Classification of handwriting kinematics in automated diagnosis and monitoring of Parkinson's disease

Parkinson's disease is one of the most prevalent neurodegenerative conditions. Currently, there is no standard clinical tool available to diagnose PD. One of the research priorities is to come up with biomarkers which will improve the diagnostic process and can be used for the clinical test. At present, the only way to assess this disease is by visually observing the symptoms of the patient which is performed only by expert neurologists. As of now, there is no treatment to prevent the progression of PD. However, there is an elemental drug `Levodopa' (L-dopa) available to control the disease by increasing dopamine cells in the brain. It is important to detect PD and start treatment in the early stages as it helps to control the symptoms and significantly delays the development of motor complications. In this study fine motor symptoms handwriting has been studied. As a first objective I have conducted the experiments on the significant number of patients and age-matched control (112 Participants:56 PD and 56 controls), and thus completed the task of data collection. The system developed extracts the dynamic features of the handwriting/drawing, reports the possible strength of dynamic features providing a basis for automated analysis. The advantage of this approach is that patients are not required to follow complex commands, and the analysis can be fully automized. I anticipate that following appropriate clinical tests already planned, the system will be able to detect early disease symptoms remotely outside hospitals or clinics. It could also be used for self-evaluation by patients with neuromuscular and motor neuron disorders. This device can be used without compromising on the comfort level of Patients who may still prefer writing with an ink pen on plain paper. This study proposes a new feature `Composite Index of Speed and Pen-pressure' (CISP) to distinguish between different stages of Parkinson's disease. The experiment also demonstrated a method which can be used with guided spiral drawing to improve classification results to predict Parkinson's disease. Further, I recommend using a panel of writing tasks which might prove to be an effective biomarker for cell loss in the substantia nigra and the associated dopamine deficiency. Thus, models developed can be used in designing an automated application for predicting and monitoring Parkinson's diseas

Distinguishing different stages of Parkinson's disease using composite index of speed and pen-pressure of sketching a spiral

The speed and pen-pressure while sketching a spiral are lower among Parkinson's disease (PD) patients with higher severity of the disease. However, the correlation between these features and the severity level (SL) of PD has been reported to be 0.4. There is a need for identifying parameters with a stronger correlation for considering this for accurate diagnosis of the disease. This study has proposed the use of the Composite Index of Speed and Pen-pressure (CISP) of sketching as a feature for analyzing the severity of PD. A total of 28 control group (CG) and 27 PD patients (total 55 participants) were recruited and assessed for Unified Parkinson's Disease Rating Scale (UPDRS). They drew guided Archimedean spiral on an A3 sheet. Speed, pen-pressure, and CISP were computed and analyzed to obtain their correlation with severity of the disease. The correlation of speed, pen-pressure, and CISP with the severity of PD was -0.415, -0.584, and -0.641, respectively. Mann-Whitney U test confirmed that CISP was suitable to distinguish between PD and CG, while non-parametric k-sample Kruskal-Wallis test confirmed that it was significantly different for PD SL-1 and PD SL-3. This shows that CISP during spiral sketching may be used to differentiate between CG and PD and between PD SL-1 and PD SL-3 but not SL-2

Efficacy of guided spiral drawing in the classification of Parkinson's Disease

Background: Change of handwriting can be an early marker for severity of Parkinson's disease but suffers from poor sensitivity and specificity due to inter-subject variations. Aim: This study has investigated the group-difference in the dynamic features during sketching of spiral between PD and control subjects with the aim of developing an accurate method for diagnosing PD patients. Method: Dynamic handwriting features were computed for 206 specimens collected from 62 Subjects (31 Parkinson's and 31 Controls). These were analyzed based on the severity of the disease to determine group-difference. Spearman rank correlation coefficient was computed to evaluate the strength of association for the different features. Results: Maximum area under ROC curve (AUC) using the dynamic features during different writing and spiral sketching tasks were in the range of 67 to 79 %. However, when angular features (φ and pn) and count of direction inversion during sketching of the spiral were used, AUC improved to 93.3%. Spearman correlation coefficient was highest for φ and pn. Conclusion: The angular features and count of direction inversion which can be obtained in real-time while sketching the Archimedean guided spiral on a digital tablet can be used for differentiating between Parkinson's and healthy cohort

Parkinson's Disease Diagnosis Based on Multivariate Deep Features of Speech Signal

Parkinson's disease (PD) is known as neurodegenerative disorder causing speech impairment in patients. Therefore, voice recording has been used as useful tool for diagnosis of PD. For the first time in this study, we have tested the effectiveness of deep convolutional neural network (DCNN) in distinguishing between Parkinson's and healthy voices using spectral features from sustained phoneme /a/ (as pronounced in car). Various designs of the DCNN architecture were investigated on raw pathological and healthy voices of varying lengths. This study also investigated the effect of parameters such as frame size, number of convolutional layers and feature maps as well as topology of fully connected layers on the accuracy of the classification outcome. The best network achieved accuracy of 75.7% corresponding on 815 ms of data for distinguishing between Parkinson's and healthy samples. This work has demonstrated that online speech recording has the potential for being used to screening people for Parkinson's disease

A Kinematic Study of Progressive Micrographia in Parkinson's Disease

Progressive micrographia is decrement in character size during writing and is commonly associated with Parkinson's disease (PD). This study has investigated the kinematic features of progressive micrographia during a repetitive writing task. Twenty-four PD patients with duration since diagnosis of < 10 years and 24 age-matched controls wrote the letter "e" repeatedly. PD patients were studied in defined off states, with scoring of motor function on the Unified Parkinson's Disease Rating Scale Part III. A digital tablet captured x-y coordinates and ink-pen pressure. Customized software recorded the data and offline analysis derived the kinematic features of pen-tip movement. The average size of the first and the last five letters were compared, with progressive micrographia defined as > 10% decrement in letter stroke length. The relationships between dimensional and kinematic features for the control subjects and for PD patients with and without progressive micrographia were studied. Differences between the initial and last letter repetitions within each group were assessed by Wilcoxon signed-rank test, and the Kruskal-Wallis test was applied to compare the three groups. There are five main conclusions from our findings: (i) 66% of PD patients who participated in this study exhibited progressive micrographia; (ii) handwriting kinematic features for all PD patients was significantly lower than controls (p < 0.05); (iii) patients with progressive micrographia lose the normal augmentation of writing speed and acceleration in the x axis with left-to-right writing and show decrement of pen-tip pressure (p = 0.034); (iv) kinematic and pen-tip pressure profiles suggest that progressive micrographia in PD reflects poorly sustained net force; and (v) although progressive micrographia resembles the sequence effect of general bradykinesia, we did not find a significant correlation with overall motor disability, nor with the aggregate UPDRS-III bradykinesia scores for the domin

A Decision Tree for Automatic Diagnosis of Parkinson’s Disease from Offline Drawing Samples: Experiments and Findings

We address the problem of designing a machine learning tool for the automatic diagnosis of Parkinson's disease that is capable of providing an explanation of its behavior in terms that are easy to understand by clinicians. For this purpose, we consider as machine learning tool the decision tree, because it provides the decision criteria in terms of both the features which are actually useful for the purpose among the available ones and how their values are used to reach the final decision, thus favouring its acceptance by clinicians. On the other side, we consider the random forest and the support vector machine, which are among the top performing machine learning tool that have been proposed in the literature, but whose decision criteria are hidden into their internal structures. We have evaluated the effectiveness of different approaches on a public dataset, and the results show that the system based on the decision tree achieves comparable or better results that state-of-the-art solutions, being the only one able to provide a plain description of the decision criteria it adopts in terms of the observed features and their values