2016
DOI: 10.3233/idt-160264
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ECG Morphological Marking using Discrete Wavelet Transform

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Cited by 6 publications
(5 citation statements)
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“…The system based on convolutional ANN proposed in this paper showed results slightly superior to the previous system based on pattern recognition [2] and similar to the work [20].…”
Section: Resultssupporting
confidence: 74%
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“…The system based on convolutional ANN proposed in this paper showed results slightly superior to the previous system based on pattern recognition [2] and similar to the work [20].…”
Section: Resultssupporting
confidence: 74%
“…Based on the fact that the normal cardiocycle shape changes slowly over time and each current cardiocycle can be distorted by artifacts and noises, it makes sense to recognize the characteristic segments of average cardiocycle shape. The average cardiocycle of several (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) cardiocycles having a high correlation coefficient with each other is calculated. Then the selected cardiocycles are averaged pointwise.…”
Section: The Previous Version Of the Cardiac Cycle Morphology Analysismentioning
confidence: 99%
“…Related literature review of this study almost 67 % paper used all using wavelet at the extraction stage of its feature. With the use of Discrete Wavelet Transform-DWT method consists of 50% journals used using such methods include: [15], [18], [20], [24][25], [28][29][30][31][32], [35], [38], and [41]. Continuous Wavelet Transform (CWT) include: [17], [21], [23], and [39][40].…”
Section: B Feature Extractionmentioning
confidence: 99%
“…Some researchers are using methods other than Wavelet at the extraction stage of the feature as seen in Table II. PCA [24] Filtering raw signal and extraction of beats [25] P & R peak detection [27] Denoising using notch filter [29] Noise Repair [30] Denoising [31] Key Features and Frequencies [32] Denoising [38] Baseline Drift Removal and Denoising, Noise Removal [39] Data Denoising and Data Segmentation [40] Noise Identification [41] Mean [41]…”
Section: B Feature Extractionmentioning
confidence: 99%
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