Taha Khan

Automatic and objective assessment of alternating tapping performance in Parkinson’s disease

This paper presents the development and evaluation of a method for enabling quantitative and automatic scoring of alternating tapping performance of patients with Parkinsons disease (PD). Ten healthy elderly subjects and 95 patients in different clinical stages of PD have utilized a touch-pad handheld computer to perform alternate tapping tests in their home environments. First, a neurologist used a web-based system to visually assess impairments in four tapping dimensions (‘speed’, ‘accuracy’, ‘fatigue’ and ‘arrhythmia’) and a global tapping severity (GTS). Second, tapping signals were processed with time series analysis and statistical methods to derive 24 quantitative parameters. Third, principal component analysis was used to reduce the dimensions of these parameters and to obtain scores for the four dimensions. Finally, a logistic regression classifier was trained using a 10-fold stratified cross-validation to map the reduced parameters to the corresponding visually assessed GTS scores. Results showed that the computed scores correlated well to visually assessed scores and were significantly different across Unified Parkinsons Disease Rating Scale scores of upper limb motor performance. In addition, they had good internal consistency, had good ability to discriminate between healthy elderly and patients in different disease stages, had good sensitivity to treatment interventions and could reflect the natural disease progression over time. In conclusion, the automatic method can be useful to objectively assess the tapping performance of PD patients and can be included in telemedicine tools for remote monitoring of tapping.

Motion cue analysis for parkinsonian gait recognition.

This paper presents a computer-vision based marker-free method for gait-impairment detection in Patients with Parkinson’s disease (PWP). The system is based upon the idea that a normal human body attains equilibrium during the gait by aligning the body posture with Axis-of-Gravity (AOG) using feet as the base of support. In contrast, PWP appear to be falling forward as they are less-able to align their body with AOG due to rigid muscular tone. A normal gait exhibits periodic stride-cycles with stride-angle around 45o between the legs, whereas PWP walk with shortened stride-angle with high variability between the stride-cycles. In order to analyze Parkinsonian-gait (PG), subjects were videotaped with several gait-cycles. The subject’s body was segmented using a color-segmentation method to form a silhouette. The silhouette was skeletonized for motion cues extraction. The motion cues analyzed were stride-cycles (based on the cyclic leg motion of skeleton) and posture lean (based on the angle between leaned torso of skeleton and AOG). Cosine similarity between an imaginary perfect gait pattern and the subject gait patterns produced 100% recognition rate of PG for 4 normal-controls and 3 PWP. Results suggested that the method is a promising tool to be used for PG assessment in home-environment.

Computer Vision Methods for Parkinsonian Gait Analysis: A Review on Patents

Gait disturbance is an important symptom of Parkinson’s disease (PD). This paper presents a review of patents reported in the area of computerized gait disorder analysis. The feasibility of marker- ...

Pattern matching approach towards real-time traffic sign recognition

This paper addresses the problem of traffic sign recognition in real-time conditions. The algorithm presented in this paper is based on detecting traffic signs in life images and videos using pattern matching of the unknown signs shape with standard shapes of the traffic signs. The pattern matching algorithm works with shape vertices rather than the whole image. This reduces the computation time which is a crucial factor to fit real-time demands. The algorithm is translation and scaling invariant. It shows high robustness as it is tested with 500 images and several videos and a recognition rate of 97% is achieved.

Methods for Detection of Speech Impairment Using Mobile Devices

Speech impairment is an important symptom of Parkinson’s disease (PD). This paper presents a detailed systematic literature review on speech impairment assessment through mobile devices. A two-tier ...

A Case Study in Healthcare Informatics: A Telemedicine Framework for Automated Parkinson’s Disease Symptom Assessment

This paper reports the development and evaluation of a mobile-based telemedicine framework for enabling remote monitoring of Parkinson’s disease (PD) symptoms. The system consists of different measurement devices for remote collection, processing and presentation of symptom data of advanced PD patients. Different numerical analysis techniques were applied on the raw symptom data to extract clinically symptom information which in turn were then used in a machine learning process to be mapped to the standard clinician-based measures. The methods for quantitative and automatic assessment of symptoms were then evaluated for their clinimetric properties such as validity, reliability and sensitivity to change. Results from several studies indicate that the methods had good metrics suggesting that they are appropriate to quantitatively and objectively assess the severity of motor impairments of PD patients.

