Nadi Sadr1, Madhuka Jayawardhana, Thuy T Pham, Rui Tang, Asghar Tabatabaei Balaei, Philip de Chazal. 1. School of Electrical and Information Engineering, University of Sydney, Sydney, Australia. Charles Perkins Centre, University of Sydney, Sydney, Australia. School of Electrical and Information Engineering, University of Sydney, NSW 2006, Sydney, Australia.
Abstract
OBJECTIVES: We present a method for automatic processing of single-lead electrocardiogram (ECG) with duration of up to 60 s for the detection of atrial fibrillation (AF). The method categorises an ECG recording into one of four categories: normal, AF, other and noisy rhythm. For training the classification model, 8528 scored ECG signals were used; for independent performance assessment, 3658 scored ECG signals. APPROACH: Our method was based on features derived from RR interbeat intervals. The features included time domain, frequency domain and distribution features. We assessed the performance of three different classifiers (linear and quadratic discriminant analysis, and quadratic neural network (QNN)) on the training set using 100-fold cross-validation. The QNN was selected as the highest performing classifier, and a further performance assessment on the test data made. MAIN RESULTS: On the test set, our method achieved an F1 score for the normal, AF, other and noisy classes of 0.90, 0.75, 0.68 and 0.32, respectively. The overall F1 score was 0.78. SIGNIFICANCE: The computational cost of our algorithm is low as all features are derived from RR intervals and are processed by a single hidden layer neural network. This makes it potentially suitable for low-power devices.
OBJECTIVES: We present a method for automatic processing of single-lead electrocardiogram (ECG) with duration of up to 60 s for the detection of atrial fibrillation (AF). The method categorises an ECG recording into one of four categories: normal, AF, other and noisy rhythm. For training the classification model, 8528 scored ECG signals were used; for independent performance assessment, 3658 scored ECG signals. APPROACH: Our method was based on features derived from RR interbeat intervals. The features included time domain, frequency domain and distribution features. We assessed the performance of three different classifiers (linear and quadratic discriminant analysis, and quadratic neural network (QNN)) on the training set using 100-fold cross-validation. The QNN was selected as the highest performing classifier, and a further performance assessment on the test data made. MAIN RESULTS: On the test set, our method achieved an F1 score for the normal, AF, other and noisy classes of 0.90, 0.75, 0.68 and 0.32, respectively. The overall F1 score was 0.78. SIGNIFICANCE: The computational cost of our algorithm is low as all features are derived from RR intervals and are processed by a single hidden layer neural network. This makes it potentially suitable for low-power devices.
Authors: Ali Bahrami Rad; Conner Galloway; Daniel Treiman; Joel Xue; Qiao Li; Reza Sameni; Dave Albert; Gari D Clifford Journal: PLoS One Date: 2021-11-16 Impact factor: 3.240