FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2938507687> ?p ?o ?g. }

• W2938507687 endingPage "156" @default.
• W2938507687 startingPage "145" @default.
• W2938507687 abstract "Electrocardiogram (ECG) is a diagnostic tool for recording electrical activities of the human heart non-invasively. It is detected by electrodes placed on the surface of the skin in a conductive medium. In medical applications, ECG is used by cardiologists to observe heart anomalies (cardiovascular diseases) such as abnormal heart rhythms, heart attacks, effects of drug dosage on subject's heart and knowledge of previous heart attacks. Recorded ECG signal is generally corrupted by various types of noise/distortion such as cardiac (isoelectric interval, prolonged depolarization and atrial flutter) or extra cardiac (respiration, changes in electrode position, muscle contraction and power line noise). These factors hide the useful information and alter the signal characteristic due to low Signal-to-Noise Ratio (SNR). In such situations, any failure to judge the ECG signal correctly may result in a delay in the treatment and harm a subject (patient) health. Therefore, appropriate pre-processing technique is necessary to improve SNR to facilitate better treatment to the subject. Effects of different pre-processing techniques on ECG signal analysis (based on R-peaks detection) are compared using various Figures of Merit (FoM) such as sensitivity (Se), accuracy (Acc) and detection error rate (DER) along with SNR. In this research article, a new fractional wavelet transform (FrWT) has been proposed as a pre-processing technique in order to overcome the disadvantages of other existing commonly used techniques viz. wavelet transform (WT) and the fractional Fourier transform (FrFT). The proposed FrWT technique possesses the properties of multiresolution analysis and represents signal in the fractional domain which consists of representation in terms of rotation of signals in the time–frequency plane. In the literature, ECG signal analysis has been improvised using statistical pre-processing techniques such as principal component analysis (PCA), and independent component analysis (ICA). However, both PCA and ICA are prone to suffer from slight alterations in either signal or noise, unless the basis functions are prepared with a worldwide set of ECG. Independent Principal Component Analysis (IPCA) has been used to overcome this shortcoming of PCA and ICA. Therefore, in this paper three techniques viz. FrFT, FrWT and IPCA are selected for comparison in pre-processing of ECG signals. The selected methods have been evaluated on the basis of SNR, Se, Acc and DER of the detected ECG beats. FrWT yields the best results among all the methods considered in this paper; 34.37dB output SNR, 99.98% Se, 99.96% Acc, and 0.036% DER. These results indicate the quality of biology-related information retained from the pre-processed ECG signals for identifying different heart abnormalities. Correct analysis of the acquired ECG signal is the main challenge for cardiologist due to involvement of various types of noises (high and low frequency). Twenty two real time ECG records have been evaluated based on various FoM such as SNR, Se, Acc and DER for the proposed FrWT and existing FrFT and IPCA preprocessing techniques. Acquired real-time ECG database in normal and disease situations is used for the purpose. The values of FoMs indicate high SNR and better detection of R-peaks in a ECG signal which is important for the diagnosis of cardiovascular disease. The proposed FrWT outperforms all other techniques and holds both analytical attributes of the actual ECG signal and alterations in the amplitudes of various ECG waveforms adequately. It also provides signal portrayals in the time-fractional-frequency plane with low computational complexity enabling their use practically for versatile applications." @default.
• W2938507687 created "2019-04-25" @default.
• W2938507687 creator A5011734370 @default.
• W2938507687 creator A5084545093 @default.
• W2938507687 date "2019-06-01" @default.
• W2938507687 modified "2023-10-12" @default.
• W2938507687 title "A Comparison of ECG Signal Pre-processing Using FrFT, FrWT and IPCA for Improved Analysis" @default.
• W2938507687 cites W1964348390 @default.
• W2938507687 cites W1968897584 @default.
• W2938507687 cites W1972858380 @default.
• W2938507687 cites W1973387901 @default.
• W2938507687 cites W1978142137 @default.
• W2938507687 cites W1980881714 @default.
• W2938507687 cites W1982626236 @default.
• W2938507687 cites W1983100643 @default.
• W2938507687 cites W1993609051 @default.
• W2938507687 cites W1998718579 @default.
• W2938507687 cites W2005728158 @default.
• W2938507687 cites W2012991100 @default.
