Adaptive QRS Detection: A Study of Performance

QRS detectors are often evaluated in terms of statistical measures, e.g., rate of false and true detections. These measures are usually calculated for a detector with fixed parameter values. In this paper, detector properties are studied by varying those parameters which seem to have the most profound effect on performance. By applying techniques known from optimal estimation to a stochastic model for the ECG, two different detectors have been derived. The detector structures are described in detail. In order to study and compare the performance of the two detectors, a database of "difficult" ECG's has been collected, containing various "artifacts" of physiological and electrical/mechanical origin. The performance of this database is presented in terms of receiver-operating characteristics (ROC's), and several examples are discussed.     

Vectorcardiographic loop alignment and morphologic beat-to-beatvariability

The measurement of subtle morphologic beat-to-beat variations in the electrocardiogram is complicated by the presence of respiration-induced movements of the heart. A statistical signal model is developed which accounts for such movements by means of scaling, rotation, and time synchronization of vector-cardiographic loops. The maximum-likelihood estimator of the parameters describing these three transformations is presented and is extended to the case of multiple loop alignment. The performance of the method is assessed by measuring morphologic variability before and after loop alignment. It is shown that the effects of respiration on morphologic variability can be considerably reduced by the new method. Measurements on morphologic variability were typically reduced by a factor of 0.53 after loop alignment. The results show also that beat-to-beat measurements are strongly dependent on the selected sampling rate and that a rate of 1 kHz is too low     

Block adaptive filters with deterministic reference inputs forevent-related signals: BLMS and BRLS

Adaptive estimation of the linear coefficient vector in truncated expansions is considered for the purpose of modeling noisy, recurrent signals. Two different criteria are studied for block-wise processing of the signal: the mean square error (MSE) and the least squares (LS) error. The block LMS (BLMS) algorithm, being the solution of the steepest descent strategy for minimizing the MSE, is shown to be steady-state unbiased and with a lower variance than the LMS algorithm. It is demonstrated that BLMS is equivalent to an exponential averager in the subspace spanned by the truncated set of basis functions. The block recursive least squares (BRLS) solution is shown to be equivalent to the BLMS algorithm with a decreasing step size. The BRLS is unbiased at any occurrence number of the signal and has the same steady-state variance as the BLMS but with a lower variance at the transient stage. The estimation methods can be interpreted in terms of linear, time-variant filtering. The performance of the methods is studied on an ECG signal, and the results show that the performance of the block algorithms is superior to that of the LMS algorithm. In addition, measurements with clinical interest are found to be more robustly estimated in noisy signals     

An atrioventricular node model for analysis of the ventricular response during atrial fibrillation

This paper introduces a model of the atrioventricular node function during atrial fibrillation (AF), and describes the related ECG-based estimation method. The proposed model is defined by parameters that characterize the arrival rate of atrial impulses, the probability of an impulse choosing either one of the two atrioventricular nodal pathways, the refractory periods of these pathways, and the prolongation of the refractory periods. These parameters are estimated from the RR intervals using maximum likelihood estimation, except for the shorter refractory period which is estimated from the RR interval Poincaré plot, and the mean arrival rate of atrial impulses by the AF frequency. Simulations indicated that 200-300 RR intervals are generally needed for the estimates to be accurate. The model was evaluated on 30-min ECG segments from 36 AF patients. The results showed that 88% of the segments can be accurately modeled when the estimated probability density function (PDF) and an empirical PDF were at least 80% in agreement. The model parameters were estimated during head-up tilt test to assess differences caused by sympathetic stimulation. Both refractory periods decreased as a result of stimulation, and the likelihood of an impulse choosing the pathway with the shorter refractory period increased.     

