Reduce the Energy Consumption of IOTs in the Medical Field

Moutaib, Mohammed; Ahajjam, Tarik; Fattah, Mohammed; Farhaoui, Yousef; Aghoutane, Badraddine; Bekkali, Moulhime El

doi:10.1007/978-3-031-02447-4_27

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…Fetal health is a crucial indicator of an unborn fetus's overall well-being and development. A significant aspect of this monitoring is assessing fetal heart health, often performed via the fetal electrocardiogram (fetal ECG) [1]. These cardiac data can be indicative of a variety of medical conditions, including congenital disabilities and warning signs of fetal distress.…”

Section: Introductionmentioning

confidence: 99%

Fetal electrocardiogram prediction using machine learning: a random forest-based approach

Moutaib,

Fattah,

Farhaoui

et al. 2024

IJEECS

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<p class="IOPText">Monitoring fetal health during pregnancy ensures safe delivery and the newborn’s well-being. The fetal electrocardiogram (fetal ECG) is a valuable tool for assessing fetal cardiac health, but interpretation of ECG data can be challenging due to its complexity and variability. In this work, we explore the application of machine learning, particularly random forest, to predict and analyze fetal ECGs. With its ability to manage large datasets and provide precise insights, random forest is a promising solution for this challenge. By comparing our random forest-based approach with other standard machine learning techniques such as artificial neural network (ANN), support vector machines (SVM), and recurrent neural networks (RNN), we observed that our solution outperformed these methods in accuracy, robustness, and reliability. This article details the methodology used, the implementation of the algorithm, as well as the comparative results obtained. Emphasis is placed on the benefits of random forest in this specific medical context, highlighting its potential as a future tool for fetal ECG prediction. Ultimately, our research suggests a shift toward random forest-based solutions for more efficient and accurate analysis of fetal ECGs, with direct implications for clinical practice and fetal well-being.</p>

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Section: Introductionmentioning

confidence: 99%

Fetal electrocardiogram prediction using machine learning: a random forest-based approach

Moutaib,

Fattah,

Farhaoui

et al. 2024

IJEECS

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“…It relies on remote medical monitoring systems and telemedicine, capable of collecting, transmitting, and displaying real-time data via the Internet. (1,2) Our previous research unveiled an adaptable intelligent healthcare system capable of segregating fetal electrocardiogram (ECG) signal data using the k-means machine-learning algorithm. (3) This system comprised a smart gateway, a series of sensor nodes, and wireless communication links capable of continuously acquiring, processing, and routing human vital signals to a remote medical server.…”

Section: Introductionmentioning

confidence: 99%

“…Such a system facilitates remote health monitoring of patients' conditions by medical professionals such as doctors and nurses, thus opening promising prospects. (1,3) The prediction of fetal electrocardiogram (ECG) signal data using deep learning is a significant approach in the field of healthcare. Technological advancements play a vital role in improving prenatal monitoring and care.…”

Section: Introductionmentioning

confidence: 99%

Fetal and Maternal Electrocardiogram ECG Prediction using Convolutional Neural Networks

Moutaib,

Fattah,

Farhaoui

et al. 2023

Data and Metadata

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Predicting fetal and maternal electrocardiograms (ECGs) is crucial in advanced prenatal monitoring. In this study, we explore the effectiveness of Convolutional Neural Networks (CNNs), using a carefully developed methodology to predict the category of fetal (F) or maternal (M) ECGs. In the first part, we trained a CNN model to predict fetal and maternal ECG images. In the following sections, the study results will be revealed. The CNN model demonstrated its ability to effectively discriminate between fetal and maternal patterns using automatically learned features.

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