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An electrocardiography (ECG) machine is an essential piece of medical equipment used to monitor the electrical activity of the heart. It operates by detecting small electrical changes on the skin that result from the depolarization of the heart muscle during each heartbeat. However, these signals are in the microvolt range and can be easily overwhelmed by noise or interference.
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Augmenting ECG Data with Multiple Filters for a Better Emotion Recognition System.

Muhammad Anas Hasnul1, Nor Azlina Ab Aziz1, Azlan Abd Aziz1

  • 1Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia.

Arabian Journal for Science and Engineering
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Summary
This summary is machine-generated.

This study introduces a new method to improve emotion recognition using electrocardiogram (ECG) data. The technique enhances small datasets, significantly boosting the accuracy of emotion classification systems.

Keywords:
Affective computingAugmentationElectrocardiogramEmotion recognitionFilterMachine learning

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Area of Science:

  • Physiological computing
  • Affective computing
  • Biomedical signal processing

Background:

  • Unimodal electrocardiogram (ECG)-based emotion recognition systems (ERS) offer lower costs but face challenges due to limited, small-sample-size datasets.
  • The potential for mass adoption of ECG-based ERS is high, driven by the proliferation of wearable and mobile ECG devices.
  • Current research in ECG-based ERS is hindered by a scarcity of quality, large-scale affective datasets.

Purpose of the Study:

  • To address the data scarcity issue in unimodal ECG-based ERS.
  • To propose and evaluate a novel multi-filtering augmentation technique for ECG signals.
  • To enhance the performance of machine learning classifiers for emotion recognition using augmented ECG data.

Main Methods:

  • A novel multi-filtering augmentation technique was developed to increase the sample size of ECG data by applying various data cleaning methods.
  • Three small, emotion-labelled ECG datasets were utilized to assess the augmentation technique.
  • The effectiveness of the augmentation was evaluated by measuring the classification accuracy of five machine learning algorithms: k-nearest neighbours (KNN), support vector machine, decision tree, random forest, and multilayer perceptron.

Main Results:

  • The proposed multi-filtering augmentation technique led to significant performance improvements across all tested datasets and classifiers.
  • The k-nearest neighbours (KNN) classifier demonstrated the most substantial improvement, achieving classification accuracies exceeding 90% with augmented data.
  • The study confirmed the efficacy of the augmentation method in enhancing the robustness and accuracy of ECG-based emotion recognition.

Conclusions:

  • The novel multi-filtering augmentation technique effectively addresses the data limitations in unimodal ECG-based ERS.
  • This approach significantly improves the accuracy of emotion recognition, with KNN showing remarkable performance gains.
  • The findings suggest a viable pathway for developing more accurate and widely adoptable ECG-based emotion recognition systems.