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Detection of essential tremor at the S-band.

  • Xiaodong Yang
    ,
  • Syed Aziz Shah
    ,
  • Aifeng Ren
    ,
  • Dou Fan
    ,
  • Nan Zhao
    ,
  • Dongjian Cao
  • Xidian University
    ,
  • Xi'an Jiaotong University
    ,
  • CAS - Institute of Automation
Research Output: Contribution to journal Article Peer-review

Open access

Abstract

Essential 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.

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Journal (Volume, Issue Number)

IEEE Journal of Translational Engineering in Health and Medicine (Volume 6)

Publication milestones

  • Published - 24/01/2018

Publication status

Published - 24/01/2018

External Publication IDs

  • handle.net: 10547/623827
  • Scopus: 85040965403

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