Integrasi Sinyal ECG Dan EEG Sebagai Biofeedback Serta Biomarker Untuk Asesmen Stres

Ichsan, Yasmin Fakhira (2024) Integrasi Sinyal ECG Dan EEG Sebagai Biofeedback Serta Biomarker Untuk Asesmen Stres. Other thesis, Institut Teknologi Sepuluh Nopember.

Text [BUKU] TUGAS AKHIR_YASMIN FAKHIRA ICHSAN_5023201032_TANPA WATERMARK.pdf - Accepted Version Restricted to Repository staff only until 1 October 2026. Download (7MB) | Request a copy

Abstract

Stres adalah respons fisiologis terhadap emosi negatif atau situasi mengancam yang dapat berdampak serius pada kondisi fisik dan mental manusia. Peningkatan level stres dikaitkan dengan risiko penyakit mental kompleks serta berbagai penyakit fisik. Meskipun penelitian sebelumnya telah membuktikan efektivitas penggunaan parameter fisiologis untuk deteksi stres, fokus utamanya masih terbatas pada deteksi stres tanpa mempertimbangkan potensi biomarker dan biofeedback dalam asesmen stres. Penelitian ini mengambil pendekatan holistik dengan mengeksplorasi deteksi stres melalui Electrocardiography (ECG) dan Electroencephalography (EEG), terhubung dengan Mikromedia 7 FPI Capacitive dan chipset STM32F746ZG. Sinyal ECG diambil melalui surface electrode dengan aturan Segitiga Einthoven, diproses menjadi HRV, dan dievaluasi dalam time domain, frequency domain, serta analisis non-linear. Sinyal EEG diambil melalui EEG headband dengan titik sisipan electrode Fp1 dan F3, dievaluasi dalam frequency domain. Pengujian dilakukan pada kelompok pria dan wanita sehat berusia 18-25 tahun dengan tahapan untuk menginduksi stres, diverifikasi dengan kuisioner STAI-Y1. Fitur HRV dan EEG dievaluasi dengan Analysis of Variance (ANOVA) untuk pemilihan fitur sebelum digunakan sebagai input pada Artificial Neural Network (ANN) untuk klasifikasi tingkat stres (rendah, sedang, tinggi). Metode klasifikasi ANN dengan Stratified K-Fold mencapai akurasi 98,964% pada data latihan dan 98,913% pada data testing, dengan akurasi kesesuaian antara hasil klasifikasi objektif dan subjektif sebesar 94% melalui kuisioner STAI-Y1 dan 80% melalui kuisioner DASS21. Hasil penelitian ini diharapkan dapat berfungsi sebagai biomarker praktis bagi profesional kesehatan mental serta memberikan biofeedback yang efektif bagi individu dalam mengelola stres.

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Stress is a physiological response to negative emotions or threatening situations that can significantly impact both physical and mental well-being. Elevated stress levels are associated with complex mental health issues and various physical diseases. While previous research has proven the effectiveness of physiological parameters in stress detection, the primary focus has often been limited to stress detection without considering the potential benefits as biomarkers and biofeedback in stress assessment. This study takes a holistic approach by exploring stress detection through Electrocardiography (ECG) and Electroencephalography (EEG), connected to Mikromedia 7 FPI Capacitive and STM32F746ZG chipset. ECG signals are captured using surface electrodes following the Einthoven's Triangle rule, processed into Heart Rate Variability (HRV), and evaluated in time domain, frequency domain, and non-linear analysis. EEG signals are acquired via an EEG headband with electrode insertion points at Fp1 and F3, evaluated in the frequency domain. Testing was conducted on a group of healthy males and females aged 18-25 years, inducing stress stages validated by the STAI-Y1 questionnaire. HRV and EEG features were evaluated using Analysis of Variance (ANOVA) for feature selection before inputting into an Artificial Neural Network (ANN) for stress level classification (low, medium, high). The ANN classification method with Stratified K-Fold achieved 98,964% accuracy in training data and 98,913% in testing data, with consistency accuracy between objective and subjective stress classification 94% by the STAI-Y1 questionnaire and 80% by the DASS21 questionnaire. This research is expected to serve as a practical biomarker for mental health professionals and provide effective biofeedback for individuals in managing stress.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Asesmen Stres, ECG, EEG, Artificial Neural Network. ============================================= Stress Assessment, ECG, EEG, Artificial Neural Network.
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5102.9 Signal processing.
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Biomedical Engineering > 11410-(S1) Undergraduate Thesis
Depositing User:	YASMIN FAKHIRA ICHSAN
Date Deposited:	05 Aug 2024 07:50
Last Modified:	05 Aug 2024 07:50
URI:	http://repository.its.ac.id/id/eprint/111738

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