Mahmood Awais, Mehroz Khan Muhammad, Imran Muhammad, Alhajlah Omar, Dhahri Habib, Karamat Tehmina
College of Applied Computer Science, Almuzahmiyah Campus, King Saud University, Riyadh 11543, Saudi Arabia.
Department of Robotic, and Artificial Intelligence, Shaheed Zulifkar Ali Bhutto Institute of Science and Technology, Islamabad 44000, Pakistan.
Diagnostics (Basel). 2023 Mar 13;13(6):1088. doi: 10.3390/diagnostics13061088.
Parkinson's disease directly affects the nervous system are causes a change in voice, lower efficiency in daily routine tasks, failure of organs, and death. As an estimate, nearly ten million people are suffering from Parkinson's disease worldwide, and this number is increasing day by day. The main cause of an increase in Parkinson's disease patients is the unavailability of reliable procedures for diagnosing Parkinson's disease. In the literature, we observed different methods for diagnosing Parkinson's disease such as gait movement, voice signals, and handwriting tests. The detection of Parkinson's disease is a difficult task because the important features that can help in detecting Parkinson's disease are unknown. Our aim in this study is to extract those essential voice features which play a vital role in detecting Parkinson's disease and develop a reliable model which can diagnose Parkinson's disease at its early stages. Early diagnostic systems for the detection of Parkinson's disease are needed to diagnose Parkinson's disease early so that it can be controlled at the initial stages, but existing models have limitations that can lead to the misdiagnosing of the disease. Our proposed model can assist practitioners in continuously monitoring the Parkinson's disease rating scale, known as the Total Unified Parkinson's Disease Scale, which can help practitioners in treating their patients. The proposed model can detect Parkinson's disease with an error of 0.10 RMSE, which is lower than that of existing models. The proposed model has the capability to extract vital voice features which can help detect Parkinson's disease in its early stages.
帕金森病直接影响神经系统，会导致声音改变、日常任务效率降低、器官功能衰竭以及死亡。据估计，全球近一千万人患有帕金森病，且这一数字日益增加。帕金森病患者增多的主要原因是缺乏可靠的帕金森病诊断程序。在文献中，我们观察到不同的帕金森病诊断方法，如步态运动、语音信号和笔迹测试。帕金森病的检测是一项艰巨任务，因为有助于检测帕金森病的重要特征尚不清楚。我们在本研究中的目标是提取那些在检测帕金森病中起关键作用的基本语音特征，并开发一个可靠的模型，能够在帕金森病早期阶段进行诊断。需要早期诊断系统来尽早检测帕金森病，以便在初始阶段对其进行控制，但现有模型存在局限性，可能导致疾病误诊。我们提出的模型可以帮助从业者持续监测帕金森病评定量表，即统一帕金森病评定量表，这有助于从业者治疗患者。所提出的模型检测帕金森病的均方根误差为0.10，低于现有模型。所提出的模型有能力提取重要的语音特征，有助于在早期阶段检测帕金森病。
