CNN‐Based Neurodegenerative Disease Classification Using QR‐Represented Gait Data

• Published in: Brain and behavior Vol. 14; no. 10; pp. e70100 - n/a
• Main Authors: Erdaş, Çağatay Berke, Sümer, Emre
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.10.2024; John Wiley and Sons Inc; Wiley
• Subjects: Adult; Aged; Amyotrophic lateral sclerosis; Amyotrophic Lateral Sclerosis - classification; Amyotrophic Lateral Sclerosis - diagnosis; Amyotrophic Lateral Sclerosis - physiopathology; Brain; Classification; Deep Learning; Feature selection; Female; Fourier transforms; Gait; Gait - physiology; Gait Analysis - methods; Humans; Huntington Disease - classification; Huntington Disease - diagnosis; Huntington Disease - physiopathology; Huntingtons disease; Kinematics; Male; Middle Aged; Movement disorders; Neural networks; Neural Networks, Computer; Neurodegenerative Diseases - classification; Neurodegenerative Diseases - diagnosis; Neurodegenerative Diseases - physiopathology; Original; Parkinson Disease - classification; Parkinson Disease - diagnosis; Parkinson Disease - physiopathology; Parkinson's disease; Patients; Sensors; Signal processing; Spinal cord; Walking
• ISSN: 2162-3279; 2162-3279
• DOI: 10.1002/brb3.70100

ABSTRACT Purpose The primary aim of this study is to develop an effective and reliable diagnostic system for neurodegenerative diseases by utilizing gait data transformed into QR codes and classified using convolutional neural networks (CNNs). The objective of this method is to enhance the precision of diagnosing neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), and Huntington's disease (HD), through the introduction of a novel approach to analyze gait patterns. Methods The research evaluates the CNN‐based classification approach using QR‐represented gait data to address the diagnostic challenges associated with neurodegenerative diseases. The gait data of subjects were converted into QR codes, which were then classified using a CNN deep learning model. The dataset includes recordings from patients with Parkinson's disease (n = 15), Huntington's disease (n = 20), and amyotrophic lateral sclerosis (n = 13), and from 16 healthy controls. Results The accuracy rates obtained through 10‐fold cross‐validation were as follows: 94.86% for NDD versus control, 95.81% for PD versus control, 93.56% for HD versus control, 97.65% for ALS versus control, and 84.65% for PD versus HD versus ALS versus control. These results demonstrate the potential of the proposed system in distinguishing between different neurodegenerative diseases and control groups. Conclusion The results indicate that the designed system may serve as a complementary tool for the diagnosis of neurodegenerative diseases, particularly in individuals who already present with varying degrees of motor impairment. Further validation and research are needed to establish its wider applicability. The study proposes a CNN‐based method using QR‐represented gait data for neurodegenerative disease classification. The approach effectively distinguishes between different gait patterns, highlighting the potential of QR encoding in enhancing CNN classification performance for medical diagnostics.