Jovanovic Luka, Damaševičius Robertas, Matic Rade, Kabiljo Milos, Simic Vladimir, Kunjadic Goran, Antonijevic Milos, Zivkovic Miodrag, Bacanin Nebojsa
Faculty of Technical Sciences, Singidunum University, Belgrade, Serbia.
Department of Applied Informatics, Vytautas Magnus University, Akademija, Lithuania.
PeerJ Comput Sci. 2024 May 13;10:e2031. doi: 10.7717/peerj-cs.2031. eCollection 2024.
Neurodegenerative conditions significantly impact patient quality of life. Many conditions do not have a cure, but with appropriate and timely treatment the advance of the disease could be diminished. However, many patients only seek a diagnosis once the condition progresses to a point at which the quality of life is significantly impacted. Effective non-invasive and readily accessible methods for early diagnosis can considerably enhance the quality of life of patients affected by neurodegenerative conditions. This work explores the potential of convolutional neural networks (CNNs) for patient gain freezing associated with Parkinson's disease. Sensor data collected from wearable gyroscopes located at the sole of the patient's shoe record walking patterns. These patterns are further analyzed using convolutional networks to accurately detect abnormal walking patterns. The suggested method is assessed on a public real-world dataset collected from parents affected by Parkinson's as well as individuals from a control group. To improve the accuracy of the classification, an altered variant of the recent crayfish optimization algorithm is introduced and compared to contemporary optimization metaheuristics. Our findings reveal that the modified algorithm (MSCHO) significantly outperforms other methods in accuracy, demonstrated by low error rates and high Cohen's Kappa, precision, sensitivity, and F1-measures across three datasets. These results suggest the potential of CNNs, combined with advanced optimization techniques, for early, non-invasive diagnosis of neurodegenerative conditions, offering a path to improve patient quality of life.
神经退行性疾病对患者的生活质量有重大影响。许多此类疾病无法治愈，但通过适当及时的治疗，可以减缓疾病的进展。然而，许多患者直到病情发展到对生活质量产生重大影响时才寻求诊断。有效的非侵入性且易于获取的早期诊断方法可以显著提高受神经退行性疾病影响患者的生活质量。这项工作探索了卷积神经网络（CNN）在与帕金森病相关的患者冻结步态方面的潜力。从位于患者鞋底的可穿戴陀螺仪收集的传感器数据记录行走模式。使用卷积网络进一步分析这些模式，以准确检测异常行走模式。所建议的方法在一个从受帕金森病影响的患者及其对照组个体收集的公共真实世界数据集上进行评估。为了提高分类的准确性，引入了一种改进的小龙虾优化算法变体，并与当代优化元启发式算法进行比较。我们的研究结果表明，改进算法（MSCHO）在准确性方面显著优于其他方法，这在三个数据集上的低错误率以及高科恩卡方值、精度、灵敏度和F1分数中得到体现。这些结果表明，结合先进优化技术的CNN在神经退行性疾病的早期非侵入性诊断方面具有潜力，为改善患者生活质量提供了一条途径。
