Parkinson’s Disease (PD) is characterized as the commonest neurodegenerative illness that gradually degenerates the central nervous system. The goal of this review is to come out with a summary of the recent progress of numerous forms of sensors and systems that are related to diagnosis of PD in the past decades. The paper reviews the substantial researches on the application of technological tools (objective techniques) in the PD field applying different types of sensors proposed by previous researchers. In addition, this also includes the use of clinical tools (subjective techniques) for PD assessments, for instance, patient self-reports, patient diaries and the international gold standard reference scale, Unified Parkinson Disease Rating Scale (UPDRS). Comparative studies and critical descriptions of these approaches have been highlighted in this paper, giving an insight on the current state of the art. It is followed by explaining the merits of the multiple sensor fusion platform compared to single sensor platform for better monitoring progression of PD, and ends with thoughts about the future direction towards the need of multimodal sensor integration platform for the assessment of PD.

Parkinson's disease (PD) is a type of progressive neurodegenerative disorder that has affected a large part of the population till now. Several symptoms of PD include tremor, rigidity, slowness of movements and vocal impairments. In order to develop an effective diagnostic system, a number of algorithms were proposed mainly to distinguish healthy individuals from the ones with PD. However, most of the previous works were conducted based on a binary classification, with the early PD stage and the advanced ones being treated equally. Therefore, in this work, we propose a multiclass classification with three classes of PD severity level (mild, moderate, severe) and healthy control. The focus is to detect and classify PD using signals from wearable motion and audio sensors based on both empirical wavelet transform (EWT) and empirical wavelet packet transform (EWPT) respectively. The EWT/EWPT was applied to decompose both speech and motion data signals up to five levels. Next, several features are extracted after obtaining the instantaneous amplitudes and frequencies from the coefficients of the decomposed signals by applying the Hilbert transform. The performance of the algorithm was analysed using three classifiers - K-nearest neighbour (KNN), probabilistic neural network (PNN) and extreme learning machine (ELM). Experimental results demonstrated that our proposed approach had the ability to differentiate PD from non-PD subjects, including their severity level - with classification accuracies of more than 90% using EWT/EWPT-ELM based on signals from motion and audio sensors respectively. Additionally, classification accuracy of more than 95% was achieved when EWT/EWPT-ELM is applied to signals from integration of both signal's information.

New positron emission tomography (PET) tracers could have a substantial impact on early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) progression, particularly if they are accompanied by optimised deep learning methods. To realize the full potential of deep learning for PET imaging, large datasets are required for training. However, dataset sizes are restricted due to limited availability. Meanwhile, most of the AD classification studies have been based on structural MRI rather than PET. In this paper, we propose a novel application of conditional Generative Adversarial Networks (cGANs) to the generation of 𝐹 18 -florbetapir PET images from corresponding MRI images. Furthermore, we show that generated PET images can be used for synthetic data augmentation, and improve the performance of 3D Convolutional Neural Networks (CNN) for predicting progression to AD. Our method is applied to a dataset of 79 PET images, obtained from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We generate high quality PET images from corresponding MRIs using cGANs, and we evaluate the quality of generated PET images by comparison to real images. We then use the trained cGANs to generate synthetic PET images from additional MRI dataset. Finally we build a 152-layer ResNet to compare the MCI classification performance using both traditional data augmentation method and our proposed synthetic data augmentation method. Mean Structural Similarity (SSIM) index was 0.95± 0.05 for generated PET and real PET. For MCI progression classification, the traditional data augmentation method showed 75% accuracy while the synthetic data augmentation improved this to 82%.

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