206 research outputs found

    Comparison of CNN-Learned vs. Handcrafted Features for Detection of Parkinson's Disease Dysgraphia in a Multilingual Dataset

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    Parkinson's disease dysgraphia (PDYS), one of the earliest signs of Parkinson's disease (PD), has been researched as a promising biomarker of PD and as the target of a noninvasive and inexpensive approach to monitoring the progress of the disease. However, although several approaches to supportive PDYS diagnosis have been proposed (mainly based on handcrafted features (HF) extracted from online handwriting or the utilization of deep neural networks), it remains unclear which approach provides the highest discrimination power and how these approaches can be transferred between different datasets and languages. This study aims to compare classification performance based on two types of features: features automatically extracted by a pretrained convolutional neural network (CNN) and HF designed by human experts. Both approaches are evaluated on a multilingual dataset collected from 143 PD patients and 151 healthy controls in the Czech Republic, United States, Colombia, and Hungary. The subjects performed the spiral drawing task (SDT; a language-independent task) and the sentence writing task (SWT; a language-dependent task). Models based on logistic regression and gradient boosting were trained in several scenarios, specifically single language (SL), leave one language out (LOLO), and all languages combined (ALC). We found that the HF slightly outperformed the CNN-extracted features in all considered evaluation scenarios for the SWT. In detail, the following balanced accuracy (BACC) scores were achieved: SL—0.65 (HF), 0.58 (CNN); LOLO—0.65 (HF), 0.57 (CNN); and ALC—0.69 (HF), 0.66 (CNN). However, in the case of the SDT, features extracted by a CNN provided competitive results: SL—0.66 (HF), 0.62 (CNN); LOLO—0.56 (HF), 0.54 (CNN); and ALC—0.60 (HF), 0.60 (CNN). In summary, regarding the SWT, the HF outperformed the CNN-extracted features over 6% (mean BACC of 0.66 for HF, and 0.60 for CNN). In the case of the SDT, both feature sets provided almost identical classification performance (mean BACC of 0.60 for HF, and 0.58 for CNN). Copyright © 2022 Galaz, Drotar, Mekyska, Gazda, Mucha, Zvoncak, Smekal, Faundez-Zanuy, Castrillon, Orozco-Arroyave, Rapcsak, Kincses, Brabenec and Rektorova

    Bradykinesia in non-parkinsonian conditions: the emerging concept of a network disorder

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    Bradykinesia (movement slowness) is one of the cardinal motor symptoms of Parkinson‟s disease and atypical parkinsonism and it has hystorically been interpreted as a motor disorder due to basal ganglia dysfunction. Clinical and experimental studies, however, indicate that it may be also observed in the context of various neurological conditions not primarily characterized by parkinsonism. These conditions include hyperkinetic movement disorders, such as dystonia and chorea, as well conditions primarily characterized by tremor (e.g. essential tremor) or other nervous diseases characterized by the involvement of brain areas and network including not only the basal ganglia but also the cerebellum and upper motoneurons. Also, movement slowness may be observed in patients with neurodegenerative or inflammatory diseases of the central nervous system of various origins, like dementia or multiple sclerosis. From a pathophysiological standpoint, the observation of movement slowness in neurological conditions not primarily characterized by parkinsonism is possibly explained by a brain network dysfunction, as hypothesized in parkinsonism. In the present thesis, we will first provide an updated overview on bradykinesia in non-parkinsonian conditions and discuss major findings of clinical reports and experimental studies. In the experimental part of the present thesis, we will provide the results from three original studies, which investigated the presence of bradykinesia and its possible pathophysiological mechanisms in (i) patients with essential tremor, (ii) patients with Alzheimer‟s disease, and (iii) patients with amyotrophic lateral sclerosis. Finally, we will provide a unifying pathophysiological interpretation of bradykinesia in non-parkinsonian conditions from a network perspective and emphasize possible terminological implications

    Motor symptoms in Parkinson's disease: A unified framework

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    Parkinson’s disease (PD) is characterized by a range of motor symptoms. Besides the cardinal symptoms (akinesia and bradykinesia, tremor and rigidity), PD patients show additional motor deficits, including: gait disturbance, impaired handwriting, grip force and speech deficits, among others. Some of these motor symptoms (e.g., deficits of gait, speech, and handwriting) have similar clinical profiles, neural substrates, and respond similarly to dopaminergic medication and deep brain stimulation (DBS). Here, we provide an extensive review of the clinical characteristics and neural substrates of each of these motor symptoms, to highlight precisely how PD and its medical and surgical treatments impact motor symptoms. In conclusion, we offer a unified framework for understanding the range of motor symptoms in PD. We argue that various motor symptoms in PD reflect dysfunction of neural structures responsible for action selection, motor sequencing, and coordination and execution of movement

