Nitish V. Thakor

Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection

Several adaptive filter structures are proposed for noise cancellation and arrhythmia detection. The adaptive filter essentially minimizes the mean-squared error between a primary input, which is the noisy electrocardiogram (ECG), and a reference input, which is either noise that is correlated in some way with the noise in the primary input or a signal that is correlated only with ECG in the primary input. Different filter structures are presented to eliminate the diverse forms of noise: baseline wander, 60 Hz power line interference, muscle noise, and motion artifact. An adaptive recurrent filter structure is proposed for acquiring the impulse response of the normal QRS complex. The primary input of the filter is the ECG signal to be analyzed, while the reference input is an impulse train coincident with the QRS complexes. This method is applied to several arrhythmia detection problems: detection of P-waves, premature ventricular complexes, and recognition of conduction block, atrial fibrillation, and paced rhythm.<<ETX>>

Electric field and stimulating influence generated by deep brain stimulation of the subthalamic nucleus

OBJECTIVE The goal of this project was to develop a quantitative understanding of the volume of axonal tissue directly activated by deep brain stimulation (DBS) of the subthalamic nucleus (STN). METHODS The 3-dimensionally inhomogeneous and anisotropic tissue medium surrounding DBS electrodes complicates our understanding of the electric field and tissue response generated by the stimulation. We developed finite element computer models to address the effects of DBS in a homogeneous isotropic medium, and a medium with tissue conductivity properties derived from human diffusion tensor magnetic resonance data. The second difference of the potential distribution generated in the tissue medium was used as a predictor of the volume of tissue supra-threshold for axonal activation. RESULTS The model predicts that clinically effective stimulation parameters (-3 V; 0.1 ms; 150 Hz) result in activation of large diameter (5.7 microm) myelinated axons over a volume that spreads outside the borders of the STN. The shape of the activation volume was dependent on the strong dorsal-ventral anisotropy of the internal capsule, and the moderate anterior-posterior anisotropy of the region around zona incerta. CONCLUSIONS Small deviations ( approximately 1 mm) in the electrode position within STN can substantially alter the shape of the activation volume as well as its spread to neighboring structures. SIGNIFICANCE STN DBS represents an effective treatment for medically refractory movement disorders such as Parkinsons disease. However, stimulation induced side effects such as tetanic muscle contraction, speech disturbance and ocular deviation are not uncommon. Quantitative characterization of the spread of stimulation will aid in the development of techniques to maximize the efficacy of DBS.

Estimation of QRS Complex Power Spectra for Design of a QRS Filter

We present power spectral analysis of ECG waveforms as well as isolated QRS complexes and episodes of noise and artifact. The power spectral analysis shows that the QRS complex could be separated from other interfering signals. A bandpass filter that maximizes the signal (QRS complex)-to-noise (T-waves, 60 Hz, EMG, etc.) ratio would be of use in many ECG monitoring instruments. We calculate the coherence function and, from that, the signal-to-noise ratio. Upon carrying out this analysis on experimentaly obtained ECG data, we observe that a bandpass filter with a center frequency of 17 Hz and a Q of 5 yields the best signal-to-noise ratio.

Detecting ventricular tachycardia and fibrillation by complexity measure

Sinus rhythm (SR), ventricular tachycardia (VT) and ventricular fibrillation (VF) belong to different nonlinear physiological processes with different complexity. In this study, the authors present a novel, and computationally fast method to detect VT and VF, which utilizes a complexity measure suggested by Lempel and Ziv (1976). For a specific window length (i.e., the length of data segment to be analyzed), the method first generates a 0-1 string by comparing the raw electrocardiogram (ECG) data to a selected suitable threshold. The complexity measure can be obtained from the 0-1 string only using two simple operations, comparison and accumulation. When the window length is 7 s, the detection accuracy for each of SR, VT, and VF is 100% for a test set of 204 body surface records (34 SR, 85 monomorphic VT, and 85 VF). Compared with other conventional time- and frequency-domain methods, such as rate and irregularity, VF-filter leakage, and sequential hypothesis testing, the new algorithm is simple, computationally efficient, and well suited for real-time implementation in automatic external defibrillators (AEDs).

