Spectral Energy of ECG Morphologic Differences to Predict Death
Abstract
Unstable conduction system bifurcations following ischemia and infarction are associated with variations in the electrocardiographic activity spanning the heart beat. In this paper, we investigate a spectral energy measure of morphologic differences (SE-MD) that quantifies aspects of these changes. Our measure uses a dynamic time-warping approach to compute the time-aligned morphology differences between pairs of successive sinus beats in an electrocardiographic signal. While comparing beats, the entire heart beat signal is analyzed in order to capture changes affecting both depolarization and repolarization. We show that variations in electrocardiographic activity associated with death can be distinguished by their spectral characteristics. We developed the SE-MD metric on holter data from 764 patients from the TIMI DISPERSE2 dataset and tested it on 600 patients from the TIMI MERLIN dataset. In the test population, high SE-MD was strongly associated with death over a 90 day period following non-ST-elevation acute coronary syndrome (HR 10.45, p < 0.001) and showed significant discriminative ability (c-statistic 0.85). In comparison with heart rate variability and deceleration capacity, SE-MD was also the most significant predictor of death in the study population. Furthermore, SE-MD had low correlation with these other measures, suggesting that complementary use of the risk variables may allow for more complete assessment of cardiac health.
Keywords
Electrocardiogram (ECG) Risk stratification Acute coronary syndromes Heart rate variability Deceleration capacity Morphologic differencesIntroduction
In a stationary and homogenous myocardial conducting system, the activated pathways through excitable cells are usually similar for consecutive beats. However, in the presence of ischemia, the conducting system has multiple irregular islands of severely depressed and unexcitable myocardium (El-Sherif et al. 1977) that leads to discontinuous electrophysiological characteristics (Josephson and Wit 1984). The presence of several possible adjacent pathways that can invade the nonfunctioning area leads to variations in the spatial direction of the invading vector (Ben-Haim et al. 1991). Measured electrical activity in this phase can only be described in probabilistic terms because of beat-to-beat activation and repolarization variability, stemming from subtle unstable conduction bifurcations. Furthermore, propagation of a beat may be dependent on the route of propagation of the previous beat. The overall effect of such minor conduction inhomogeneities is not well understood, but it is possible that they correlate with myocardial electrical instability and have potentially predictive value for ventricular arrhythmias (Ben-Haim et al. 1991) or other adverse events.
In this paper, we propose and evaluate a new method to risk stratify patients by measuring seemingly random morphologic differences in electrocardiographic (ECG) signals. Our method uses dynamic time-warping to compute the time-aligned morphology changes between consecutive sinus beats. While comparing beats, the entire heart beat signal is analyzed and changes affecting both depolarization and repolarization are quantified. This approach reduces the original electrocardiographic signal to a time series of morphologic differences (MD). We describe how, when a large enough sequence of beats is analyzed, increased spectral energy in the MD time series (SE-MD) within a characteristic frequency range may have value in predicting patients at increased future risk of cardiovascular death.
We developed our SE-MD metric on a training set of 764 patients from the TIMI DISPERSE2 study, who were followed up for the endpoint of death for a 90 day period following non-ST-elevation acute coronary syndromes (NSTEACS). We then tested SE-MD on 600 patients from the TIMI MERLIN dataset. For both these datasets, SE-MD identified patients at increased risk of a future adverse outcome. SE-MD was also more strongly associated with death in the test dataset than either heart rate variability (HRV) or deceleration capacity (DC). Furthermore, SE-MD had low correlation with these other measures, suggesting that complementary use of these risk variables may allow for more complete assessment of cardiac health.
The rest of this paper is organized as follows. “Morphologic Differences” details the process of measuring time-aligned morphology differences between pairs of consecutive heart beats. “Spectral Energy of Morphologic Differences” describes the approach of identifying characteristic frequencies in the MD time series that are associated with future cardiovascular death. “Evaluation” proposes a study to evaluate the prognostic information provided by SE-MD, HRV and DC. “Results presents the results of this study. “Summary and Discussion” concludes the paper with a summary and discussion.
Morphologic Differences
This section describes the different stages involved in calculating the MD time series.
