Shah, Syed Aziz
von Deneen, Karen M.
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AbstractEssential tremor (ET) is a neurological disorder characterized by rhythmic, involuntary shaking of a part or parts of the body. The most common tremor is seen in the hands/arms and fingers. This paper presents an evaluation of ETs monitoring based on finger-to-nose test measurement as captured by small wireless devices working in shortwave or [Formula: see text]-band frequency range. The acquired signals in terms of amplitude and phase information are used to detect a tremor in the hands. Linearly transforming raw phase data acquired in the [Formula: see text]-band were carried out for calibrating the phase information and to improve accuracy. The data samples are used for classification using support vector machine algorithm. This model is used to differentiate the tremor and nontremor data efficiently based on secondary features that characterize ET. The accuracy of our measurements maintains linearity, and more than 90% accuracy rate is achieved between the feature set and data samples.
CitationYang X, Shah SA, Ren A, Fan D, Zhao N, Cao D, Hu F, Ur Rehman M, Wang W, Von Deneen KM, Tian J (2018) 'Detection of essential tremor at the S-band.', IEEE Journal of Translational Engineering in Health and Medicine, 6 (), pp.-.
PubMed Central IDPMC5808945
SponsorsThis work was supported in part by the National Natural Science Foundation of China under Grant 61671349, in part by the Fundamental Research Funds for the Central Universities, and in part by the International Scientific and Technological Cooperation and Exchange Projects in Shaanxi Province under Grant 2017KW-005.
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