Abstract
Premature births are on the rise all around the world, and there is currently no way to prevent them. The recent study is focused on the examination of ECG records. It includes data on the electrophysiological characteristics of the mother’s and foetal heart signals. The purpose of this study is to employ the KNN classifier to categorise foetal ECG heartbeats and predict premature delivery. In this study, 50 ECG signals were collected and preprocessed with the filters NLMS and FIR. FFT was used to extract the function from the preprocessed data. It is uncertain how to classify the signals using the retrieved characteristics. As a result, the classification is carried out using the MATLAB software’s Classification Learner programme. By analysing the ECG signals using qualifying criteria, selected features, and target value. ECG signals were classified as either term or preterm.
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Goud, K.N.N., Reddy, K.M.S., Mahesh, A., Raju, G.R. (2023). Preterm Birth Classification Using KNN Machine Learning Algorithm. In: Kumar, A., Mozar, S., Haase, J. (eds) Advances in Cognitive Science and Communications. ICCCE 2023. Cognitive Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-19-8086-2_102
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DOI: https://doi.org/10.1007/978-981-19-8086-2_102
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