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All Journal Jurnal Keperawatan Idea Nursing Journal Jurnal Ilmu Keperawatan Jurnal Kesehatan Vokasional Belitung Nursing Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Keperawatan Silampari Window of Health : Jurnal Kesehatan Jurnal Kepemimpinan dan Manajemen Keperawatan JURNAL PENELITIAN PERAWAT PROFESIONAL Journal of Telenursing (JOTING) Jurnal Assyifa: Jurnal Ilmu Kesehatan Lhokseumawe KESANS : International Journal of Health and Science DUNIA KEPERAWATAN: JURNAL KEPERAWATAN DAN KESEHATAN Jurnal Ilmiah Mahasiswa Fakultas Keperawatan Asian Journal of Public Health and Nursing Jurnal Kesehatan
Syarifah Rauzatul Jannah
Bagian Keilmuan Keperawatan Jiwa Fakultas Keperawatan, Universitas Syiah Kuala, Banda Aceh
Chemistry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Health Professions Immunology & microbiology Medicine & Pharmacology Neuroscience Nursing Physics Public Health Veterinary

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Electrocardiogram Signal Analysis Based on Discrete Wavelet Transform with Machine Learning Method in Autistic Children Muhammad Irhamsyah; Hanum Aulia; Yunidar Yunidar; Melinda Melinda; Muhsin Muhsin; Syarifah Rauzatul Jannah
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2750

Abstract

ASD is a neurodevelopmental disorder that affects a child's ability to manage emotions, interact socially, and respond to the environment. The main challenge in monitoring children's physiological condition is the limited availability of objective observation methods that rely heavily on health professionals. One potential objective approach is to analyze the ECG signal. However, ECG signals in children with ASD generally have high levels of noise due to body movements during recording, making manual analysis and conventional methods difficult. This study aims to develop a classification system for the physiological condition of children with ASD based on ECG signals, specifically to distinguish between quiet and active states. The dataset consists of 1000 from each of the two active classes and 1000 from the quiet class. ECG signals were processed using DWT for filtering, and then classified using three machine learning algorithms: SVM, RF, and AdaBoost. The performance of each model was evaluated using accuracy, precision, recall, and F1-score metrics. The evaluation results showed that Random Forest provided the best performance, with an accuracy value of 93%. Meanwhile, SVM achieved an accuracy of 91.25%, while AdaBoost showed slightly lower performance at 90.00%. Based on these results, Random Forest was selected as the most optimal model and integrated into a web-based system using Streamlit. This study demonstrates that the combination of DWT and Random Forest is effective for classifying the physiological conditions of autistic children and has the potential to serve as an objective tool for monitoring them.
Co-Authors 'Aisy, Rohadatul Adinda Oktaviani Adlim Afrida, Fitry Aiyub Aiyub Aiyub Alfiandi, Rudi Asniar Asniar Ayu Lestari Azhar Mu'alim Cut Husna cut rita maharani Dara Febriana Daralina, Monaris Dewi Marianthi Dewi Marianti Dewiyuliana Dhenok Megawulandari Elly Wardani Elvi Mardhiah Elvi Mardhiah Farah Dineva R Farah Shiba Hanin Ferinadia Ferinadia Fiddini, Azzahra Fithria Fithria Fithria Fithria Fithria Fithria Fithria, Fithria Fithria, Fithria Hajjul Kamil Hanum Aulia Hasni Rauzana Hermansyah Hermansyah Hilman Syarif Hilman Syarif Hilman, Syarif Iklima Iklima intan sukma sari Juanita, Juanita Juliana Juliana Khalida, Hayyuni Loreni Loreni M. Marthoenis Mardhiah, Elvi Martina Martina Melinda Melinda miftahul jannah Miftahurrahmi Monaris Daralina Mudatsir Mudatsir Muhammad Ikhsan Muhammad Irhamsyah Muhsin Muhsin Muzar Hafni nelfi ahul lishani Nevi Hasrati Nizami Nova Fajri Novi Afrianti Novitayani, Sri Permana, Hendrian Hadi Pertiwi, Eri Riana putri hayatul jannah Rahma Septiana Putri Rahmi Soraya Ridha Fitri Rifka Amna Rini Asnuriyanti robertus malau Rudi Alfiandi Rudi Alfiandi Said Usman Said Usman Sapitri, Yunyka Wira Saputra, Farhan Siska Damayanti siska millasari Sri Novitayani Suryane Sulistiana Susanti Teguh Mitra Surya Teuku Tahlil Teuku Tahlil tria fitri deswinda Widia riski pratiwi Wika Utari Wika Utari Wirda Hayati Wiwik Handayani Yunidar Yunidar Zafira Nabilah ZURNILA MARLI KESUMA