High-Frequency Electrocardiogram Analyzer

We have developed a microprocessor-based analyzer for processing the high-frequency electrocardiogram. It has a signal band-width of 500 Hz, five times that of the standard clinical ECG. The device is programmed to isolate QRS complexes, to compute their first derivatives, and to dilate the time base so that the high frequency ECG's and their derivatives can be recorded on a restricted-bandwidth hard copy device such as a strip chart recorder or ECG machine. Also, the analyzer interfaces directly to a laboratory computer system for additional signal processing.     

R-wave detection in the presence of muscle artifacts

Ambulatory electrocardiogram (ECG) recordings from patients undergoing severe physical stress are corrupted by large muscle artifacts (EMG) and other noise sources, such as baseline drifts and currents induced by motion artifacts and electromechanical devices. A simple and reliable QRS filter and R-wave detectot unit, for use under moderate to low QRS signal-to-noise ratio conditions, is described. It is found that a narrow bandpass sampled data filter design with a center frequency and bandwidth equal to 4 Hz maximizes the QRS signal-to-noise ratio under such conditions. The detector includes automatic gain control and a differencing integrator for improved R-wave detection.     

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.     

A robust method for ECG-based estimation of the respiratory frequency during stress testing

A robust method is presented for electrocardiogram (ECG)-based estimation of the respiratory frequency during stress testing. Such ECGs contain highly nonstationary noise and exhibit changes in QRS morphology which, when combined with the dynamic nature of the respiratory frequency, make most existing methods break down. The present method exploits the oscillatory pattern of the rotation angles of the heart's electrical axis as induced by respiration. The series of rotation angles, obtained from least-squares loop alignment, is subject to power spectral analysis and estimation of the respiratory frequency. Robust techniques are introduced to handle the nonstationary properties of exercise ECGs. The method is evaluated by means of both simulated signals, and ECG/airflow signals recorded from 14 volunteers and 20 patients during stress testing. The resulting respiratory frequency estimation error is, for simulated signals, equal to 0.5% +/- 0.2%, mean +/- SD (0.002 +/- 0.001 Hz), whereas the error between respiratory frequencies of the ECG-derived method and the airflow signals is 5.9% +/- 4% (0.022 +/- 0.016Hz). The results suggest that the method is highly suitable for analysis of noisy ECG signals recorded during stress testing.     

A theoretical analysis of acute ischemia and infarction using ECG reconstruction on a 2-D model of myocardium

We developed a two-dimensional ventricular tissue model in order to probe the determinants of electrocardiographic (ECG) morphology during acute and chronic ischemia. Hyperkalemia was simulated by step changes in [K+]out, while acidosis was induced by reducing Na+ and Ca2+ conductances. Hypoxia was introduced by its effect on potassium activity. During the initial moments of ischemia, ECG changes were characterized by increases in QRS amplitude and ST segment shortening, followed in the advanced phase by ST baseline elevation, T conformation changes, widening of the QRS and significant decreases in QRS amplitude in spite of an enlarged Q. During each phase, potential proarrhythmic mechanisms were investigated. The presence of unexcitable regions of simulated myocardial infarction led to polymorphic ECG. We also observed a nonuniform deflection of the ST segment from beat to beat. We used similar protocols to explore the responses of infarcted myocardium after impairment resolving. We found that despite irreversible uncoupling of the necrotic region, the restored normal ionic concentrations produced an isopotential ST segment and monomorphic ECG complexes, while an enlarged Q wave was still visible. In summary, these numerical experiments indicate the possibility to track in the ECG pathologic changes following the altered electrophysiology of the ischemic heart.     

A comparison of the noise sensitivity of nine QRS detectionalgorithms

The noise sensitivities for nine different QRS detection algorithms were measured for a normal, single-channel lead II, synthesized ECG corrupted with five different types of synthesized noise. The noise types were electromyographic interference, 60 Hz powerline interference, baseline drift due to respiration, abrupt baseline shift, and a composite noise constructed from all of the other noise types. The percentage of QRS complexes detected, the number of false positives, and the detection delay were measured. None of the algorithms were able to detect all QRS complexes without any false positives for all of the noise types at the highest noise level. Algorithms based on amplitude and slope had the highest performance for EMG-corrupted ECG. An algorithm using a digital filter had the best performance for the composite noise corrupted data.     

