Rancang Bangun Sistem Deteksi Dini Stroke Berbasis Akuisisi Sinyal Electrocardiogram Dan Phonocardiogram Dengan Integrasi Kasur Cerdas Dan Stetoskop

Izzatadini, Raisya (2026) Rancang Bangun Sistem Deteksi Dini Stroke Berbasis Akuisisi Sinyal Electrocardiogram Dan Phonocardiogram Dengan Integrasi Kasur Cerdas Dan Stetoskop. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Stroke merupakan salah satu penyebab utama kecacatan dan kematian sehingga diperlukan sistem yang dapat mendukung pemantauan kondisi jantung secara real-time sebagai upaya deteksi dini. Penelitian ini bertujuan merancang dan mengimplementasikan sistem deteksi dini stroke berbasis dual sinyal Electrocardiogram (ECG) dan Phonocardiogram (PCG) yang terintegrasi dengan kasur cerdas, stetoskop, mikrokontroler STM32, dan dashboard monitoring. Sinyal ECG diperoleh menggunakan kain elektroda, sedangkan sinyal PCG diperoleh menggunakan sensor stetoskop. Sinyal hasil akuisisi diproses menggunakan Fast Fourier Transform (FFT) untuk memperoleh karakteristik spektrum frekuensi, Dolphine Ecolocation untuk menentukan frekuensi karakteristik, serta Pearson Correlation untuk menganalisis tingkat kemiripan sinyal terhadap data referensi. Hasil pengujian rangkaian menunjukkan nilai akurasi sebesar 92,64% pada rangkaian proteksi ECG, 87,42% pada Low Pass Filter (LPF) ECG, 89,12% pada Notch Filter ECG, 66,88% pada High Pass Filter (HPF) ECG, 95,11% pada HPF PCG, 97,97% pada LPF PCG, dan 93,48% pada Notch Filter PCG. Sistem juga berhasil mengintegrasikan rangkaian dengan mikrokontroler STM32 dan dashboard sehingga sinyal ECG dan PCG dapat diakuisisi serta ditampilkan secara real-time pada beberapa posisi tubuh, yaitu terlentang, miring ke kanan, dan miring ke kiri. Namun, hasil akuisisi sinyal biologis masih dipengaruhi noise sehingga karakteristik kompleks P-QRS-T pada ECG serta komponen S1 dan S2 pada PCG belum dapat teridentifikasi secara optimal. Dengan demikian, sistem telah berhasil mengintegrasikan proses akuisisi dan monitoring sinyal ECG dan PCG secara real-time, tetapi masih memerlukan pengembangan lebih lanjut pada tahap pengkondisian dan pengolahan sinyal untuk meningkatkan kualitas hasil deteksi. Hasil sistem merupakan informasi pendukung dan tidak digunakan sebagai dasar diagnosis medis
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Stroke is one of the leading causes of disability and death, highlighting the need for a system that can support real-time cardiac monitoring as an effort toward early detection. This study aims to design and implement an early stroke detection system based on dual Electrocardiogram (ECG) and Phonocardiogram (PCG) signals integrated with a smart bed, stethoscope, STM32 microcontroller, and monitoring dashboard. ECG signals are acquired using conductive fabric electrodes, while PCG signals are acquired using a stethoscope sensor. The acquired signals are processed using Fast Fourier Transform (FFT) to obtain frequency spectrum characteristics, Dolphine Ecolocation to determine characteristic frequencies, and Pearson Correlation to analyze the similarity between the acquired signals and reference data. The circuit testing results showed accuracy values of 92.64% for the ECG protection circuit, 87.42% for the ECG Low Pass Filter (LPF), 89.12% for the ECG Notch Filter, 66.88% for the ECG High Pass Filter (HPF), 95.11% for the PCG HPF, 97.97% for the PCG LPF, and 93.48% for the PCG Notch Filter. The system also successfully integrated the circuits with the STM32 microcontroller and monitoring dashboard, enabling ECG and PCG signals to be acquired and displayed in real time under several body positions, namely supine, right lateral, and left lateral positions. However, the acquired biological signals were still affected by noise, resulting in the P-QRS-T complex of the ECG and the S1 and S2 components of the PCG not being optimally identified. Therefore, the system successfully integrated real-time ECG and PCG signal acquisition and monitoring, but further development is still required in signal conditioning and processing to improve the quality of the detection results. The system output is

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Stroke, Electrocardiogram (ECG), Phonocardiogram (PCG), Fast Fourier Transform (FFT), Dolphine Echolocation, Pearson Correlation ======================================================================================================================== Stroke, Electrocardiogram (ECG), Phonocardiogram (PCG), Fast Fourier Transform (FFT), Dolphine Echolocation, Pearson Correlation
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK105.8883 Web authoring software (include web server) T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7878 Electronic instruments
Divisions:	Faculty of Vocational > Instrumentation Engineering
Depositing User:	Raisya Izzatadini
Date Deposited:	04 Aug 2026 07:21
Last Modified:	04 Aug 2026 07:21
URI:	http://repository.its.ac.id/id/eprint/143400

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