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Heart Rate Analysis and Telemedicine: New concepts & Maths
Our paper deals with some new aspects of ambulatory (Holter) ECG monitoring extending its indications and using for risk management purpose. Remote sensing consists of the transmittal of patient information, such as ECG, X-rays, or patient records, from a remote site to a collaborator in a distant s...
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| creator | Khoor, S. Kecskes, I. Kovacs, I. Verner, D. Remete, A. Jankovich, P. Bartus, R. Stanko, N. Schramm, N. Domijan, M. Domijan, E. |
| description | Our paper deals with some new aspects of ambulatory (Holter) ECG monitoring extending its indications and using for risk management purpose. Remote sensing consists of the transmittal of patient information, such as ECG, X-rays, or patient records, from a remote site to a collaborator in a distant site. Our earlier developed internet based ECG system was unique for on/off-line analysis of long-term ECG registrations. After the 5-year experience in a smaller region of Budapest, Hungary involving a municipal hospital and the surrounding outpatient cardiology departments and general practitioners, we decided to integrate into our new ECG equipment, the CardioClient the results. In the first clinical study of the four was a wavelet, non-linear heart rate analysis in sudden cardiac death patients using the Internet and the GPRS mobile communication. After the wavelet transformation by the Haar wavelet and the Daubechies 10-tap wavelet, the phase-space of the wavelet-coefficient standard deviation and the scale parameters showed an excellent separation in the scale-range of 3-6 between the two groups: in that region, the average scaling exponents was 0.14plusmn0.04 for Group-A, and 1.22plusmn0.27 for Group-B (p |
| doi_str_mv | 10.1109/SISY.2007.4342620 |
| format | conference_proceeding |
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Remote sensing consists of the transmittal of patient information, such as ECG, X-rays, or patient records, from a remote site to a collaborator in a distant site. Our earlier developed internet based ECG system was unique for on/off-line analysis of long-term ECG registrations. After the 5-year experience in a smaller region of Budapest, Hungary involving a municipal hospital and the surrounding outpatient cardiology departments and general practitioners, we decided to integrate into our new ECG equipment, the CardioClient the results. In the first clinical study of the four was a wavelet, non-linear heart rate analysis in sudden cardiac death patients using the Internet and the GPRS mobile communication. After the wavelet transformation by the Haar wavelet and the Daubechies 10-tap wavelet, the phase-space of the wavelet-coefficient standard deviation and the scale parameters showed an excellent separation in the scale-range of 3-6 between the two groups: in that region, the average scaling exponents was 0.14plusmn0.04 for Group-A, and 1.22plusmn0.27 for Group-B (p<0.001). In the next study, we used the Internet database of long-term ambulatory, mobile, GPRS electrocardiograms for the for risk stratification of patients through the cardiovascular continuum. From our ambulatory mobile GPRS ECG database the following a priori groups were defined after a 24 months follow-up: G1: N=227 patients (without manifest cardiovascular disease, clusterized "boxes" based on the age, sex, cholesterol level, diabetes, hypertension ); G2: N=89 patients (postinfarction group); G3: N=66 (patients with chronic heart failure) with (+) or without (-): all-cause death (acD), myocardial infarction (MI), malignant ventricular arrhythmia (MVA), sudden cardiac death (SCD). The actual vs. predicted values were analyzed with chi-square test. The best significance levels (p<0.001) were found with method in G1/MI+, G2/SCD+, G3/acD+, G3/SCD+ groups. In the third study a wavelet analysis of late potentials based on long-term, high-resolution, mobile, GPRS ECG data was performed. These pathological changes were also detected by the Haar and Daubechies_4 wavelets, but in a narrower