gms | German Medical Science

33. Internationale Konferenz für Elektrokardiographie

Internationale Konferenz für Elektrokardiographie

Clusterization of the Relationship between SNS and PSNS activity by Heart Rate Variability Analysis

Meeting Abstract

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33rd International Congress on Electrocardiology. Cologne, 28.06.-01.07.2006. Düsseldorf, Köln: German Medical Science; 2007. Doc06ice111

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Veröffentlicht: 8. Februar 2007

© 2007 Riftine.
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Question: The introduction of HRV analysis - especially, the identification of the power of low-frequency band of HRV spectral function with the activity of Sympathetic Nervous System (SNS) and the power of its high-frequency band with the activity of Parasympathetic Nervous System (PSNS) - opened up new theoretical opportunities for ANS assessment. But to make practical use of this important scientific discovery one had to solve the problem of deriving some form of quantitative relationship between SNS and PSNS from the spectral function. HRV analysis is based on measuring variability in heart rate; specifically, variability in intervals between R waves - “RR intervals”. These RR intervals are then analyzed by spectral or some other form of mathematical analysis (e.g., chaos, wavelet theories). Such mathematical analysis generates multiple parameters; typically 15-30. The problem of SNS-PSNS quantification, which has remained for many years the principal dilemma of HRV analysis, is specifically in reducing all possible variations of these multiple parameters to a quantitative relationship between only two parameters: SNS and PSNS.

Method used: We solve the problem of SNS-PSNS quantification by using proprietary algorithms and a new approach based on one of the leading theories of Artificial Intelligence - Marvin Minsky's Frame Theory.

The analysis of the SNS – PSNS relationship uses the following parameters:

1. HF (0.15 - 0.5 Hz);

2. LF (0.04 - 0.15 Hz);

3. LF1 (0.07 - 0.15 Hz);

4. LF2 (0.04 – 0.07 Hz);

5. Smax(HF) – peak of amplitude in HF;

6. Smax(LF) – peak of amplitude in LF(0.04-0.15);

7. Smax(MF) - peak in mid-frequency range (0.15 ± 0.02) if the maximum falls within this range;

8. F max(HF) - Value of frequency at Smax(HF);

9. F max(LF) - Value of frequency at Smax(LF);

10. F max(MF) - Value of frequency at Smax(MF);

11. HF/LF1 ratio;

12. HF/(LF1 + LF2) ratio;

13. HR.

Result: The proposed algorithm for HRV analyses, called “IntelWave”, automatically recognizes 74 clusters of ANS states that represent different relationships between SNS and PSNS activities and variations in their balance. IntelWave then graphs the Parasympathetic activity on the horizontal or X-axis and the Sympathetic activity on the vertical or Y-axis (Figure 1 [Fig. 1]). The intersection point of the Sympathetic and Parasympathetic axes is the point of Autonomic Balance. To the right of and above this balance point, IntelWave displays an area of increased Parasympathetic and Sympathetic activities in 4 gradations. Decreases in PSNS and SNS activities are shown to the left and below the balance point. 74 ANS states are subdivided into nine categories (circled in red in Figure 1 [Fig. 1], with corresponding numbers marking each category - e.g. 1, 2).

Conclusion: The developed algorithm opens new perspectives for quantitative assessment of the autonomic reaction of the human organism on any therapeutic and other interventions.