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Pattern recognition and time-frequency representation of cardiac rhythm dynamics
Journal of Electrocardiology 40 (2007) S30 – S31 www.elsevier.com/locate/jelectrocard
Pattern recognition and time-frequency representation of cardiac rhythm dynamics Vladimir Shusterman4 University of Pittsburgh, Pittsburgh, USA
The structure of beat-to-beat patterns of cardiac rhythm is complex and includes a multitude of time-varying components. Analysis of changes in these components has proven useful for analysis of the autonomic nervous system activity, as well as stress responses that might be related to clinically significant ischemic and arrhythmic events. However, because of the multicomponent structure of cardiac rhythm, tracking changes in this signal, in particular abrupt and irregular perturbations, is technically challenging. One important goal of the time-series analysis of cardiac rhythm is an identification of patterns and mechanisms predisposing to the initiation of ventricular tachyarrhythmias (VTA). Changes in cardiac rhythm have been consistently found before the onset of VTA in different patient populations. The predominant pattern of changes
Fig. 1. Changes in heart rate before the onset of VTA relative to the 24-hour maximum heart rate (N = 53 patients). The Heart Rate (HR) Delta Index was computed as 100% [(HRVT HR24)/(HRMax HR24)], where HRVT indicates HR during 15 minutes before VTA; HR24, 24-hour-mean HR; and HRMax, maximum HR in the 24-hour period.
4 Tel.: +1 412 647 6272. E-mail address: [email protected] 0022-0736/$ – see front matter D 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.jelectrocard.2006.10.025
Table 1 Prevalence of early and late coupled ventricular extrasystoles and shortlong-short sequences before the onset of VTA Late (%)
Early (%)
Short-LongShort (%)
Patients/ VTA
Study
86 92 – 85 56 –
14 7 – 13 – –
14 – 25 2 – 8
22/73 59/1102 38/286 90/260 52/68 309/4973
Roelke et al, JACC 1994 Anderson et al, JACC, 1995 Meyerfeldt et al, EHJ, 1997 Taylor et al, JCE, 2000 Saeed et al, AJC, 2000 Gronefeld et al, PACE 2002
included an increase in heart rate during 5 to 45 minutes before the onset. Yet, analysis of the magnitude of the increase (Fig. 1) shows that only a minority (4%) of patients reached a 24-hour maximum heart rate before the event. The heart rate increase before VTA was greater than 30% and 60% of the 24-hour-maximum in only 35% and 17% of the patients, respectively (Fig. 1). Therefore, analysis of the heart rate alone does not allow sufficiently accurate identification of the specific changes in rhythm structure that precede and, possibly, facilitate initiation of VTA. Similarly, analysis of the frequency of short-long-short sequences of cardiac cycle lengths and early coupled ventricular extrasystoles also proved ineffective because most VTA episodes are not preceded by either frequent short-long-short sequences or early coupled extrasystoles (Table 1). To improve identification of changes in the cardiac rhythm structure, we developed a 2-step approach. First, we extracted the most significant components of the signal structure by applying a pattern-recognition approach based on a linear orthogonal transformation of the time series of cardiac cycle lengths with the use of the basis vectors derived from the covariance matrix of the signal. Our experiments showed that the first 6 most significant components (ie, the projection coefficients associated with
V. Shusterman / Journal of Electrocardiology 40 (2007) S30–S31
S31
the greatest eigenvalues) provided sufficiently accurate information about patterns of changes in the signal structure in most patients (Fig. 2). In particular, this allowed us to identify the time intervals of enhanced instabilities. Second, we used time-frequency representation of the most significant eigenvectors during the time intervals of enhanced instabilities for detailed analysis of the most significant signal components. This 2-step approach has proven useful in detecting and quantifying the rhythm instabilities that precede the onset of VTA and syncope (Fig. 2). Those features of cardiac rhythm could not be exposed by the traditional statistical or spectral methods. Thus, pattern recognition combined with time-frequency representation is a promising new approach for the analysis of instabilities in the cardiac rhythm dynamics.
Fig. 2. Changes in cardiac rhythm during 16 hours before the initiation of ventricular tachyarrhythmia. The onset of the arrhythmia was at the end of the recording. Top panel: series of cardiac cycle lengths. Lower panels: time series of the most significant projection coefficients onto the linear, orthogonal basis vectors derived from the covariance matrix of the signal shown in the top panel. Adapted from J Electrocardiol 2003; 36(Suppl):219.
Pattern recognition and time-frequency representation of cardiac rhythm dynamics Vladimir Shusterman4 University of Pittsburgh, Pittsburgh, USA
The structure of beat-to-beat patterns of cardiac rhythm is complex and includes a multitude of time-varying components. Analysis of changes in these components has proven useful for analysis of the autonomic nervous system activity, as well as stress responses that might be related to clinically significant ischemic and arrhythmic events. However, because of the multicomponent structure of cardiac rhythm, tracking changes in this signal, in particular abrupt and irregular perturbations, is technically challenging. One important goal of the time-series analysis of cardiac rhythm is an identification of patterns and mechanisms predisposing to the initiation of ventricular tachyarrhythmias (VTA). Changes in cardiac rhythm have been consistently found before the onset of VTA in different patient populations. The predominant pattern of changes
Fig. 1. Changes in heart rate before the onset of VTA relative to the 24-hour maximum heart rate (N = 53 patients). The Heart Rate (HR) Delta Index was computed as 100% [(HRVT HR24)/(HRMax HR24)], where HRVT indicates HR during 15 minutes before VTA; HR24, 24-hour-mean HR; and HRMax, maximum HR in the 24-hour period.
4 Tel.: +1 412 647 6272. E-mail address: [email protected] 0022-0736/$ – see front matter D 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.jelectrocard.2006.10.025
Table 1 Prevalence of early and late coupled ventricular extrasystoles and shortlong-short sequences before the onset of VTA Late (%)
Early (%)
Short-LongShort (%)
Patients/ VTA
Study
86 92 – 85 56 –
14 7 – 13 – –
14 – 25 2 – 8
22/73 59/1102 38/286 90/260 52/68 309/4973
Roelke et al, JACC 1994 Anderson et al, JACC, 1995 Meyerfeldt et al, EHJ, 1997 Taylor et al, JCE, 2000 Saeed et al, AJC, 2000 Gronefeld et al, PACE 2002
included an increase in heart rate during 5 to 45 minutes before the onset. Yet, analysis of the magnitude of the increase (Fig. 1) shows that only a minority (4%) of patients reached a 24-hour maximum heart rate before the event. The heart rate increase before VTA was greater than 30% and 60% of the 24-hour-maximum in only 35% and 17% of the patients, respectively (Fig. 1). Therefore, analysis of the heart rate alone does not allow sufficiently accurate identification of the specific changes in rhythm structure that precede and, possibly, facilitate initiation of VTA. Similarly, analysis of the frequency of short-long-short sequences of cardiac cycle lengths and early coupled ventricular extrasystoles also proved ineffective because most VTA episodes are not preceded by either frequent short-long-short sequences or early coupled extrasystoles (Table 1). To improve identification of changes in the cardiac rhythm structure, we developed a 2-step approach. First, we extracted the most significant components of the signal structure by applying a pattern-recognition approach based on a linear orthogonal transformation of the time series of cardiac cycle lengths with the use of the basis vectors derived from the covariance matrix of the signal. Our experiments showed that the first 6 most significant components (ie, the projection coefficients associated with
V. Shusterman / Journal of Electrocardiology 40 (2007) S30–S31
S31
the greatest eigenvalues) provided sufficiently accurate information about patterns of changes in the signal structure in most patients (Fig. 2). In particular, this allowed us to identify the time intervals of enhanced instabilities. Second, we used time-frequency representation of the most significant eigenvectors during the time intervals of enhanced instabilities for detailed analysis of the most significant signal components. This 2-step approach has proven useful in detecting and quantifying the rhythm instabilities that precede the onset of VTA and syncope (Fig. 2). Those features of cardiac rhythm could not be exposed by the traditional statistical or spectral methods. Thus, pattern recognition combined with time-frequency representation is a promising new approach for the analysis of instabilities in the cardiac rhythm dynamics.
Fig. 2. Changes in cardiac rhythm during 16 hours before the initiation of ventricular tachyarrhythmia. The onset of the arrhythmia was at the end of the recording. Top panel: series of cardiac cycle lengths. Lower panels: time series of the most significant projection coefficients onto the linear, orthogonal basis vectors derived from the covariance matrix of the signal shown in the top panel. Adapted from J Electrocardiol 2003; 36(Suppl):219.