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Influence of noise on the analysis of late potentials
Journal of Electrocardiology
Vol. 25 Supplement
Influence of Noise on the Analysis of Late Potentials
Owe Svensson, MSc, Leif Siimmo, PhD, and Olle Pahlm, MD, PhD
Detection and quantification of ventricular late potentials are possible thanks to time-coherent averaging of several beats. Averaging has usually been based on a predetermined number of beats (normally 200-250 beats), but recent studies indicate that it may be more appropriate to continue averaging until a certain preset noise level is reached. Most clinical studies have been realized by using equipment with expensive, high-resolution A/D conversion (resolution range, 0. l- 1 kV). This study aimed at investigating the effects of quantization when the electrocardiographic (ECG) signal is subject to averaging, as well as at providing a basis for selecting sufficient quantization step size. It was also of interest to determine how noise in general could influence traditional time-domain analysis of late potentials. The error introduced by quantization can be treated in a way similar to, for example, muscle noise, that is, as being uncorrelated from interval to interval and thus attenuated by averaging. The effects of quantization on the averaging process depend, however, on the amplitude distribution of the ECG samples. Such statistical information was acquired from a population of 22 patients after myocardial infarction. The amplitude distribution was studied by computing histograms for a time interval subsequent to the J point over the ensemble of time-aligned beats (typically 3 50-450 beats). These histograms were then merged for each individual lead for all patients in the database. It was found that the histogram for each individual lead was well approximated by a Gaussian distribution (the main contributing factor to the amplitude distribution is the low-frequency drift caused by respiration and electrode impedance changes). Based on the Gaussian assumption, the accuracy in averaging was studied as a function of reso-
lution and noise level (ie, SD of the amplitude distribution). An important result of this study is that unbiased reproduction of low-amplitude activity buried in noise can be achieved when the quantization interval selected is smaller than twice the SD of the Gaussian noise. If this relation is not fulfilled, accurate reproduction cannot be achieved no matter how long the averaging process is continued. The quantization noise contributes to the overall noise level and should be related to the analog ECG signal. The latter noise level was in all cases larger than 10 I.LVroot-mean-square. Based on these findings, the digital resolution 2.5 (IV results in an overall noise level that is virtually identical to that obtained with 0.625 and 1.25 pV, respectively. The slightly higher noise level, which resulted for 5 pV (commonly used in modern ECG carts), can be compensated for by increasing acquisition time by 25-30%. Requirements for unbiased averaging were found to be sufficient for the measurement situation of interest. The effects of quantization were also studied in relation to determination of the endpoint of late potentials using traditional time-domain methods. The averaged beats from the X, Y, and Z leads were then subjected to late potential analysis using the method described by Simson.’ In that method, bandpass liltering of the orthogonal leads are done by using a four-pole Butterworth filter with cut-off frequencies at 40 and 250 Hz. In order to avoid ringing in the filter output due to large R and S waves, the beat averages are filtered forward until mid-QRS and backward until the same mid-QRS point is reached (bidirectional filtering). The three bandpass filtered leads are combined into a vector magnitude. All measurements are then performed with reference to an endpoint, which is found from a backwards search in the vector magnitude. The search employs a threshold, which is determined as a function of the noise level.
From the Departments of Signal Processing and Clinical Physiology, Lund University, Lund, Sweden. Reprint requests: Leif Siirmno, PhD, Lund University, Department of Clinical Physiology, Lasarettet, S-22185 Lund, Sweden.
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influence of Noise on Late Potentials 200
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Svensson et al.
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r
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Histograminterval Fig. 1. Example of vector magnitude
with added noise.
40
t -
20
-
0
h 0
The accuracy in defining an endpoint for late potentials by the Simson method was studied by determining the endpoint for different quantization levels. In most cases, the endpoints determined for A = 0.625 PV and 5 p.V, respectively, only differed by l-2 ms. However, a large change in endpoint (ie, 40 ms) was noted in one case aIthough only a slight increase in the noise level occurred (well below 0.1 p,V root-mean-square). The vector magnitude exhibited a pronounced “peak-and-valley” behavior in that case. In order to further substantiate this finding, a “noise-free” vector magnitude was created from an original vector magnitude and different sequences of noise were then added (Fig. 1). The noise level in the simulations was held constant and equal to the overall noise level of the original averaged signal (A = 5 WV).The resulting vector magnitude simulations were very close in character to the original one. It was found that large differences in endpoints occurred for a fixed noise level. The resulting histogram
1 50
time after fiducial point Fig. 2. LP endpoints obtained from vector magnitude quantized at 5 (LV.The histogram was obtained from 1,000 realizations.
for 1,000 realizations of the vector magnitude is shown in Figure 2. The multi-modal character of the histograms indicate that the Simson method can produce an endpoint, which may differ up to 40 ms from one recording to another of the same patient. This behavior can thus seriously degrade the reproducibility of the method. The endpoint histogram was also determined for a higher resolution ( A = 2.5 FV) and a similar multi-modal histogram was found.
