… An active learning approach is … active learning strategies for ECG signal classification. All
the proposed strategies are based on iterative procedures and use SVM to classify the signals. …

… learning to achieve two main objectives: (i) learn a suitable feature representation of the ECG
signals in an automatic way unlike state-of-the-art methods … active learning (AL) techniques …

… with CNN and active learning method is introduced to classify ECG signals. The wearable
ECG device is utilized to obtain ECG signals. Then, obtained ECG signals are sent to the …

… obtain for ECG signals. Active learning is well-suited for ECG signal classification as it aims
at selecting the best set of labeled data in order to maximize the classification performance. …

… novel active learning-based electrocardiogram (ECG) signal … unsupervised approach for
active classification of ECG signals using … by eigenvalues of ECG signals. The basic idea of this …

… The key challenges faced in the automatic diagnosis of arrhythmia by electrocardiogram (ECG)
is enormous differences among individual patients and high cost of labeling clinical ECG …

… -cost way to choose learning samples and developing an incremental learning system for
AF detection. Methods Based on transfer learning and active learning, this paper proposed a …

… In 24 hours an electrocardiogram (ECG) can record over … using supervised machine learning
techniques to do the same. … This work employed a mixture of experts approach, combining …

… (k-NN) machine learning methods is performed to measure the classification performance
of the models on classifying electrocardiogram (ECG) signals as normal and abnormal. In this …

… is used to classify the ECG beats. In the fine-tuning phase, an active learning is added to
improve the performance. In the active learning phase, we use the method that relies on the …