In Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE : Automated R-wave detection plays a vital role in electrocardiography (ECG) and ECG-based computer-aided diagnosis. Recently, a multi-level one-dimensional (1D) deep learning approach was presented that shows good performance as compared to traditional methods.
METHODS : In this paper, we present several improvements of the multi-level 1D convolutional neural network (CNN)-based deep learning approach using: (i) adaptive deep learning, (ii) cross-database training, and (iii) cross-lead training. For this, we consider ECG signals from four publicly available databases: MIT-BIH, INCART, TELE, and SDDB, having 109,404, 175,660, 6,708, and 1,684,447 annotated beats, respectively. Except for TELE, all databases provide at least two-lead recordings. To evaluate the improvements, experiments are performed with adaptive k-times cross-trained databases validation scheme (k = 5). The hypothesis tested are: (i) the improvements outperform the state-of-the-art, (ii) cross-database training and adaptive deep learning contribute, and (iii) additional databases or cross-lead training further improves the results.
RESULTS : Our proposed approach outperforms the state-of-the-art. In terms of F-measure, F = 99.75% and F = 95.25% is obtained for the MIT-BIH and TELE databases, respectively. Further, cross-database training (F = 98.02%) is found to be more effective than training on individual databases (F = 97.33%). The performance of our approach further improves when additional databases and different leads are used for training.
CONCLUSION : Existing state-of-the-art methods perform low on noisy and pathological signals. Adaptive cross-data training identifies the optimal model. Using multiple datasets and leads allows analyzing noisy, pathological and mobile-recorded long-term ECG signals without ground truths. These conclusions are based on the comprehensive evaluation of four different databases, and in total, about 4.5 million annotated beats.
Ganapathy Nagarajan, Swaminathan Ramakrishnan, Deserno Thomas M
Adaptive model, Adaptive training, Convolutional neural network, Cross-database training, Cross-lead training, Deep learning, Electrocardiography, R-wave detection