Reference 1. Simson MB: Use of signals in the terminal QRS complex to identify patients with ventricular tachycardia after myocardial infarction. Circulation 64:23 5, 198 1
Vol. 25 Supplement
Influence of Noise on the Analysis of Late Potentials
Owe Svensson, MSc, Leif Siimmo, PhD, and Olle Pahlm, MD, PhD
Detection and quantification of ventricular late potentials are possible thanks to time-coherent averaging of several beats. Averaging has usually been based on a predetermined number of beats (normally 200-250 beats), but recent studies indicate that it may be more appropriate to continue averaging until a certain preset noise level is reached. Most clinical studies have been realized by using equipment with expensive, high-resolution A/D conversion (resolution range, 0. l- 1 kV). This study aimed at investigating the effects of quantization when the electrocardiographic (ECG) signal is subject to averaging, as well as at providing a basis for selecting sufficient quantization step size. It was also of interest to determine how noise in general could influence traditional time-domain analysis of late potentials. The error introduced by quantization can be treated in a way similar to, for example, muscle noise, that is, as being uncorrelated from interval to interval and thus attenuated by averaging. The effects of quantization on the averaging process depend, however, on the amplitude distribution of the ECG samples. Such statistical information was acquired from a population of 22 patients after myocardial infarction. The amplitude distribution was studied by computing histograms for a time interval subsequent to the J point over the ensemble of time-aligned beats (typically 3 50-450 beats). These histograms were then merged for each individual lead for all patients in the database. It was found that the histogram for each individual lead was well approximated by a Gaussian distribution (the main contributing factor to the amplitude distribution is the low-frequency drift caused by respiration and electrode impedance changes). Based on the Gaussian assumption, the accuracy in averaging was studied as a function of reso-
lution and noise level (ie, SD of the amplitude distribution). An important result of this study is that unbiased reproduction of low-amplitude activity buried in noise can be achieved when the quantization interval selected is smaller than twice the SD of the Gaussian noise. If this relation is not fulfilled, accurate reproduction cannot be achieved no matter how long the averaging process is continued. The quantization noise contributes to the overall noise level and should be related to the analog ECG signal. The latter noise level was in all cases larger than 10 I.LVroot-mean-square. Based on these findings, the digital resolution 2.5 (IV results in an overall noise level that is virtually identical to that obtained with 0.625 and 1.25 pV, respectively. The slightly higher noise level, which resulted for 5 pV (commonly used in modern ECG carts), can be compensated for by increasing acquisition time by 25-30%. Requirements for unbiased averaging were found to be sufficient for the measurement situation of interest. The effects of quantization were also studied in relation to determination of the endpoint of late potentials using traditional time-domain methods. The averaged beats from the X, Y, and Z leads were then subjected to late potential analysis using the method described by Simson.’ In that method, bandpass liltering of the orthogonal leads are done by using a four-pole Butterworth filter with cut-off frequencies at 40 and 250 Hz. In order to avoid ringing in the filter output due to large R and S waves, the beat averages are filtered forward until mid-QRS and backward until the same mid-QRS point is reached (bidirectional filtering). The three bandpass filtered leads are combined into a vector magnitude. All measurements are then performed with reference to an endpoint, which is found from a backwards search in the vector magnitude. The search employs a threshold, which is determined as a function of the noise level.
From the Departments of Signal Processing and Clinical Physiology, Lund University, Lund, Sweden. Reprint requests: Leif Siirmno, PhD, Lund University, Department of Clinical Physiology, Lasarettet, S-22185 Lund, Sweden.
212
influence of Noise on Late Potentials 200
l
Svensson et al.
213
r
180 160
60
Histograminterval Fig. 1. Example of vector magnitude
with added noise.
40
t -
20
-
0
h 0
The accuracy in defining an endpoint for late potentials by the Simson method was studied by determining the endpoint for different quantization levels. In most cases, the endpoints determined for A = 0.625 PV and 5 p.V, respectively, only differed by l-2 ms. However, a large change in endpoint (ie, 40 ms) was noted in one case aIthough only a slight increase in the noise level occurred (well below 0.1 p,V root-mean-square). The vector magnitude exhibited a pronounced “peak-and-valley” behavior in that case. In order to further substantiate this finding, a “noise-free” vector magnitude was created from an original vector magnitude and different sequences of noise were then added (Fig. 1). The noise level in the simulations was held constant and equal to the overall noise level of the original averaged signal (A = 5 WV).The resulting vector magnitude simulations were very close in character to the original one. It was found that large differences in endpoints occurred for a fixed noise level. The resulting histogram
1 50
time after fiducial point Fig. 2. LP endpoints obtained from vector magnitude quantized at 5 (LV.The histogram was obtained from 1,000 realizations.
for 1,000 realizations of the vector magnitude is shown in Figure 2. The multi-modal character of the histograms indicate that the Simson method can produce an endpoint, which may differ up to 40 ms from one recording to another of the same patient. This behavior can thus seriously degrade the reproducibility of the method. The endpoint histogram was also determined for a higher resolution ( A = 2.5 FV) and a similar multi-modal histogram was found.
Reference 1. Simson MB: Use of signals in the terminal QRS complex to identify patients with ventricular tachycardia after myocardial infarction. Circulation 64:23 5, 198 1