ECG Signal Preprocessing
The process of analyzing ECG morphology is more sensitive to noise than techniques focusing exclusively on the heart rate. This is because the heart rate can often be estimated robustly, even in the presence of significant amounts of noise, by searching for high amplitude R-waves in the signal. In contrast, characterizing the morphology requires using information even from those parts of the ECG that are low amplitude and where small amounts of noise can significantly affect the signal-to-noise ratio. To minimize this effect, we employ different techniques for noise removal and automated signal rejection.
Noise removal is carried out in three steps. Baseline wander is first removed by subtracting an estimate of the wander obtained by median filtering the original ECG signal as described in (DeChazal et al. 2004). The ECG signal is then filtered using wavelet denoising with a soft-threshold (Donoho 2005). Finally, sensitivity to calibration errors is decreased by normalizing the entire ECG signal by the mean R-wave amplitude.
While the noise removal steps help remove artifacts commonly encountered in long-term electrocardiographic records, the signal rejection process is designed to remove segments of the ECG signal where the signal-to-noise ratio is sufficiently low that meaningful analysis of the morphology is challenging even after noise removal. Such regions are typically dominated by artifacts unrelated to cardiac activity but that have similar spectral characteristics to the ECG signal, e.g., segments recorded during periods when there was substantial muscle artifact.
The process of signal rejection proceeds in two stages. Parts of the ECG signal with a low signal quality index (Li et al. 2008) are identified by combining four analysis methods: disagreement between multiple beat detection algorithms on a single ECG lead, disagreement between the same beat detection algorithm on different ECG leads, the kurtosis of a segment of ECG, and the ratio of power in the spectral distribution of a given ECG segment between 5–14 and 5–50 Hz. In our work, we use the Physionet SQI package implementation (Li et al. 2008) to automatically remove parts of the ECG signal with a low signal quality index from further analysis. The remaining data is divided into half hour windows, and the standard deviation of the R-waves during each half hour window is calculated. We discard any window with a standard deviation greater than 0.2887. Given the earlier normalization of the ECG signal, under a conservative model that allows the R-wave amplitude to uniformly vary between 0.5 and 1.5 every beat (i.e., up to 50% of its mean amplitude), we expect the standard deviation of the R-wave amplitudes to be less than 0.2887 for any half hour window. This heuristic identifies windows that are likely corrupted by significant non-physiological additive noise, and where the morphology of the ECG cannot be meaningfully analyzed.
ECG Segmentation and Removal of Ectopy
To segment the ECG signal into beats, we use two open-source QRS detection algorithms with different noise sensitivities. The first of these makes use of digital filtering and integration (Hamilton and Tompkins 1986) and has been shown to achieve a sensitivity of 99.69%, while the second is based on a length transform after filtering (Zong et al. 2003) and has a sensitivity of 99.65%. Both techniques have a positive predictivity of 99.77%. QRS complexes were marked only at locations where these algorithms agreed. These algorithms are used as part of the Physionet SQI package implementation described earlier.
In order to study sinus conduction, prior to further analysis, ectopic parts of the signal were also removed in a fully automated manner using the beat classification algorithm of (Hamilton 2002) present in the Physionet SQI package. The beat classification algorithm characterizes each beat by a number of features such as width, amplitude and RR interval, and then compares it to previously detected beat types to assign it a label.
We removed each ectopic beat, as well as the beats occurring immediately before and after it. To address the splicing introduced by this step, we also made changes to the subsequent stages of measuring SE-MD. We restricted the process of measuring pairwise energy differences (“Morphologic Distance (MD) Time Series”) to pairs of beats that occurred consecutively, i.e., did not span a gap corresponding to spliced out beats. We also used the Lomb-Scargle periodogram to measure spectral energy (“Spectral Energy of Morphologic Differences”), since this method has been shown to be well-suited for irregularly sampled signals (Clifford and Tarassenko 2005).
Morphologic Distance (MD) Time Series
We use a variant of dynamic time-warping (DTW) (Rabiner 1978) to align samples that correspond to the same underlying physiological activity. As depicted in the drawing on the right side of Fig. 1, this can require aligning a single sample in one beat with multiple samples in another beat. The algorithm uses dynamic programing to search for an alignment that minimizes the overall distortion. Distortion is measured using the method described in (Syed et al. 2007), which captures differences in both amplitude and timing of ECG waves.
