Appl Psychophysiol Biofeedback (2010) 35:257–259
DOI 10.1007/s10484-010-9136-8

A Measurement of Electrocardiography and Photoplethesmography in Obese Children

C. V. Russoniello • V. Pougtachev • E. Zhirnov • M. T. Mahar

Published online: 15 June 2010
Springer Science+Business Media, LLC 2010

Abstract
The purpose of this study was to establish heart rate variability normative data on obese children and to comparing the accuracy of two medical technologies photoplethesmography (PPG) with electrocardiography (ECG) while measuring heart rate variability (HRV). PPG is a relatively new technique that holds promise for health care practitioners as an evaluative tool and biofeedback instrument due to its cost and easy administration. This study involved ten children who were recruited for an after-school program designed to reduce obesity. Three-five-minute recordings of HRV were collected while the children were lying in the supine position on a therapy bed. PPG was measured from a thumb sensor and ECG from sensors placed under wristbands on both wrists. The results indicate that PPG is as effective as ECG in measuring the eleven parameters of heart rate variability.

Keywords: Heart rate variability · Electrocardiography · Photoplethesmography · Obesity

Background
Calling it a public health epidemic the Center for Disease Control¹ has estimated the prevalence of seriously overweight children and adolescents in the United States at 13 percent noting this figure has doubled since the 1970s. Children are likely to remain overweight as adolescents and adults² and as a result numerous medical problems eventually emerge₃. Because the efficacy of treatment programs for adults is poor⁴, emphasis needs to be placed on children⁵. It has been reported that obese people have greater autonomic nervous system stress due to excessive weight and the psychological variables associated⁶. In order to study the effects of various stimuli on obese children normative data parameters are needed. Thus, obese children were chosen for this study to establish baseline heart rate variability data for present and future studies that compare lean and normal weight children using this psychophysiological measurement.

Electrocardiogarphy (ECG) is the standard method of measuring the rhythm of the heart by tracing electrical impulses as the heart contracts and relaxes⁷. Cardiac rhythm derived from ECG is the best way to detect not only true sinus rhythm but also all types of ectopic heartbeats. Electorcardiograms are expensive and usually conducted in a physician’s office or hospital however and therefore its usefulness for other practitioners is limited.

Photoplethysmography (PPG) was developed in the 1960s and 1970s by psychophysiology researchers. PPG is based upon the premise that all living tissue and blood have different light-absorbing properties. PPG works by placing an individual’s finger upon a photocell that converts light to electrical energy. The blood in the finger scatters light in the infrared range, and the amount of light reaching the cell is inversely related to the amount of blood in the finger. Hence, when blood vessels in the finger dilate, the increased blood flow allows less light to reach the photo- cell, when blood vessels constrict, blood flow is decreased and increased light reaches the photocell⁸.

PPG measures pulse volume or phasic changes, which are related to beat variations in the force of blood flow. These beat-to-beat changes in peripheral blood flow reflect the heart’s interbeat intervals similar to ECG. PPG therefore, gives summary information reflecting both cardiac and blood vessel components and may be an accurate measure of cardiac function when compared to electro-cardiography. This method does not deal with measuring blood volume pulse where measuring amplitude of PPG is important. In contrast it deals with recognizing pulse waveform patterns to measure time intervals between peaks of the waves or points on the waves representing biggest differences between consecutive signal samples. These time intervals have very high correlations with those obtained from EKG.

Heart Rate Variability (HRV) is a physiological measurement that directly reflects a balance of the autonomic nervous system regulation, which has control over the human body. HRV is a multidimensional measurement of sympathetic and parasympathetic nervous system innervations of the heart. HRV reflects the state of sympathetic (stress, anxiety) or parasympathetic (relaxation, calmness) activation in the body. Heart rate variability (HRV) is considered a marker of cardiac parasympathetic and sympathetic activity,^9,10,11 and is of great interest to health care practitioners. In recent years HRV has been used as a measurement of autonomic nervous system dysfunction¹² as a treatment outcome measure in conditions such as depression¹³, and anxiety and panic¹⁴ as well as a measurement of the effects of various stimuli including video games¹⁵.

Methods
Data was obtained from 10 obese children ages 8–11 years old. All children and their parents signed informed consent forms. The local Institutional Review Board approved the study. Children were included if their body mass index was in the 95th percentile the definition of obesity weight status by the Centers of Disease Control and Prevention¹⁶. Three-five minute recordings of heart rate variability data was collected simultaneously using both ECG and PPG while the children were lying in the supine position on a therapy bed. PPG was measured from a thumb sensor and ECG from sensors placed under wristbands on both wrists. The children were instructed to lie in a relaxed position and to avoid movement if possible.

Measurements
Data was collected using the Heart Rhythm Scanner 2.0 (Biocom Technologies, Poulsbo WA). Since the Heart Rhythm Scanner 2.0 is not capable of simultaneous recording of both ECG and PPG, both signals were recorded synchronously from the same subject using 2 units on 2 separate computers. ECG data was recorded by means of Biocom 1500 ECG Recorder at sampling rate of 256 samples per second, PPG data was recorded by means of Biocom HRM-02 USB Pulse Wave Sensor at sampling rate of 240 samples per second. Upon completing of signal recording they were visually checked and edited to eliminate abnormal heartbeat intervals using the Heart Rhythm Scanner signal editing capabilities. All abnormal intervals were replaced with linearly interpolated values.

