Beat-to-beat changes in cardiac signals or heart rate variability (HRV) are controlled by the two branches of autonomic nervous system (ANS) in a very complex manner. Although traditional HRV (tHRV) analysis has shown to provide information on cardiac ANS control, it often fails to isolate the effect of two branches in HRV signals. This problem becomes more obvious especially at low respiratory rates since parasympathetic activity shifts into lower frequencies and overlaps the frequency interval where sympathetic region is defined. To investigate the effect of respiration in HRV analysis we have analyzed the data of 17 healthy subjects while they were performing normal breathing (NB) and deep breathing (DB). Data were analyzed and compared using both tHRV analysis and enhanced HRV (eHRV) analysis where we used respiration to locate the frequency interval of parasympathetic activity in HRV signal. eHRV analysis provided proper isolation and more accurate detection of parasympathetic and sympathetic control of the heart.

Heart rate rose, whereas LFP, LFPn and LFP/HFP fell before the onset of VT. This pattern of changes could be explained by a rise in sympathetic activity and saturation of the HRV signal resulting in dissociation of the average and rhythmical effects of sympathetic activity. These findings suggest that alterations in autonomic activity contributed to arrhythmogenesis in this group of patients.

Heart Rate Variability (HRV) analysis has become an important tool in assessing human Autonomic Nervous System (ANS) activity in recent years. Orthostatic challenge is one of the most common tests to detect ANS dysfunction. In this study we looked at the changes in ANS activity of normal subjects to orthostatic challenge and compared the results of 3 different HRV analysis methods: Time-Domain Methods, HRV spectral analysis without respiratory analysis (RA) and with RA. Although all three methods have indicated an increase in sympathetic activity and a decrease in parasympathetic activity from baseline to stand, the only significant increase in sympathetic activity was observed in HRV with RA method. Additional information from RA enables isolating the sympathetic and parasympathetic branches in HRV signals and therefore reflects ANS changes more accurately. On the other hand, sympathetic and parasympathetic power may not be separated properly if respiration-dependent fluctuations in HRV are ignored. It is expected that the differences between methods would be very clear with low respiratory rates. However, we focused on studies with normal respiratory rates and have also found significant differences among the methods.

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Abstract-Cardiac cycle dynamics reflect underlying physiological changes that could predict imminent arrhythmias but are obscured by high complexity, nonstationarity, and large interindividual differences. To overcome these problems, we developed an adaptive technique, referred to as the modified Karhunen-Loeve transform (MKLT), that identifies an individual characteristic ("core") pattern of cardiac cycles and then tracks the changes in the pattern by projecting the signal onto characteristic eigenvectors. We hypothesized that disturbances in the core pattern, indicating progressive destabilization of cardiac rhythm, would predict the onset of spontaneous sustained ventricular tachyarrhythmias (VTAs) better than previously reported methods. We analyzed serial ambulatory ECGs recorded in 57 patients at the time of VTA and non-VTA 24-hour periods. The disturbances in the pattern were found in 82% of the recordings before the onset of impending VTA, and their dimensionality, defined as the number of unstable orthogonal projections, increased gradually several hours before the onset. MKLT provided greater sensitivity and specificity (70% and 93%) compared with the best traditional method (68% and 67%, respectively). We present a theoretical analysis of MKLT and describe the effects of ectopy and slow changes in cardiac cycles on the disturbances in the pattern. We conclude that MKLT provides greater predictive accuracy than previously reported methods. The improvement is due to the use of individual patterns as a reference for tracking the changes. Because this approach is independent of the group reference values or the underlying clinical context, it should have substantial potential for predicting other forms of arrhythmic events in other populations. Key Words: ventricular arrhythmias Ⅲ cardiac cycle dynamics Ⅲ orthogonal decomposition A lthough substantial progress has been made in the understanding of arrhythmia mechanisms and identification of individuals at risk, short-term prediction of the timing of onset of sustained ventricular tachyarrhythmias (VTAs) has lagged, delaying development of preventive treatments. 1 Because autonomic activity is thought to be an important trigger of VTA and because cardiac cycle lengths (CCLs) are modulated by autonomic tone, it has been assumed that the analysis of the changes in CCL could predict the timing as well as the triggers of VTA. 2 This has been confirmed by studies that demonstrated heart rate increase before the VTA onset in many patients. [2][3][4][5] However, the change in heart rate before the onset of VTA is usually small and indistinguishable from random daily variations. 2,6 Descriptors of heart rate variability proved useful in general risk assessment but failed to predict the timing of VTA. 5,7 Probable reasons for the failure include the high complexity of the interacting physiological influences and violation of the statistical assumptions that underlie traditional techniques. 8 In addition, the attempts to summarize highly nonstationary and indivi...

Beat-to-beat changes in cardiac signals or heart rate variability (HRV) is controlled by the two branches of Autonomic Nervous System (ANS) in a very complex manner. Although traditional HRV (tHRV) analysis has shown to provide information on cardiac ANS control, it often fails to isolate the effect of two branches in HRV signals. This problem becomes more obvious especially at low respiratory rates since parasympathetic activity shifts into lower frequencies and overlaps the frequency interval where sympathetic region is defined. To investigate the effect of respiration in HRV analysis we have analyzed the data of 17 healthy subjects while they were performing normal breathing (NB) and deep breathing (DB). Data were analyzed and compared using both tHRV analysis and enhanced HRV (eHRV) analysis where we used respiration to locate the frequency interval of parasympathetic activity in HRV signal. eHRV analysis provided proper isolation and more accurate detection of parasympathetic and sympathetic control of the heart.

HR and RRV did not change before type 1 VTsm, suggesting that short-term changes in autonomic activity were not essential to initiation of apparent PVC-triggered VTsm. In contrast, RR interval dynamics before type 2 VTsm suggested that short-term changes in neurohormonal activity contributed to arrhythmia initiation. Heterogeneities in arrhythmia onset may reflect distinct triggers and substrate properties that could provide a basis for effective therapeutic targets.

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