Assessment of Autonomic Function in Subjects Practicing Yoga Using Spectral Analysis and Approximate Entropy Method

Volume 5 | Issue 3 Assessment of Autonomic Function in Subjects Practicing Yoga Using Spectral Analysis and Approximate Entropy Method Kamal Ahmed* MET Department, Tennessee Tech University, Lewis Hall, Cookeville, TN, USA *Corresponding author: Kamal Ahmed, Ph.D, SMIEE, MET Department, Tennessee Tech University Box 5003, Lewis Hall, Cookeville, TN, USA 38501, E-mail: akamal@tntech.edu Case Report Open Access

The aim of this study to investigate the auto power spectra of HRV signals at different breathing rates as well as producing the gain frequency response for both healthy subjects group (Control) and subjects practicing Yoga group.Another aim of this study is to introduce other statistics to assess the autonomic function developed from nonlinear dynamics.One such statistics, approximate entropy (ApEn) which quantifies the regularity of patterns in data set [7][8][9][10][11][12][13][14][15][16].ApEn has been applied to HRV signal for both control and Yoga subjects.
The study group was composed of fifty one healthy subjects (25±8.7)years and fifty two Yoga Subjects (26±9.6)years has been investigated in this study at Johns Hopkins hospital, MD, USA during 2013/ 2014.None of the Yoga subjects and normal healthy subjects had clinical signs of myocardial infraction, arterial hypertension, diabetes mellitus including type I and II or pulmonary disease.No drugs that could affect HRV parameters were used by the subjects and the control groups under study.The final group consisted of 52 Yoga practicing subjects and 51 of normal healthy subjects.All subjects agreed to participate in the research prior to their inclusion in the study and the consent of ethical committee was obtained and approved the study protocol.Each subject lies supine on a bed.The breathing signal is measured using a thermistor placed in the nose.The Electrocardiogram (ECG) is taken from wrists and ankle (Lead II).All the measurements were recorded for the duration of the experiments using laptop computer.After the subject has settled comfortably, a base line of the physiological measurements, ECG, derived HRV and breathing signal were measured for 5 min for both healthy subjects and patients with Yoga subjects at rest in supine position to calculate the ApEn and heart rate for each group.The subject is then asked to breathe deeply at different rates for 2 min following the light sequence, green inhale and red exhale.The cycle length is varied using frequency generator and the light indicator automatically divides the cycle length into 40% inhalation and 60% exhalation [10].A rest period is allowed between each breathing rate sequence.The following breathing rates were examined for each subject 3,4,6,10,12,14,16,18,24 and 30 breath/min. Figure 1 shows the block diagram of the experiments.The ECG for every patient participated in this study is fed into an electronic device which detects the R-wave and measures the time until the next R wave occurs.Alternately, the peak of QRS was identified for each beat using rate-detector algorithm after exclusion of artifacts and ectopics.Those periods in which beat identification was poor were excluded from the analysis.This R-R time is converted into voltage directly proportional to that time.The reconstructed voltage signals as shown in Figure 2 may now place as HRV signals and can be interfaced to Laptop computer to obtain auto power and cross power spectra of HRV signal and breathing signals at different rates using Fast Fourier transform (FFT) to calculate the gain frequency response and ApEn (Appendix).

Identification Procedure of Frequency Gain Response
Consider a linear system with an input-output relationship as shown in Figure 3.It can be represented by a transfer function which can be found by several conventional methods e.g.impulse response, H(jw).R(jw), where H(jw) and R(jw) are the Fourier transform (using FFT algorithm) of the output signal h(t) and input signal r(t), respectively.If we define the transfer function between H(jw) and R(jw) as shown in figure 3 as: ( )