Denoising and R-Peak Detection of Electrocardiogram Signal Based on EMD and Improved Approximate Envelope

The electrocardiogram (ECG ) signal is prone to various high and low frequency noises, including baseline wandering and power-line interference, which become the source of errors in QRS and in other extracted features. This paper presents a new ECG signal-processing approach based on empirical mode decomposition (EMD) and an improved approximate envelope method. To reduce the number of the initial intrinsic mode functions (IMFs), a Butterworth lowpass filter is used to eliminate high frequency noises before the EMD. To correct baseline wandering and to eliminate low frequency noises, the two last-order IMFs are abandoned. An improved approximate envelope is proposed and applied after the Hilbert transform to enhance the energy of QRS complexes and to suppress unwanted P/T waves and noises. Then, an algorithm based on the slope threshold is used for R-peak detection. The proposed denoising and R-peak detection algorithm are validated using the MIT-BIH Arrhythmia Database. The simulation results show that the proposed method can effectively eliminate the Gaussian noise, baseline wander, and power-line interference added to the ECG signal. The method can also function reliably even under poor signal quality and with long P and T peaks. The QRS detector has an average sensitivity of Se=99.94 % and a positive predictivity of +P=99.87 % over the first lead of the MIT-BIH Arrhythmia Database.     

Computer analysis of the electrocardiogram during esophageal pacingcardiac stress

It has been estimated that 15 to 30% of patients with suspected or known coronary artery disease are unable to perform an adequate exercise stress test due to a variety of reasons such as obesity, poor physical condition, claudication, etc. Transesophageal atrial pacing has been proposed as a noninvasive alternative for inducing cardiac stress in patients who cannot exercise. Although computer analysis is commonly employed to analyze the electrocardiogram (ECG) during the conventional exercise stress test, the surface ECG recorded during transesophageal atrial pacing is contaminated with large pacing artifacts which confound beat identification by standard computer software. We report the development of a robust signal processing algorithm for interpretation of the surface ECG during transesophageal atrial pacing stress. The algorithm employs novel schemes using both linear and nonlinear transformations to detect and differentiate between the pacing artifact and QRS complex even in difficult situations where the pacing artifact is in proximity to or superimposed on the QRS complex. The algorithm uses sophisticated logic for automatic recognition of sustained capture. It subsequently calculates beat-by-beat and average (over five beats) ST segment amplitude and slope. The algorithm also reports the instantaneous heart rate, RR interval, pace-to-R interval, R-wave amplitude, and estimated sinus node recovery time upon loss of sustained capture. The limitations of present exercise ECG computer methods in processing the ECG during transesophageal atrial pacing stress are evaluated and significantly improved performance by our algorithm is demonstrated.     

Nonstationary harmonic modeling for ECG removal in surface EMG signals

We present a compact approach for mitigating the presence of electrocardiograms (ECG) in surface electromyographic (EMG) signals by means of time-variant harmonic modeling of the cardiac artifact. Heart rate and QRS complex variability, which often account for amplitude and frequency time variations of the ECG, are simultaneously captured by a set of third-order constant-coefficient polynomials modulating a stationary harmonic basis in the analysis window. Such a characterization allows us to significantly suppress ECG from the mixture by preserving most of the EMG signal content at low frequencies (less than 20?Hz). Moreover, the resulting model is linear in parameters and the least-squares solution to the corresponding linear system of equations efficiently provides model parameter estimates. The comparative results suggest that the proposed method outperforms two reference methods in terms of the EMG preservation at low frequencies.     

A Compact, Microprocessor-Based ECG ST-Segment Analyzer for the Operating Room

We describe a microprocessor-based device for analyzing ST-segment changes of the electrocardiogram in the operating room during general anesthesia. The device identifies significant components of the ECG waveform, detects arrhythmias, and performs ST-segment measurements. Measured values of heart rate, QRS duration, R-wave magnitude, and ST-segment level, slope, area, and index are selectively displayed on a front-panel numeric display and can be recorded in analog form with an FM tape recorder. Provided are details of the battery-operated hardware, the ST-analysis software, and the results of 60 h of clinical trials. Possible uses are proposed for trend analysis during anesthesia.     

Alignment methods for averaging of high-resolution cardiac signals:a comparative study of performance

Accurate signal estimation by means of coherent averaging techniques needs temporal alignment methods. A known low-pass filtering effect is yielded when alignment errors are present. This is very critical in the estimation of low-level high-frequency potentials in high-resolution ECG analysis. A comparative study of the performance of three alignment methods (the double-level method, a new time-delay estimation method based on normalized integrals, and matched filtering) is presented in this paper. A real signal and additive random noise for several signal-to-noise ratios (SNR's) are selected to make an ensemble of computer-simulated beats. The relation between the standard deviation of temporal misalignment versus SNR is discussed. A second study with real ECG signals is also presented. Several morphologies of QRS and P waves are tested. The results are in agreement with the computer simulation study. Nevertheless, the power spectrum of the noise process can affect the results. Matched filter estimation has been tested in the presence of power line interference (50 Hz), with poor results. An application of the three alignment methods as a function of the SNR is proposed. The new time-delay estimation method has been observed to be robust, even in the presence of nonwhite noise.     