    Objective evaluation of Parkinson's disease bradykinesia

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    Bradykinesia is the fundamental motor feature of Parkinson’s disease - obligatory for diagnosis and central to monitoring. It is a complex clinicalsign that describes movements with slow speed, small amplitude, irregular rhythm, brief pauses and progressive decrements. Clinical ascertainment of the presence and severity of bradykinesia relies on subjective interpretation of these components, with considerable variability amongst clinicians, and this may contribute to diagnostic error and inaccurate monitoring in Parkinson’s disease. The primary aim of this thesis was to assess whether a novel non-invasive device could objectively measure bradykinesia and predict diagnostic classification of movement data from Parkinson’s disease patients and healthy controls. The second aim was to evaluate how objective measures of bradykinesia correlate with clinical measures of bradykinesia severity. The third aim was to investigate the characteristic kinematic features of bradykinesia. Forty-nine patients with Parkinson’s disease and 41 healthy controls were recruited in Leeds. They performed a repetitive finger-tapping task for 30 seconds whilst wearing small electromagnetic tracking sensors on their finger and thumb. Movement data was analysed using two different methods - statistical measures of the separable components of bradykinesia and a computer science technique called evolutionary algorithms. Validation data collected independently from 13 patients and nine healthy controls in San Francisco was used to assess whether the results generalised. The evolutionary algorithm technique was slightly superior at classifying the movement data into the correct diagnostic groups, especially for the mildest clinical grades of bradykinesia, and they generalised to the independent group data. The objective measures of finger tapping correlated well with clinical grades of bradykinesia severity. Detailed analysis of the data suggests that a defining feature of Parkinson’s disease bradykinesia called the sequence effect may be a physiological rather than a pathological phenomenon. The results inform the development of a device that may support clinical diagnosis and monitoring of Parkinson’s disease and also be used to investigate bradykinesia

    Comparison of One- Two- and Three- Dimensional CNN models for Drawing-Test-Based Diagnostics of the Parkinson's Disease

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    Subject: In this article, convolutional networks of one, two, and three dimensions are compared with respect to their ability to distinguish between the drawing tests produced by Parkinson's disease patients and healthy control subjects. Motivation: The application of deep learning techniques for the analysis of drawing tests to support the diagnosis of Parkinson's disease has become a growing trend in the area of Artificial Intelligence. Method: The dynamic features of the handwriting signal are embedded in the static test data to generate one-dimensional time series, two-dimensional RGB images and three-dimensional voxelized point clouds, and then one-, two-, and three-dimensional CNN can be used to automatically extract features for effective diagnosis. Novelty: While there are many results that describe the application of two-dimensional convolutional models to the problem, to the best knowledge of the authors, there are no results based on the application of three-dimensional models and very few using one-dimensional models. Main result: The accuracy of the one-, two- and three-dimensional CNN models was 62.50%, 77.78% and 83.34% in the DraWritePD dataset (acquired by the authors) and 73.33%, 80.00% and 86.67% in the PaHaW dataset (well known from the literature), respectively. For these two data sets, the proposed three-dimensional convolutional classification method exhibits the best diagnostic performance

    Study and characterisation of the prodromal motor phase of Parkinson’s Disease

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    There is sufficient evidence that a neurodegenerative process in Parkinson’s Disease (PD) starts many years before the clinical diagnosis. The progression of PD is generally slow and, because it is diagnosed based on established motor features, it is probable that subtle motor manifestations appear in the pre-diagnostic phase of PD. Isolated rapid eye movement (REM) sleep behaviour disorder (iRBD) is a condition known to be part of the prodromal phase of PD. The PREDICT-PD study is a population-based cohort which aims to identify individuals at risk of PD based on the presence and absence of risk factors. The first project of this thesis investigated the association between first presentation of motor symptoms (tremor, rigidity and balance difficulties) and subsequent PD in a large primary care dataset in East London, including almost 3 decades of clinical information from over a million individuals. People who went on to develop PD reported motor symptoms up to 10 years before PD diagnosis. Tremor had the highest association with future PD followed by balance difficulties and rigidity. The second project aimed to identify the range of motor features in the elderly population participating in the PREDICT-PD cohort study and document their rate of progression over time. People classified as having a higher risk of future PD (using the PREDICT-PD algorithm) were more likely to have early parkinsonian signs than the lower risk group. Six years later, they also showed a bigger motor decline compared with people in the lower risk group. The third project was focused on developing two new objective motor tools, the Distal Finger Tapping test and the Slow-Motion Analysis of Repetitive Tapping. Both tests were able to detect abnormal patterns of movement amongst people with early PD. Finally, a motor battery was created and implemented in a group of patients with iRBD. A higher proportion of patients with iRBD had early parkinsonian signs compared with controls. The motor battery was able to detect early patterns of motor dysfunction not captured by standardised clinical scales. The work presented in this thesis demonstrates that motor features start in the pre-diagnostic phase of PD and describes new motor signatures in the prodromal phase of PD
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