space (110-128 ms and 180-240) and with lesser significance (p<0.01). Late potentials were found in Group-A (N=21) in 18 cases with Morlet, 16 with Haar, 19 with Daub-4 analysis, and in 15 cases using all the 3 waves; for Group-B the data were 5, 9, 8, 5, respectively. In the fourth clinical study the prognostic value of the nonlinear dynamicity measurement of atrial fibrillation waves detected by GPRS Internet long-term ECG monitoring were analyzed. The multivariate discriminant model selects the best parameters stepwise, the entry or removal based on the minimalization of the Wilks' lambda. Three variables remained finally: x1 = CI mean-value at log r=-1.0 (m9-14), x2 = CI mean-value at log r=-0.5 (m12-17), and x3 = CD_cg. The Wilks' lambda was 0.011, chi-square 299.68, significancy: p<0.001.</description><identifier>ISSN: 1949-047X</identifier><identifier>ISBN: 1424414423</identifier><identifier>ISBN: 9781424414420</identifier><identifier>EISSN: 1949-0488</identifier><identifier>EISBN: 9781424414437</identifier><identifier>EISBN: 1424414431</identifier><identifier>DOI: 10.1109/SISY.2007.4342620</identifier><language>eng</language><publisher>IEEE</publisher><subject>Cardiology ; Electrocardiography ; Ground penetrating radar ; Heart rate ; Internet ; Patient monitoring ; Remote monitoring ; Telemedicine ; Wavelet analysis</subject><ispartof>2007 5th International Symposium on Intelligent Systems and Informatics, 2007, p.39-43</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4342620$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54555,54920,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4342620$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Khoor, S.</creatorcontrib><creatorcontrib>Kecskes, I.</creatorcontrib><creatorcontrib>Kovacs, I.</creatorcontrib><creatorcontrib>Verner, D.</creatorcontrib><creatorcontrib>Remete, A.</creatorcontrib><creatorcontrib>Jankovich, P.</creatorcontrib><creatorcontrib>Bartus, R.</creatorcontrib><creatorcontrib>Stanko, N.</creatorcontrib><creatorcontrib>Schramm, N.</creatorcontrib><creatorcontrib>Domijan, M.</creatorcontrib><creatorcontrib>Domijan, E.</creatorcontrib><title>Heart Rate Analysis and Telemedicine: New concepts & Maths</title><title>2007 5th International Symposium on Intelligent Systems and Informatics</title><addtitle>SISY</addtitle><description>Our paper deals with some new aspects of ambulatory (Holter) ECG monitoring extending its indications and using for risk management purpose. Remote sensing consists of the transmittal of patient information, such as ECG, X-rays, or patient records, from a remote site to a collaborator in a distant site. Our earlier developed internet based ECG system was unique for on/off-line analysis of long-term ECG registrations. After the 5-year experience in a smaller region of Budapest, Hungary involving a municipal hospital and the surrounding outpatient cardiology departments and general practitioners, we decided to integrate into our new ECG equipment, the CardioClient the results. In the first clinical study of the four was a wavelet, non-linear heart rate analysis in sudden cardiac death patients using the Internet and the GPRS mobile communication. After the wavelet transformation by the Haar wavelet and the Daubechies 10-tap wavelet, the phase-space of the wavelet-coefficient standard deviation and the scale parameters showed an excellent separation in the scale-range of 3-6 between the two groups: in that region, the average scaling exponents was 0.14plusmn0.04 for Group-A, and 1.22plusmn0.27 for Group-B (p<0.001). In the next study, we used the Internet database of long-term ambulatory, mobile, GPRS electrocardiograms for the for risk stratification of patients through the cardiovascular continuum. From our ambulatory mobile GPRS ECG database the following a priori groups were defined after a 24 months follow-up: G1: N=227 patients (without manifest cardiovascular disease, clusterized "boxes" based on the age, sex, cholesterol level, diabetes, hypertension ); G2: N=89 patients (postinfarction group); G3: N=66 (patients with chronic heart failure) with (+) or without (-): all-cause death (acD), myocardial infarction (MI), malignant ventricular arrhythmia (MVA), sudden cardiac death (SCD). The actual vs. predicted values were analyzed with chi-square test. The best significance levels (p<0.001) were found with method in G1/MI+, G2/SCD+, G3/acD+, G3/SCD+ groups. In the third study a wavelet analysis of late potentials based on long-term, high-resolution, mobile, GPRS ECG data was performed. These pathological changes were also detected by the Haar and Daubechies_4 wavelets, but in a narrower space (110-128 ms and 180-240) and with lesser significance (p<0.01). Late potentials were found in Group-A (N=21) in 18 cases with Morlet, 16 with Haar, 19 with Daub-4 analysis, and in 15 cases using all the 3 waves; for Group-B the data were 5, 9, 8, 5, respectively. In the fourth clinical study the prognostic value of the nonlinear dynamicity measurement of atrial fibrillation waves detected by GPRS Internet long-term ECG monitoring were analyzed. The multivariate discriminant model selects the best parameters stepwise, the entry or removal based on the minimalization of the Wilks' lambda. Three variables remained finally: x1 = CI mean-value at log r=-1.0 (m9-14), x2 = CI mean-value at log r=-0.5 (m12-17), and x3 = CD_cg. The Wilks' lambda was 0.011, chi-square 299.68, significancy: p<0.001.</description><subject>Cardiology</subject><subject>Electrocardiography</subject><subject>Ground penetrating radar</subject><subject>Heart rate</subject><subject>Internet</subject><subject>Patient monitoring</subject><subject>Remote monitoring</subject><subject>Telemedicine</subject><subject>Wavelet analysis</subject><issn>1949-047X</issn><issn>1949-0488</issn><isbn>1424414423</isbn><isbn>9781424414420</isbn><isbn>9781424414437</isbn><isbn>1424414431</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2007</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNo9kEtLw0AUhccXWGt-gLiZlbvEedx5dVeK2kJVsFnoqkwyNzjSxpIJlP57A1bP5iw--DgcQm44Kzhn7n61WH0UgjFTgAShBTshmTOWgwDgANKckhF34HIG1p6Rqz8g5Pk_MO-XJEvpiw0BNSAxIpM5-q6nb75HOm395pBior4NtMQNbjHEOrY4oS-4p_V3W-OuT_SOPvv-M12Ti8ZvEmbHHpPy8aGczfPl69NiNl3m0bE-d0oLKWu0wgf02ISgHddSKBYsaFt7p6GxyhkJoQEuVWUCk40b9glRqUqOye2vNiLietfFre8O6-ML8gf6k0oU</recordid><startdate>200708</startdate><enddate>200708</enddate><creator>Khoor, S.</creator><creator>Kecskes, I.</creator><creator>Kovacs, I.</creator><creator>Verner, D.</creator><creator>Remete, A.</creator><creator>Jankovich, P.</creator><creator>Bartus, R.</creator><creator>Stanko, N.</creator><creator>Schramm, N.</creator><creator>Domijan, M.</creator><creator>Domijan, E.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200708</creationdate><title>Heart Rate Analysis and Telemedicine: New concepts & Maths</title><author>Khoor, S. ; Kecskes, I. ; Kovacs, I. ; Verner, D. ; Remete, A. ; Jankovich, P. ; Bartus, R. ; Stanko, N. ; Schramm, N. ; Domijan, M. ; Domijan, E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-956233ce82adeaefdd69163250d8468ca964f859734df4135b7d03f954222b5b3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Cardiology</topic><topic>Electrocardiography</topic><topic>Ground penetrating radar</topic><topic>Heart rate</topic><topic>Internet</topic><topic>Patient monitoring</topic><topic>Remote monitoring</topic><topic>Telemedicine</topic><topic>Wavelet analysis</topic><toplevel>online_resources</toplevel><creatorcontrib>Khoor, S.</creatorcontrib><creatorcontrib>Kecskes, I.</creatorcontrib><creatorcontrib>Kovacs, I.</creatorcontrib><creatorcontrib>Verner, D.</creatorcontrib><creatorcontrib>Remete, A.</creatorcontrib><creatorcontrib>Jankovich, P.</creatorcontrib><creatorcontrib>Bartus, R.</creatorcontrib><creatorcontrib>Stanko, N.</creatorcontrib><creatorcontrib>Schramm, N.</creatorcontrib><creatorcontrib>Domijan, M.</creatorcontrib><creatorcontrib>Domijan, E.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Khoor, S.</au><au>Kecskes, I.</au><au>Kovacs, I.</au><au>Verner, D.</au><au>Remete, A.</au><au>Jankovich, P.</au><au>Bartus, R.</au><au>Stanko, N.</au><au>Schramm, N.</au><au>Domijan, M.</au><au>Domijan, E.