Abstract
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.
Highlights
Cardiovascular diseases are the main cause of death in the world [1,2]
This paper has evaluated whether the tandem feature extraction approach is useful for ECG arrhythmia identification
Tandem features have been integrated within a tandem feature extraction approach for a Gaussian mixture model (GMM)-based arrhythmia identification system
Summary
Cardiovascular diseases are the main cause of death in the world [1,2]. 18 million people died from cardiovascular diseases in 2019, representing 33% of all deaths worldwide. The electrocardiogram (ECG) is a fundamental test in the clinical routine for the diagnosis and monitoring of cardiovascular diseases. In the ECG, a lead is a measure of the electrical activity of the heart given by the difference in potential between two points. This difference can be measured between two electrodes (bipolar lead) or between a virtual point and an electrode (monopolar lead). Different leads provide different perspectives of the electrical stimulus, and complementary information
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