A Real-Time QRS Detection Algorithm

We have developed a real-time algorithm for detection of the QRS complexes of ECG signals. It reliably recognizes QRS complexes based upon digital analyses of slope, amplitude, and width. A special digital bandpass filter reduces false detections caused by the various types of interference present in ECG signals. This filtering permits use of low thresholds, thereby increasing detection sensitivity. The algorithm automatically adjusts thresholds and parameters periodically to adapt to such ECG changes as QRS morphology and heart rate. For the standard 24 h MIT/BIH arrhythmia database, this algorithm correctly detects 99.3 percent of the QRS complexes.     

Modeling QRS complex in dECG

It is proposed to model the derivative of electrocardiogram (ECG) signal, which we refer to as dECG, instead of the ECG signal. It is shown that the QRS complex in the dECG signal can be represented in the frequency domain by an all-pole model of appropriate order, the coefficients of the model being determined using the covariance method of linear prediction applied over an analysis interval that includes the QRS complex and that is centered about the R-peak. Modeling of dECG, instead of ECG, gives a better spectral representation of the QRS complex.     

Analysis of abnormal signals within the QRS complex of thehigh-resolution electrocardiogram

Presents a new, quantitative approach to measuring abnormal intra-QRS signals, using the high-resolution electrocardiogram (HRECG). These signals are conventionally known as QRS “notches and slurs.” They are measured qualitatively and form the basis for the ECG identification of myocardial infarction. The HRECG is used for detection of ventricular late potentials (LP), which are linked with the presence of a reentry substrate for ventricular tachycardia (VT) after a myocardial infarction. LP's are defined as signals from areas of delayed conduction which outlast the normal QRS period. The authors' objective is to quantify very low-level abnormal signals that may not outlast the normal QRS period. In this work, abnormal intra-QRS potentials (AIQP) were characterized by removing the predictable, smooth part of the QRS from the original waveform. This was represented as the impulse response of an ARX parametric model, with model order selected empirically from a training data set. AIQP were estimated using the residual of the modeling procedure. Critical AIQP parameters to separate VT and non-VT subjects were obtained using discriminant functions. Results suggest that AIQP indexes are a new predictive index of the HRECG for VT. The concept of abnormal intra-QRS potentials permits the characterization of pathophysiological signals contained wholly within the normal QRS period, but related to arrhythmogenesis. The new method may have other applications, such as detection of myocardial ischemia and improved ECG identification of the site of myocardial infarction, particularly in the absence of Q waves     

Dominant frequency analysis of EEG reveals brain's response duringinjury and recovery

A new method of monitoring and analyzing electroencephalogram (EEG) signals during brain injury is presented, EEG signals are modeled using the autoregressive (AR) technique to obtain the frequencies where there are peaks in the spectrum. The powers at these dominant frequencies are analyzed to reveal the state of brain injury during an experimental study involving progressive hypoxia, asphyxia, and recovery. Neonatal piglets (n=8) were exposed to a sequence of 30 min of hypoxia (10% oxygen), 5 min of room air, and 7 min of asphyxia. They then received cardiopulmonary resuscitation and were subsequently monitored for 4 h. An optimal AR model order of 6 was obtained for these data, resulting in 3 dominant frequencies. These dominant frequencies, referred to as the low, medium, and high frequency components, fell in the bands 1.0-5.5 Hz, 9.0-14.0 Hz, and 18.0-21.0 Hz, respectively. A remarkable feature of the authors' data is the spectral dispersion, or diverging trends in the 3 frequency bands. During hypoxia, the relative powers of the medium and high-frequency components of EEG increased up to 160% and 176%, from their respective baseline values. During the first minute of asphyxia the medium- and high-frequency powers (relative to baseline) increased by 280-400%. The power in all 3 frequency components went down to nearly zero within 40-80 s of asphyxia. During recovery, the phenomenon of burst-suppression was clearly exhibited in the low-frequency component. A new index, called mean normalized separation, representing the degree of disproportionality in the recovery of powers of the 3 dominant components relative to their mean recovered power, is presented as a possible single indicator of electrical function recovery. In conclusion, dominant frequency analysis helps reveal the brain's graded electrical response to injury and recovery     

On the Weibull shape factor of intrinsic breakdown of dielectric films and its accurate experimental determination. Part II: experimental results and the effects of stress conditions

For pt. I see ibid., vol. 49, no. 12, p.2131 (2002).The Weibull slope measurement techniques described in Part I are used to determine Weibull slopes as function of thickness, voltage, and temperature. The effect of stress temperature and voltage on Weibull slopes is investigated over a wide range of voltage and temperatures for several different oxide thickness values. It was found that Weibull slopes show a strong thickness dependence while Weibull slopes are essentially independent of stress conditions such as voltages and temperature. The implications of the voltage-independent Weibull slope on voltage-dependent acceleration factors are discussed. In addition, the impact of electron injection polarity on Weibull slopes is studied in detail. To further advance understanding, we compare the measured Weibull slopes with different nitrogen incorporation processes under gate injection mode. It was found that for ultrathin oxides below 3 nm to the first order, the Weibull slopes are relatively insensitive to the nitrogen incorporation process for which we investigated. Finally, we discuss the validity of the stress-induced leakage current measurement as an experimental means to measure the critical defect density.     