The final energy difference between the two beats x_{1} and x_{2}, is given by the cost of their optimal alignment, which depends on the amplitude differences between the two signals and the length, K, of the alignment (which increases if the two signals differ in their timing characteristics). In a typical formulation of DTW, this difference is divided by K to remove the dependence of the cost on the length of the original observations. A problem with applying this correction in our context is that some paths are long not because the segments to be aligned are long, but rather because the observations are time-warped differently. In these cases, dividing by K is inappropriate since a difference in the length of a beats (or of parts of beats) often provides diagnostic information that is complementary to the information provided by the morphology. Consequently, in our algorithm we omit the division by K.
The process described above transforms the original ECG signal from a sequence of beats to a sequence of energy differences. We call the resulting time series the morphologic distance (MD) time series for the patient. This new signal, comprising pair-wise, time-aligned energy differences between beats, is then smoothed using a median filter of length 8. The median filtering process addresses noisy and ectopic heart beats that may have passed through the earlier preprocessing stage and lead to high morphologic distances. The smoothing process is geared towards ensuring that high values in the MD time series correspond to locally persistent morphology changes, i.e., sustained differences in beat-to-beat morphology.
Spectral Energy of Morphologic Differences
We estimate the power spectral density of the MD time series using the Lomb-Scargle periodogram (Lomb 1976), which is well-suited to measure the spectral content of an irregularly sampled signal by taking into account both the signal value and the time of each sample. The Lomb-Scargle periodgram provides a natural way to exclude noisy samples from the computation, unlike other spectral estimation techniques that require interpolation methods to deal with missing data.
Evaluation
We tested the ability of SE-MD to discriminate between low and high risk patients in a study on 600 patients randomly selected from the placebo population for the TIMI MERLIN (Morrow et al. 2007) trial. Each of these patients had 24 h of holter ECG recorded at 128 Hz within 48 h of admission due to NSTEACS. There were 12 cardiovascular deaths in this cohort during a follow-up period of 90 days.
For comparison, we also studied the discriminative ability of two other commonly used electrocardiographic risk variables: heart rate variability (HRV) (Malik 1996) and deceleration capacity (DC) (Bauer et al. 2006). For HRV, we considered the SDNN, SDANN, ASDNN, RMSSD, HRVI, pNN50 and LF/HF metrics that were proposed by the Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology (Malik 1996), but report only data from the best performing (i.e., LF/HF) metric. To measure DC, we used the PRSA implementation available at: http://www.psra.eu (Bauer et al. 2006).
To evaluate the risk variables, we carried out two separate analyses. We calculated the c-statistic for the predicted survival of patients over a 90 day period following NSTEACS. We then dichotomized the risk variables using the values reported in the literature for HRV (i.e., 1.2) (Malik 1996) and DC (2.5) (Bauer et al. 2006), and the highest quartile value for SE-MD in the DISPERSE2 dataset (52.5). For the dichotomized risk variables, we subsequently estimated hazard ratios using Cox proportional hazards regression models (Cox and Oakes 1984).
In addition to using values reported in the literature as cutoffs for dichotomization, we also estimated hazard ratios for high risk groups comprising patients in the lowest quartiles for HRV and DC (where low values correspond to high risk) and patients in the highest quartile for SE-MD (where high values correspond to high risk). This approach helped provide a comparison of the different methods on equal sized populations. We further investigated using a high risk group for each risk variable that was chosen to match the percentage of deaths observed in the TIMI DISPERSE2 dataset over the 90 day follow-up period (i.e., patients in the lowest 2.5% HRV and DC, and in the highest 2.5% SE-MD). These alternate dichotomization cutoffs only affect the hazard ratios obtained. The measurement of the c-statistic does not require dichotomization, and calculation of this value is not repeated for changes in cutoff.
The decision to calculate both c-statistics and hazard ratios was based on the complementary information provided by these statistical techniques. The c-statistics are calculated by measuring the area under the ROC curve and indicate the discriminative ability of risk variables. This data does not factor in the survival time for patients who die. Furthermore, it ignores censoring, i.e., patients dropping out before the study is complete. Conversely, the hazard ratios for univariate association are derived using a Cox proportional regression model, and relate the survival times of patients in high and low risk groups, while accounting for censoring. Intuitively, the c-statistics can be considered as a measure of discriminative ability of a risk variable, while hazard ratios report the survival characteristics of patients in high and low risk populations based on a dichotomized classification of patients using the risk variable.
