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- W2364783896 abstract "In the process of ECG signal data acquisition,it was easy to involve white noise. Based on morphological component analysis( MCA),a novel method for ECG de-noising was proposed. The method uses a variety of forms of decomposition dictionaries. MCA is a new method which takes advantage of the sparse representation of structured data in overcomplete dictionaries to separate features in the data based on the diversity of their morphology. According to the morphological differences in ECG waveform characteristics,Discrete Cosine transform( DCT) dictionary was used to sparsely represent ECG smoothing component( such as P wave), and undecimated Wavelet Transform( UWT)dictionary was used to sparsely represent mutations component( such as QRS wave),then the white noise component could be filtered out. Through using the MIT-BIH database of ECG signal for simulation,conclusion shows that the method proposed in this paper performs better than wavelet de-noising method significantly,not only in suppressing white noise effectively,but also in preserving the primary characteristics waveform of ECG." @default.
- W2364783896 created "2016-06-24" @default.
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- W2364783896 date "2015-01-01" @default.
- W2364783896 modified "2023-09-23" @default.
- W2364783896 title "Study on ECG Signal De-noising Based on Morphological Component Analysis" @default.
- W2364783896 hasPublicationYear "2015" @default.
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