ECG-based heartbeat classification for arrhythmia detection: A survey

By Eduardo José da S. Luz, William Robson Schwartz, Guillermo Cámara Chávez, David Menotti. Comput. Methods Programs Biomed., 2016

808 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/a3cd318986aea973612bdf49ed9a984b0a71b590

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cardiology and Cardiovascular Medicine

1. M. Zhou
2. Masoud Daneshtalab
3. Zahra Ebrahimi
4. Arash Gharehbaghi
5. Taiyong Li
6. Mohammad Loni

In Cognitive Neuroscience

1. M. Zhou
2. Masoud Daneshtalab
3. Zahra Ebrahimi
4. Arash Gharehbaghi
5. Taiyong Li
6. Mohammad Loni

Citations in the corpus

• A review on deep learning methods for ECG arrhythmia classification (2020, background): “(Jambukia et al., 2015) is a short survey papers that mainly focus on machine learning techniques such as SVM and MLP. Luz et al. (Luz et al., 2016) review automatic ECG-based abnormalities classification papers that consider ECG signal preprocessing, heartbeat segmen-415 tation, feature description ”
• Mel Frequency Cepstral Coefficient and its Applications: A Review (2022, background): “An automatic ECG classification system is an experimental area and there are still many efforts and challenges in identifying and classifying several waveforms in the ECG signal [121].”
• ECG Classification Using Wavelet Packet Entropy and Random Forests (2016, methodology): “the particularities of the patient’s heartbeat are learned in the training phase [2].”