Biomedical subjects

Valeriy Nenov

Publications and source records attributed to Valeriy Nenov.

5 recordsLinked to original sources

PubMed2006

A single-lead ECG enhancement algorithm using a regularized data-driven filter.

We presented a novel way of deriving a subspace filter for enhancing a noisy electrocardiogram (ECG) signal contaminated by electromyogram (EMG). The new subspace filter was based on a multiple cycle prediction (MCP) modeling of a single-lead ECG. The adoption of an MCP model resulted in a data matrix more suitable for separating noise and signal subspaces than the linear prediction (LP) model that is implicitly assumed in many existing subspace filters. Alignment of ECG cycles of different length is required for MCP modeling and was handled by a dynamic time warping (DTW) algorithm. A run-time procedure was designed for automatically determining the signal space dimension adaptively. To validate the new filter in a quantitative way, 12 clean realistic ECG segments with different degrees of heart rate variability generated using the ECGSyn program were mixed with different realizations of EMG noise in the MIT-BIH Noise Stress Test Database and locally acquired EMG at a typical 10-dB signal-to-noise ratio. The performance of the proposed method was compared to three existing ECG enhancement algorithms and achieved encouraging results. In addition, various ECG recordings from MIT-Arrythmia database were also mixed with EMG noise and subjected to the same four filters resulting in a qualitative comparison of them.

Algorithms↗

PubMed2006

Nonlinear analysis of cerebral hemodynamic and intracranial pressure signals for characterization of autoregulation.

The objective of this study was to determine whether or not the underlying physiological systems that generates spontaneous arterial blood pressure (ABP), cerebral blood flow velocity (CBFV), and intracranial pressure signals could be adequately approximated as a linear stochastic process. Furthermore, a new measure (C) capable of capturing the degree of nonlinear dependency between two ABP and CBFV signals (including a time-varying situation) was proposed for quantifying the degree of cerebral blood flow autoregulation. A surrogate data test of fifteen ABP, CBFV, and intracranial pressure (ICP) segments was conducted for detecting whether there exists a statistically significant deviation from the null hypothesis of linear signals. The extension of the established block computation method of C measure to an adaptive one was achieved. This new algorithm was then applied to study the C evolution using brain injury patients data from a hyperventilation study and two propofol studies. Nonlinearity has not been detected for all the fifteen recordings, neither has nonlinear dependency between CBFV and ABP. However, their presences in some of the signal segments justified the adoption of a nonlinear measure of dependency capable of characterizing both linear and nonlinear correlations for inferring autoregulation status. C measure started to decrease with the introduction of hypocapnia state indicating that hyperventilation may reduce the dependency of CBFV on ABP fluctuations. On the other hand, complex patterns of C measure evolution were observed among 14 cases of propofol data indicating a nontrivial effect of propofol on the dependency of CBFV on ABP.

Adaptation, Physiological↗

PubMed2004

Robust measure for characterizing generalized synchronization.

Generalized synchronization between two coupled systems can be characterized by recently proposed interdependency measures calculated from two simultaneously observed time series from them. However, numerical tests have shown that these measures cannot consistently indicate the direction of the coupling for strongly coupled systems or in situations with a large phase space neighbor size. An interdependency measure is proposed here quantifying how close a conditional neighbor is to a true neighbor in terms of the degree of alignment of their principal axes. Numerical tests are carried out on time series generated from a coupled Hénon map and a Lorenz model driven by a Rossler model. Given that a driving system is more dependent on a response system, the results show that the direction of the coupling is consistently detected by using the proposed measure even in those unfavorable cases for the measures mentioned above.

Journal Article↗

PubMed2004

Multivariate AR modeling of electromyography for the classification of upper arm movements.

OBJECTIVE: We compared the performance of two feature extraction methods for multichannel electromyography (EMG) based arm movement classification. One method was to use a scalar autoregressive model (sAR) for each channel. Another was to model all channels as a whole by a multivariate AR model (mAR). METHODS: The classified arm movements included elbow flexion, elbow extension, forearm pronation and internal shoulder rotation. Six-channel bipolar EMG signals were collected from four electrodes fixed on the biceps, triceps, brachioradialis and deltoid. Fifteen two-channel and four three-channel configurations were formed out of these six-channel signals for a comparison of different channel combinations. Leave-one-out cross-validation was adopted for evaluating the classification performance using a parametric statistical classifier. RESULTS: We processed a total of 216 EMG segments obtained from repeated 18 performances by three normal subjects. mAR model based feature set achieved a better classification accuracy than sAR did for each configuration. Moreover, significance of improvement was greater than 0.95 for those configurations which consisted of EMG channels that were close spatially. CONCLUSIONS: The stronger the cross-correlation among EMG channels the more improvement of classification accuracy one would expect from using a mAR model.

Arm↗

PubMed2002

Early and persistent impaired percent alpha variability on continuous electroencephalography monitoring as predictive of poor outcome after traumatic brain injury.

OBJECT: Early prediction of outcomes in patients after they suffer traumatic brain injury (TBI) is often nonspecific and based on initial imaging and clinical findings alone, without direct physiological testing. Improved outcome prediction is desirable for ethical, social, and financial reasons. The goal of this study was to determine the usefulness of continuous electroencephalography (EEG) monitoring in determining prognosis early after TBI, while the patient is in the intensive care unit. METHODS: The authors hypothesized that the reduced percentage of alpha variability (PAV) in continuous EEG tracings indicates a poor prognosis. Prospective continuous EEG monitoring was performed in 89 consecutive patients with moderate to severe TBI (Glasgow Coma Scale [GCS] Scores 3-12) from 0 to 10 days after injury. The PAV was calculated daily, and the time course and trends of the PAV were analyzed in comparison with the patient's Glasgow Outcome Scale (GOS) score at the time of discharge. In patients with GCS scores of 8 or lower, a PAV value of 0.1 or lower is highly predictive of a poor outcome or death (positive predictive value 86%). The determinant PAV value was obtained by Day 3 after injury. Persistent PAV values of 0.1 or lower over several days or worsening of the PAV to a value of 0.1 or lower indicated a high likelihood of poor outcome (GOS Scores 1 and 2). In comparison with the combination of traditional initial clinical indicators of outcome (GCS score, pupillary response to light, patient age, results of computerized tomography scanning, and early hypotension or hypoxemia), the early PAV value during the initial 3 days after injury independently improved prognostic ability (p < 0.01). CONCLUSIONS: Continuous EEG monitoring performed with particular attention paid to the PAV is a sensitive and specific method of prognosis that can indicate outcomes in patients with moderate to severe TBI within 3 days postinjury.

Adult↗