Ventricular tachycardia and fibrillation detection by a sequential hypothesis testing algorithm

An algorithm for detecting ventricular fibrillation (VF) and ventricular tachycardia (VT) by the method of sequential hypothesis testing is presented. The algorithm first generates a binary sequence by comparing the signal to a threshold. The probability distribution of the time intervals of the binary sequence is obtained, and the sequential hypothesis testing procedure of A.J. Wald and J. Wolfowitz (1948) is employed to discriminate the arrhythmias. Sequential hypothesis testing of 85 cases resulted in identification of (1) 97.64% VF and 97.65% VT episodes after 5 s and (2) 100% identification of both VF and VT after 7 s. The desired false positive and false negative error probabilities can be programmed into the algorithm. An important feature of the sequential method is that extra time for detection can be traded off for improved accuracy, and vice versa.<<ETX>>

Determination of current density distributions generated by electrical stimulation of the human cerebral cortex

With the use of a 3-dimensional finite element model of the human brain based on structural data from MRI scans, we simulated patterns of current flow in the cerebral hemisphere with different types of electrical stimulation. Five different tissue types were incorporated into the model based on conductivities taken from the literature. The boundary value problem derived from Laplaces equation was solved with a quasi-static approximation. Transcranial electrical stimulation with scalp electrodes was poorly focussed and required high levels of current for stimulation of the cortex. Direct cortical stimulation with bipolar (adjacent) electrodes was found to be very effective in producing localized current flows. Unipolar cortical stimulation (with a more distant reference electrode) produced higher current densities at the same stimulating current as did bipolar stimulation, but stimulated a larger region of the cortex. With the simulated electrodes resting on the pia-arachnoid, as usually occurs clinically, there was significant shunting of the current (7/8 of the total current) through the CSF. Possible changes in electrodes and stimulation parameters that might improve stimulation procedures are considered.

Decoding of Individuated Finger Movements Using Surface Electromyography

Upper limb prostheses are increasingly resembling the limbs they seek to replace in both form and functionality, including the design and development of multifingered hands and wrists. Hence, it becomes necessary to control large numbers of degrees of freedom (DOFs), required for individuated finger movements, preferably using noninvasive signals. While existing control paradigms are typically used to drive a single-DOF hook-based configurations, dexterous tasks such as individual finger movements would require more elaborate control schemes. We show that it is possible to decode individual flexion and extension movements of each finger (ten movements) with greater than 90% accuracy in a transradial amputee using only noninvasive surface myoelectric signals. Further, comparison of decoding accuracy from a transradial amputee and able-bodied subjects shows no statistically significant difference ( p < 0.05) between these subjects. These results are encouraging for the development of real-time control strategies based on the surface myoelectric signal to control dexterous prosthetic hands.

Simulation of Action Potentials From Metabolically Impaired Cardiac Myocytes Role of ATP-Sensitive K+ Current

The role of the ATP-sensitive K+ current (IK-ATP) and its contribution to electrophysiological changes that occur during metabolic impairment in cardiac ventricular myocytes is still being discussed. The aim of this work was to quantitatively study this issue by using computer modeling. A model of IK-ATP is formulated and incorporated into the Luo-Rudy ionic model of the ventricular action potential. Action potentials under different degrees of activation of IK-ATP are simulated. Our results show that in normal ionic concentrations, only approximately 0.6% of the KATP channels, when open, should account for a 50% reduction in action potential duration. However, increased levels of intracellular Mg2+ counteract this shortening. Under conditions of high [K+]0, such as those found in early ischemia, the activation of only approximately 0.4% of the KATP channels could account for a 50% reduction in action potential duration. Thus, our results suggest that opening of IK-ATP channels should play a significant role in action potential shortening during hypoxic/ischemic episodes, with the fraction of open channels involved being very low ( < 1%). However, the results of the model suggest that activation of IK-ATP alone does not quantitatively account for the observed K+ efflux in metabolically impaired cardiac myocytes. Mechanisms other than KATP channel activation should be responsible for a significant part of the K+ efflux measured in hypoxic/ischemic situations.

Adaptive Fourier estimation of time-varying evoked potentials

In this estimation procedure the evoked response is modeled as a dynamic Fourier series and the Fourier coefficients are estimated adaptively by the least-mean-square algorithm. Approximate expressions have been developed for the estimation error and time constant of adaptation. A procedure for optimizing the estimator performance is also presented. The effectiveness of the estimator with simulated as well as actual evoked responses is demonstrated.<<ETX>>

Adaptive Filterng of Evoked Potentials

We present an adaptive filtering (AF) technique for rapid processing of evoked potentials (EP). The AF algorithm minimizes the mean-square error (MSE) between successive ensembles. We demonstrate theoretically that the filter output converges to the least square estimate of the underlying signal. Computer simulations with known signal and added noise show that AF produces lower MSE than ensemble averaging. We also compare results of AF to those obtained by ensemble averaging for some EP recorded from animals and humans. For very noisy EP recordings, we propose techniques that combine AF and ensemble averaging. The AF technique shows promise for requiring fewer ensembles than averaging to attain adequate signal quality.