ECG Pattern Analysis for Emotion Detection

By Foteini Agrafioti, Dimitrios Hatzinakos, Adam A. K. Anderson. IEEE Transactions on Affective Computing, 2012

442 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/1df44f1c7800ecea195dfb7457a25af172168f11

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. Pritam Sarkar
2. Tengfei Song
3. Zhen Cui
4. Peng Song
5. Ali Etemad
6. Wenming Zheng

In Cognitive Neuroscience

1. Pritam Sarkar
2. Tengfei Song
3. Lin Shu
4. Zhen Cui
5. Peng Song
6. Dan Liao
7. Zhenqi Li
8. Ziyi Li

In Experimental and Cognitive Psychology

1. Pritam Sarkar
2. Tengfei Song
3. Zhen Cui
4. Peng Song
5. Ali Etemad
6. Wenming Zheng
7. Dongrui Wu

Citations in the corpus

• A Review of Emotion Recognition Using Physiological Signals (2018, methodology): “In the reference [36], the authors extracted the Hilbert instantaneous frequency and local oscillation from Intrinsic Mode Functions (IMFs) after applying Em irical Mode Decomposition (EMD).”
• EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks (2020, background): “The second one is based on physiological signal, such as electroencephalogram (EEG) [8], electromyogram (EMG) [9], and electrocardiogram (ECG) [10].”
• Self-Supervised ECG Representation Learning for Emotion Recognition (2020, background): “In particular, ECG, which is the focus of our study, has been explored in the past with works such as [15] showing a strong correlation between emotional attributes and the ECG waveform, and investigating the feasibility and limitations of using ECG signals for emotion detection.”