Augmenting ECG Data with Multiple Filters for a Better Emotion Recognition System.

Autor: Hasnul MA; Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia., Ab Aziz NA; Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia., Abd Aziz A; Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia.
Jazyk: angličtina
Zdroj: Arabian journal for science and engineering [Arab J Sci Eng] 2023 Jan 11, pp. 1-22. Date of Electronic Publication: 2023 Jan 11.
DOI: 10.1007/s13369-022-07585-9
Abstrakt: A physiological-based emotion recognition system (ERS) with a unimodal approach such as an electrocardiogram (ECG) is not as popular compared to a multimodal approach. However, a single modality has the advantage of lower development and computational cost. Therefore, this study focuses on a unimodal ECG-based ERS. The ECG-based ERS has the potential to become the next mass-adopted consumer application due to the wide availability of wearable and mobile ECG devices in the market. Currently, ECG-inclusive affective datasets are limited, and many of the existing datasets have small sample sizes. Hence, ECG-based ERS studies are stunted by the lack of quality data. A novel multi-filtering augmentation technique is proposed here to increase the sample size of the ECG data. This technique augments the ECG signals by cleaning the data in different ways. Three small ECG datasets labelled according to emotion state are used in this study. The benefit of the proposed augmentation techniques is measured using the classification accuracy of five machine learning algorithms; k-nearest neighbours (KNN), support vector machine, decision tree, random forest and multilayer perceptron. The results show that with the proposed technique, there is a significant improvement in performance for all the datasets and classifiers. KNN classifier improved the most with the augmented data and the reported classification accuracies of over 90%.
Competing Interests: Conflict of interestThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(© King Fahd University of Petroleum & Minerals 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje