Time-frequency plane Wiener filtering of the high-resolution ECG: background and time-frequency representations

Real-Time Quality Assessment of Long-Term ECG Signals Recorded by Wearables in Free-Living Conditions

OBJECTIVE

Nowadays, methods for ECG quality assessment are mostly designed to binary distinguish between good/bad quality of the whole signal. Such classification is not suitable to long-term data collected by wearable devices. In this paper, a novel approach to estimate long-term ECG signal quality is proposed.

METHODS

The real-time quality estimation is performed in a local time window by calculation of continuous signal-to-noise ratio (SNR) curve. The layout of the data quality segments is determined by analysis of SNR waveform. It is distinguished between three levels of ECG signal quality: signal suitable for full wave ECG analysis, signal suitable only for QRS detection, and signal unsuitable for further processing.

RESULTS

The SNR limits for reliable QRS detection and full ECG waveform analysis are 5 and 18 dB respectively. The method was developed and tested using synthetic data and validated on real data from wearable device.

CONCLUSION

The proposed solution is a robust, accurate and computationally efficient algorithm for annotation of ECG signal quality that will facilitate the subsequent tailored analysis of ECG signals recorded in free-living conditions.

SIGNIFICANCE

The field of long-term ECG signals self-monitoring by wearable devices is swiftly developing. The analysis of massive amount of collected data is time consuming. It is advantageous to characterize data quality in advance and thereby limit consequent analysis to useable signals.

Adaptive wavelet Wiener filtering of ECG signals

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Arrhythmia ECG noise reduction by ensemble empirical mode decomposition

A novel noise filtering algorithm based on ensemble empirical mode decomposition (EEMD) is proposed to remove artifacts in electrocardiogram (ECG) traces. Three noise patterns with different power—50 Hz, EMG, and base line wander – were embedded into simulated and real ECG signals. Traditional IIR filter, Wiener filter, empirical mode decomposition (EMD) and EEMD were used to compare filtering performance. Mean square error between clean and filtered ECGs was used as filtering performance indexes. Results showed that high noise reduction is the major advantage of the EEMD based filter, especially on arrhythmia ECGs.

A nonlinear Bayesian filtering framework for ECG denoising

In this paper, a nonlinear Bayesian filtering framework is proposed for the filtering of single channel noisy electrocardiogram (ECG) recordings. The necessary dynamic models of the ECG are based on a modified nonlinear dynamic model, previously suggested for the generation of a highly realistic synthetic ECG. A modified version of this model is used in several Bayesian filters, including the Extended Kalman Filter, Extended Kalman Smoother, and Unscented Kalman Filter. An automatic parameter selection method is also introduced, to facilitate the adaptation of the model parameters to a vast variety of ECGs. This approach is evaluated on several normal ECGs, by artificially adding white and colored Gaussian noises to visually inspected clean ECG recordings, and studying the SNR and morphology of the filter outputs. The results of the study demonstrate superior results compared with conventional ECG denoising approaches such as bandpass filtering, adaptive filtering, and wavelet denoising, over a wide range of ECG SNRs. The method is also successfully evaluated on real nonstationary muscle artifact. This method may therefore serve as an effective framework for the model-based filtering of noisy ECG recordings.

Abnormal intra-QRS potentials associated with percutaneous transluminal coronary angiography–induced transient myocardial ischemia

This article introduces a novel concept of abnormal intra-QRS potentials (AIQPs) associated with myocardial ischemia. AIQPs are microvolt-level potentials--subtle alterations in the QRS of the high-resolution electrocardiogram (ECG)--isolated from the unfiltered signal-averaged ECG (SAECG) by a method of mathematical modeling. The aims of the study were (1) to determine the characteristics of potentials in the SAECG related to ischemically altered activation during percutaneous transluminal coronary angiography (PTCA), (2) to determine their relationship with standard 12-lead ECG variables, and (3) to investigate whether AIQPs have a specific pathophysiologic basis in myocardial ischemia. Continuous high-resolution ECG data were acquired from 12 patients before, during, and after PTCA. SAECGs were computed every 60 seconds using an enhanced method of signal averaging. AIQP, ST-segment deviation, and changes in standard ECG QRS duration were measured in each 1-minute SAECG. AIQP amplitudes increased significantly during balloon inflation, compared with the preinflation state. AIQPs exhibited a greater prevalence (12 of 12 patients) than ST-segment deviation changes of more than 100 microV (7 of 12 patients), or measurable changes in standard QRS duration (4 of 12 patients). In patients with significant changes in 12-lead ECG variables during balloon inflation, AIQPs were strongly correlated with both ST-segment and QRS-duration changes. AIQP timing was correlated with the artery occluded, suggesting a specific, ischemia-influenced origin of the signal. AIQPs show promise as a time-localized, sensitive new ECG marker of ischemically altered ventricular activation.

Reduction of electromyographic noise in the signal-averaged electrocardiogram by spectral decomposition

This paper proposes a technique to improve the quality of high-resolution electrocardiogram by weighting the coherent average of beats by a function of the energy of the corrupting myoelectric noise, prior to subsequent detection of ventricular late potentials. The results obtained with 20 patients indicate the method requires fewer beats than conventional nonweighted average to achieve the same noise level.

Wavelet-based enhancement of signal-averaged electrocardiograms for late potential detection

An optimal wavelet filter to improve the signal-to-noise ratio (SNR) of the signal-averaged electrocardiogram is described. As the averaging technique leads to the best unbiased estimator, the challenge is to attenuate the noise while preserving the low amplitude signals that are usually embedded in it. An optimal, in the mean-square sense, wavelet-based filter has been derived from the model of the signal. However, such a filter needs exact knowledge of the noise statistic and the noise-free signal. Hence, to implement such a filter, a method based on successive sub-averaging and wavelet filtering is proposed. Its performance was evaluated using simulated and real ECGs. An improvement in SNR of between 6 and 10 dB can be achieved compared to a classical averaging technique which uses an ensemble of 64 simulated ECG beats. Tests on real ECGs demonstrate the utility of the method as it has been shown that by using fewer beats in the filtered ensemble average, one can achieve the same noise reduction. Clinical use of this technique would reduce the ensemble needed for averaging while obtaining the same diagnostic result.

Time-frequency plane Wiener filtering of the high-resolution ECG: development and application

The time-frequency plane Wiener (TFPW) filter is a new method, based on a posteriori Wiener filtering principles, to enhance the performance of ensemble averaging. This paper develops the mathematical aspects of the TFPW filter, and assesses its performance with elementary signals, such as sine waves and chirps, and authentic high-resolution electrocardiogram (HRECG) ensembles. The principal feature of the TFPW filter is its use of the time-frequency plane to accommodate signal nonstationarity. Using a posteriori computed statistics of the ensemble, the filter matches itself to the time-frequency structure of the signals to be estimated. The method is sufficiently general to be applicable to any class of repetitive signal with a deterministic time-frequency structure and additive noise in the ensemble. It is concluded that significant improvements in both estimated signal fidelity and noise reduction are possible with the TFPW filter, compared to conventional ensemble averaging.