A knowledge-based technique for automated detection of ischaemic episodes in long duration electrocardiograms
Abstract A novel method for the detection of ischaemic episodes in long duration ECGs is proposed. It includes noise handling, feature extraction, rule-based beat classification, sliding window classification and ischaemic episode identification, all integrated in a four-stage procedure. It can be e...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Papaloukas, C. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2001 |
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| Schlagwörter: |
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| Anmerkung: |
© IFMBE 2001 |
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| Übergeordnetes Werk: |
Enthalten in: Medical & biological engineering & computing - Springer-Verlag, 1977, 39(2001), 1 vom: Jan., Seite 105-112 |
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| Übergeordnetes Werk: |
volume:39 ; year:2001 ; number:1 ; month:01 ; pages:105-112 |
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| DOI / URN: |
10.1007/BF02345273 |
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| 520 | |a Abstract A novel method for the detection of ischaemic episodes in long duration ECGs is proposed. It includes noise handling, feature extraction, rule-based beat classification, sliding window classification and ischaemic episode identification, all integrated in a four-stage procedure. It can be executed in real time and is able to provide explanations for the diagnostic decisions obtained. The method was tested on the ESC ST-T database and high scores were obtained for both sensitivity and positive predictive accuracy (93.8% and 78.5% respectively using aggregate gross statistics, and 90.7% and 80.7% using aggregate average statistics). | ||
| 650 | 4 | |a Ischaemic episodes detection | |
| 650 | 4 | |a Knowledge-based method | |
| 650 | 4 | |a ECG noise handling | |
| 700 | 1 | |a Fotiadis, D. I. |4 aut | |
| 700 | 1 | |a Liavas, A. P. |4 aut | |
| 700 | 1 | |a Likas, A. |4 aut | |
| 700 | 1 | |a Michalis, L. K. |4 aut | |
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10.1007/BF02345273 doi (DE-627)OLC2038676852 (DE-He213)BF02345273-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Papaloukas, C. verfasserin aut A knowledge-based technique for automated detection of ischaemic episodes in long duration electrocardiograms 2001 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © IFMBE 2001 Abstract A novel method for the detection of ischaemic episodes in long duration ECGs is proposed. It includes noise handling, feature extraction, rule-based beat classification, sliding window classification and ischaemic episode identification, all integrated in a four-stage procedure. It can be executed in real time and is able to provide explanations for the diagnostic decisions obtained. The method was tested on the ESC ST-T database and high scores were obtained for both sensitivity and positive predictive accuracy (93.8% and 78.5% respectively using aggregate gross statistics, and 90.7% and 80.7% using aggregate average statistics). Ischaemic episodes detection Knowledge-based method ECG noise handling Fotiadis, D. I. aut Liavas, A. P. aut Likas, A. aut Michalis, L. K. aut Enthalten in Medical & biological engineering & computing Springer-Verlag, 1977 39(2001), 1 vom: Jan., Seite 105-112 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:39 year:2001 number:1 month:01 pages:105-112 https://doi.org/10.1007/BF02345273 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT GBV_ILN_32 GBV_ILN_34 GBV_ILN_70 GBV_ILN_105 GBV_ILN_2004 GBV_ILN_2006 GBV_ILN_4012 GBV_ILN_4219 GBV_ILN_4306 AR 39 2001 1 01 105-112 |
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10.1007/BF02345273 doi (DE-627)OLC2038676852 (DE-He213)BF02345273-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Papaloukas, C. verfasserin aut A knowledge-based technique for automated detection of ischaemic episodes in long duration electrocardiograms 2001 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © IFMBE 2001 Abstract A novel method for the detection of ischaemic episodes in long duration ECGs is proposed. It includes noise handling, feature extraction, rule-based beat classification, sliding window classification and ischaemic episode identification, all integrated in a four-stage procedure. It