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Recently, researchers in the area of biosensor based human emotion recognition have used different types of machine learning models for recognizing human emotions. However, most of them still lack the ability to recognize human emotions with higher classification accuracy incorporating a limited number of bio-sensors. In the domain of machine learn...

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... de-trending, the resulting signal was further smoothed using a Gaussian kernel, and it was made sure that the smoothing procedure preserves the vital information of the wave. Figure 4 illustrates the resulting signals after conducting each step. ...

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... Several studies have used the ECG signal to detect emotional changes [162][163][164][165]. In the research of Dissanayake et al. (2019) [166], the authors used three ECG signal-based techniques and the EMD method to recognize the primary human emotions: anger, joy, sadness, and pleasure. Tey achieved an accuracy gain of 6.8% as compared to the other methods. ...
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The joint time-frequency analysis method represents a signal in both time and frequency. Thus, it provides more information compared to other one-dimensional methods. Several researchers recently used time-frequency methods such as the wavelet transform, short-time Fourier transform, empirical mode decomposition and reported impressive results in various electrophysiological studies. The current review provides comprehensive knowledge about different time-frequency methods and their applications in various ECG-based analyses. Typical applications include ECG signal denoising, arrhythmia detection, sleep apnea detection, biometric identification, emotion detection, and driver drowsiness detection. The paper also discusses the limitations of these methods. The review will form a reference for future researchers willing to conduct research in the same field.
... Thus, the performance of ensemble learning models is generally higher than single classification algorithms [27]. There are various applications in which ensemble learning methods are utilized such as cyber security [28][29][30][31][32][33], energy [34][35][36][37], and health informatics [38][39][40][41][42][43][44][45][46][47]. ...
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italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Emotion analysis has been employed in many fields such as human-computer interaction, rehabilitation, and neuroscience. But most emotion analysis methods mainly focus on healthy controls or depression patients. This paper aims to classify the emotional expressions in individuals with hearing impairment based on EEG signals and facial expressions. Two kinds of signals were collected simultaneously when the subjects watched affective video clips, and we labeled the video clips with discrete emotional states (fear, happiness, calmness, and sadness). We extracted the differential entropy (DE) features based on EEG signals and converted DE features into EEG topographic maps (ETM). Next, the ETM and facial expressions were fused by the multichannel fusion method. Finally, a deep learning classifier CBAM_ResNet34 combined Residual Network (ResNet) and Convolutional Block Attention Module (CBAM) was used for subject-dependent emotion classification. The results show that the average classification accuracy of four emotions recognition after multimodal fusion achieves 78.32%, which is higher than 67.90% for facial expressions and 69.43% for EEG signals. Moreover, visualization by the Gradient-weighted Class Activation Mapping (Grad-CAM) of ETM showed that the prefrontal, temporal and occipital lobes were the brain regions closely related to emotional changes in individuals with hearing impairment.</i
... In [19], the authors explained a machine learning method to recognize four major types of human emotions which are anger, sadness, joy, and pleasure. The authors incorporated electrocardiogram (ECG) signals to recognize emotions. ...
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... However, older studies focusing on linear and quadratic discriminant analysis remain relevant, achieving suitable accuracy for their respective classifications [5], [22]. Furthermore, adapted ML classifiers and combinations of ML classifiers forming ensembles have demonstrated potential for binary classifications in emotion detection [23], [24]. In comparison to other studies, [19] achieved the highest accuracy for multiple emotion detection from ECG data and reported setting the new state of the art for ECG emotion detection. ...
... Certain windows may include multiple emotive annotations; hence to identify the most pertinent emotion, the mean of all annotation values per window is calculated and rounded to the nearest annotation (1-4) using Euclidean distance. Alternative approaches [24] omit these windows and the neighbouring segments to prevent confusion from mixed emotions. ...
