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Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions
Chapters
Summary June 5th, 2019
This protocol describes a method for calculating Heart Rate Variability (HRV) from electrocardiogram (ECG) waveforms. Waveforms from continuous heart rate (HR) recordings during active video game (AVG) sessions were used to measure the aerobic performance of youth with cerebral palsy (CP).
Transcript
Protocol provides an analytical method for measuring heart rate variability using electrocardiogram data to determine aerobic function. And with this, it will help us gain insight into the autonomic system functions within physical therapy protocols. This technique may be used to measure aerobic workload, demands and physical therapy strategies for youth with cerebral palsy.
For example, when in physical therapy, we're doing techniques to promote health and fitness using active video games. Heart rate variability measures aerobic workload and provides cardiovascular function measures in patients and allows us to dose physical therapy at the level that meets and challenges the patient most appropriately. The methodology is useful in cardiac care and cardio rehabilitation.
And with that, the calculations could be integrated into cardiac monitoring devices where they would provide heart rate variability from ECG or electrocardiograms as an outcome. The calculations can be challenging at first. But by creating a training video, we can aid clinicians and researchers in better utilizing these measures for evaluating the effectiveness of cardiac care.
For active video game data collection sessions, select active video games with hand icons only, feet icons only, or both hand and feet icons for object collection to determine which game is more effective in promoting physical activity and fitness without being too demanding and causing early undue fatigue. To equip the youth subjects with cerebral palsy with a heart rate monitor, first plug a fully charged Bluetooth module into the data computer, be it the charge cradle, and open the config tool. Use a wet hand to moisten the conductive areas on the heart rate monitor chest strap.
Place the Bluetooth module into the chest strap with the conductive surfaces of the module lined up with those of the chest strap. The monitor will click into place. Then apply the heart rate monitor chest strap and Bluetooth module to the subject with the module aligned with the left mid axillary line and the strap just under the pectoral muscles.
Once properly positioned, tighten the device so that it will not move during the session but is not uncomfortable for the subject. Have the subject sit on a bench with their feet flat on the floor and with their knees and hips flexed to 90 degrees for postural support and stability. Plug the connector into the USB port of the computer that will be used to view the data and use the live mode tab to monitor the heart rate, respiratory rate and posture of the subject in real time.
Then record the subject for a period of rest to obtain a baseline recording of the subject's heart rate before starting the first active video game for the subject. At the end of the gaming session, download the electrocardiogram data from the heart rate monitor and remove the strap from the subject. To calculate the heart rate variability from the electrocardiogram data, open MATLAB and organize the data into five-minute intervals to ensure comparable data between phases.
Calculate the rest period as the five minutes prior to the game start and the recovery phase as the five minutes after the end of the cool down phase to perform R peak detection on the raw electrocardiogram signal. Once these times have been obtained, identify the location of the game phase of interest within the ECG file and select the five-minute section of interest. Calculate a threshold for peak detection based upon the average and standard deviation of the waveform.
And along with the minimum height for the R peak, assign a minimum distance of 0.3 seconds between the peaks to minimize the detection of incorrect peaks around the desired R.Once the threshold has been set, run peakdetection. m to process the waveform and attempt to discern all of the R peaks for the inter beat interval and heart rate variability calculations. Generate a preliminary plot so that it can be reviewed for irregularities as illustrated.
Many data sessions are fairly uniform and will therefore only require a few corrections. Some cases however are fairly messy and require more time to review and obtain the proper R locations. To correct these irregularities, manually edit the detection variable that contains the microvolt reading of the peak in column one and the location in the current game session in column two.
In most cases, the proper R peaks can easily be found by zooming in to the problem location. Obtain the matrix of intervals ignoring any interval greater than 1.5 seconds and save these inter beat intervals for further calculations in the verification of data. Then use these intervals to calculate the root mean square of the successive differences.
Prior to calculating heart rate variability measures, open a waveform to check for correct peak detection. Use the magnifying tool to select an area of the plot that is output zooming in or out if the window is not easily inspected visually. Use the data tips tool to find the location of the missed peak as necessary.
For peak correction, locate the detection variable and double-click in the workspace to correct the incorrectly detected or missing peaks. If the point is a false positive, remove the point from the array and change the values of any incorrectly marked peaks adjacent to unmarked peaks to that of the unmarked peaks. Then to obtain heart rate variability measure calculations, run hrvmeasures.
m and open the heart rate variability variable from the workspace window. This method provides data for analyzing the effect that the newly developed active video gaming method has on the heart rate variability of a specific subject by locating the R portion of the QRS waveform in the subject's ECG data. If the heart rate monitor makes proper contact with the subject, the data will be uniform substantially reducing the need for corrections.
If the data are sufficiently variable due to momentary changes in the heart rate monitor skin contact, the initial analysis may incorrectly label the peaks as indicated. Altering the threshold levels in the minimum time between the peaks can also help to clean up the detection values for the production and adjusted plot. It is important to pan through each waveform to ensure proper peak detection, remove false positives, and shift mislabeled peaks prior to calculating heart rate variability data.
Other considerations and influences using heart rate variability measurements include physiological considerations, hydration status, and environmental influences. This method is useful in activity-based physical therapy interventions as in the past decade there's been a movement from disability-based interventions to more health promotion and fitness and this method allows us to measure fitness and function mobility. Before applying the heart rate monitor, you want to ensure that the skin is not irritated and that the device is in good repair to avoid skin abrasions.
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