Spectral Analysis of Respiratory Activity Provides a Second Autonomic Measure Associated With Spectral Analysis of Heart Rate Variability

The premise for the difference is that the AFC method starts with two independent, but concurrent signals, RA and EKG. Mathematically, two independent signals are required to fully describe (mathematically) a system with two independent parts. The SA package selected as a continuous wavelet transform (CWT) and is used to analyze both signals independently. Peerreviewed published reports indicate that RA analysis is necessary to isolate parasympathetic from sympathetic activity. Our recent studies have borne this out and have shown that the MITbased technique, (including RA spectral analysis) provides more specific and sensitive autonomic indices. Further the CWT analysis improves the mathematical accuracy of the AFC parameters over the fast Fourier transform (FFT) method discussed in the Special Report in two ways. First, the FFT as a result of windowing requires a compromise between time and frequency fidelity. More accurate temporal resolution is achieved at the expense of frequency resolution, and vise versa. Second, the FFT requires stationary signals, biological (including autonomic) signals are inherently non-stationary. Clinically, this issue forces longer recording times and causes the FFT approach to miss rapid changes in autonomic responses. The CWT method resolves these mathematical issues. An example of the difficulty with the spectral domain (SD) HRV (without SA of respiratory activity) method is that during paced, deep breathing when the breathing frequency is in the LF region, the HRV spectrum show little or no activity in the HF region and most of the activity in the LF region. Based on the definitions from the Special Report, this would suggest little of no PSNS (HF) activity during the pace breathing challenge and a significant amount of SNS (LF) activity. This is counter-intuitive. Paced, deep breathing should increase the PSNS output and possibly decrease the SNS. In this study we compare and contrast the differences between the two approaches and present the results of the two approaches for the same clinical data set. METHODS ANS function testing using the ANX-3.0 system (Ansar, Medical Device Technologies, Inc., Philadelphia, PA) was performed on 5752 patients (Table 1) in 38 ambulatory clinics nationwide. The 15.5’ test clinical exam included a 5’ resting baseline. The ANX-3.0 utilizes SA of RA and SA of HRVto compute SNS and PSNS output at rest and during various