A Tandem Feature Extraction Approach for Arrhythmia Identification

• Published in: Electronics (Basel) Vol. 10; no. 8; p. 976
• Main Authors: Tejedor, Javier, Marquez, David G., Garcia, Constantino A., Otero, Abraham
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 19.04.2021
• Subjects: Arrhythmia; Cardiac arrhythmia; Classification; Conditional probability; Discriminant analysis; Electrocardiography; Electroencephalography; Feature extraction; Health care facilities; Heart; Heart diseases; Identification; Model accuracy; Morphology; Neural networks; Patients; Pattern recognition; Principal components analysis; Probabilistic models; Recording; Standard deviation; Statistical analysis; Support vector machines; Voice recognition; Wavelet transforms; Wearable computers
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics10080976

Heart disease is currently the leading cause of death in the world. The electrocardiogram (ECG) is the recording of the electrical activity generated by the heart. Its low cost and simplicity have made it an essential test for monitoring heart disease, especially for the identification of arrhythmias. With the advances in electronic technology, there are nowadays sensors that enable the recording of the ECG during the daily life of the patient and its wireless transmission to healthcare facilities. This type of information has a great potential to detect cardiac diseases in their early stages and to permit early interventions before the patient’s health deteriorates. However, to usefully exploit the large volume of information obtained from ambulatory ECG, pattern recognition techniques that are capable of automatically analyzing it are required. Tandem feature extraction techniques have proven to be useful for the processing of physiological parameters such as the electroencephalogram (EEG) and speech. However, to the best of our knowledge, they have never been applied to the ECG. In this paper, the utility of tandem feature extraction for the identification of arrhythmias is studied. The coefficients of a regression using Hermite functions are used to create a feature vector that represents the heartbeat. A multiple-layer perceptron (MLP) is trained using these features and its posterior probability outputs are used to extend the original feature vector. Finally, a Gaussian mixture model (GMM) is trained on the extended feature vectors, which is then used in a GMM-based arrhythmia identification system. This approach has been validated using the MIT-BIH Arrhythmia database. The accuracy of the Gaussian mixture model increased by 15.8% when applied over the extended feature vectors, compared to its application over the original feature vectors, showing the potential of tandem feature extraction for ECG analysis and arrhythmia identification.