Task-Oriented Intelligent Solution to Measure Parkinson’s Disease Tremor Severity
Why is this work in the frame?
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No Canadian affiliation. An affiliation-only frame — the usual design — would never have seen this work. It is one of the works that make the case for inverting the frame.
Post-publication record
- Nature
- Retraction
- Reason
- Concerns/Issues about Data;Concerns/Issues about Results and/or Conclusions;Concerns/Issues about Referencing/Attributions;Concerns/Issues about Peer Review;Investigation by Journal/Publisher;Investigation by Third Party;Paper Mill;Computer-Aided Content or Computer-Generated Content;Unreliable Results and/or Conclusions;
- Date
- 11/1/2023 0:00
- Flagged by OpenAlex?
- Yes
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement — it reports them as false, which reads as “fine”.
Abstract
Tremor is a common symptom of Parkinson's disease (PD). Currently, tremor is evaluated clinically based on MDS-UPDRS Rating Scale, which is inaccurate, subjective, and unreliable. Precise assessment of tremor severity is the key to effective treatment to alleviate the symptom. Therefore, several objective methods have been proposed for measuring and quantifying PD tremor from data collected while patients performing scripted and unscripted tasks. However, up to now, the literature appears to focus on suggesting tremor severity classification methods without discrimination tasks effect on classification and tremor severity measurement. In this study, a novel approach to identify a recommended system is used to measure tremor severity, including the influence of tasks performed during data collection on classification performance. The recommended system comprises recommended tasks, classifier, classifier hyperparameters, and resampling technique. The proposed approach is based on the above-average rule of five advanced metrics results of four subdatasets, six resampling techniques, six classifiers besides signal processing, and features extraction techniques. The results of this study indicate that tasks that do not involve direct wrist movements are better than tasks that involve direct wrist movements for tremor severity measurements. Furthermore, resampling techniques improve classification performance significantly. The findings of this study suggest that a recommended system consists of support vector machine (SVM) classifier combined with BorderlineSMOTE oversampling technique and data collection while performing set of recommended tasks, which are sitting, stairs up and down, walking straight, walking while counting, and standing.
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The record
- Venue
- Journal of Healthcare Engineering
- Topic
- Parkinson's Disease Mechanisms and Treatments
- Field
- Medicine
- Canadian institutions
- —
- Funders
- Trent UniversityNottingham Trent UniversityMichael J. Fox Foundation for Parkinson's Research
- Keywords
- Computer sciencePhysical medicine and rehabilitationArtificial intelligenceSupport vector machineResamplingMachine learningMedicine
- Has abstract in OpenAlex
- yes