Automated Levodopa-induced dyskinesia assessment

Abstract

An automated methodology for Levodopa-induced dyskinesia (LID) assessment is presented in this paper. The methodology is based on the analysis of the signals recorded from accelerometers and gyroscopes, which are placed on certain positions on the subject's body. The obtained signals are analyzed and several features are extracted. Based on these features a classification technique is used for LID detection and classification of its severity. The method has been evaluated using a group of 10 subjects. Results are presented related to each individual sensor as well as for various sensor combinations. The obtained results indicate high classification ability (93.73% classification accuracy).

Assessing Motor Fluctuations in Parkinson’s Disease Patients Based on a Single Inertial Sensor

“…The classification algorithm employed was based on artificial neural networks and labelled 15-min segments. In contrast, similar results were achieved by Tsipouras et al but on smaller segments [44]. In this case, signals from four PD patients and six control subjects doing a number of previously scripted activities were collected.…”

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“…In contrast, Tsipouras et al [65] were able to achieve similar results but on smaller segments. While Keijsers et al [30] used fifteen minute segments, here two second intervals with 75% overlap are utilized.…”

A holistic approach to assess the feasability of telehealth for Parkinson's disease management

“…The ability of the methodology to be generalized was also evaluated using leave-one-patient-out cross validation. The obtained results indicate high classification ability (93.73% classification accuracy) [476], [480], [481]. Gait performance has been largely studied and it is part of this thesis work [5], [6], [17], [482], also, Pansera et al studied the variation of Sample Entropy in the acceleration signals as indicator of the gait performance.…”

“…Finally, Figure 86 shows graphically the quantitative assessment of the system by plotting together the weights of the user needs and the weighted scores of the experts. About the technical performance of the system it shows an accuracy of up to 93.73% of accuracy for the classification of Levodopa Induced Dyskinesias (LID) severity [476], an 86% for the classification of bradykinesia severity [477] and 87% for tremor severity [478]. Also, a specific module was developed for the assessment of gait [5].…”

“…Also, it has been showed that mutual entrainment can facilitate gait relearning effectively [513]. [476], [480], [481]. Gait assessment [5], [6], [482].…”