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Optimising complexity of CNN models for resource constrained devices: QRS detection case study
We hypothesize that a shallow CNN model can offer satisfactory level of performance in combination by leveraging other essential solution-components, such as post-processing that is suitable for resource constrained environment.
Learning post-processing for QRS detection using Recurrent Neural Network
To the best of our knowledge, the use of GRU to learn QRS-detection post-processing from CNN model generated prediction streams is the first of its kind.
Choosing a sampling frequency for ECG QRS detection using convolutional networks
Automated QRS detection methods depend on the ECG data which is sampled at a certain frequency, irrespective of filter-based traditional methods or convolutional network (CNN) based deep learning methods.
Impact of ECG Dataset Diversity on Generalization of CNN Model for Detecting QRS Complex
In real life, a promising QRS detector may be expected to achieve acceptable accuracy over diverse ECG recordings and, thus, investigation of the model's generalization capability is crucial.
Ranked #1 on
QRS Complex Detection
on MIT-BIH AR
