Ambulatory (Holter) electrocardiography signal-averaging: A current perspective

Kennedy, Harold L.; Bavishi, Neil; Buckingham, Thomas A.

doi:10.1016/0002-8703(92)90421-q

Cited by 22 publications

(8 citation statements)

References 28 publications

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“…Some authors used the wavelet method which would be the best solution for the analysis of this kind of data [11][12][13][14].…”

Section: Wavelet Analysis Of Late Potentials Based On Longterm Himentioning

confidence: 99%

“…The Haar, the Daubechies_4 and the Morlet (frequency parameter (c) = 2*π*5.33) wavelets were used in the calculations. The Morlet wavelet HR-ECG analysis used a combined wavelet stratification method of Couderc and Selmaoui [10,11]. In this case, the wavelet transform was applied on 512 (=2 9 ) points, from 128 ms before the beginning of the QRS to 384 ms after QRS onset.…”

Section: Wavelet Analysis Of Late Potentials Based On Longterm Himentioning

confidence: 99%

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Heart Rate Analysis and Telemedicine: New concepts & Maths

Khoor

Kecskés²,

Kovács

et al. 2007

2007 5th International Symposium on Intelligent Systems and Informatics

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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 0.14+-0.04 for Group-A, and 1.22+-0.27 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, highresolution, 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 meanvalue at log r=-1.0 (m9-14), x2 = CI mean-value at log r=-0.5 (m12-17), and x3 = CD_cg. The Wilks' lam...

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“…Some authors used the wavelet method which would be the best solution for the analysis of this kind of data [11][12][13][14].…”

Section: Wavelet Analysis Of Late Potentials Based On Longterm Himentioning

confidence: 99%

Section: Wavelet Analysis Of Late Potentials Based On Longterm Himentioning

confidence: 99%

Heart Rate Analysis and Telemedicine: New concepts & Maths

Khoor

Kecskés²,

Kovács

et al. 2007

2007 5th International Symposium on Intelligent Systems and Informatics

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show abstract

“…1,2,5,[7][8][9] Electrocardiographic (ECG) Holter monitoring is also generally regarded as a tool for the detection and documentation of spontaneous ventricular arrhythmias; recently ambulatory ECG has been innovatively used to examine the amplified high resolution SAECG. [10][11][12][13] Holter analysis may confer significant benefits due to the ability to perform late potentials analysis on the same tape used for conventional arrhythmia and heart rate variability analysis. Guidelines for deriving the high resolution ECG from Holter tapes remain to be established, although early experiences with this approach show promising close correlation and good agreement between the two methodologies.…”

Section: Introductionmentioning

confidence: 99%

“…Guidelines for deriving the high resolution ECG from Holter tapes remain to be established, although early experiences with this approach show promising close correlation and good agreement between the two methodologies. [10][11][12][13] Furthermore, the ECG Holter recorder enables late potential analysis during repeated registration periods whether a time-course occurence of ventricular late potentials, especially during phases with accelerated heart rate, had been recently demonstrated in patients prone to ventricular fibrillation. 14 Despite early promising prospective results, clinical investigations are required to confirm the predictive value of such a high resolution technique in classical postinfarction arrhythmic risk stratification.…”

Section: Introductionmentioning

confidence: 99%

Arrhythmic Risk Stratification After Myocardial Infarction Using Ambulatory Electrocardiography Signal Averaging

Roche

Dacosta

Karnib

et al. 2002

Pacing Clinical Electrophis

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Ambulatory ECG had been proposed to examine the amplified high resolution signal-averaged electrocardiogram (SAECG). Clinical investigations are required to confirm the predictive value of such a high resolution technique in arrhythmic risk stratification. The prognostic value of ambulatory Holter SAECG was evaluated in 108 postinfarction patients for the purpose of predicting the occurrence of serious arrhythmic (SARR) events (sudden cardiac death [SCD], VT, or VF) in comparison with classical real-time SAECG. During the 42+/-8 months of follow-up, the sudden cardiac death mortality was 4.6% (five deaths), six (5.6%) patients had VT, and one (0.9%) VF. QRSd was found to be the most predictive parameter using ROC curves analysis for SAAR + outcome (W = 0.833 and W = 0.803 for 25-250 Hz and 40-250 Hz filters, respectively) followed by RMS (W = 0.766 and W = 0.721) and LAS (W = 0.759, W = 0.709) (all P < 0.01). Abnormal Holter SAECG for 25 and 40-Hz LP filter were significant predictors of SARR+ by log-rank test (P < 0.01, P < 0.05, respectively). This study confirms that valuable prognostic information can be obtained from the ambulatory high resolution ECG technique and that Holter SAECG may predict arrhythmic risk in a postinfarction population.

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“…However, for higher levels noise (20-100µV) as were found in ambulatory Holter signal-averaged systems [16], the standard method introduces an important alignment error. According to our results, the temporal method does not satisfy the maximum allowed value of trigger jitter of 1 msec, when the noise level increases over 50µV.…”

Section: Discussionmentioning

confidence: 99%

A new alignment method based on the wavelet multi-scale cross-correlation for noisy high resolution ECG records

Laciar

Jané

Brooks³

2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-The coherent signal averaging process requires an accurate estimation of the fiducial point in all beats to be averaged. The temporal cross-correlation between the detected beat and a template beat is the typical alignment method used with high-resolution ECG (HRECG) records. However, this technique does not produce a precise fiducial mark in records with high noise levels. In this study, we propose a new alignment method based on the multi-scale cross-correlation between the wavelet transforms of the template and the detected beat, respectively. The wavelet and temporal methods were tested for several simulated records corrupted with white noise and electromyographic (EMG) noise of different RMS levels. The results indicate that wavelet alignment method produces a lower trigger jitter than the temporal method in all tests. We conclude that the proposed alignment method can be used in records with high noise levels, like those found in Holter HRECG systems.

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Ambulatory (Holter) electrocardiography signal-averaging: A current perspective

Cited by 22 publications

References 28 publications

Heart Rate Analysis and Telemedicine: New concepts & Maths

Heart Rate Analysis and Telemedicine: New concepts & Maths

Arrhythmic Risk Stratification After Myocardial Infarction Using Ambulatory Electrocardiography Signal Averaging

A new alignment method based on the wavelet multi-scale cross-correlation for noisy high resolution ECG records

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Ambulatory (Holter) electrocardiography signal-averaging: A current perspective

Cited by 22 publications

References 28 publications

Heart Rate Analysis and Telemedicine: New concepts &#x00026; Maths

Heart Rate Analysis and Telemedicine: New concepts &#x00026; Maths

Arrhythmic Risk Stratification After Myocardial Infarction Using Ambulatory Electrocardiography Signal Averaging

A new alignment method based on the wavelet multi-scale cross-correlation for noisy high resolution ECG records

Contact Info

Product

Resources

About

Heart Rate Analysis and Telemedicine: New concepts & Maths

Heart Rate Analysis and Telemedicine: New concepts & Maths