Performance Evaluation of Time-Frequency Distributions for ECG Signal Analysis.
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| Title: | Performance Evaluation of Time-Frequency Distributions for ECG Signal Analysis. |
|---|---|
| Authors: | Hussein, Ahmed Faeq1,2, Hashim, Shaiful Jahari1 sjh@upm.edu.my, Aziz, Ahmad Fazli Abdul3, Rokhani, Fakhrul Zaman1, Adnan, Wan Azizun Wan1 |
| Source: | Journal of Medical Systems. Jan2018, Vol. 42 Issue 1, p1-1. 16p. |
| Subjects: | Electrocardiography, Probability theory, Research funding, Signal processing |
| Abstract: | The non-stationary and multi-frequency nature of biomedical signal activities makes the use of time-frequency distributions (TFDs) for analysis inevitable. Time-frequency analysis provides simultaneous interpretations in both time and frequency domain enabling comprehensive explanation, presentation and interpretation of electrocardiogram (ECG) signals. The diversity of TFDs and specific properties for each type show the need to determine the best TFD for ECG analysis. In this study, a performance evaluation of five TFDs in term of ECG abnormality detection is presented. The detection criteria based on extracted features from most important ECG signal components (QRS) to detect normal and abnormal cases. This is achieved by estimating its energy concentration magnitude using the TFDs. The TFDs analyse ECG signals in one-minute interval instead of conventional time domain approach that analyses based on beat or frame containing several beats. The MIT-BIH normal sinus rhythm ECG database total records of 18 long-term ECG sampled at 128 Hz have been analysed. The tested TFDs include Dual-Tree Wavelet Transform, Spectrogram, Pseudo Wigner-Ville, Choi-Williams, and Born-Jordan. Each record is divided into one-minute slots, which is not considered previously, and analysed. The sample periods (slots) are randomly selected ten minutes interval for each record. This result with 99.44% detection accuracy for 15,735 ECG beats shows that Choi-Williams distribution is most reliable to be used for heart problem detection especially in automated systems that provide continuous monitoring for long time duration. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Medical Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 127145166 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Performance Evaluation of Time-Frequency Distributions for ECG Signal Analysis. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hussein%2C+Ahmed+Faeq%22">Hussein, Ahmed Faeq</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Hashim%2C+Shaiful+Jahari%22">Hashim, Shaiful Jahari</searchLink><relatesTo>1</relatesTo><i> sjh@upm.edu.my</i><br /><searchLink fieldCode="AR" term="%22Aziz%2C+Ahmad+Fazli+Abdul%22">Aziz, Ahmad Fazli Abdul</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Rokhani%2C+Fakhrul+Zaman%22">Rokhani, Fakhrul Zaman</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Adnan%2C+Wan+Azizun+Wan%22">Adnan, Wan Azizun Wan</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Medical+Systems%22">Journal of Medical Systems</searchLink>. Jan2018, Vol. 42 Issue 1, p1-1. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Electrocardiography%22">Electrocardiography</searchLink><br /><searchLink fieldCode="DE" term="%22Probability+theory%22">Probability theory</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The non-stationary and multi-frequency nature of biomedical signal activities makes the use of time-frequency distributions (TFDs) for analysis inevitable. Time-frequency analysis provides simultaneous interpretations in both time and frequency domain enabling comprehensive explanation, presentation and interpretation of electrocardiogram (ECG) signals. The diversity of TFDs and specific properties for each type show the need to determine the best TFD for ECG analysis. In this study, a performance evaluation of five TFDs in term of ECG abnormality detection is presented. The detection criteria based on extracted features from most important ECG signal components (QRS) to detect normal and abnormal cases. This is achieved by estimating its energy concentration magnitude using the TFDs. The TFDs analyse ECG signals in one-minute interval instead of conventional time domain approach that analyses based on beat or frame containing several beats. The MIT-BIH normal sinus rhythm ECG database total records of 18 long-term ECG sampled at 128 Hz have been analysed. The tested TFDs include Dual-Tree Wavelet Transform, Spectrogram, Pseudo Wigner-Ville, Choi-Williams, and Born-Jordan. Each record is divided into one-minute slots, which is not considered previously, and analysed. The sample periods (slots) are randomly selected ten minutes interval for each record. This result with 99.44% detection accuracy for 15,735 ECG beats shows that Choi-Williams distribution is most reliable to be used for heart problem detection especially in automated systems that provide continuous monitoring for long time duration. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Medical Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10916-017-0871-8 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 1 Subjects: – SubjectFull: Electrocardiography Type: general – SubjectFull: Probability theory Type: general – SubjectFull: Research funding Type: general – SubjectFull: Signal processing Type: general Titles: – TitleFull: Performance Evaluation of Time-Frequency Distributions for ECG Signal Analysis. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hussein, Ahmed Faeq – PersonEntity: Name: NameFull: Hashim, Shaiful Jahari – PersonEntity: Name: NameFull: Aziz, Ahmad Fazli Abdul – PersonEntity: Name: NameFull: Rokhani, Fakhrul Zaman – PersonEntity: Name: NameFull: Adnan, Wan Azizun Wan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2018 Type: published Y: 2018 Identifiers: – Type: issn-print Value: 01485598 Numbering: – Type: volume Value: 42 – Type: issue Value: 1 Titles: – TitleFull: Journal of Medical Systems Type: main |
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