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Found 6 papers, 0 papers with code
NeuroSleepNet: A Multi-Head Self-Attention Based Automatic Sleep Scoring Scheme with Spatial and Multi-Scale Temporal Representation Learning
Our model employs supervised spatial and multi-scale temporal context learning and incorporates a transformer encoder to enhance representation learning.
Machine Learning Models for the Identification of Cardiovascular Diseases Using UK Biobank Data
Therefore, we aimed to develop machine learning models for CVD detection using primary healthcare data, compare the performance of different models, and identify the best models.
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
