Rompi Untuk Mendeteksi Risiko Kematian Jantung Mendadak Pada Fibrilasi Ventrikel Menggunakan Artificial Neural Network

Wulandari, Retno (2024) Rompi Untuk Mendeteksi Risiko Kematian Jantung Mendadak Pada Fibrilasi Ventrikel Menggunakan Artificial Neural Network. Diploma thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Sudden Cardiac Death (SCD) adalah kejadian tragis yang terjadi secara tiba-tiba akibat kegagalan fungsi jantung dengan gejala utama adalah Ventricular Fibrillation pada jantung. SCD jika tidak ditangani segera, dapat berakibat kematian. Untuk mengatasi tantangan ini, perlu dikembangkan teknologi pemantauan ECG yang wearable. Sistem pemantauan ECG berbasis wearable ini menggunakan elektroda non-kontak pada rompi dengan tiga lead : Right Chest (RC), Left Chest (LC), dan Lower Body (LB). Dua material yang diuji yaitu conductive textile dan copper foil tape. Copper foil tape menghasilkan sinyal ECG dengan noise lebih rendah dibandingkan kain konduktif, tetapi kurang ergonomis untuk penggunaan jangka panjang. Rangkaian penangkapan sinyal ECG non kontak yaitu amplifier, Low Pass Filter, High Pass Filter, dan Non-Inverting Adder yang terhubung dengan Mikromedia 7 FPI Capacitive dan terdapat chipset STM32F746ZG. Enam parameter yang digunakan dalam pendeteksian yaitu RR interval, TpTe/QT, JTp/JTe, TpTe/JTp ,TpTe/QRS, TpTe/(QT×QRS). Pengolahan data menggunakan Discrete Wavelet Transform dengan lima level digunakan untuk mendapatkan parameter tersebut. Pengujian dilakukan dengan pengambilan data secara langsung pada 16 subjek dengan 8 perempuan dan 8 laki-laki yang berusia 20 – 50 tahun. Metode klasifikasi menggunakan Artificial Neural Network (ANN) menunjukkan akurasi 97,4%, sensitivitas 96%, spesifisitas 98,4%, dan presisi 97,9%. Posisi tubuh dan material pakaian mempengaruhi kualitas sinyal ECG. Posisi duduk dan tidur telentang menghasilkan sinyal ECG terbaik karena tekanan antara kulit dan elektroda lebih konsisten. Material katun memberikan noise paling sedikit dibandingkan dengan polyester, linen, dan rajut karena kapasitansi yang lebih tinggi antara kulit dan elektroda. Hasil penelitian menunjukkan bahwa rompi yang dirancang dapat secara efektif mendeteksi risiko SCD, memberikan kontribusi dalam bidang kesehatan jantung dan pencegahan kematian jantung mendadak.
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Sudden Cardiac Death (SCD) is a tragic event that occurs suddenly due to heart failure, with the main symptom being Ventricular Fibrillation in the heart. SCD, if not treated immediately, can result in death. To address this challenge, wearable ECG monitoring technology needs to be developed. This wearable ECG monitoring system uses non-contact electrodes on a vest with three leads: Right Chest (RC), Left Chest (LC), and Lower Body (LB). Two materials tested are conductive textile and copper foil tape. Copper foil tape produces ECG signals with lower noise than conductive fabric but is less ergonomic for long-term use. The non-contact ECG signal capture circuit includes an amplifier, Low Pass Filter, High Pass Filter, and Non-Inverting Adder connected to Mikromedia 7 FPI Capacitive with an STM32F746ZG chipset. Six parameters used for detection are RR interval, TpTe/QT, JTp/JTe, TpTe/JTp, TpTe/QRS, and TpTe/(QT×QRS). Data processing using a five-level Discrete Wavelet Transform is employed to obtain these parameters. Testing was done by collecting data directly on 16 subjects with 8 women and 8 men aged 20 – 50 years. The classification method using Artificial Neural Network (ANN) showed an accuracy of 97.4%, precision of 97.9%, sensitivity of 96%, and specificity of 98.4%. Body position and clothing material affect the quality of the ECG signal. Sitting and lying supine positions produce the best ECG signals because the pressure between the skin and electrodes is more consistent. The cotton material provides the least noise compared to polyester, linen, and knitwear due to the higher capacitance between the skin and electrodes. The research results show that the designed vest can effectively detect the risk of SCD, making a contribution to cardiac health and the prevention of sudden cardiac death.

Item Type:	Thesis (Diploma)
Uncontrolled Keywords:	Artificial Neural Network, Elektroda Non Kontak, SCD, Ventricular Fibrillation. Artificial Neural Network, Non-Contact Electrodes, SCD, Ventricular Fibrillation.
Subjects:	Q Science > Q Science (General) > Q325.5 Machine learning. Support vector machines. Q Science > QA Mathematics > QA336 Artificial Intelligence Q Science > QA Mathematics > QA76.87 Neural networks (Computer Science) Q Science > QA Mathematics > QA76 Computer software > QA76.8 Microprocessor R Medicine > R Medicine (General) > R856.2 Medical instruments and apparatus. R Medicine > R Medicine (General) > R858 Deep Learning R Medicine > RC Internal medicine > RC683.5.E5 Electrocardiography T Technology > T Technology (General) T Technology > T Technology (General) > T58.62 Decision support systems T Technology > T Technology (General) > T59.7 Human-machine systems. T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5102.9 Signal processing. T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7878 Electronic instruments
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Biomedical Engineering > 11410-(S1) Undergraduate Thesis
Depositing User:	Retno Wulandari
Date Deposited:	06 Aug 2024 05:29
Last Modified:	06 Aug 2024 05:29
URI:	http://repository.its.ac.id/id/eprint/112072

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