… CPU counterpart in the classification of the heartbeats. The computation … , the entire system
can be trained extremely fast with a GPU. … We show that our heartbeat classification method …

… classification. The cross-database training and testing with promising results is the uniqueness
of our proposed machine-learning model. … learning algorithms were used for classification …

… Recently, deep learning techniques have been used by many … novel deep learning approach
for ECG beats classification is … for developing a fast classifier for heartbeats classification. …

… [36] proposed another deep-learning approach for the detection of cardiac arrhythmia, …
model. The proposed method is simple, efficient, and fast (it allows for real-time classification), …

… , the deep learning model is used as a second judge for a well-known R-peak detector
algorithm. … The popularization of deep learning, especially CNNs, has led to a fast increase in the …

… irregularity may either be a slow or fast heartbeat. A … heartbeat classification model that
effectively handles large pools of data, this work proposes an ensemble of deep-learning models. …

… the classification accuracy of the ML classifiers. To this end and to develop an efficient classifier
model, we propose to optimize the learning … faster before they detect the prey such that: …

… system based on the intelligent ECG classification with the aid of fast residual … assessed
the deep learning techniques used to classify a heartbeat. We improved the model accuracy by …

… An arrhythmia is an abnormality in the heart rhythm, or heartbeat pattern. The heartbeat
can be too slow, too fast, have extra beats, or skip a beat. Left Bundle Branch Block (LBBB), …

… the problem of ECG beat classification. At the same time, deep learning has advanced rapidly
… In this paper, we propose a novel deep learning approach for ECG beat classification. We …