In addition, the variety of cardiac pathologies (more than 20 types) is a problem in diagnosing the disease. However, this technique can present difficulties, such as the high cost of private health services or the time the public health system takes to refer the patient to a cardiologist. When working with electrocardiogram signals, they can be represented in the time and frequency domains (one-dimensional signals). Usually, cardiac pathologies are detected using one-dimensional electrocardiogram signals or two-dimensional images. Wide validation over five different databases proves the robustness of this method. The proposed method is based on dynamic thresholding and simple decision rules, which makes this method computationally efficient.
The overall sensitivity rate of 99.70% and positive predictivity rate of 99.69% have been achieved. The proposed method was applied to Massachusetts Institute of Technology-Beth Israel Hospital Arrhythmia Database (MIT-BIH AD), Fantasia Database (FTD), European ST-T Database (ESTD), MIT-BIH Noise Stress Test Database (NSTD), and Direct Fetal ECG Database (FTD) for its evaluation and validation. Kurtosis coefficient computation is used for discarding prominent T-wave and further this technique located the QRS-complex accurately in the raw ECG signal. The threshold value was automatically updated using the previous threshold value, R-peak amplitude, RR-interval, and RR-intervals means.
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Then baseline and root mean square (RMS) value of first three seconds of the signal are used for initial thresholding, later dynamic thresholding process was utilized to update the threshold value after the detection of four R-peaks. Next, the ECG is enhanced to the power third after multiplication followed by normalization and moving average process to retain dynamic QRS-complex. In this paper, A window-based FIR filter is used to eliminate the high-frequency noise. Abnormal and varying peaks, baseline wander and other noise are the main challenges in accurate QRS-complex detection. QRS-complex detection is a primitive step in the detection of cardiac disorder using electrocardiogram (ECG). Numerical examples from synthetic data and natural phenomena are given to demonstrate the power of this new method.
For complex signals that are a superposition of several MIMFs with well-differentiated phase functions $\phi(t)$, a new recursive scheme based on Gauss-Seidel iteration and diffeomorphisms is proposed to identify these MIMFs, their multiresolution expansion coefficients, and shape function series.
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OL20704992W Page_number_confidence 95.87 Pages 414 Partner Innodata Pdf_module_version 0.0.15 Ppi 360 Rcs_key 24143 Republisher_date 20211021122204 Republisher_operator Republisher_time 565 Scandate 20211019221921 Scanner Scanningcenter cebu Scribe3_search_catalog isbn Scribe3_search_id 9781496306906 Tts_version 4.This paper proposes the \emph$ provide innovative features for adaptive time series analysis. Access-restricted-item true Addeddate 14:25:53 Associated-names Coviello, Jessica Shank, editor Boxid IA40268718 Camera USB PTP Class Camera Collection_set printdisabled External-identifier
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