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HRV analysis of patients prone to atrial fibrillation using a Neural Network approach.

HRV analysis of patients prone to atrial fibrillation using a Neural Network approach. Yuriy Chesnokov, Dmitry Nerukh, Robert Glen.

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HRV analysis of patients prone to atrial fibrillation using a Neural Network approach.

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  1. HRV analysis of patients prone to atrial fibrillation using a Neural Network approach. Yuriy Chesnokov, Dmitry Nerukh, Robert Glen. Simple and robust fully automated methods for the screening and prediction of PAF events is of high clinical importance for the detection of the most frequent cardiac arrhythmias. Method Automatic ECG annotation 30 min segments Training data normalization 5 layer Screening ANN [41] [15 10 5] [1] PSD spectrum 0.01 – 0.4 Hz HRV extraction Training data normalization 5 layer Prediction ANN [41] [15 10 5] [1] Results PAF screening on single 30 min non-PAF HRV segment 10 min distant PAF prediction on 20 min HRV segment PAF screening training data: 45 – prone to PAF 635 – healthy PAF screening results: Test set – 48 healthy and PAF Se: 74.0%, Sp: 61.9%, Ac: 68.7% PAF prediction training data: 78 – 10 min distant from PAF 1997 – healthy and more than 45 min PAF distant Normal HRV database testing: Test set – 6385 healthy 30 min segments screening ANN: 83.3% accuracy prediction ANN: 87.1% accuracy Immediate PAF prediction on single 30 min HRV segment PAF prediction results: Test set – 175 healthy and PAF preceding Se: 67.3%, Sp: 64.2%, Ac: 65.1% with energy normalization PAF prediction training data: 21 – immediately before PAF 658 – healthy and PAF distant PAF prediction results: Test set – 175 healthy and PAF preceding Se: 69.3%, Sp: 68.2%, Ac: 68.5% with minmax normalization Se: 81.6%, Sp: 53.9%, Ac: 61.7% with sigmoidal normalization Conclusions: 1) Differentiation of PAF patients from healthy patients 2) Immediate prediction of PAF 3) Prediction of PAF 10 minutes in advance This automated method produced good results with high Se and Sp values providing the possibility of implementing the method in portable devices for people at risk of cardiac arrhythmia and also the possible adaptation of the neural network for individual patients. Unilever Centre for Molecular Sciences Informatics, University Chemical Laboratory, Cambridge University Lensfield Road, Cambridge, CB2 1EW, UK

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