can be executed in real time and is able to provide explanations for the diagnostic decisions obtained. The method was tested on the ESC ST-T database and high scores were obtained for both sensitivity and positive predictive accuracy (93.8% and 78.5% respectively using aggregate gross statistics, and 90.7% and 80.7% using aggregate average statistics). Ischaemic episodes detection Knowledge-based method ECG noise handling Fotiadis, D. I. aut Liavas, A. P. aut Likas, A. aut Michalis, L. K. aut Enthalten in Medical & biological engineering & computing Springer-Verlag, 1977 39(2001), 1 vom: Jan., Seite 105-112 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:39 year:2001 number:1 month:01 pages:105-112 https://doi.org/10.1007/BF02345273 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT GBV_ILN_32 GBV_ILN_34 GBV_ILN_70 GBV_ILN_105 GBV_ILN_2004 GBV_ILN_2006 GBV_ILN_4012 GBV_ILN_4219 GBV_ILN_4306 AR 39 2001 1 01 105-112 |
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10.1007/BF02345273 doi (DE-627)OLC2038676852 (DE-He213)BF02345273-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Papaloukas, C. verfasserin aut A knowledge-based technique for automated detection of ischaemic episodes in long duration electrocardiograms 2001 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © IFMBE 2001 Abstract A novel method for the detection of ischaemic episodes in long duration ECGs is proposed. It includes noise handling, feature extraction, rule-based beat classification, sliding window classification and ischaemic episode identification, all integrated in a four-stage procedure. It can be executed in real time and is able to provide explanations for the diagnostic decisions obtained. The method was tested on the ESC ST-T database and high scores were obtained for both sensitivity and positive predictive accuracy (93.8% and 78.5% respectively using aggregate gross statistics, and 90.7% and 80.7% using aggregate average statistics). Ischaemic episodes detection Knowledge-based method ECG noise handling Fotiadis, D. I. aut Liavas, A. P. aut Likas, A. aut Michalis, L. K. aut Enthalten in Medical & biological engineering & computing Springer-Verlag, 1977 39(2001), 1 vom: Jan., Seite 105-112 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:39 year:2001 number:1 month:01 pages:105-112 https://doi.org/10.1007/BF02345273 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT GBV_ILN_32 GBV_ILN_34 GBV_ILN_70 GBV_ILN_105 GBV_ILN_2004 GBV_ILN_2006 GBV_ILN_4012 GBV_ILN_4219 GBV_ILN_4306 AR 39 2001 1 01 105-112 |
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10.1007/BF02345273 doi (DE-627)OLC2038676852 (DE-He213)BF02345273-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Papaloukas, C. verfasserin aut A knowledge-based technique for automated detection of ischaemic episodes in long duration electrocardiograms 2001 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © IFMBE 2001 Abstract A novel method for the detection of ischaemic episodes in long duration ECGs is proposed. It includes noise handling, feature extraction, rule-based beat classification, sliding window classification and ischaemic episode identification, all integrated in a four-stage procedure. It can be executed in real time and is able to provide explanations for the diagnostic decisions obtained. The method was tested on the ESC ST-T database and high scores were obtained for both sensitivity and positive predictive accuracy (93.8% and 78.5% respectively using aggregate gross statistics, and 90.7% and 80.7% using aggregate average statistics). Ischaemic episodes detection Knowledge-based method ECG noise handling Fotiadis, D. I. aut Liavas, A. P. aut Likas, A. aut Michalis, L. K. aut Enthalten in Medical & biological engineering & computing Springer-Verlag, 1977 39(2001), 1 vom: Jan., Seite 105-112 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:39 year:2001 number:1 month:01 pages:105-112 https://doi.org/10.1007/BF02345273 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT GBV_ILN_32 GBV_ILN_34 GBV_ILN_70 GBV_ILN_105 GBV_ILN_2004 GBV_ILN_2006 GBV_ILN_4012 GBV_ILN_4219 GBV_ILN_4306 AR 39 2001 1 01 105-112 |