... The full model comparison is shown in Figure 1. In contrast with the state of the art [19], [24] the performance achieved is much lower for ECG and PPG; however, this work focuses on the variance between the signals for affective analysis rather than achieving high classification accuracy. Analysing the ROC curves from RF demonstrates the true and false positive rates per signal for each affective state, see Figure 4. On average, ECG demonstrates increased capabilities for affective classification by achieving a higher ROC area than PPG, varying with a range of 0.02-0.10. ...
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... As a result of the development of unsupervised DNN algorithms, researchers have become more interested in identifying hidden ill state signals in a 12-lead ECG. It has been demonstrated that utilising a 12-lead ECG, it is possible to identify hyperkalemia, cardiac failure, hypoglycemia, [68] and even changes in mental states [69].Mayo Clinic Rochester researchers Attia et al. [70] investigated whether an AIenabled 12-lead ECG in normal sinus rhythm might be used to detect prior or impending AF events. They used 0.65 million ECGs in a 7:1:2 ratio to train, validate, and test the AI algorithm and discovered that an AI-enabled The accuracy of an ECG recorded during normal sinus rhythm as a screening test for AF was 79. 4 percent. ...
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... For ECG signals, there are large number of researches focusing on different types of feature extraction methods. Few such methods include heart rate variability (HRV), empirical mode decomposition (EMD) with-in beat analysis (WIB), FFT analysis, and various methods of wavelet transformations [23]. For GSR, as the skin conductance is mainly related with arousal level, thus useful information related with its amplitude and frequency is analyzed in time and frequency domains by applying various techniques and extracting some statistical parameters as: median, mean, standard deviation, minimum, maximum, as well as ratio of minimum and maximum [24]. ...
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Detection of mental states like stress/anxiety, mediation is a widely researched topic and is important for ensuring overall well-being of an individual. Several approaches have been reported in the literature for prediction or assessment of mental states. Recently, with advances in sensor technology, various physiological signals are being used by researchers for detecting mental states. In the present study, we have used a light weight deep convolutional neural network (CNN) for creating a mental state prediction model. The proposed detection model is created using publicly available WESAD dataset. The dataset contains electrocardiogram (ECG), galvanic skin response (GSR), skin temperature and electromyogram (EMG) signals recorded using a wearable device. Results show that for binary classification of stress vs no-stress condition our results are comparable with that reported in state-of-the-art machine learning/deep learning-based approaches. However, for three class classification of baseline vs stress vs amusement states, our model gives an accuracy of 90% which is much higher compared to that reported in the literature. In addition, we have also tried to classify various binary states like stress vs baseline,stress vs amusement and stress vs meditation conditions. The f1 score obtained for these classes are 0.96, 0.87 and 0.91, respectively, which are much higher than that reported in state-of-the-art literature using same dataset. Proposed light weight CNN-based mental state classification model is computationally less complex compared to other deep networks used by the researchers. Thus, it can be used for monitoring mental state successfully in real-life scenarios.
... This proposed model is called the Discrete Emotion Model.The proposed study aims to identify discrete emotions through ECG signals. A study by Dissanayake et al. [15] recognized 6 emotional classes through HRV features obtained from ECG signal. An accuracy of 80% was achieved using Extra Tree Classifier with feature selection. ...
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Smart wearables have played an integral part in our day to day life. From recording ECG signals to analysing body fat composition, the smart wearables can do it all. The smart devices encompass various sensors which can be employed to derive meaningful information regarding the user's physical and psychological conditions. Our approach focuses on employing such sensors to identify and obtain the variations in the mood of a user at a given instance through the use of supervised machine learning techniques. The study examines the performance of various supervised learning models such as Decision Trees, Random Forests, XGBoost, LightGBM on the dataset. With our proposed model, we obtained a high recognition rate of 92.5% using XGBoost and LightGBM for 9 different emotion classes. By utilizing this, we aim to improvise and suggest methods to aid emotion recognition for better mental health analysis and mood monitoring.