Deep Convolutional Neural Network for Parkinsons Disease Based Handwriting Screening

Parkinson?s disease (PD) is a neuro-degenerative disorder whose symptoms include slowness of movement, tremors, muscle stiffness, changes in speech and writing, depression, anxiety, sleep and emotional problems. According to the Parkinson?s foundation, almost one million Americans live with PD and approximately 60,000 Americans are diagnosed with the disease every year. Deep learning approaches have been shown promising for medical image analysis (Litjens et al., 2017). Deep learning is a branch of machine learning that aims at learning various features of the provided data or images in an automated fashion without the need for extracting specific handcrafted features. The most popular deep learning approaches are: Convolutional Neural Networks (CNN), Deep Belief Networks (DBF) and Deep Boltzmann Machines (DBM). Few works have investigated the use of CNN for diagnosing and classifying PD based on speech patterns, handwriting exams and handwriting dynamics captured by a smart pen (Frid et al., 2016; Pereira et al., 2016; Eskofier et al., 2016; Pereira et al., 2017; Um et al., 2017). In the current work, two novel CNN models of two convolutional, two maximum pooling and fully connected layers are introduced. The proposed models were trained on spiral and wave handwriting datasets of 102 images each to distinguish between controls and subjects with PD.A validation accuracy of 83% and 87% was achieved when the proposed models were trained on 70% of both datasets for 300 epochs respectively with 30% of the datasets reserved for testing. The proposed models provide a promising solution for early diagnosis and screening of PD patients and may possibly support the clinical assessments for medical specialists that may be subjective and insensitive. In addition to using handwritten datasets for PD diagnosis, frequency analysis of the Electroencephalography (EEG) data may contain useful information about motor and non-motor dysfunction of PD by exploiting theta [4-8 Hz], alpha [8-12 Hz], beta [12-35 Hz] and gamma [35-45 Hz] waves. The future research will aim to analyze a public EEG dataset for controls and PD patients and extract unique features from both the beta and gamma frequency bands with an objective to detect the cognitive decline of PD patients and hence predict the existence of the disease.