Maximum likelihood analysis of cardiac late potentials

This study presents a new time-domain method for the detection of late potentials in individual leads. Basic statistical properties of the ECG samples are modeled in order to estimate the amplitude and duration of late potentials. The signal model accounts for correlation in both time and across the ensemble of beats. Late potentials are modeled as a colored process with unknown amplitude which is disturbed by white, Gaussian noise. Maximum likelihood estimation is applied to the model for estimating the amplitude of the late potentials. The resulting estimator consists of an eigenvector-based filter followed by a nonlinear operation. The performance of the maximum likelihood procedure was compared to that obtained by traditional time-domain analysis based on the vector magnitude. It was found that the new technique yielded a substantial improvement of the signal-to-noise ratio in the function used for endpoint determination. This improvement leads to a prolongation of the filtered QRS duration in cases with late potentials     

A Method for Evaluation of QRS Shape Features Using a Mathematical Model for the ECG

Automated classification of ECG patterns is facilitated by careful selection of waveform features. This paper presents a method for evaluating the properties of features that describe the shape of a QRS complex. By examining the distances in the feature space for a class of nearly similar complexes, shape transitions which are poorly described by the feature under investigation can be readily identified. To obtain a continuous range of waveforms, which is required by the method, a mathematical model is used to simulate the QRS complexes.     

Adaptive QRS Detection Based on Maximum A Posteriori Estimation

A mathematical model for the occurrence of nonoverlapping pulse-shaped waveforms corrupted with colored Gaussian noise is considered for the purpose of QRS detection. The number of waveforms, the arrival times, amplitudes, and widths are regarded as random variables. The joint MAP estimation of all the unknown quantities consists of linear filtering followed by an optimization procedure. A class of filters is introduced which is easy to implement. The mismatching obtained by using this class for detection of model QRS complexes is investigated. The optimization procedure is time-consuming and is modified so that a threshold test is obtained. The model formulation with nonoverlapping waveforms leads to an "eye-closing" procedure covering a segment before as well as after an accepted event. Adaptivity of the detector is gained by utilizing past as well as future signal properties in determining thresholds for QRS acceptance.     

Remote processing server for ECG-based clinical diagnosis support

In this paper, we present the development of a remote server that provides a user-friendly access to advanced electrocardiographic (ECG) signal processing techniques. The prototype supplies telemedicine facilities to doctors for clinical indexes remote computation to support diagnosis through the Internet. The user-friendly interface is based on the selection of the desired ECG signal processing tools on a Web browser window. The centralized structure of the system permits unique and user-independent update and management of the software and, therefore, is especially suitable for remote or rural regions to have access to the new ECG information techniques.     

Digital implementation of a wavelet-based event detector for cardiac pacemakers

This paper presents a digital hardware implementation of a novel wavelet-based event detector suitable for the next generation of cardiac pacemakers. Significant power savings are achieved by introducing a second operation mode that shuts down 2/3 of the hardware for long time periods when the pacemaker patient is not exposed to noise, while not degrading performance. Due to a 0.13-/spl mu/m CMOS technology and the low clock frequency of 1 kHz, leakage power becomes the dominating power source. By introducing sleep transistors in the power-supply rails, leakage power of the hardware being shut off is reduced by 97%. Power estimation on RTL-level shows that the overall power consumption is reduced by 67% with a dual operation mode. Under these conditions, the detector is expected to operate in the sub-/spl mu/W region. Detection performance is evaluated by means of databases containing electrograms to which five types of exogenic and endogenic interferences are added. The results show that reliable detection is obtained at moderate and low signal to noise-ratios (SNRs). Average detection performance in terms of detected events and false alarms for 25-dB SNR is P/sub D/=0.98 and P/sub FA/=0.014, respectively.     

Signal-to-noise ratio enhancement of cardiac late potentials usingensemble correlation

An optimal one-weight filter is presented for the purpose of improving the signal-to-noise ratio of averaged ECG recordings in the analysis of late potentials. Based on a simple statistical model, the filter is estimated from the ensemble correlation of available beats. The correlation estimator is found by a maximum likelihood procedure in which the observed signal is assumed to have a Gaussian distribution. The performance of the optimal filter is studied in relation to an ensemble with individual or subaveraged beats