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All Journal Journal of Holistic Medical Technologies
Harisma Nugraha
PT. C-Tech Lab Edwar Teknologi
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Neuroscience

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Characteristics of Brain Signals in Response to Audiovisual Stimulation Using the Electrical Capacitance Volume Tomography (ECVT) Principle Dewi Retno Kinasih; Heni Sumarti; Sheilla Rully Anggita; Irman Said Prastyo; Marlin Ramadhan Baidillah; Harisma Nugraha; Rohmadi
Journal of Holistic Medical Technologies (JHMT) Vol. 2 No. 2 (2026): June
Publisher : Konsorsium Pengetahuan Innoscientia (KOPINNOS)

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The human brain continuously generates electrical activity that changes measurably when it processes external stimuli. Electrical Capacitance Volume Tomography (ECVT) is a non-invasive, radiation-free volumetric sensing technique that has been adapted, as a helmet-shaped capacitive sensor, to monitor human brain electrical activity in real time. This study characterizes the ECVT-based brain signal response to audiovisual stimulation and identifies the measurement frequency and sensor-channel combination most sensitive to stimulus-related change. A single healthy male subject (21 years old) was measured using a 16-sensor ECVT helmet connected to an Arbitrary Function Generator and an oscilloscope. Peak-to-peak voltage (Vpp) was recorded for six representative sensor-channel pairs at three carrier frequencies (500 kHz, 1 MHz, and 5 MHz) under three conditions – audiovisual (video and music) stimulation, a water-filled helmet reference, and an empty (air-filled) helmet reference – with six repeated measurements per combination. Across the three frequencies, the 500 kHz measurements showed the most pronounced differences between channels and between conditions, while the 1 MHz and 5 MHz measurements showed comparatively little variation. At 500 kHz, channel responses during audiovisual stimulation were visibly more variable than during the water-filled or empty reference conditions. Among the tested frequencies, 500 kHz was the most sensitive for detecting ECVT signal changes associated with audiovisual stimulation, indicating that both operating frequency and channel selection are important considerations for ECVT-based studies of brain responses to sensory stimulation.
Classification of Alzheimer's Disease Based on Texture Characteristics in Magnetic Resonance Imaging Images Using the Random Forest Method Ibnul Bahar Al Basith; Muhammad Ulil Albab; Nani Kartika Dewi; Hartono; Affa Ardhi Saputri; Susilawati; Harisma Nugraha
Journal of Holistic Medical Technologies (JHMT) Vol. 2 No. 2 (2026): June
Publisher : Konsorsium Pengetahuan Innoscientia (KOPINNOS)

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Alzheimer's disease (AD) is a progressive neurodegenerative disorder and one of the leading causes of dementia worldwide. Early detection is critical to slowing disease progression and improving patient outcomes. While Magnetic Resonance Imaging (MRI) is commonly used for AD diagnosis, analyzing the high-dimensional data remains challenging. This study explores the use of texture features from MRI images to improve the accuracy of AD classification using machine learning. This research utilized MRI brain images from publicly available datasets to classify early-stage Alzheimer's disease. The Random Forest algorithm handled large, complex datasets while minimizing overfitting. Texture features, including Histogram, Gray Level Co-Occurrence Matrix (GLCM), and Gray Level Run-Length Matrix (GLRLM), were extracted to highlight microstructural changes in the brain, particularly in the hippocampus and cortex—critical areas affected by AD. The model achieved an accuracy of 88%, precision of 85%, and Recall of 92% in the 10th fold using the WEKA platform, demonstrating its effectiveness in detecting early neurodegenerative changes. The Random Forest model, combined with texture feature extraction, provides a computationally efficient approach for the early detection of Alzheimer's disease. By identifying subtle changes in brain tissue, this method can potentially support clinicians in early diagnosis and intervention planning. Future research should focus on integrating additional imaging modalities and clinical data to enhance diagnostic accuracy and reliability.
Co-Authors Affa Ardhi Saputri Dewi Retno Kinasih Hartono Ibnul Bahar Al Basith Marlin Ramadhan Baidillah Muhammad Ulil Albab Nani Kartika Dewi Prastyo, Irman Said Rohmadi Sheilla Rully Anggita Sumarti, Heni Susilawati