HRV analysis of patients prone to atrial fibrillation
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 HRV extraction PSD spectrum 0. 01 – 0. 4 Hz Training data normalization 5 layer Screening ANN [41] [15 10 5] [1] Training data normalization 5 layer Prediction ANN [41] [15 10 5] [1] Results PAF screening on single 30 min non-PAF HRV segment PAF screening training data: PAF screening results: 45 – prone to PAF Test set – 48 healthy and PAF 635 – healthy Se: 74. 0%, Sp: 61. 9%, Ac: 68. 7% Immediate PAF prediction on single 30 min HRV segment PAF prediction training data: PAF prediction results: 21 – immediately before PAF Test set – 175 healthy and PAF preceding 658 – healthy and PAF distant Se: 69. 3%, Sp: 68. 2%, Ac: 68. 5% with minmax normalization Se: 81. 6%, Sp: 53. 9%, Ac: 61. 7% with sigmoidal normalization 10 min distant PAF prediction on 20 min HRV segment 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 PAF prediction results: screening ANN: 83. 3% accuracy Test set – 175 healthy and PAF preceding prediction ANN: 87. 1% accuracy Se: 67. 3%, Sp: 64. 2%, Ac: 65. 1% with energy normalization Conclusions: 1) Differentiation of PAF patients from healthy patients 3) Prediction of PAF 10 minutes in advance 2) Immediate prediction of PAF 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, CB 2 1 EW, UK
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