Atrial fibrillation frequency tracking in ambulatory ECG signals: The significance of signal quality assessment

Pseudo anomalies enhanced deep support vector data description for electrocardiogram quality assessment

Electrocardiogram (ECG) recordings obtained from wearable devices are susceptible to noise interference that degrades the signal quality. Traditional methods for assessing the quality of electrocardiogram signals (SQA) are mostly supervised and typically rely on limited types of noise in the training data, which imposes limitations in detecting unknown anomalies. The high variability of both ECG signals and noise presents a greater challenge to the generalization of traditional methods. In this paper, we propose a simple and effective unsupervised SQA method by modeling the SQA of ECG as a problem of anomaly detection, in which, a model of pseudo anomalies enhanced deep support vector data description is introduced to learn a more discriminative and generalized hypersphere of the high-quality ECG in a self-supervised manner. Specifically, we propose a series of ECG noise-generation methods to simulate the noise of real scenarios and use the generated noise samples as the pseudo anomalies to correct the hypersphere learned solely by the high-quality ECG samples. Finally, the quality of ECG can be measured based on the distance to the center of the hypersphere. Extensive experimental results on multiple public datasets and our constructed real-world 12-lead dataset demonstrate the effectiveness of the proposed method.

A comparative study of the performance of methods for f-wave extraction

Objective.This study proposes a novel technique for atrial fibrillatory waves (f-waves) extraction and investigates the performance of the proposed method comparing with different f-wave extraction methods.Approach.We propose a novel technique combining a periodic component analysis (PiCA) and echo state network (ESN) for f-waves extraction, denoted PiCA-ESN. PiCA-ESN benefits from the advantages of using both source separation and nonlinear adaptive filtering. PiCA-ESN is evaluated by comparing with other state-of-the-art approaches, which include template subtraction technique based on principal component analysis, spatiotemporal cancellation, nonlinear adaptive filtering using an echo state neural network, and a source separation technique based on PiCA. Quality assessment is performed on a recently published reference database including a large number of simulated ECG signals in atrial fibrillation (AF). The performance of the f-wave extraction methods is evaluated in terms of signal quality metrics (SNR, ΔSNR) and robustness of f-wave features.Main results.The proposed method offers the best signal quality performance, with a ΔSNR of approximately 22 dB across all 8 sets of the reference database, as well as the most robust extraction of f-wave features, with 75% of all estimates of dominant atrial frequency well below 1 Hz.

Quality evaluation of signals collected by portable ECG devices using dimensionality reduction and flexible model integration

OBJECTIVE

Portable devices for collecting electrocardiograms (ECGs) and telemedicine systems for diagnosis are available to residents in deprived areas, but ECGs collected by non-professionals are not necessarily reliable and may impair the accuracy of diagnosis. We propose an algorithm for accurate ECG quality assessment, which can help improve the reliability of ECGs collected by portable devices.

APPROACH

Using challenge data from CinC (2019), signals were classified as 'acceptable' and 'unacceptable' by annotators. The training set contained 998 12-lead ECGs and the test set contained 500. A 998 × 84 feature matrix, S, was formed by feature extraction and three basic models were obtained through training SVM, DT and NBC on S. The feature subsets S1, S2 and S3 were obtained by dimensionality reduction on S using SVM, DT and NBC, respectively. Three other basic models were obtained through training SVM on S1, DT on S2 and NBC on S3. By combining these six basic models, several integrated models were formed. An iterative method was proposed to select the integrated model with the highest accuracy on the training set. Having compared differences between the output labels and the original data labels, evaluation criteria were calculated.

MAIN RESULTS

An accuracy of 98.70% and 98.60% was achieved on the training and test datasets, respectively. High F1 score and Kappa values were also obtained.

SIGNIFICANCE

The proposed algorithm has advantages over previously reported approaches during automatic assessment of ECG quality and can thus help to reduce reliance on highly trained professionals when assessing the quality of ECGs.

Addressing challenges of quantitative methodologies and event interpretation in the study of atrial fibrillation

Atrial fibrillation (AF) is the commonest arrhythmia, yet the mechanisms of its onset and persistence are incompletely known. Although techniques for quantitative assessment have been investigated, there have been few attempts to integrate this information to advance disease treatment protocols. In this review, key quantitative methods for AF analysis are described, and suggestions are provided for the coordination of the available information, and to develop foci and directions for future research efforts. Quantitative biologists may have an interest in this topic in order to develop machine learning and tools for arrhythmia characterization, but they may perhaps have a minimal background in the clinical methodology and in the types of observed events and mechanistic hypotheses that have thus far been developed. We attempt to address these issues via exploration of the published literature. Although no new data is presented in this review, examples are shown of current lines of investigation, and in particular, how electrogram analysis and whole-chamber quantitative modeling of the left atrium may be useful to characterize fibrillatory patterns of activity, so as to propose avenues for more efficacious acquisition and interpretation of AF data.