Finally, in contrast to the TIMI DISPERSE2 dataset used for training, where 90 day follow-up data was available, patients in the MERLIN study were followed up for a median duration of 348 days. We therefore also investigated risk stratification of patients using the risk variables over a year following NSTEACS. During this period 22 cardiovascular deaths occurred.
Results
Univariate and Multivariate Analysis with Dichotomization Cutoffs from Literature
Discriminative ability of SE-MD, HRV and DC measured using the c-statistic to predict cardiovascular death over 90 days following NSTEACS (n = 600)
Parameter | c-Statistic |
---|---|
SE-MD | 0.85 |
HRV | 0.64 |
DC | 0.75 |
Univariate and multivariate association between risk variables and cardiovascular death over 90 days following NSTEACS (n = 600)
Parameter | Univariate hazard ratio | 95% confidence interval | p value | Multivariate hazard ratio | 95% confidence interval | p value |
---|---|---|---|---|---|---|
SE-MD | 10.45 | 2.83–38.59 | <0.001 | 9.05 | 2.05–39.97 | 0.004 |
HRV | 2.12 | 0.68–6.58 | 0.192 | 0.54 | 0.15–2.03 | 0.364 |
DC | 7.37 | 2.34–23.23 | <0.001 | 3.27 | 0.87–12.33 | 0.080 |
Of the evaluated measures, SE-MD was strongly associated with death over a 90 day period following NSTEACS and showed the highest hazard ratio (HR 10.45, p < 0.001). DC was also associated with death in this population. The c-statistic for SE-MD was the highest of the three measures studied, and exceeded the threshold of 0.8 associated with genuine clinical utility (Ohman et al. 2000).
The results of multivariate analysis including all three electrocardiographic measures are presented in Table 2. SE-MD was the only electrocardiographic risk variable independently associated with death during follow-up in this population (HR 9.05, p = 0.004). The association between DC and the endpoint of cardiovascular death marginally exceeded the threshold of significance.
Correlation coefficients between SE-MD, HRV and DC
Parameter | SE-MD | HRV | DC |
---|---|---|---|
SE-MD | 1.00 | −0.31 | −0.32 |
HRV | 1.00 | 0.24 | |
DC | 1.00 |
Analysis with Alternate Dichotomization Cutoffs
Univariate and multivariate association between risk variables and cardiovascular death over 90 days following NSTEACS using high risk quartiles for dichotomization (n = 600)
Parameter | Univariate hazard ratio | 95% confidence interval | p value | Multivariate hazard ratio | 95% confidence interval | p value |
---|---|---|---|---|---|---|
SE-MD | 9.14 | 2.48–33.77 | <0.001 | 6.61 | 1.53–28.58 | 0.011 |
HRV | 3.08 | 0.99–9.54 | 0.052 | 1.15 | 0.33–4.06 | 0.823 |
DC | 4.38 | 1.39–13.80 | 0.012 | 1.88 | 0.51–6.88 | 0.340 |
Patients common to high risk groups for all three risk variables using high risk quartiles for dichotomization (n = 600)
Parameter | Patients | Cardiovascular deaths |
---|---|---|
SE-MD | 150 | 9 |
HRV | 150 | 6 |
DC | 150 | 7 |
SE-MD ∩ HRV | 79 | 6 |
SE-MD ∩ DC | 77 | 5 |
HRV ∩ DC | 77 | 5 |
SE-MD ∩ HRV ∩ DC | 55 | 5 |
Univariate and multivariate association between risk variables and cardiovascular death over 90 days following NSTEACS using high risk group sizes matching the rate of cardiovascular death in the TIMI DISPERSE2 dataset (n = 600)
Parameter | Univariate hazard ratio | 95% confidence interval | p value | Multivariate hazard ratio | 95% confidence interval | p value |
---|---|---|---|---|---|---|
SE-MD | 8.10 | 1.77–36.97 | 0.007 | 6.93 | 1.23–39.10 | 0.028 |
HRV | 0.00 | – | – | 0.00 | – | – |
DC | 3.84 | 0.50–29.76 | 0.198 | 2.07 | 0.20–21.38 | 0.540 |
Analysis of 365 Day Follow-Up
Discriminative ability of SE-MD, HRV and DC measured using the c-statistic to predict cardiovascular death over 365 days following NSTEACS (n = 600)
Parameter | c-Statistic |
---|---|
SE-MD | 0.78 |
HRV | 0.66 |
DC | 0.73 |
Univariate and multivariate association between risk variables and cardiovascular death over 365 days following NSTEACS (n = 600)
Parameter | Univariate hazard ratio | 95% confidence interval | p value | Multivariate hazard ratio | 95% confidence interval | p value |
---|---|---|---|---|---|---|
SE-MD | 7.78 | 3.17–19.09 | <0.001 | 4.89 | 1.70–14.08 | 0.003 |
HRV | 3.77 | 1.58–8.99 | 0.003 | 1.50 | 0.55–4.11 | 0.432 |
DC | 6.13 | 2.57–14.62 | <0.001 | 2.26 | 0.85–6.03 | 0.104 |
Summary and Discussion
This manuscript investigates use of a new method to risk stratify patients for future cardiovascular death by measuring morphologic differences in ECG affecting the entire heart beat signal. Our method uses dynamic time-warping to compute time-aligned morphology changes between consecutive sinus beats, and then estimates energy at frequencies between 0.30 and 0.55 Hz in the resulting time series of morphologic differences.
