An Ensemble Learning Approach for Electrocardiogram Sensor Based Human Emotion Recognition

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 learning, ensemble learning methods have been successfully applied to solve different types of real-world machine learning problems which require improved classification accuracies. Emphasising on that, this research suggests an ensemble learning approach for developing a machine learning model that can recognize four major human emotions namely: anger; sadness; joy; and pleasure incorporating electrocardiogram (ECG) signals. As feature extraction methods, this analysis combines four ECG signal based techniques, namely: heart rate variability; empirical mode decomposition; with-in beat analysis; and frequency spectrum analysis. The first three feature extraction methods are well-known ECG based feature extraction techniques mentioned in the literature, and the fourth technique is a novel method proposed in this study. The machine learning procedure of this investigation evaluates the performance of a set of well-known ensemble learners for emotion classification and further improves the classification results using feature selection as a prior step to ensemble model training. Compared to the best performing single biosensor based model in the literature, the developed ensemble learner has the accuracy gain of 10.77%. Furthermore, the developed model outperforms most of the multiple biosensor based emotion recognition models with a significantly higher classification accuracy gain.

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title: Emotion recognition via random forest and galvanic skin response: Comparison of time based feature sets, window sizes and wavelet approaches
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title: Real-Time Epileptic Seizure Detection from EEG Signals via Random Subspace Ensemble Learning
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abstract: The use of the fast Fourier transform in power spectrum analysis is described. Principal advantages of this method are a reduction in the number of computations and in required core storage, and convenient application in nonstationarity tests. The method involves sectioning the record and averaging modified periodograms of the sections.

title: Feature Selection for Ensembles
abstract: The traditional motivation behind feature selection algorithms is to find the best subset of features for a task using one particular learning algonthm. Given the recent success of ensembles, however, we investigate the notion of ensemble feature selection in this paper. This task is harder than traditional feature selection in that one not only needs to find features germane to the learning task and learning algorithm, but one also needs to find a set of feature subsets that will promote disagreement among the ensemble's classifiers. In this paper, we present an ensemble feature selection approach that is based on genetic algorithms. Our algorithm shows improved performance over the popular and powerful ensemble approaches of AdaBoost and Bagging and demonstrates the utility of ensemble feature selection.

title: Improving software quality estimation by combining feature selection strategies with sampled ensemble learning
abstract: The efficiency (prediction accuracy) of a classification model is affected by the quality of training data. High dimensionality and class imbalance are two main problems that may cause low quality of training datasets, making data preprocessing a very important step for a classification problem. Feature (software metric) selection and data sampling are frequently used to overcome these problems. Feature selection (FS) is a process of selecting the most important attributes from the original dataset. Data sampling copes with class imbalance by adding/removing instances to/from training datasets. Another interesting method, called boosting (building multiple models, with each model tuned to work better on instances misclassifled by previous models), is found also effective for addressing the class imbalance problem. In this study, we investigate two types of FS approaches: individual FS and repetitive sampled FS. Following feature selection, models are built either using a plain learner or using a boosting algorithm, where random undersampling integrates with the AdaBoost algorithm. We focus on studying the impact of two FS methods (individual FS vs. repetitive sampled FS) and two model-building processes (boosting vs. plain learner) on software quality prediction. Six feature ranking techniques are examined in the experiment. The results demonstrate that the repetitive sampled FS generally has better performance than the individual FS technique when a plain learner is used for the subsequent learning process, and that boosting is more effective in improving classification performance than not using boosting.

title: Scikit-learn: Machine Learning in Python
abstract: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.

title: Emotion Recognition through Physiological Signals for Human-Machine Communication
abstract: The ability to recognize emotion is one of the hallmarks of emotion intelligence. This paper proposed to recognize emotion using physiological signals obtained from multiple subjects. IAPS (International Affective Picture System) images were used to elicit target emotions. Five physiological signals: Blood volume pulse (BVP), Electromyography (EMG), Skin Conductance (SC), Skin Temperature (SKT) and Respiration (RESP) were selected to extract 30 features for recognition. Two pattern classification methods, Fisher discriminant and SVM method are used and compared for emotional state classification. The experimental results indicate that the proposed method provides very stable and successful emotional classification performance as 92% over six emotional states.

title: Emotion recognition system using short-term monitoring of physiological signals
abstract: A physiological signal-based emotion recognition system is reported. The system was developed to operate as a user-independent system, based on physiological signal databases obtained from multiple subjects. The input signals were electrocardiogram, skin temperature variation and electrodermal activity, all of which were acquired without much discomfort from the body surface, and can reflect the influence of emotion on the autonomic nervous system. The system consisted of preprocessing, feature extraction and pattern classification stages. Preprocessing and feature extraction methods were devised so that emotion-specific characteristics could be extracted from short-segment signals. Although the features were carefully extracted, their distribution formed a classification problem, with large overlap among clusters and large variance within clusters. A support vector machine was adopted as a pattern classifier to resolve this difficulty. Correct-classification ratios for 50 subjects were 78.4% and 61.8%, for the recognition of three and four categories, respectively.

title: An ultra-wearable, wireless, low power ECG monitoring system
abstract: Wearable electrocardiograph (ECG) monitoring systems today use electrodes that require skin preparation in advance, and require pastes or gels to make electrical contact to the skin. Moreover, they are not suitable for subjects at high levels of activity due to high noise spikes that can appear in the data. To address these problems, a new class of miniature, ultra low noise, capacitive sensor that does not require direct contact to the skin, and has comparable performance to gold standard ECG electrodes, has been developed. This paper presents a description and evaluation of a wireless version of a system based on these innovative ECG sensors. We use a wearable and ultra low power wireless sensor node called Eco. Experimental results show that the wireless interface will add minimal size and weight to the system while providing reliable, untethered operation.

title: Unobtrusive Sensing of Emotions (USE)
abstract: Emotions are acknowledged as a crucial element for artificial intelligence; this is, as is illustrated, no different for Ambient Intelligence (AmI). Unobtrusive Sensing of Emotions (USE) is introduced to enrich AmI with empathic abilities. USE coins the combination of speech and the electrocardiogram (ECG) as a powerful and unique combination to unravel people's emotions. In a controlled study, 40 people watched film scenes, in either an office or a home-like setting. It is shown that, when people's gender is taken into account, both heart rate variability (derived from the ECG) and the standard deviation of the fundamental frequency of speech indicate people's experienced valence and arousal, in parallel. As such, both measures validate each other. Thus, through USE reliable cues can be derived that indicate the emotional state of people, in particular when also people's environment is taken into account. Since all this is crucial for both AI and true AmI, this study provides a first significant leap forward in making AmI a success.

title: Development of a Wireless Capacitive Sensor for Ambulatory ECG Monitoring over Clothes
abstract: We present a wireless capacitive sensor for ambulatory ECG monitoring. The device is capable of detecting electrocardiographic potential through underwear. It is based on capacitive coupling involving the electrode, the underwear, and the skin. Validation of the system revealed the following: (1) The frequency response of the device with the underwear revealed that the gain of the detected signal was reduced, but distortion of the signal within the pass-band (from 6 to 45 Hz) was minimal even with the underwear. (2) The signal displayed a periodic waveform synchronized with the simultaneously recorded ECG, even when the underwear was inserted between the electrode and the skin. (3) Although the signal was seemed deteriorated slightly while walking, R-waves were recognized easily. Although there is still room for improvement in terms of its practical use, the proposed approach appears promising for application to ambulatory ECG monitoring.