A 1-D convolutional neural network classifier for QRS detection achieved a sensitivity of 99.81% and a positive predictive value of 99.93% on the MIT-BIH arrhythmia database.
A 1-D CNN-based algorithm demonstrates high sensitivity and positive predictive value for QRS detection, comparable to state-of-the-art solutions.
In this paper we present a QRS detection algorithm based on pattern recognition as well as a new approach to ECG baseline wander removal and signal normalization. Each point of the zero-centred and normalized ECG signal is a QRS candidate, while a 1-D CNN classifier serves as a decision rule. Positive outputs from the CNN are clustered to form final QRS detections. The data is obtained from the 44 non-pacemaker recordings of the MIT-BIH arrhythmia database. Classifier was trained on 22 recordings and the remaining ones are used for performance evaluation. Our method achieves a sensitivity of 99.81% and 99.93% positive predictive value, which is comparable with most state-of-the-art solutions. This approach opens new possibilities for improvements in heartbeat classification as well as P and T wave detection problems.
Šarlija et al. (Fri,) conducted a other in Arrhythmia (n=44). 1-D convolutional neural network (CNN) classifier vs. State-of-the-art solutions was evaluated on Sensitivity and positive predictive value for QRS detection. A 1-D convolutional neural network classifier for QRS detection achieved a sensitivity of 99.81% and a positive predictive value of 99.93% on the MIT-BIH arrhythmia database.
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