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All Journal Sinergi Narra J
Gazali, Syahrul
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Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Medicine & Pharmacology Public Health

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Plasma interleukin 6 as an outcome predictor of traumatic brain injury patients Ichwan, Khairunnisa; Gazali, Syahrul; Suherman, Suherman; Desiana, Desiana; Nurjannah, Nurjannah
Narra J Vol. 3 No. 3 (2023): December 2023
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v3i3.234

Abstract

Traumatic brain injury is one of the leading causes of death and disability in young adults. Previous studies have suggested that neuroinflammatory process involves the overexpression of interleukin 6 (IL-6); however, data on the predictive ability of IL-6 is limited and conflicting in traumatic head injury patients. The aim of this study was to assess the ability of plasma IL-6 as a predictor of outcome in head injury patients. A cross-sectional study was conducted between June and December 2020 among traumatic head injury patients admitted to Dr. Zainoel Abidin Hospital, Banda Aceh, Indonesia. Demographic, clinical data, and IL-6 level were collected and measured on admission. The outcome was assessed by the Glasgow outcome scale extended (GOSE) in the first- and third-month of post-injury. A total of 50 traumatic brain injury patients were recruited of which 54% were male, 64% had mild head injury, 82% had leukocytosis, and 60% had non-bleeding head CT scan. The mean of IL-6 level was 79.32 pg/mL while the GOSE scores ranged from 1 (death) to 8 (upper good recovery). Early IL-6 level (<24 hours post-injury) was significantly correlated with worse outcome in traumatic head injury, though the correlation strength was moderate (p<0.001; r=-0.42). As a predictor, IL-6 yielded the area under curve (AUC) value of 93.5% (p<0.001) and a cut-off point of 46.33 pg/mL. The sensitivity and specificity of this predictor were 87.5% and 95.24%, respectively. In conclusion, early IL-6 level can be used as a predictor for traumatic head injury. Nevertheless, further multi-center study with a bigger sample size is needed to confirm this finding.
Comparative analysis of EEG pre-processing in ASD using Hanning and Blackman Harris filters Melinda, Melinda; Waladah, Buleun; Yunidar, Yunidar; Mahfuzha, Raudhatul; Gazali, Syahrul; Rusdiana, Siti; Basir, Nurlida
SINERGI Vol 30, No 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.1.023

Abstract

This study investigates the effectiveness of two Finite Impulse Response (FIR) filter designs based on the Hanning and Blackman-Harris windows for preprocessing electroencephalography (EEG) signals collected from both neurotypical individuals and those diagnosed with Autism Spectrum Disorder (ASD). EEG signals were recorded using a 16-channel setup and band-pass filtered between 0.5 and 40 Hz to isolate relevant neural activity. Subsequently, the signals were processed independently using each FIR filter type. Performance evaluation was conducted using four quantitative metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Signal-to-Noise Ratio (SNR), and Power Spectral Density (PSD). The Hanning window filter showed MAE values ranging from 0.079 to 0.325, MSE from 0.026 to 0.177, SNR between 7.56 and 15.86 dB, and PSD values from 5.3 to 9.08 × 10⁻³. These results demonstrate good noise attenuation while preserving signal morphology. In contrast, the Blackman-Harris window produced higher MAE (0.061–0.318) and MSE (0.019–0.172) but achieved significantly greater SNR improvements (7.77–17.4 dB) and tighter control over PSD (4.904 – 8.442 × 10⁻³), indicating superior noise suppression and reduced spectral leakage. A paired t-test confirmed that differences in all four performance metrics were statistically significant (p < 0.05) across both neurotypical and ASD subject groups. Despite the Hanning filter's computational simplicity, the Blackman-Harris filter demonstrated more robust performance, making it a more suitable choice for high-fidelity EEG signal analysis in clinical diagnostics and neuroscience research.  
Co-Authors Basir, Nurlida Desiana Desiana, Desiana Ichwan, Khairunnisa Junaidi Junaidi Mahfuzha, Raudhatul Melda Fitria Melinda Melinda Nurwijayanti Siti Rusdiana Suherman, Suherman Waladah, Buleun Yunidar