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Heart Rate Analysis and Telemedicine: New concepts & Maths</atitle><btitle>2007 5th International Symposium on Intelligent Systems and Informatics</btitle><stitle>SISY</stitle><date>2007-08</date><risdate>2007</risdate><spage>39</spage><epage>43</epage><pages>39-43</pages><issn>1949-047X</issn><eissn>1949-0488</eissn><isbn>1424414423</isbn><isbn>9781424414420</isbn><eisbn>9781424414437</eisbn><eisbn>1424414431</eisbn><abstract>Our paper deals with some new aspects of ambulatory (Holter) ECG monitoring extending its indications and using for risk management purpose. Remote sensing consists of the transmittal of patient information, such as ECG, X-rays, or patient records, from a remote site to a collaborator in a distant site. Our earlier developed internet based ECG system was unique for on/off-line analysis of long-term ECG registrations. After the 5-year experience in a smaller region of Budapest, Hungary involving a municipal hospital and the surrounding outpatient cardiology departments and general practitioners, we decided to integrate into our new ECG equipment, the CardioClient the results. In the first clinical study of the four was a wavelet, non-linear heart rate analysis in sudden cardiac death patients using the Internet and the GPRS mobile communication. After the wavelet transformation by the Haar wavelet and the Daubechies 10-tap wavelet, the phase-space of the wavelet-coefficient standard deviation and the scale parameters showed an excellent separation in the scale-range of 3-6 between the two groups: in that region, the average scaling exponents was 0.14plusmn0.04 for Group-A, and 1.22plusmn0.27 for Group-B (p<0.001). In the next study, we used the Internet database of long-term ambulatory, mobile, GPRS electrocardiograms for the for risk stratification of patients through the cardiovascular continuum. From our ambulatory mobile GPRS ECG database the following a priori groups were defined after a 24 months follow-up: G1: N=227 patients (without manifest cardiovascular disease, clusterized "boxes" based on the age, sex, cholesterol level, diabetes, hypertension ); G2: N=89 patients (postinfarction group); G3: N=66 (patients with chronic heart failure) with (+) or without (-): all-cause death (acD), myocardial infarction (MI), malignant ventricular arrhythmia (MVA), sudden cardiac death (SCD). The actual vs. predicted values were analyzed with chi-square test. The best significance levels (p<0.001) were found with method in G1/MI+, G2/SCD+, G3/acD+, G3/SCD+ groups. In the third study a wavelet analysis of late potentials based on long-term, high-resolution, mobile, GPRS ECG data was performed. These pathological changes were also detected by the Haar and Daubechies_4 wavelets, but in a narrower space (110-128 ms and 180-240) and with lesser significance (p<0.01). Late potentials were found in Group-A (N=21) in 18 cases with Morlet, 16 with Haar, 19 with Daub-4 analysis, and in 15 cases using all the 3 waves; for Group-B the data were 5, 9, 8, 5, respectively. In the fourth clinical study the prognostic value of the nonlinear dynamicity measurement of atrial fibrillation waves detected by GPRS Internet long-term ECG monitoring were analyzed. The multivariate discriminant model selects the best parameters stepwise, the entry or removal based on the minimalization of the Wilks' lambda. Three variables remained finally: x1 = CI mean-value at log r=-1.0 (m9-14), x2 = CI mean-value at log r=-0.5 (m12-17), and x3 = CD_cg. The Wilks' lambda was 0.011, chi-square 299.68, significancy: p<0.001.</abstract><pub>IEEE</pub><doi>10.1109/SISY.2007.4342620</doi><tpages>5</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1949-047X |
| ispartof | 2007 5th International Symposium on Intelligent Systems and Informatics, 2007, p.39-43 |
| issn | 1949-047X 1949-0488 |
| language | eng |
| recordid | cdi_ieee_primary_4342620 |
| source | IEEE Xplore All Conference Series |
| subjects | Cardiology Electrocardiography Ground penetrating radar Heart rate Internet Patient monitoring Remote monitoring Telemedicine Wavelet analysis |
| title | Heart Rate Analysis and Telemedicine: New concepts & Maths |
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