Abstract
Deep Neural Networks (DNNs) are state-of-the-art machine learning algorithms, the application of which in electrocardiographic signals is gaining importance. So far, limited studies or optimizations using DNN can be found using ECG databases. To explore and achieve effective ECG recognition, this paper presents a convolutional neural network to perform the encoding of a single QRS complex with the addition of entropy-based features. This study aims to determine what combination of signal information provides the best result for classification purposes. The analyzed information included the raw ECG signal, entropy-based features computed from raw ECG signals, extracted QRS complexes, and entropy-based features computed from extracted QRS complexes. The tests were based on the classification of 2, 5, and 20 classes of heart diseases. The research was carried out on the data contained in a PTB-XL database. An innovative method of extracting QRS complexes based on the aggregation of results from established algorithms for multi-lead signals using the k-mean method, at the same time, was presented. The obtained results prove that adding entropy-based features and extracted QRS complexes to the raw signal is beneficial. Raw signals with entropy-based features but without extracted QRS complexes performed much worse.
Highlights
The analysis of electrocardiographic signals (ECG) is one of the most important steps in diagnosing cardiac disorders
The entropy-based features extracted from QRS complexes turned out to be better at encoding class-specific information compared to entropy measures of the raw signal
The paper presents the use of PTB-XL in the operation of a convolutional neural network, which uses distinguished QRS complexes and entropy-based features
Summary
The analysis of electrocardiographic signals (ECG) is one of the most important steps in diagnosing cardiac disorders. An electrocardiogram is a commonly employed non-invasive physiological signal used for screening and diagnosing cardiovascular disease. The most common reference point for assessing ECG signals is the QRS complex and detection of R-waves [2,3,4,5,6]. These studies are complemented by the R–R distance assessment and heart rate analysis as an additional feature of the signal [7,8,9,10,11,12]. It should be noted that these methods usually use databases such as Physionet, PhysioBank, and PhysioToolkit datasets to confirm their performance [13]
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