Sampling rate and the estimation of ensemble variability for repetitive signals

Laguna, Pablo; Sörnmo, Leif

doi:10.1007/bf02345750

Cited by 31 publications

(22 citation statements)

References 19 publications

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“…Hz (Laguna and Sornmo, 2000). To implement the upsampling by the ratio of p/q, where p and q are coprime integer numbers and p>q, the input data is first upsampled by a factor of the integer p by inserting zeros.…”

Section: Methodsmentioning

confidence: 99%

Diffusion geometry approach to efficiently remove electrical stimulation artifacts in intracranial electroencephalography

et al. 2019

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Objective: Cortical oscillations, electrophysiological activity patterns, associated with cognitive functions and impaired in many psychiatric disorders can be observed in intracranial electroencephalography (iEEG). Direct cortical stimulation (DCS) may directly target these oscillations and may serve as therapeutic approaches to restore functional impairments. However, the presence of electrical stimulation artifacts in neurophysiological data limits the analysis of the effects of stimulation. Currently available methods suffer in performance in the presence of nonstationarity inherent in biological data.Approach: Our algorithm, Shape Adaptive Nonlocal Artifact Removal (SANAR) is based on unsupervised manifold learning. By estimating the Euclidean median of k-nearest neighbors of each artifact in a nonlocal fashion, we obtain a faithful representation of the artifact which is then subtracted. This approach overcomes the challenges presented by nonstationarity.Main results: SANAR is effective in removing stimulation artifacts in the time domain while preserving the spectral content of the endogenous neurophysiological signal. We demonstrate the performance in a simulated dataset as well as in human iEEG data. Using two quantitative measures, that capture how much of information from endogenous activity is retained, we demonstrate that SANAR's performance exceeds that of one of the widely used approaches, independent component analysis, in the time domain as well as the frequency domain.Significance: This approach allows for the analysis of iEEG data, single channel or multiple channels, during DCS, a crucial step in advancing our understanding of the effects of periodic stimulation and developing new therapies. Rogasch et al., 2014) in the case of multi-channel recordings. Apart from these approaches, Kalman Filtering have been used to suppress stimulation artifacts in neurophysiological data (Morbidi et al., 2007;Morbidi et al., 2008). The approach involves fitting separate generative models for the artifact and for the neurophysiological data and applying the Kalman filter to extract the artifact-free data of interest. Yet another approach is to use the spectral information of the artifacts using matched filters (Allen et al., 2010;Sun et al., 2014) or empirical mode decomposition (Al-ani et al., 2011;Santillan-Guzman et al., 2013) to separate neurophysiological signal from artifacts.While these methods have been successfully applied, there are some limitations. The TS algorithm tends to suffer from the template bias, which arises from the possible deviation of the designed

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Section: Methodsmentioning

confidence: 99%

Diffusion geometry approach to efficiently remove electrical stimulation artifacts in intracranial electroencephalography

et al. 2019

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“…To preserve the non-stationary nature of the signal, we do not apply any other linear filtering technique to the signal. If the signal is sampled at a frequency lower than 1000 Hz, to enhance the R peak alignment needed in the nonlocal median step, the signal is upsampled to 1000 Hz [48]. To simplify the notation, we use the same notation f s and N to denote the resulting sampling rate and the resulting number of sampling points, and denote the resulting signal as…”

Section: Step 0: Preprocessingmentioning

confidence: 99%

Extract Fetal ECG from Single-Lead Abdominal ECG by De-Shape Short Time Fourier Transform and Nonlocal Median

2017

Front. Appl. Math. Stat.

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The multiple fundamental frequency detection problem and the source separation problem from a single-channel signal containing multiple oscillatory components and a nonstationary noise are both challenging tasks. To extract the fetal electrocardiogram (ECG) from a single-lead maternal abdominal ECG, we need to solve both challenges. We propose a novel method to extract the fetal ECG from a single-lead maternal abdominal ECG, without any additional measurement. The algorithm is composed of three components. First, the maternal and fetal heart rates are estimated by the de-shape short time Fourier transform (STFT), which is a recently proposed nonlinear time-frequency analysis technique. The beat tracking technique is the second component which is applied to accurately obtain the maternal and fetal R peaks. The third component consists of establishing the maternal and fetal ECG waveforms by the nonlocal median. The algorithm is tested on two real databases with the annotation provided by experts (adfecgdb database and CinC2013 database) and a simulated database (fecgsym), and provides the state-of-the-art results. We conclude that with the proposed algorithm, the fetal ECG waveform and the fetal heart rate could be accurately obtained from the single-lead maternal abdominal ECG.

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“…Contrasting the work in [39], we use FIR filters to prepare for an implementation of the algorithm in real-time. If f s is lower than 1000 Hz, the signal is upsampled to 1000 Hz to enhance R peak alignment [26,3]. To preserve local information in the upsampling process and to prepare for an implementation of the algorithm in real-time, we apply the blending operator introduced in [8], which is a local version of cubic spline interpolation.…”

Section: Diffusion-based F-wave Extraction Algorithm -Dd-nlemmentioning

confidence: 99%