Extracción de la señal del electrocardiograma fetal basada en el agrupamiento K-means

Mohammed Moutaib; Mohammed Fattah; Yousef Farhaoui; Badraddine Aghoutane; Moulhime El Bekkali

doi:10.56294/dm202384

Original

Published: 2023-12-29

DOI: https://doi.org/10.56294/dm202384

Extraction of fetal electrocardiogram signal based on K-means Clustering

Abstract

Fetal electrocardiograms (ECG) provide crucial information for the interventions and diagnoses of pregnant women at the clinical level. Maternal signals are robust, making retrieval and detection of Fetal ECGs difficult. In this article, we propose a solution based on Machine Learning by adapting the k-means clustering to detect the fetal ECG by recording the ECGs. In our first preprocessing part, we tried normalized and segmented ECG waveform. Next, we used the Euclidean distance to measure similarity. To identify a certain number of centroids in our data, the results classified into two classes are represented in the last part through graphs and compared with other algorithms, such as the CNN classifier, to demonstrate the effectiveness of this innovative approach, which can be deployed in real-time.

Keywords:

ECG,

Machine Learning,

K-means,

Fetal Electrocardiograms,

How to Cite

Moutaib M, Fattah M, Farhaoui Y, Aghoutane B, El Bekkali M. Extraction of fetal electrocardiogram signal based on K-means Clustering. Data and Metadata [Internet]. 2023 Dec. 29 [cited 2024 Jul. 27];2:84. Available from: https://dm.saludcyt.ar/index.php/dm/article/view/84

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The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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