We developed the spectral energy of morphologic differences (SE-MD) metric on 764 patients from the TIMI DISPERSE2 study. The SE-MD metric was then evaluated for value in predicting patients at risk of cardiovascular death following NSTEACS in a study using previously unseen data from the TIMI MERLIN study. On a test population of 600 patients from the MERLIN study, SE-MD was strongly associated with the endpoint of cardiovascular death over a 90 day follow-up period. This relationship was consistent for a longer follow-up period of 1 year, and when all risk variables were dichotomized into high risk quartiles or into groups matching the rate of death in the TIMI DISPERSE2 dataset.
For the endpoint of cardiovascular death, SE-MD showed a higher c-statistic and hazard ratio than both HRV and DC for the different experiments considered. We note that these findings hold on a specific endpoint and test population (i.e., cardiovascular death in patients following NSTEACS), and caution against interpreting the results as a general comparison of the different electrocardiographic risk variables. We further observe that a more comprehensive comparison of the methods would explore the risk stratification utility of the different risk variables over periods longer than the 1 year maximum considered by our work and for other endpoints, e.g., MI.
The decision to calculate SE-MD, HRV and DC on 128 Hz holter ECG signals also represents a practical use-case of these methods, i.e., the ability to compute prognostic parameters on data that is typically available for all admitted patients. With higher sampling rates the performance of these metrics could potentially be improved.
A further limitation of this study is the omission of the significant body of work related to the analysis of T-wave morphology (Acar et al. 1999; Zabel et al. 2000). In particular, T-wave alternans (TWA) (Rosenbaum et al. 1994) is commonly used as a measure of repolarization abnormalities that may help identify patients at increased risk. The measurement of TWA typically requires the use of specialized equipment and maneuvers to increase heart rate. We were therefore unable to measure TWA on the holter recordings available in the MERLIN study for comparison with SE-MD. However, we observe that our work differs from TWA both in terms of using information that can be obtained directly from standard holter recordings, and in that SE-MD does not rely on changes in any particular segment of the ECG signal.
Finally, we observe that while we used the endpoint of death due to cardiovascular causes in the reported study, we did not have a more fine-grained description of the cause of death available to us. We believe that death in the period following NSTEACS is likely to be the result of fatal arrhythmias, and SE-MD may help identify patients in a proarrhythmic state. This would be consistent with the results in “Analysis of 365 Day Follow-up” showing SE-MD to be particularly well-suited for risk stratification immediately following the index event. However, we were unable to prove this hypothesis on the current dataset. For future work, we hope to explore the use of SE-MD on a larger cohort, with a more precise categorization of cardiovascular death and a measure of risk variables such as TWA obtained through specialized gold-standard tests.
Notes
Acknowledgments
We would like to thank Gari Clifford for providing tools from Physionet for preprocessing ECG signals and for his technical input on heart rate variability metrics, and Dorothy Curtis for helping with the computational needs for this project. This work was supported, in part, by the Center for Integration of Medicine and Innovative Technology (CIMIT), the Harvard-MIT Division of Health Sciences and Technology (HST), and the Industrial Technology Research Institute (ITRI).
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