Multiple Slope Switching Waveform Approximation to Improve Conducted EMI Spectral Analysis of Power Converters

An improved and simplified electromagnetic interference (EMI) modeling method based on multiple slope approximation of device-switching transitions for EMI analysis of power converters is presented. The traditional noise source modeling method, which uses single slope for rise and fall transition, is studied, and the criteria for reasonable modeling in the frequency range is analyzed. The turn-on and turn-off dynamics are investigated by dividing the nonlinear transitions into several stages based on an insulated gate bipolar transistor (IGBT) behavior circuit model. Real device-switching voltage and current waveforms are approximated by piece-wise linear lines and modeled by multiple dv/dt and di/dt slopes. The predicted EMI spectra suggest that high-frequency EMI noise is modeled with an acceptable accuracy. The proposed method was verified experimentally for a dc-dc buck converter     

Neural-network-based adaptive matched filtering for QRS detection

We have developed an adaptive matched filtering algorithm based upon an artificial neural network (ANN) for QRS detection. We use an ANN adaptive whitening filter to model the lower frequencies of the ECG which are inherently nonlinear and nonstationary. The residual signal which contains mostly higher frequency QRS complex energy is then passed through a linear matched filter to detect the location of the QRS complex. We developed an algorithm to adaptively update the matched filter template from the detected QRS complex in the ECG signal itself so that the template can be customized to an individual subject. This ANN whitening filter is very effective at removing the time-varying, nonlinear noise characteristic of ECG signals. Using this novel approach, the detection rate for a very noisy patient record in the MIT/BIH arrhythmia database is 99.5%, which compares favorably to the 97.5% obtained using a linear adaptive whitening filter and the 96.5% achieved with a bandpass filtering method.     

Analysis of beat-to-beat variability of frequency contents in theelectrocardiogram using two-dimensional Fourier transforms

Late potentials are very small signals (1-20 μV) in the surface ECG with high-frequency components, which are found in patients prone to sustained ventricular tachycardia. Evaluation of these signals requires either very sophisticated recording techniques for single-beat analysis or signal averaging. Signal averaging, however, might disregard information about risk stratification. Therefore, the authors developed the Single-Beat Spectral Variance (SBSV) based on two-dimensional (2-D) Fourier transform of 80 ms segments of 128 consecutive beats. This approach depicts the beat-to-beat variability of the frequency contents of these ECG segments. An index function enables an objective detection of late potentials. The authors investigated 35 patients after myocardial infarction and sustained ventricular tachycardia (Group 1), 50 patients after myocardial infarction without ventricular arrhythmias (Group 2) and ten healthy volunteers, SBSV classified 29 of 35 patients (83%) of Group 1 as pathologic, 14 of these 29 patients (48%) exclusively on the basis of marked Wenckebach-like conduction pattern. In Group 2, only five of 50 patients showed abnormal SBSV. In Group 3, the authors found no pathologic result. Thus, SBSV is a promising new method to investigate late potentials inpatients after myocardial infarction, SBSV-contains not only the results of frequency analysis after signal averaging, but also evaluates variable ECG components     

Analysis of first-derivative based QRS detection algorithms.

Accurate QRS detection is an important first step for the analysis of heart rate variability. Algorithms based on the differentiated ECG are computationally efficient and hence ideal for real-time analysis of large datasets. Here, we analyze traditional first-derivative based squaring function (Hamilton-Tompkins) and Hilbert transform-based methods for QRS detection and their modifications with improved detection thresholds. On a standard ECG dataset, the Hamilton-Tompkins algorithm had the highest detection accuracy (99.68% sensitivity, 99.63% positive predictivity) but also the largest time error. The modified Hamilton-Tompkins algorithm as well as the Hilbert transform-based algorithms had comparable, though slightly lower, accuracy; yet these automated algorithms present an advantage for real-time applications by avoiding human intervention in threshold determination. The high accuracy of the Hilbert transform-based method compared to detection with the second derivative of the ECG is ascribable to its inherently uniform magnitude spectrum. For all algorithms, detection errors occurred mainly in beats with decreased signal slope, such as wide arrhythmic beats or attenuated beats. For best performance, a combination of the squaring function and Hilbert transform-based algorithms can be applied such that differences in detection will point to abnormalities in the signal that can be further analyzed.