Comparison of methods for tremor frequency analysis for patients with Parkinson's disease

Niazmand, K.; Kalaras, A.; Dai, Houde; Lueth, Tim C.

doi:10.1109/bmei.2011.6098449

Cited by 13 publications

(11 citation statements)

References 13 publications

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“…This is most pronounced for dyskinesia, but is also true for tremor. This is an interesting result because a lot of existing classification work on both these movement types has focused on the frequency domain [1,9,14]. For tremor, a frequency analysis seems natural, since it is defined as a regular rhythmic oscillation of a body part, and the frequency ranges for di↵erent kinds of tremor are relatively well characterised.…”

Section: Resultsmentioning

confidence: 98%

Classification and characterisation of movement patterns during levodopa therapy for parkinson's disease

Lones

Alty

Duggan-Carter

et al. 2014

Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

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Parkinson's disease is a chronic neurodegenerative condition that manifests clinically with various movement disorders. These are often treated with the dopamine-replacement drug levodopa. However, the dosage of levodopa must be kept as low as possible in order to avoid the drug's side e↵ects, such as the involuntary, and often violent, muscle spasms called dyskinesia, or levodopa-induced dyskinesia. In this paper, we investigate the use of genetic programming for training classifiers that can monitor the e↵ectiveness of levodopa therapy. In particular, we evolve classifiers that can recognise tremor and dyskinesia, movement states that are indicative of insu cient or excessive doses of levodopa, respectively. The evolved classifiers achieve clinically useful rates of discrimination, with AUC>0.9. We also find that temporal classifiers generally out-perform spectral classifiers. By using classifiers that respond to low-level features of the data, we identify the conserved patterns of movement that are used as a basis for classification, showing how this approach can be used to characterise as well as classify abnormal movement.

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Section: Resultsmentioning

confidence: 98%

Classification and characterisation of movement patterns during levodopa therapy for parkinson's disease

Lones

Alty

Duggan-Carter

et al. 2014

Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

View full text Add to dashboard Cite

show abstract

“…Depending on symptom and utilized sensors, various features have been proposed and applied in literature (e.g. entropy [9], [50], [14], [43], spectral or fractal features [64], [61], [66], [5], [57], [44], [47], [29], [10], [11]) are known to be used in this context. In the course of this section a strong focus will be on the most common symptoms that are experienced by PD patients.…”

Section: Identifying Parkinson's Disease and Its Symptomsmentioning

confidence: 99%

“…This is different from most algorithms for tremor indication. More common approaches rely on spectral features alone [64], [61], [66], [5], [57], [44], [47], [29], [10], [11] while other classify based on NNs [30], [15], [5], [9], [55], [21], [16], [14] or SVMs [9], [50], [14]. Rigas et al state that HMMs are suitable for tremor indication because "tremor presents time-dependency" [54].…”

Section: Tremor At Restmentioning

confidence: 99%