IRN-MLSQR: An improved iterative reweight norm approach to the inverse problem of electrocardiography incorporating factorization-free preconditioned LSQR.

The inverse problem of electrocardiography (ECG) of computing epicardial potentials from body surface potentials, is an ill-posed problem and needs to be solved by regularization techniques. The L2-norm regularization can cause considerable smoothing of the solution, while the L1-norm scheme promotes a solution with sharp boundaries/gradients between piecewise smooth regions, so L1-norm is widely used in the ECG inverse problem. However, large amount of computation and long computation time are needed in the L1-norm scheme. In this paper, by combining iterative reweight norm (IRN) with a factorization-free preconditioned LSQR algorithm (MLSQR), a new IRN-MLSQR method was proposed to accelerate the convergence speed of the L1-norm scheme. We validated the IRN-MLSQR method using experimental data from isolated canine hearts and clinical procedures in the electrophysiology laboratory. The results showed that the IRN-MLSQR method can significantly reduce the number of iterations and operation time while ensuring the calculation accuracy. The number of iterations of the IRN-MLSQR method is about 60%-70% that of the conventional IRN method, and at the same time, the accuracy of the solution is almost the same as that of the conventional IRN method. The proposed IRN-MLSQR method may be used as a new approach to the inverse problem of ECG.

Detection of atrial fibrillation from ECG recordings using decision tree ensemble with multi-level features.

OBJECTIVE: Detecting atrial fibrillation (AF) from electrocardiogram (ECG) recordings remains a challenging task. In this paper, a new AF detection method was proposed to classify the ECG recordings into one of four classes: Normal rhythm, AF, Other rhythm, and Noisy recordings. APPROACH: The proposed method comprised preprocessing, feature extraction, and classification. In preprocessing, R-peaks were detected, and RR intervals and delta RR intervals were extracted. In feature extraction, 30 multi-level features were extracted, including AF features (n = 4), morphology features (n = 20), RR interval features (n = 2), and features of similarity index between beats (n = 4). In classification, these features were used to train an AdaBoosted decision tree ensemble for classifying ECG recordings into the four classes. The decision tree ensemble was trained with 100-fold cross-validation on the training dataset (n = 8528) provided by the 2017 PhysioNet/Computing in Cardiology (CinC) Challenge. MAIN RESULTS: The trained classifier was submitted to the Challenge for testing on the unavailable test dataset (n = 3658); the official F 1 scores for 'Normal', 'AF', 'Other' were 0.91, 0.82, and 0.73, respectively, and the overall F 1 score was 0.82 (ranking equal 5th with eight other algorithms in the 2017 PhysioNet/CinC Challenge). SIGNIFICANCE: The proposed algorithm may be used as a new method for AF detection.