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10.1007/BF02345273 doi (DE-627)OLC2038676852 (DE-He213)BF02345273-p DE-627 ger DE-627 rakwb eng 610 660 570 VZ 12 ssgn Papaloukas, C. verfasserin aut A knowledge-based technique for automated detection of ischaemic episodes in long duration electrocardiograms 2001 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © IFMBE 2001 Abstract A novel method for the detection of ischaemic episodes in long duration ECGs is proposed. It includes noise handling, feature extraction, rule-based beat classification, sliding window classification and ischaemic episode identification, all integrated in a four-stage procedure. It can be executed in real time and is able to provide explanations for the diagnostic decisions obtained. The method was tested on the ESC ST-T database and high scores were obtained for both sensitivity and positive predictive accuracy (93.8% and 78.5% respectively using aggregate gross statistics, and 90.7% and 80.7% using aggregate average statistics). Ischaemic episodes detection Knowledge-based method ECG noise handling Fotiadis, D. I. aut Liavas, A. P. aut Likas, A. aut Michalis, L. K. aut Enthalten in Medical & biological engineering & computing Springer-Verlag, 1977 39(2001), 1 vom: Jan., Seite 105-112 (DE-627)129858552 (DE-600)282327-5 (DE-576)015165507 0140-0118 nnns volume:39 year:2001 number:1 month:01 pages:105-112 https://doi.org/10.1007/BF02345273 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT GBV_ILN_32 GBV_ILN_34 GBV_ILN_70 GBV_ILN_105 GBV_ILN_2004 GBV_ILN_2006 GBV_ILN_4012 GBV_ILN_4219 GBV_ILN_4306 AR 39 2001 1 01 105-112 |
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a knowledge-based technique for automated detection of ischaemic episodes in long duration electrocardiograms |
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A knowledge-based technique for automated detection of ischaemic episodes in long duration electrocardiograms |
| abstract |
Abstract A novel method for the detection of ischaemic episodes in long duration ECGs is proposed. It includes noise handling, feature extraction, rule-based beat classification, sliding window classification and ischaemic episode identification, all integrated in a four-stage procedure. It can be executed in real time and is able to provide explanations for the diagnostic decisions obtained. The method was tested on the ESC ST-T database and high scores were obtained for both sensitivity and positive predictive accuracy (93.8% and 78.5% respectively using aggregate gross statistics, and 90.7% and 80.7% using aggregate average statistics). © IFMBE 2001 |
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Abstract A novel method for the detection of ischaemic episodes in long duration ECGs is proposed. It includes noise handling, feature extraction, rule-based beat classification, sliding window classification and ischaemic episode identification, all integrated in a four-stage procedure. It can be executed in real time and is able to provide explanations for the diagnostic decisions obtained. The method was tested on the ESC ST-T database and high scores were obtained for both sensitivity and positive predictive accuracy (93.8% and 78.5% respectively using aggregate gross statistics, and 90.7% and 80.7% using aggregate average statistics). © IFMBE 2001 |
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Abstract A novel method for the detection of ischaemic episodes in long duration ECGs is proposed. It includes noise handling, feature extraction, rule-based beat classification, sliding window classification and ischaemic episode identification, all integrated in a four-stage procedure. It can be executed in real time and is able to provide explanations for the diagnostic decisions obtained. The method was tested on the ESC ST-T database and high scores were obtained for both sensitivity and positive predictive accuracy (93.8% and 78.5% respectively using aggregate gross statistics, and 90.7% and 80.7% using aggregate average statistics). © IFMBE 2001 |
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A knowledge-based technique for automated detection of ischaemic episodes in long duration electrocardiograms |
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Fotiadis, D. I. Liavas, A. P. Likas, A. Michalis, L. K. |
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Fotiadis, D. I. Liavas, A. P. Likas, A. Michalis, L. K. |
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10.1007/BF02345273 |
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2024-07-03T19:48:29.252Z |
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