Improved Performance of Human Emotion Detection Using ECG Signal Processing

Simulation and identification of emotions have attracted much interest from fields like cognitive science, psychology, and, recently, engineering. Even though a good quantity of investigation has been conducted on behavioral modalities, there are some underresearched aspects such as physiological signals. This research brings forth the ECG signal and introduces a complete study of its psychological characteristics. The very institution of this signal as a biometric property justifies subject-reliant emotion identifiers that record the immediate changeability of the signal from its homeostatic standard level. We are enhancing the implementation of the Emotion Identification through the application of ECG signals in this work. We recommend Ensemble Pragmatic Mode Decomposition or EPMD technique to diminish the operation duration and enhance the categorization rate.