ECG signal analysis through hidden Markov models
By Rodrigo Varejão Andreão, Bernadette Dorizzi, Jérôme Boudy. IEEE Transactions on Biomedical Engineering, 2006
308 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine, Cognitive Neuroscience
View Paper: https://www.semanticscholar.org/paper/a469ee9a33b0f83135af9e7b4771830d2610860b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
- Farid Melgani
- Yakoub Bazi
- Seong-Gon Kong
- Eduardo José da S. Luz
- Wei Jiang
- Guillermo Cámara Chávez
- David Menotti
- William Robson Schwartz
In Cardiology and Cardiovascular Medicine
- Farid Melgani
- Yakoub Bazi
- Seong-Gon Kong
- Eduardo José da S. Luz
- Wei Jiang
- Guillermo Cámara Chávez
- David Menotti
- William Robson Schwartz
In Cognitive Neuroscience
- Farid Melgani
- Yakoub Bazi
- Seong-Gon Kong
- Eduardo José da S. Luz
- Wei Jiang
- Guillermo Cámara Chávez
- David Menotti
- William Robson Schwartz
Citations in the corpus
- Block-Based Neural Networks for Personalized ECG Signal Classification (2007, methodology): “Classification techniques for ECG patterns include linear discriminant analysis [5], support vector machines [8], artificial neural networks (NNs) [9]‐[14], mixture-of-experts method [12], and statistical Markov models [15], [ 16 ].”
- ECG-based heartbeat classification for arrhythmia detection: A survey (2016, methodology): “[143] validated the use of HMM for ECG analysis in medcal clinics (real world).”
- Classification of Electrocardiogram Signals With Support Vector Machines and Particle Swarm Optimization (2008, methodology): “In [7], an automatic online beat segmentation and classification system based on a Markovian approach is proposed.”