Classification of Electrocardiogram Signals With Support Vector Machines and Particle Swarm Optimization
By Farid Melgani, Yakoub Bazi. IEEE Transactions on Information Technology in Biomedicine, 2008
506 citations recorded by the source corpus.
Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience, Signal Processing
View Paper: https://www.semanticscholar.org/paper/781eb5c143a596e085192ef4ecc5327467b0deaa
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
In Cognitive Neuroscience
- Matin Hashemi
- Yashar Mehdad
- Mohammadhosein Oveisi
- Saeed Saadatnejad
- Abdulhamit Subasi
- Mahmut Ozer
- Mahmut Hekim
- Umut Orhan
In Signal Processing
- Matin Hashemi
- Yashar Mehdad
- Mohammadhosein Oveisi
- Saeed Saadatnejad
- Abdulhamit Subasi
- Amjed Subhi Al-Fahoum
- Ausilah Alfraihat
- Dimitrios I. Fotiadis
Citations in the corpus
- Automatic Cost Estimation for Tree Edit Distance Using Particle Swarm Optimization (2009, background): “It is usually selected slightly less than 1 for better global exploration (Melgani and Bazi, 2008).”
- Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders (2013, methodology): “In order to achieve this, the system is derived from an optimization framework based on PSO [19].”
- LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices (2018, methodology): “Many previous ECG classification algorithms are mainly focused on signal processing techniques including extraction of morphological features [7], frequency domain analysis [8], Hermite function decomposition [9], wavelet transform [10], [11], support vector machines [12] and hidden Markov models [1”