Results
And multiply the numerator and denominator of the right side by R*(jw),i.e. the complex conjugate of the Fourier transformed input r(t),the result is Where Prh(jw) is the complex conjugate of the cross power spectrum between r(t) and h(t) and Prr(w) is the auto-power spectrum of r(t).Assuming that n(t) and r(t) are not auto-correlated then Prh*(jw) is not influenced by the presence of n(t).Since Prr(w) does not involve n(t) at all, this estimate of Grh(jw) from h(t) and r(t) becomes a good method of overcoming the presence of noise to estimate H/R or the frequency gain response.The clinical data of fifty two Yoga subjects are summarized in Table 1.There were no significant differences between Yoga practicing and control in these parameters.However, the results of HRV analysis and Approximate Entropy (ApEn) are shown in Figure 4 and Table 2 and Table 3. Figure 4 shows the gain frequency response for both Yoga group and control group at different breathing rates.The gain frequency response of control healthy group is significantly lower (p=0.003)than Yoga Subjects.Table 2 shows significant difference (p=0.001) between the value of ApEn of HRV signal at rest for both groups.Table 3 shows the duration of Yoga practicing group and ApEn as well as HRV parameters (heart rate, SDNN (standard deviation of all R-R intervals) at rest.2, There is significant reduction (p<0.5) in both ApEn and SDNN in Schizophrenia patients group.Also, Table 3 shows significant increase in ApEn index of Yoga subjects related to the duration of the disease.However, SDNN and heart rate did not show significant decrease with the duration of Yoga practicing.This is important finding to quantify the autonomic function of Yoga subjects with respect to their duration of Yoga practicing using ApEn index.In fact, a low ApEn correlates with greater system isolation i.e. less interaction between multiple inputs that makeup a normal control system [10,13].Several normal physiologic processes have been studied to demonstrate the relationship between ApEn and the integrity of the biological system.Kaplan et al. [14] compared healthy young and elderly subjects and found reduced complexity in the elderly.Similarly, Rayan et al. [15] demonstrated ApEn decreased with aging.In this study, we observed the lowest normalized ApEn values in Schizophrenia patients with autonomic dysfunction.Also, we found correlation between increasing ApEn values and Yoga practicing duration as shown in Table 3.The significance of ApEn index is to represent one value (between 0 and 1) which indicates the quality of connection, interaction, regularity and complexity in the system (Appendix A).Applying this concept to the autonomic nervous system of Yoga subjects, it seems their autonomic system has the integrity and interaction with respect to normal healthy subjects which may be manifested in low ApEn values in Healthy normal subjects group as shown in Table 3.The frequency gain response as shown in Figure 4 demonstrates the influence of breathing on gain response with respect to Yoga subjects and healthy subjects.The Yoga subjects group exhibits two distinct peaks at nearly 6 cycles/min (0.1 Hz) and nearly 15 cycles/min (2.5 Hz).However, healthy subjects group who did not practicing Yoga shows only one lower distinct peak at the same breathing rate 6 cycles/min.It is clear that Yoga subjects show higher gain response to breathing than the healthy subjects at all breathing frequencies.This may be attributed to increase as well as balance in autonomic function especially parasympathetic activities.These observations might be important with respect to the decreased rate of sudden death occurred in Yoga subjects which correlated with increased Vagus (parasympathetic) activity [7][8][9].Our finding which correlates the duration of Practicing Yoga group and ApEn may be considered a simple prognostic value to assess the autonomic function of Yoga subjects to screening the autonomic function quantitative way.

Conclusion and Future Work
This study demonstrates the significance of using the spectral analysis Method and Max Entropy Method in assessing the function of autonomic nervous system for Yoga practicing subjects compared to normal subjects However, further study is required to

Figure 1 :
Figure 1: Block Diagram of Experimental Procedure

Figure 2 :
Figure 2: Derivation of heart rate variability (HRV) signal from Electrocardiogram (ECG) (a) ECG; (b) Detection of R-R interval; (c) Construction of HRV signal and (d) Smoothed Derivation HRV signal

Figure 4 :Table 3 :
Figure 4: The average frequency gain response of healthy subjects and Yoga subjects at different breathing rates Submit your next manuscript to Annex Publishers and benefit from: Submit your manuscript at http://www.annexpublishers.com/paper-submission.php→ Easy online submission process → Rapid peer review process → Open access: articles available free online → Online article availability soon after acceptance for Publication → Better discount on subsequent article submission → More accessibility of the articles to the readers/researchers within the field

Table 2 :
ApEn and HRV parameters (heart rate and SDNN(standard deviation of all R-R intervals) at rest in Yoga and control groups