Application of principal component analysis to ECG signals for automated diagnosis of cardiac health

By Roshan Joy Martis, Usha R. Acharya, K. Mandana, Ajoy Kumar Ray, Chandan Chakraborty. Expert systems with applications, 2012

300 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/63f6655f2f394886d6acb3f97ddbaf401b5419da

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. Naomie Binti Salim
2. Arief R. Harris
3. Tan Tian Swee
4. Taqwa Ahmed
5. Fatin A. Elhaj

In Cognitive Neuroscience

1. Naomie Binti Salim
2. Sadasivan K. Puthusserypady
3. Arief R. Harris
4. Tan Tian Swee
5. Taqwa Ahmed
6. Fatin A. Elhaj

Citations in the corpus

• A deep learning approach for real-time detection of atrial fibrillation (2019, background): “…reported in (Alonso-Atienza ; Yu & Chou, 2008; Pawiak, 2018; Homaeinezhad et al., 2012; Shadmand & Mashoufi, 2016; Khalaf et ; Ceylan et al., 2009; Khorrami & Moavenian, 2010; Martis et al., 2012; Moavenian & Khorrami, This study presents DL model capable of detecting AF in long-term ECG recordings.”
• Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals (2016, methodology): “(Hu et al.,1997) Time domain features Mixture of experts 2 94.00% (Ince et al.,2009) DWT+PCA MDPSO 5 95.58% (Martis et al., 2012) PCA SVM-RBF 5 98.11% (Martis et al., 2013c) DWT+PCA SVM-RBF 5 96.92% DWT+PCA NN 5 98.78%”