Analysis of Electrocardiogram (ECG) Signals for Human Emotional Stress Classification

Bong, Siao Zheng; Murugappan, M.; Yaacob, Sazali

doi:10.1007/978-3-642-35197-6_22

Cited by 24 publications

(23 citation statements)

References 7 publications

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“…Although the number of emotion classes in the DEM varies, there are similarities between them. Among the emotion labels, the most common are happiness, sadness, and anger [ 20 , 37 , 38 , 39 , 40 , 41 ]. The reason for the three of them being selected the most is because of the prominent arousal level that can be easily detected compared to more relaxed emotions [ 22 ].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…A higher accuracy of training and testing models can also be expected. Figure 7 shows the emotional stress model proposed by [ 39 ]. Instead of valence, the author used a pleasantness scale to describe the region of potential mental stressors.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…The Pan–Tompkins QRS detection algorithm [ 81 ] is considered as the most common technique to find the R peak location [ 58 , 67 , 69 ]. In [ 39 , 40 ], the QRS complex was derived by applying a nonlinear transformation on the first derivative (Gaussian first-order differentiator) of the filtered ECG signal [ 82 , 83 ]. Continuous wavelet transforms (CWT) are applied to detect a precise R location and then the QS, P, and T waves [ 84 ].…”

Section: Development Of Emotion Recognition Systemsmentioning

confidence: 99%

“…Considering that one cycle or one beat can be measured between two successive R peaks, the HR can be derived simply through averaging the overall signals collected through a period. The HR is proven to show distinct feature changes [ 87 ] and has been used in various ECG-based affective studies [ 6 , 24 , 39 , 51 , 53 , 55 , 56 , 57 , 67 , 71 , 75 , 78 , 85 , 88 ]. The benefits of HR over other features are the simplicity of the calculation and not requiring a highly accurate measurement.…”

Section: Development Of Emotion Recognition Systemsmentioning

confidence: 99%

“…Other well-performing classifiers used are k-nearest neighbour (KNN) and naïve Bayes (NB). KNN was reported to perform better than SVM in [ 39 , 77 ]. Meanwhile, [ 56 ] showed that NB performed better than SVM in both valence and arousal classification using a single ECG modality.…”

Section: Development Of Emotion Recognition Systemsmentioning

confidence: 99%

See 4 more Smart Citations

Electrocardiogram-Based Emotion Recognition Systems and Their Applications in Healthcare—A Review

Hasnul

Aziz

Alelyani

et al. 2021

Sensors

118

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Affective computing is a field of study that integrates human affects and emotions with artificial intelligence into systems or devices. A system or device with affective computing is beneficial for the mental health and wellbeing of individuals that are stressed, anguished, or depressed. Emotion recognition systems are an important technology that enables affective computing. Currently, there are a lot of ways to build an emotion recognition system using various techniques and algorithms. This review paper focuses on emotion recognition research that adopted electrocardiograms (ECGs) as a unimodal approach as well as part of a multimodal approach for emotion recognition systems. Critical observations of data collection, pre-processing, feature extraction, feature selection and dimensionality reduction, classification, and validation are conducted. This paper also highlights the architectures with accuracy of above 90%. The available ECG-inclusive affective databases are also reviewed, and a popularity analysis is presented. Additionally, the benefit of emotion recognition systems towards healthcare systems is also reviewed here. Based on the literature reviewed, a thorough discussion on the subject matter and future works is suggested and concluded. The findings presented here are beneficial for prospective researchers to look into the summary of previous works conducted in the field of ECG-based emotion recognition systems, and for identifying gaps in the area, as well as in developing and designing future applications of emotion recognition systems, especially in improving healthcare.

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