Historical Analyses of Disordered Handwriting

Handwritten texts carry significant information, extending beyond the meaning of their words. Modern neurology, for example, benefits from the interpretation of the graphic features of writing and drawing for the diagnosis and monitoring of diseases and disorders. This article examines how handwriting analysis can be used, and has been used historically, as a methodological tool for the assessment of medical conditions and how this enhances our understanding of historical contexts of writing. We analyze handwritten material, writing tests and letters, from patients in an early 20th-century psychiatric hospital in southern Germany (Irsee/Kaufbeuren). In this institution, early psychiatrists assessed handwriting features, providing us novel insights into the earliest practices of psychiatric handwriting analysis, which can be connected to Berkenkotter’s research on medical admission records. We finally consider the degree to which historical handwriting bears semiotic potential to explain the psychological state and personality of a writer, and how future research in written communication should approach these sources

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

Derivation and Analysis of Dynamic Handwriting Features as Clinical Markers of Parkinson’s Disease

Parkinson’s Disease (PD) is a complex neurodegenerative disorder that is challenging to diagnose. Recent research has demonstrated predictive value in the analysis of dynamic handwriting features for detecting PD, however, consensus on clinically-useful features is yet to be reached. Here we explore and evaluate secondary kinematic handwriting features hypothesized to be diagnostically relevant to Parkinson’s Disease using a publicly-available Spiral Drawing Test PD dataset. Univariate and multivariate analysis was performed on derived features. Classification outcome was determined using logistic regression models with 10-fold cross validation. Feature correlation was based on model specificity and sensitivity. Variations in grip angle, instantaneous acceleration and pressure indices were found to have high predictive potential as clinical markers of PD, with combined classification accuracy of above 90%. Our results show that the significance of secondary handwriting features and recommend the feature expansion step for hypothesis generation, comparative evaluation of test types and improved classification accuracy

DIMETER: a haptic master device for tremor diagnosis in neurodegenerative diseases

In this study, a device based on patient motion capture is developed for the reliable and non-invasive diagnosis of neurodegenerative diseases. The primary objective of this study is the classification of differential diagnosis between Parkinson's disease (PD) and essential tremor (ET). The DIMETER system has been used in the diagnoses of a significant number of patients at two medical centers in Spain. Research studies on classification have primarily focused on the use of well-known and reliable diagnosis criteria developed by qualified personnel. Here, we first present a literature review of the methods used to detect and evaluate tremor; then, we describe the DIMETER device in terms of the software and hardware used and the battery of tests developed to obtain the best diagnoses. All of the tests are classified and described in terms of the characteristics of the data obtained. A list of parameters obtained from the tests is provided, and the results obtained using multilayer perceptron (MLP) neural networks are presented and analyzed

Characterization and personalization of botulinum toxin type A therapy for upper limb tremor in Parkinson disease and Essential tremor patients using multi-sensor kinematic technology

Tremor commonly affects the upper extremities in essential tremor (ET) and Parkinson disease (PD) patients where many experience functional disability and ultimately seek therapy. As ET and PD tremor features overlap and clinical assessment is challenging due to its highly complex nature, misdiagnosis is common resulting in unsuitable therapies and prognosis. Current treatment options for ET and PD tremor include pharmacotherapy, focal therapy with botulinum toxin type A (BoNT-A) injections, and surgical interventions which provide modest relief of tremor. However, such therapies are commonly associated with significant adverse events and lack long-term efficacy and tolerability. Hence lack of standardized, objective measures of tremor and suboptimal treatment options are two significant unmet needs faced by neurologists today. The hypothesis of this thesis was to determine whether joint tremor amplitude can differentiate between ET and PD tremor types and can be applied towards improving BoNT-A tremor therapy. The first objective was to apply motion sensor kinematic technology to investigate the role of paired tasks in modulating tremor biomechanics in 24 ET and 28 PD participants. Paired tasks involved variating limb positioning while at rest, posture, and under weight-bearing conditions. Motion sensor devices were placed over the wrist, elbow and shoulder joints capturing joint angular tremor amplitude in multiple degrees of freedom (DOF). Kinematic measures of tremor allowed detailed segmentation of tremor into directional components, which cannot be performed visually. The relationship of joint tremor severity between paired tasks and across all tasks generated unique tremor profiles and provided a simple method to differentiate ET and PD tremor types. The second objective was to apply tremor kinematics to better tailor BoNT-A injection parameters. Participants were injected in the upper limb, which exhibited their most bothersome tremor, every 16 weeks, a total of 3 injection cycles, and attended follow-up visits six weeks following treatment, for a total of 6 study visits. Clinical rating scales and kinematic recordings were conducted at each visit. Dosing was based on clinician’s experience and kinematic data, and muscle site of injection was determined kinematically. A significant decrease in mean clinical tremor rating scores during rest and action tasks and significant improvement in arm function was observed at week 6 and continued throughout the study in both ET and PD individuals. Ten PD participants and eight ET participants reported mild weakness in injected muscles that had no interference with arm function. Kinematic technology is a promising method for standardizing assessments and for personalizing BoNT-A therapy

Parkinson's disease biomarkers: perspective from the NINDS Parkinson's Disease Biomarkers Program

Biomarkers for Parkinson's disease (PD) diagnosis, prognostication and clinical trial cohort selection are an urgent need. While many promising markers have been discovered through the National Institute of Neurological Disorders and Stroke Parkinson's Disease Biomarker Program (PDBP) and other mechanisms, no single PD marker or set of markers are ready for clinical use. Here we discuss the current state of biomarker discovery for platforms relevant to PDBP. We discuss the role of the PDBP in PD biomarker identification and present guidelines to facilitate their development. These guidelines include: harmonizing procedures for biofluid acquisition and clinical assessments, replication of the most promising biomarkers, support and encouragement of publications that report negative findings, longitudinal follow-up of current cohorts including the PDBP, testing of wearable technologies to capture readouts between study visits and development of recently diagnosed (de novo) cohorts to foster identification of the earliest markers of disease onset

How Does Technology Development Influence the Assessment of Parkinson’s Disease? A Systematic Review

abstract: Parkinson’s disease (PD) is a neurological disorder with complicated and disabling motor and non-motor symptoms. The pathology for PD is difficult and expensive. Furthermore, it depends on patient diaries and the neurologist’s subjective assessment of clinical scales. Objective, accurate, and continuous patient monitoring have become possible with the advancement in mobile and portable equipment. Consequently, a significant amount of work has been done to explore new cost-effective and subjective assessment methods or PD symptoms. For example, smart technologies, such as wearable sensors and optical motion capturing systems, have been used to analyze the symptoms of a PD patient to assess their disease progression and even to detect signs in their nascent stage for early diagnosis of PD. This review focuses on the use of modern equipment for PD applications that were developed in the last decade. Four significant fields of research were identified: Assistance diagnosis, Prognosis or Monitoring of Symptoms and their Severity, Predicting Response to Treatment, and Assistance to Therapy or Rehabilitation. This study reviews the papers published between January 2008 and December 2018 in the following four databases: Pubmed Central, Science Direct, IEEE Xplore and MDPI. After removing unrelated articles, ones published in languages other than English, duplicate entries and other articles that did not fulfill the selection criteria, 778 papers were manually investigated and included in this review. A general overview of PD applications, devices used and aspects monitored for PD management is provided in this systematic review.Dissertation/ThesisMasters Thesis Computer Engineering 201