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Machine learning-based filtering system for fNIRS signals analysis purpose

Pelc Mariusz, Mikolajewski Dariusz, Luckiewicz Adrian, Sudol Adam, Mendon Patryk, Gorzelańczyk Edward Jacek, Kawala-Sterniuk Aleksandra

Bulletin of the Polish Academy of Sciences. Technical Sciences

2025

Vol. 73, nr 1

art. no. e152605

This paper presents a preliminary study delving into the application of machine learning-based methods for optimizing parameter selection in filtering techniques. The authors focus on exploring the efficacy of two prominent filtering methods: smoothing and cascade filters, known for their profound impact on enhancing the quality of brain signals. The study specifically examines signals acquired through functional near-infrared spectroscopy (fNIRS), a noninvasive neuroimaging modality offering valuable insights into brain activity. Through meticulous analysis, the research underscores the potential of machine learning approaches in discerning optimal parameters for filtering, thereby leading to a significant enhancement in the quality and reliability of fNIRS-derived signals. The results demonstrate the effectiveness of machine learning-based methods in optimizing parameter selection for filtering techniques, particularly in the context of fNIRS signals. By leveraging these approaches, the study achieves notable improvements in the quality and reliability of brain signal data. This work sheds light on promising avenues for refining neuroimaging methodologies and advancing the field of signal processing in neuroscience. The successful application of machine learning-based techniques highlights their potential for optimizing neuroimaging data processing, ultimately contributing to a deeper understanding of brain function.

Survey on the most current image processing methods in huntington's disease diagnostics and progression assessment

Kawala-Sterniuk Aleksandra, Mikolajewski Dariusz, Bryniarska Anna, Myslicka Maria, Czarnecki Damian, Junkiert-Czarnecka Anna, Sudol Adam, Mikolajewska Emilia, Pawlowski Mateusz, Wlodarczyk Anna, Walecki Piotr, Gasz Rafał, Libionka Witold, Panczyszak Bartosz, Pelc Mariusz, Zygarlicki Jarosław, Racheniuk Henryk, Bojkowska-Otrebska Katarzyna, Sterniuk Piotr, Gorzelanczyk Edward Jacek, Ferri Raffaele

Computer Science

2023

T. 24 (4)

567--588

Huntington's disease (HD) is a rare, incurable neurodegenerative disorder where fast and non-invasive diagnosis targeting patients' condition plays a crucial role. In modern medicine, various scientific areas are being combined, such as computing, medicine and biomedical engineering. This survey is focused on the most recent image processing methods applied not only for the purpose of diagnosing HD but also for the assessment of its progression severity, in order to contribute to the effort to prolong life of and to improve its quality.