• W2938507687 cites W2021044602 @default.
• W2938507687 cites W2027476193 @default.
• W2938507687 cites W2028114000 @default.
• W2938507687 cites W2028682334 @default.
• W2938507687 cites W2035381272 @default.
• W2938507687 cites W2046832318 @default.
• W2938507687 cites W2049001544 @default.
• W2938507687 cites W2053950896 @default.
• W2938507687 cites W2056105877 @default.
• W2938507687 cites W2066980082 @default.
• W2938507687 cites W2071441260 @default.
• W2938507687 cites W2074617333 @default.
• W2938507687 cites W2078852264 @default.
• W2938507687 cites W2080126518 @default.
• W2938507687 cites W2109550800 @default.
• W2938507687 cites W2112287441 @default.
• W2938507687 cites W2115340664 @default.
• W2938507687 cites W2133032394 @default.
• W2938507687 cites W2145487065 @default.
• W2938507687 cites W2152606053 @default.
• W2938507687 cites W2153198498 @default.
• W2938507687 cites W2153498619 @default.
• W2938507687 cites W2157600317 @default.
• W2938507687 cites W2160986881 @default.
• W2938507687 cites W2162273778 @default.
• W2938507687 cites W2163621334 @default.
• W2938507687 cites W2192244518 @default.
• W2938507687 cites W2221831393 @default.
• W2938507687 cites W2253210145 @default.
• W2938507687 cites W2285072859 @default.
• W2938507687 cites W22966109 @default.
• W2938507687 cites W2487714310 @default.
• W2938507687 cites W2500580066 @default.
• W2938507687 cites W2512313399 @default.
• W2938507687 cites W2515422479 @default.
• W2938507687 cites W2523688725 @default.
• W2938507687 cites W2527499963 @default.
• W2938507687 cites W2753261156 @default.
• W2938507687 cites W2768497671 @default.
• W2938507687 cites W2800704433 @default.
• W2938507687 doi "https://doi.org/10.1016/j.irbm.2019.04.003" @default.
• W2938507687 hasPublicationYear "2019" @default.
• W2938507687 type Work @default.
• W2938507687 sameAs 2938507687 @default.
• W2938507687 citedByCount "37" @default.
• W2938507687 countsByYear W29385076872019 @default.
• W2938507687 countsByYear W29385076872020 @default.
• W2938507687 countsByYear W29385076872021 @default.
• W2938507687 countsByYear W29385076872022 @default.
• W2938507687 countsByYear W29385076872023 @default.
• W2938507687 crossrefType "journal-article" @default.
• W2938507687 hasAuthorship W2938507687A5011734370 @default.
• W2938507687 hasAuthorship W2938507687A5084545093 @default.
• W2938507687 hasConcept C104267543 @default.
• W2938507687 hasConcept C115961682 @default.
• W2938507687 hasConcept C153180895 @default.
• W2938507687 hasConcept C154945302 @default.
• W2938507687 hasConcept C164705383 @default.
• W2938507687 hasConcept C196216189 @default.
• W2938507687 hasConcept C199360897 @default.
• W2938507687 hasConcept C2778134438 @default.
• W2938507687 hasConcept C2779843651 @default.
• W2938507687 hasConcept C28490314 @default.
• W2938507687 hasConcept C41008148 @default.
• W2938507687 hasConcept C47432892 @default.
• W2938507687 hasConcept C554190296 @default.
• W2938507687 hasConcept C71924100 @default.
• W2938507687 hasConcept C76155785 @default.
• W2938507687 hasConcept C99498987 @default.
• W2938507687 hasConceptScore W2938507687C104267543 @default.
• W2938507687 hasConceptScore W2938507687C115961682 @default.
• W2938507687 hasConceptScore W2938507687C153180895 @default.
• W2938507687 hasConceptScore W2938507687C154945302 @default.
• W2938507687 hasConceptScore W2938507687C164705383 @default.
• W2938507687 hasConceptScore W2938507687C196216189 @default.
• W2938507687 hasConceptScore W2938507687C199360897 @default.
• W2938507687 hasConceptScore W2938507687C2778134438 @default.
• W2938507687 hasConceptScore W2938507687C2779843651 @default.
• W2938507687 hasConceptScore W2938507687C28490314 @default.

• next