Abstract
Beat classification and denoising are two challenging and fundamental operations when processing digital electrocardiograms (ECG). This paper proposes the extended segmented beat modulation method (ESBMM) as a tool for automatic beat classification and ECG denoising. ESBMM includes four main steps: (1) beat identification and segmentation into PQRS and TU segments; (2) wavelet-based time-frequency feature extraction; (3) convolutional neural network-based classification to discriminate among normal (N), supraventricular (S), and ventricular (V) beats; and (4) a template-based denoising procedure. ESBMM was tested using the MIT–BIH arrhythmia database available at Physionet. Overall, the classification accuracy was 91.5% while the positive predictive values were 92.8%, 95.6%, and 83.6%, for N, S, and V classes, respectively. The signal-to-noise ratio improvement after filtering was between 0.15 dB and 2.66 dB, with a median value equal to 0.99 dB, which is significantly higher than 0 (p < 0.05). Thus, ESBMM proved to be a reliable tool to classify cardiac beats into N, S, and V classes and to denoise ECG tracings.
Highlights
Cardiovascular diseases (CVDs) continue to be the leading cause of death worldwide with a reported increase in the CVD mortality rate from 12.3 million in the year 1990 to approximately17.9 million in the year 2016
Electrocardiogram (ECG) is both noninvasive and the most common medical test among the procedures used by clinicians to detect and analyze cardiac arrhythmia
The current work proposes the extended segmented beat modulation method (ESBMM) as an extended and improved version of the existing segmented beat modulation method (SBMM), which is able to denoise ECG tracings characterized by sinus as well as nonsinus rhythm
Summary
Cardiovascular diseases (CVDs) continue to be the leading cause of death worldwide with a reported increase in the CVD mortality rate from 12.3 million in the year 1990 to approximately17.9 million in the year 2016. Health Organization (WHO) attributes the major causes of CVDs to behavioral factors like smoking, excessive alcohol consumption, physical inactivity, and nutritional/dietary deficiencies, in addition to pre-existing medical conditions such as diabetes, hypertension, hyperlipidaemia, or having a family history of CVD. Identifying those at highest risk of CVDs is vital to ensure that the patients receive timely and appropriate treatment, as 80% of premature heart diseases and strokes are said to be preventable [1,2]. A cardiac arrhythmia is defined as any deviation (regular or irregular/sustained or nonsustained) from normal sinus rhythm
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