FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3033380047> ?p ?o ?g. }

• W3033380047 abstract "ElCTROCARDIOGRAM (ECG) is a widely utilized tool for the diagnosis of cardiac ailments. However, considering the rapid growth of population as well as poor doctor to patient ratio, the computer-based automated monitoring of ECG signal is efficient as it provides fast processing and precise long term monitoring with less human effort. The main objective of this research work is to improve the performance of a computer-based automated ECG monitoring system incorporating efficient schemes for ECG signal quality assessment and processing, which can enhance the reliability of the medical diagnosis. Some of the existing literature on signal quality assessment (SQA) discuss ECG SQA schemes employing QRS complex morphology and R-peak information based features. But the performance of such techniques degrades due to improper detection of R-peaks. This research attempts to develop an efficient wavelet sub-bands features based ECG SQA scheme. Various wavelet sub-band level signal features are applied to machine learning-based classifier for estimating the ECG signal quality. Further, the filtering of ECG records is essential for precise identification and detection of local components when a considerable amount of noise in ECG records affects the low amplitude P and T waves significantly. Hence this research proposes an efficient ECG denoising methodology based on both empirical mode decomposition (EMD) and adaptive switching mean filter (ASMF) approach. Unlike conventional EMD based techniques, the proposed method reduces the effect of noises simultaneously preserving the high-frequency QRS complex information. For typical low-frequency noises, this method does not meet the achievable performance. Hence, this research is motivated towards developing ECG signal enhancement scheme in a dictionary learning (DL) based sparse representation framework. The proposed method completely enhances the ECG signal quality by removing both low and high-frequency noises. Continuous monitoring of ECG records for better medical diagnosis in telemetry needs a large memory space for storage and also it requires more transmission power. A low power based solution utilising for ECG compression based on beat type dictionary based compressed sensing (CS) scheme is proposed which offers high-quality signal recovery avoiding the training stage of individual ECG record. Effective detection of R-peaks in ECG plays a vital role in the extraction of clinical features in a computer-based automated system. Hence, a wavelet-based R-peak detection scheme using the Hilbert transform is proposed. The efficacy of all the proposed approaches is evaluated through MATLAB simulation based studies by comparing with the existing schemes considering ECG records of standard databases." @default.
• W3033380047 created "2020-06-12" @default.
• W3033380047 creator A5036318796 @default.
• W3033380047 date "2019-01-01" @default.
• W3033380047 modified "2023-09-26" @default.
• W3033380047 title "Development of Some Efficient Schemes for ECG Signal Quality Assessment and Processing" @default.
• W3033380047 hasPublicationYear "2019" @default.
• W3033380047 type Work @default.
• W3033380047 sameAs 3033380047 @default.
• W3033380047 citedByCount "0" @default.
• W3033380047 crossrefType "dissertation" @default.
• W3033380047 hasAuthorship W3033380047A5036318796 @default.
• W3033380047 hasConcept C104267543 @default.
• W3033380047 hasConcept C106131492 @default.
• W3033380047 hasConcept C115961682 @default.
• W3033380047 hasConcept C124101348 @default.
• W3033380047 hasConcept C153180895 @default.
• W3033380047 hasConcept C154945302 @default.
• W3033380047 hasConcept C163294075 @default.
• W3033380047 hasConcept C25570617 @default.
• W3033380047 hasConcept C28490314 @default.
• W3033380047 hasConcept C31972630 @default.
• W3033380047 hasConcept C41008148 @default.
• W3033380047 hasConcept C47432892 @default.
• W3033380047 hasConcept C84462506 @default.
• W3033380047 hasConcept C9390403 @default.
• W3033380047 hasConcept C95623464 @default.
• W3033380047 hasConcept C99498987 @default.
• W3033380047 hasConceptScore W3033380047C104267543 @default.
• W3033380047 hasConceptScore W3033380047C106131492 @default.
• W3033380047 hasConceptScore W3033380047C115961682 @default.
• W3033380047 hasConceptScore W3033380047C124101348 @default.
• W3033380047 hasConceptScore W3033380047C153180895 @default.
• W3033380047 hasConceptScore W3033380047C154945302 @default.
• W3033380047 hasConceptScore W3033380047C163294075 @default.
• W3033380047 hasConceptScore W3033380047C25570617 @default.
• W3033380047 hasConceptScore W3033380047C28490314 @default.
• W3033380047 hasConceptScore W3033380047C31972630 @default.
• W3033380047 hasConceptScore W3033380047C41008148 @default.
• W3033380047 hasConceptScore W3033380047C47432892 @default.
• W3033380047 hasConceptScore W3033380047C84462506 @default.
• W3033380047 hasConceptScore W3033380047C9390403 @default.
• W3033380047 hasConceptScore W3033380047C95623464 @default.
• W3033380047 hasConceptScore W3033380047C99498987 @default.
• W3033380047 hasLocation W30333800471 @default.
• W3033380047 hasOpenAccess W3033380047 @default.
• W3033380047 hasPrimaryLocation W30333800471 @default.
• W3033380047 hasRelatedWork W2313235157 @default.
• W3033380047 hasRelatedWork W2415045661 @default.
• W3033380047 hasRelatedWork W2518769093 @default.
• W3033380047 hasRelatedWork W2557174488 @default.
• W3033380047 hasRelatedWork W2597701414 @default.
• W3033380047 hasRelatedWork W2605629864 @default.
• W3033380047 hasRelatedWork W2749432858 @default.
• W3033380047 hasRelatedWork W2769006773 @default.
• W3033380047 hasRelatedWork W2784247182 @default.
• W3033380047 hasRelatedWork W2790497637 @default.
• W3033380047 hasRelatedWork W2791450091 @default.
• W3033380047 hasRelatedWork W2803039931 @default.
• W3033380047 hasRelatedWork W2807476393 @default.
• W3033380047 hasRelatedWork W2939641484 @default.
• W3033380047 hasRelatedWork W2967827507 @default.
• W3033380047 hasRelatedWork W2970451324 @default.
• W3033380047 hasRelatedWork W3003543939 @default.
• W3033380047 hasRelatedWork W3186913907 @default.
• W3033380047 hasRelatedWork W3210612208 @default.
• W3033380047 hasRelatedWork W3131734623 @default.
• W3033380047 isParatext "false" @default.
• W3033380047 isRetracted "false" @default.
• W3033380047 magId "3033380047" @default.
• W3033380047 workType "dissertation" @default.