use of electrocardiogram (ECG) monitoring systems, which use various technologies such as the Internet of Things (IoT), mobile applications, wireless sensor networks (WSN), and wearable devices to acquire and analyze ECG data for early diagnosis. However, despite the prevalence of these systems in the literature, there is a need for further optimization and improvement of their classification accuracy. In an effort to address this challenge, a novel heterogeneous unsupervised learning model for real-time ECG classification was proposed. The main goal of this work was to reduce the error rate and improve the classification accuracy of the system. This study presents a framework for the classification of multi-class abnormalities in electrocardiograms (ECGs) using an ensemble feature extraction technique and unsupervised learning. The framework utilizes a real-time electrocardiogramcardiotocography (ECG-CTG) system to extract features from the ECG signal, and then employs an ensemble of feature extraction techniques to enhance the discrimination of the extracted features. The extracted features are then used in an unsupervised learning-based classification algorithm to classify the ECG signals into different classes of abnormalities. The proposed framework is evaluated on a dataset of ECG signals and the results show that it can effectively classify ECG signals with high accuracy and low computational complexity.

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Section: Introductionmentioning

confidence: 99%

A Real-time ECG CTG based Ensemble Feature Extraction and Unsupervised Learning based Classification Framework for Multi-class Abnormality Prediction

Aditya¹,

Devi²,

Prasad³

2023

IJACSA

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show abstract

“…CNNs are among the most frequently utilized Deep Learning (DL) algorithms in the industry. Its ability to pick up task-specific features without any prior domain expertise is one of its most prominent traits [12]. CNNs have proven to be particularly good at object detection, image segmentation, and face recognition, among other tasks.…”

Section: Introductionmentioning

confidence: 99%

Fast Mask Recurrent Convolutional Neural Network for IoT-Based Maternal and Fetal Monitoring in High-Risk Pregnancies

Ettiyan¹,

Geetha²

2023

RIA

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Ensuring the well-being of fetuses and their timely diagnosis for potential abnormalities is a critical aspect of healthcare. Early identification of intrauterine growth restriction can facilitate appropriate interventions and improve neonatal outcomes. This study presents a novel approach incorporating the Internet of Things (IoT) and Artificial Intelligence (AI) in the medical domain for the automatic detection of fetal abnormalities. IoT sensors were employed to gather maternal clinical data, including temperature, blood pressure, oxygen saturation levels, and fetal heart rate. A Fast Mask Recurrent Convolutional Neural Network (FMRCNN) was proposed to predict and accurately classify a range of conditions affecting pregnant women and their unborn children. The developed FMRCNN model learns, segments, and classifies fetal abdominal images to identify abnormalities. Additionally, a unified fetal abnormality prediction model was established to process and classify both fetal abdomen and brain ultrasound images. Comparative performance analysis was conducted using Convolutional Neural Networks (CNN), Random Forest (RF), and Support Vector Machine (SVM) algorithms. Evaluation metrics, such as F1score, accuracy, precision, recall, and sensitivity, were employed to assess the effectiveness of the proposed approach. The results indicate that the presented FMRCNN model holds promise for IoT-based maternal and fetal monitoring in high-risk pregnancies.

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A multi-branch multi-scale convolutional neural network using automatic detection of fetal arrhythmia

Kanna,

Shajin,

Rajesh

et al. 2024

SIViP

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Ensemble feature extraction-based prediction of fetal arrhythmia using cardiotocographic signals

Cited by 11 publications

References 18 publications

A Real-time ECG CTG based Ensemble Feature Extraction and Unsupervised Learning based Classification Framework for Multi-class Abnormality Prediction

A Real-time ECG CTG based Ensemble Feature Extraction and Unsupervised Learning based Classification Framework for Multi-class Abnormality Prediction

Fast Mask Recurrent Convolutional Neural Network for IoT-Based Maternal and Fetal Monitoring in High-Risk Pregnancies

A multi-branch multi-scale convolutional neural network using automatic detection of fetal arrhythmia

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