A Computer Vision Framework For Finger-Tapping Evaluation In Parkinson's Disease

Objective:To define and evaluate a Computer-Vision (CV) method for scoring Paced Finger-Tapping (PFT) in Parkinsons disease (PD) using quantitative motion analysis of index-fingers and to compare the obtained scores to the UPDRS (Unified Parkinsons Disease Rating Scale) finger-taps (FT).Background:The naked-eye evaluation of PFT in clinical practice results in coarse resolution to determine PD status. Besides, sensor mechanisms for PFT evaluation may cause patients discomfort. In order to avoid cost and effort of applying wearable sensors, a CV system for non-invasive PFT evaluation is introduced.Methods:A database of 221 PFT videos from 6 PD patients was processed. The subjects were instructed to position their hands above their shoulders besides the face and tap the index-finger against the thumb consistently with speed. They were facing towards a pivoted camera during recording. The videos were rated by two clinicians between symptom levels 0-to-3 using UPDRS-FT.The CV method incorporates a motion analyzer and a face detector. The method detects the face of testee in each video-frame. The frame is split into two images from face-rectangle center. Two regions of interest are located in each image to detect index-finger motion of left and right hands respectively. The tracking of opening and closing phases of dominant hand index-finger produces a tapping time-series. This time-series is normalized by the face height. The normalization calibrates the amplitude in tapping signal which is affected by the varying distance between camera and subject (farther the camera, lesser the amplitude). A total of 15 features were classified using K-nearest neighbor (KNN) classifier to characterize the symptoms levels in UPDRS-FT. The target ratings provided by the raters were averaged.Results:A 10-fold cross validation in KNN classified 221 videos between 3 symptom levels with 75% accuracy. An area under the receiver operating characteristic curves of 82.6% supports feasibility of the obtained features to replicate clinical assessments.Conclusions:The system is able to track index-finger motion to estimate tapping symptoms in PD. It has certain advantages compared to other technologies (e.g. magnetic sensors, accelerometers etc.) for PFT evaluation to improve and automate the ratingsObjective: To compare the efficacy of botulinum toxin type A (BTX-A) treatment for patients with primary versus secondary blepharospasm (BS) associated with Parkinson’s disease (PD), with or without deep brain stimulation (DBS). Background: BS, a focal eyelid dystonia, can be idiopathic (primary) or secondary to other disorders such as PD. Furthermore eyelid-opening disorders are common in patients with PD undergoing deep brain stimulation (DBS). BTX-A is the treatment of choice for these conditions. Methods: 27 patients [15 males, age: 65.11 6 13.66 years, disease duration 7.7 6 8.2], newly or routinely treated with BTX-A were recruited including patients with primary BS (N 5 10), secondary BS associated with PD (N 5 6), PD1DBS (N 5 5), and other various types of BS (N 5 6). Patients were evaluated before and 4 weeks following BTX-A injections, using the Blepharospam Disability Scale (BDS), the Blepharospasm Disability Index (BDI), the Jankovic Rating Scale (JRS), the Blepharospasm Movement Scale (BMS), and the Clinical Global Impression of improvement (CGI-I). Additionally all were recorded on a 5-minute videotape and scored by a blinded rater. Results: Following BTX-A injections, our sample as a whole showed a statistically significant improvement in Severity of Illness (2.34 6 1.05 vs. 1.74 6 1.27, p 5 0.013), JRS severity scale (1.96 6 1.22 vs. 1.37 6 1.04, p 5 0.002), BMS severity scale (5.04 6 1.79 vs. 4 6 2.35, p 5 0.04), and the severity rating scale (1.61 6 0.8 vs. 1.19 6 0.84, p 5 0.013). When efficacy was compared by diagnosis group, the best effect was evident in patients with BS secondary to PD and was maximal for the PD patients without DBS who demonstrated significant improvement in Severity of Illness compared to the other two groups. Conclusions: In this study BTX-A was an effective treatment for BS. Patients with PD associated BS showed a better response than those with primary BS.Objective: To develop fMRI-based tools in tremor diagnostics and to demonstrate their clinical applicability. Background: Due to overlapping features of tremor disorders, clinical diagnostic tools are limited. Although seldomly used in diagnostic assessment of tremor, fMRI of pathological brain networks underlying tremor could aid accurate and early diagnosis. fMRI of the closed loop behaviour of the sensorimotor system may manifest itself differently with different tremor disorders and has not been fully explored yet in diagnostics. Methods: Following a literature review that we performed on neuroimaging studies in essential tremor (ET), we developed a novel fMRI setup to investigate pathological brain networks related to tremor. An MR-compatible wrist manipulator, to perturb the sensorimotor loop, is combined with movement measures. Results: Our review showed that current findings are consistent with the hypothesis that the cerebellothalamo-cortical network is involved in ET with a major role for the cerebellum. To date, imaging techniques roughly are divided into structural (n=11) and functional methods (n=24). Limitations include heterogeneity of ET symptoms, spatial resolution and inability to directly relate tremor to functional images. The typical nature of a sensorimotor loop is generally not taken into account. We have developed a high-end novel experimental setup within the MR-environment including artifact-free movement measures (EMG, accelerometry) and a MR-compatible wrist manipulator to apply perturbations. Perturbations applied close to pathological tremor frequencies provide sensory input in passive conditions and manipulate motor action in active conditions. Conclusions: We introduce a multimodal fMRI set-up manipulating the sensorimotor loop to identify faulty brain circuitries. This system can potentially lead to a novel quantitative diagnostic tool for differentiating tremor and other movement disorders.