Dynamic Segmented Beat Modulation Method for Denoising ECG Data from Wearable Sensors

Nasim, Amnah; Pinti, Federica; Gentili, Andrea; Belli, Alberto; Palma, Lorenzo; Pierleoni, Paola

doi:10.1109/issi47111.2019.9043650

Cited by 1 publication

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“…In the published articles, SBMM does not include a differentiating function for normal sinus beats and abnormal beats, hence it is a template-based denoising method with proven applicability to the normal sinus rhythm only [18,19,[22][23][24][25]. The current work overcomes this limitation of SBMM and adds a classification function based on a convolutional neural network (CNN) to classify the beats into three beat classes selected among the five beat classes defined by the American National Standards Institute (ANSI) and the Association for the Advancement of Medical Instrumentation (AAMI) standard (ANSI/AAMI EC57:1998) [26] and further apply SBMM for the denoising of arrhythmic beats.…”

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confidence: 99%

Extended Segmented Beat Modulation Method for Cardiac Beat Classification and Electrocardiogram Denoising

et al. 2020

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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.

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