After verification and editing of all heartbeat intervals both time- and frequency-domain HRV parameters were calculated in accordance with the standards set forth by the Task Force of the European Society of Cardiology and North American Society of Pacing and Electrophysiology in 1995. Data editing on the equipment used in this study can be performed by any professional having adequate knowledge of cardiovascular physiology and specifically heart rhythm or a technician specially trained to recognize heart rhythm disturbances. In this study medical doctors with expertise in cardio-respiratory functioning performed the editing.

To conduct the analyses the following time-domain parameters were calculated: Mean heart rate value (HR), Mean heartbeat interval value (RR), Standard deviation from the mean RR value (SD), Root mean square of the standard deviation (RMS-SD). To calculate frequency- domain parameters the original sequence of the RR values was re-sampled at the rate of 2 samples per second by means of linear interpolation. The Fast Fourier Transform (FFT) was used to calculate power of spectral density. The following frequency-domain parameters were calculated: Total Power (TP), Very Low Frequency (VLF), Low Frequency (LF), High Frequency (HF), Normalized Low Frequency (LFnorm), Normalized High Frequency (HFnorm), Low Frequency to High Frequency Ratio (LF/ HF). All calculated HRV parameters were exported into a spreadsheet and imported in SPSS v.15 software for statistical analysis.

Results
Means, standard deviations and Pearson Correlations for Electrocardiography (ECG) and Photoplesmography (PPG) when measuring heart rate variability parameters are presented in Table 1 above. Data indicates significant correlations between PPG and ECG when measuring all HRV param- eters. All correlations were equal to or greater than .97 demonstrating the close association between the two technologies.

Discussion
In this study PPG was an accurate measurement of short-term steady-state recordings of HRV when compared with ECG in obese children. Highly significant correlations on all parameters of HRV demonstrate PPG’s potential as a diagnostic and biofeedback treatment modality. In this study only ten obese children were measured in the supine position. Generalization to other conditions such as sitting, walking and so forth are likely to produce very different and perhaps incomparable results. Additional studies are needed to test reliability and validity of ECG and PPG under these conditions. A future study comparing PPG with all weight classifications could be helpful in learning differences in the autonomic nervous system between the groups. Health care practitioners treating conditions related to autonomic nervous system dysfunction may want to consider PPG technology as measurement and treatment instrument given its relative inexpensive costs and availability. A number of commercial products are currently available for clinical and home use.

References

1. Center for Disease Control. (2001). Obesity and overweight: A public health epidemic. Center for Disease Control, URL: http://www.cdc.gov/nccdphp/dnpa/obesity/epidemic.htm.
2. Johnston, F. E. (1985). Health implications of childhood obesity. Annals of Internal Medicine, 103(suppl. 6 pt 2), 1068-1072.
3. National Institutes of Health. (1998). Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults. Bethesda: Department of Health and Human Services; National Heart, Lung, and Blood Institute.
4. Dyer, R. G. (1994). Traditional treatment of obesity: Does it work?. Clinical Endocrinology and Metabolism, 8, 661-668.
5. United States Department of Health and Human Services. (1996). Physical activity and health. A report of the Surgeon General. Atlanta, GA: United States Department of Health and Human Services, Centers for Disease Control and Prevention and Health Promotion.
6. Rosmond, R., & Bjorntorp, P. (2000). Quality of life, overweight, and body fat distribution in middle-aged men. Behavioral Medicine, 26(2), 90-95.
7. Bickley, L. H. (1999). Bate’s guide to physical examination and history taking (7th ed.). Phiadelphia: Lippincott Williams & Wilkins.
8. Cohen, S. K. (1995). Measuring stress. New York: Oxford University Press.
9. Center for Disease Control and Prevention. (2009). Retrieved October 19, 2009, from about BMI for children and teens: http://www.cdc.gov/healthyweight/assessing/bmi/childrens_bmi/ about_childrens_bmi.html#What%20is%20BMI%20percentile.
10. Hayano, J. S. (1991). Accuracy of assessment of cardiac vagal tone by heart variability in normal subjects. American Journal of Cardiology, 67, 199-204.
11. Moser, M. L.-S. (1998). Increase heart rate in drepressed subjects in spite of unchanged automic balance? Journal of Affective Disorders, 4, 115-124.
12. Piepoli, M., Sleight, P., Leuzzi, S., Valle, F., Spadacini, G., Passino, C., et al. (1997). Origin of respiratory sinus arrhythmia in conscious humans. An important role for arterial carotid baroreceptors. Circulation, 95(7), 1813-1821.
13. Electrophysiology, T. F. (1996). Heart rate variability standards of measurement, physiological interpretation and clinical us. Cir- culation, 93, 1043-1065.
14. Hughes, J. W. (2000). Depressed mood is related to high-frequency heart rate variability during stressors. Psychosomatic Medicine, 62, 796-803.
15. Friedman, B. H., & Thayer, J. F. (1998). Anxiety and autonomic flexibility: a cardiovascular approach. Biological Psychology, 49, 303-323.
16. Russoniello, C. V. (2009). The effectiveness of casual video games in improving mood and decreasing stress. Journal of Cyber Therapy and Rehabilitation, 2, 53-66.