The NEO-CCNN algorithm achieved >99.79% R-peak detection on the MIT-BIH database and attained 97.83% classification accuracy and 96.46% sensitivity when implemented on a microcontroller.
The proposed NEO-CCNN algorithm provides highly accurate and sensitive arrhythmia classification suitable for implementation on simple microcontrollers in wearable ECG devices.
Embedded arrhythmia classification is the first step towards heart diseases prevention in wearable applications. In this paper, a robust arrhythmia classification algorithm, NEO-CCNN, for wearables that can be implemented on a simple microcontroller is proposed. The NEO-CCNN algorithm not only detects QRS complex but also accurately locates R-peak with the help of the proposed adaptive time-dependent thresholding technique, improving the accuracy and sensitivity in arrhythmia classification. An optimized compact 1D-CNN network (CCNN) with 9,701 parameters is used for classification. A QRS complex augmentation method is introduced in the training process to cater for R-peak location error (RLE). A nested k 1 k 2 -fold cross-validation method is utilized to evaluate the robustness of the proposed algorithm. Simulation results show that the proposed algorithm has the ability to detect more than 99.79% of R peaks with an RLE of 7.94 ms for the MIT-BIH database. Implemented on the STM32F407 microcontroller, NEO-CNN attains a classification accuracy of 97.83% and sensitivity of 96.46% using only 8s window size.
Sabor et al. (Mon,) conducted a other in Arrhythmia. NEO-CCNN algorithm was evaluated on Classification accuracy and sensitivity. The NEO-CCNN algorithm achieved >99.79% R-peak detection on the MIT-BIH database and attained 97.83% classification accuracy and 96.46% sensitivity when implemented on a microcontroller.