Bi-Cephalic Self-Attended Model to Classify Parkinson's Disease Patients With Freezing of Gait.
Bi-Cephalic Self-Attended Model to Classify Parkinson's Disease Patients With Freezing of Gait.
Where did the research take place?
The study site has not been established. Author addresses may differ from where the research occurred.
US · Author affiliation · country only
Biomedical and Translational Sciences, University of South Dakota, Vermillion, South Dakota, USA.Location evidence
Munich, DE · Author affiliation
Department of Neurology, LMU University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany.Location evidence
Sioux Falls, US · Author affiliation
Department of Neuroscience, Sanford School of Medicine, University of South Dakota, Sioux Falls, South Dakota, USA.Location evidence
A plain-language reading has not been prepared for this paper yet.
Original abstract
Parkinson's Disease (PD) often results in motor and cognitive impairments, including gait dysfunction, particularly in patients with freezing of gait (FOG). Current detection methods are either subjective or reliant on specialized gait analysis tools. This study aims to develop an objective, data-driven, multi-modal classification model for FOG-specific classification, distinguishing PD patients with FOG (PDFOG+) from those without FOG (PDFOG-) and healthy controls using resting-state EEG signals combined with demographic and clinical variables. For our main analysis, we utilized a dataset of 124 participants: 42 PDFOG+, 41 PDFOG-, and 41 age-matched healthy controls. Features extracted from resting-state EEG and descriptive variables (age, education, disease duration) were used to train a novel Bi-cephalic Self-Attention Model (BiSAM). We tested three modalities: signal-only, descriptive-only, and multi-modal, across different EEG channel subsets (BiSAM-63, -16, -8, and -4 for primary analysis). For the main analysis, signal-only (BiSAM-4) and descriptive-only models showed limited performance, achieving a maximum accuracy of 55% and 68%, respectively. In contrast, the multi-modal models significantly outperformed both, with BiSAM-8 and BiSAM-4 achieving the highest classification accuracy of 88%. These results demonstrate the value of integrating EEG with objective descriptive features for robust PDFOG+ classification. This study introduces a multi-modal, attention-based architecture that objectively classifies PDFOG+ using minimal EEG channels and descriptive variables. This approach offers a scalable and efficient alternative to traditional assessments, with potential applications in routine clinical monitoring and early diagnosis of PD-related gait dysfunction.