Comparison of fast Fourier transformation and autoregressive modelling as a diagnostic tool in analysis of lower extremity venous signals

Kara, Sadık; Kemalolu, Semra; Erdoan, Nuri

doi:10.1016/j.compbiomed.2005.03.001

Cited by 1 publication

(2 citation statements)

References 17 publications

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“…Yule-Walker) [32]. However, parametric overcome non-parametric methods in terms of frequency resolution, as it does not suffer from distortions caused by sidelobe leakage effects [33]. Furthermore, parametric methods do not require long segments of data, and they are based on the generation of mathematical models that are able to represent the behavior of the modeled signal, with the help of indexed parameters.…”

Section: Spectral Estimationmentioning

confidence: 99%

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Beta, gamma and High-Frequency Oscillation characterization for targeting in Deep Brain Stimulation procedures

Valderrama-Hincapié

Roldan-Vasco

Restrepo-Agudelo

et al. 2020

TecnoL.

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Deep Brain Stimulation (DBS) has been successfully used to treat patients with Parkinson’s Disease. DBS employs an electrode that regulates the oscillatory activity of the basal ganglia, such as the subthalamic nucleus (STN). A critical point during the surgical implantation of such electrode is the precise localization of the target. This is done using presurgical images, stereotactic frames, and microelectrode recordings (MER). The latter allows neurophysiologists to visualize the electrical activity of different structures along the surgical track, each of them with well-defined variations in the frequency pattern; however, this is far from an automatic or semi-automatic method to help these specialists make decisions concerning the surgical target. To pave the way to automation, we analyzed three frequency bands in MER signals acquired from 11 patients undergoing DBS: beta (13-40 Hz), gamma (40-200 Hz), and high-frequency oscillations (HFO – 201-400 Hz). In this study, we propose and assess five indexes in order to detect the STN: variations in autoregressive parameters and their derivative along the surgical track, the energy of each band calculated using the Yule-Walker power spectral density, the high-to-low (H/L) ratio, and its derivative. We found that the derivative of one parameter of the beta band and the H/L ratio of the HFO/gamma bands produced errors in STN targeting like those reported in the literature produced by image-based methods (<2 mm). Although the indexes introduced here are simple to compute and could be applied in real time, further studies must be conducted to be able to generalize their results.

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Section: Spectral Estimationmentioning

confidence: 99%

“…The beta band is mostly divided into low-band (13-20 Hz) and high-band (21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35) [20]. According to Kühn et al, the STN has an elevated frequency power in the beta band associated with bradykinesia and rigidity instead of tremor, which might serve as a potential biomarker [21], [22].…”

Section: Introductionmentioning

confidence: 99%