Atrial fibrillation (AF) electrograms (EGMs) present high heterogeneity of morphologies and amplitudes that turns the detection of their local activation waves (LAWs) a very hard task to perform. In this study, a novel fractionation-based LAW detector for bipolar EGMs is introduced. The method modifies traditional Botteron's band-pass filtering, decreasing its low cutoff frequency from 40 to 20 Hz, thus benefiting slow local activations detection. Furthermore, high and low amplitude activations in complex fractionated atrial electrograms (CFAEs) are equalized, thus facilitating the detection of low amplitude activations. A minimum refractory period of 50 ms is imposed between activations, however, additional minor activations are later sought for intervals longer than the median cycle length. All the LAWs from a set of 40 real bipolar EGMs, mostly CFAEs, were manually annotated by expert physicians and served to evaluate performance. Detections closer than 40 ms to a manual annotation were considered as correct. Detection results provided 95.41%, 92.13% and 96.37% in Sensitivity, Accuracy and Precision for CFAEs, respectively, whereas for less fractionated EGMs they were 100% in any case. Therefore, the new LAW detector has provided robust performance even under highly fractionated EGMs.

Complex fractionated atrial electrograms (CFAEs) are electrograms (EGMs) characterized by a high variability both in amplitude and waveforms, making the estimation of their cycle length (CL) a difficult task to perform. The CL is a widely employed parameter for the characterization of the electrical activity within the atria, thus serving to guide catheter ablation, one of the most effective cardiac procedures for the treatment of AF nowadays. The present study aims to compare the performance of three different methods estimating the CL from a set of 50 recordings with CFAEs previously annotated by two expert physicians. The first algorithm is based on an adaptive amplitude threshold, while the second method performs the detection of the highest activations and a later detection of peaks within intervals longer than 1.5 times the median cycle length of the EGM. Finally, we introduce a novel hybrid method which performs a main amplitude detection, which is facilitated by the equalization of high and low amplitude activations in CFAEs according to a fractionation parameter, and a subsequent search for lower activations within intervals longer than the median CL, decreasing the amplitude threshold proportionally to the intervals' length. Our method outperformed the other analyzed detectors, which confirms that the method proposed is a more accurate estimator of atrial CL for CFAEs.

Local activation waves (LAWs) detection in complex fractionated atrial electrograms (CFAEs) during catheter ablation (CA) of atrial fibrillation (AF), the commonest cardiac arrhythmia, is a complicated task due to their extreme variability and heterogeneity in amplitude and morphology. There are few published works on reliable LAWs detectors, which are efficient for regular or low fractionated bipolar electrograms (EGMs) but lack satisfactory results when CFAEs are analyzed. The aim of the present work is the development of a novel optimized method for LAWs detection in CFAEs in order to assist cardiac mapping and catheter ablation (CA) guidance. The database consists of 119 bipolar EGMs classified by AF types according to Wells’ classification. The proposed method introduces an alternative Botteron’s preprocessing technique targeting the slow and small-ampitude activations. The lower band-pass filter cut-off frequency is modified to 20 Hz, and a hyperbolic tangent function is applied over CFAEs. Detection is firstly performed through an amplitude-based threshold and an escalating cycle-length (CL) analysis. Activation time is calculated at each LAW’s barycenter. Analysis is applied in five-second overlapping segments. LAWs were manually annotated by two experts and compared with algorithm-annotated LAWs. AF types I and II showed 100% accuracy and sensitivity. AF type III showed 92.77% accuracy and 95.30% sensitivity. The results of this study highlight the efficiency of the developed method in precisely detecting LAWs in CFAEs. Hence, it could be implemented on real-time mapping devices and used during CA, providing robust detection results regardless of the fractionation degree of the analyzed recordings.

Atrial cycle length (CL) is an important feature for the analysis of electrogram (EGM) characteristics acquired during catheter ablation (CA) of atrial fibrillation (AF), the commonest cardiac arrhythmia. Nevertheless, a robust ACL estimator requires the precise detection of local activation waves (LAWs), which still remains a challenge. This work aims to compare the performance in (CL) estimation, especially under fractionated EGMs, of three different LAW detection methods relying on different operation strategies. The methods are based on the hyperbolic tangent (HT) function, an adaptive amplitude threshold (AAT) and a (CL) iteration (ACLI), respectively. For each method, LAW detection has been assessed with respect to manual annotations made by two experts and performance has been estimated by confusion matrix and mean and individual (CL) error calculation by EGM types of fractionation. The influence of EGM length on the individual (CL) error has been additionally considered. For the HT method, accuracy, sensitivity and precision were 92.77–100%, while for the AAT and ACLI methods they were 78.89–99.91% for all EGM types. The CL error on the HT method was lower than AAT and ACLI methods (up to 12 ms versus up to 20 ms), with the difference being more prominent in complex EGMs. The HT method also showed the lowest dependency on EGM length, presenting the lowest and least variable error values. Therefore, the HT method achieves higher performance in (CL) estimation in comparison with previous LAW detection techniques. The high robustness and precision demonstrated by this method suggest its implementation on CA mapping devices for a more successful location of ablation targets and improved results during CA procedures.

Algorithms developed so far for the detection of local activation waves (LAWs) in electrograms (EGMs) of atrial fibrillation (AF) analyze each channel of the catheter separately, while dominant reentries of AF are captured by most of the catheter channels simultaneously. This encompasses the risk of losing or oversensing activations in the case of complex fractionated atrial electrograms (CFAEs). The proposed method analyzes multichannel recordings of AF from the least to the most fractionated. Activations are manually annotated and then EGMs are denoised, band-pass filtered by an alternative Botteron approach and thresholded. LAW detection is performed sequentially on all the channels by ascending fractionation order. Activations in the first EGM are found by an amplitude-based search, followed by a cycle length (CL) based search. Detection algorithm in the remaining signals is based on the previously detected LAWs, applying a varying search window according to the repetition rate of each activation. A CL-based search is also applied and activation time is computed from the barycenter of each LAW. The final step contains a secondary control that deletes all the low amplitude activations showing low repetition rate, so that any bias regarding the LAW detection of the first EGM is avoided. The method was compared with the equivalent sequential single-channel analysis, with the former showing better accuracy (90.82%) and positive predictive value (96.52%) than the single-channel approach (89.65% and 92.70%, respectively). Multichannel analysis performed a more selective analysis, ignoring some obvious but not repeated LAWs. Consequently, it can localize AF triggers and propagation phenomena more robustly than single channel method.