In this paper1 a new neural networks training algorithm is presented. The algorithm originates from the Recursive Least Squares (RLS) method commonly used in adaptive filtering. It uses the QR decomposition in conjunction with the Givens rotations for solving a normal equation - resulting from minimization of the loss function. An important parameter in neural networks is training time. Many commonly used algorithms require a big number of iterations in order to achieve a satisfactory outcome while other algorithms are effective only for small neural networks. The proposed solution is characterized by a very short convergence time compared to the well-known backpropagation method and its variants. The paper contains a complete mathematical derivation of the proposed algorithm. There are presented extensive simulation results using various benchmarks including function approximation, classification, encoder, and parity problems. Obtained results show the advantages of the featured algorithm which outperforms commonly used recent state-of-the-art neural networks training algorithms, including the Adam optimizer and the Nesterov’s accelerated gradient.
Introduction Cryoballoon-based pulmonary vein isolation (CB) is an effective option for rhythm control in atrial fibrillation (AF). There have been multiple attempts to predict arrhythmia recurrence, but with moderate success. Purpose To use artificial intelligence (AI) deep electrocardiogram (ECG) analysis to predict arrythmia recurrence after CB. Methods In a single-center study of 250 consecutive pts (58.2±12.6 years, 30% female) treated with CB for AF (05.2017–04.2019), 60% had paroxysmal AF (PAF), 66.5% had hypertension, and 27.2% had a redo CB. Analyses included left atrial volume (LA vol: indexed for BSA and assed by angio-MSCT in 76% and the rest with echo), left ventricle ejection fraction (LVEF) and hypertrophy (LVH: septal/posterior wall thickness ≥11mm in ♂ and ≥10mm in ♀) along with 30s arrhythmia recurrence at 2-yr follow-up. Baseline 500Hz raw 12-lead digital ECG signals were analyzed by means of convolutional neural network (CNN) architecture that was taught to process the 12-channel ECG signal (XML). The transfer learning method of CNN parameters learned on a very large number of coded consecutive training ECG samples (n=1,000) was adopted to analyze current sample of ECG signals. Results Arrhythmia recurrence at 2-yrs was 46.0% (n=115). There was gradual increase in predictive performance being the lowest if only baseline clinical data were analyzed and the highest combing all baseline clinical data plus anatomical and functional parameters (Table 1: #1–#3). AI baseline ECG analysis alone offered predictive performance similar to that made upon analysis of all baseline clinical data or joint analysis of LA vol, LVH, and LVEF (Table 1: #4). AI results added substantially to predictive performance of models using baseline clinical data alone (#1 vs #5, p<0.001) and joint analysis of baseline clinical plus LA vol, LVH and LVEF (#3 vs #6, p=0.0130) (Fig. 1). Conclusions Prediction of recurrence after CB using raw ECG data with deep AI analysis is feasible. Joint analysis of results of AI of baseline ECG and basic clinical data offers predictive performance similar to that made upon analysis of clinical data including advanced information on LA volume and LVH and function. AI analysis of baseline ECG adds significantly to models aimed at recurrent AF prediction. Funding Acknowledgement Type of funding sources: Public hospital(s). Main funding source(s): National Institute of Cardiology, Warsaw, Poland
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