When Heart Rate Variability (HRV) data show unexpected results, there are several possible issues; most relate to the manner in which the power spectral density is computed. Before discussing those, a brief mention of data quality is in order.
To compute heart rate variability, the first step is to locate heart beats. By default, the HRV analysis in AcqKnowledge uses an algorithm similar to that used for “Detect and Classify Heartbeats” and for “Locate Human ECG Complex Boundaries” to find the times of heart beats. While the algorithm is generally robust, it is not possible to determine via inspection exactly where beats are located when the software uses the “QRS detector.” It may thus be advantageous to use “Events” instead.
When using event marks, it is possible to ensure that heart beats are not missed, added, or erroneously placed before the HRV analysis is run. That is, it is possible to know exactly what the input is to the HRV computations. Some believe HRV analysis should only be applied to normal beats, so any ectopic beats should be identified and removed prior to HRV computation. This is possible when event marks are used to define the locations of heart beats.
Another issue of concern is the units. AcqKnowledge presents the amount of power in units of seconds2. Results are often instead expressed in milliseconds2. Note that there are one million square milliseconds in one square second. Because 1 second = 1000 milliseconds, (1 second)2 = (1000 milliseconds)2 = 1 million milliseconds2. Therefore, to convert the numbers AcqKnowledge produces into numbers of square milliseconds, the numbers must be multiplied by 106.
There is no universally agreed upon method for computing power spectral density (PSD). Generally the PSD is estimated by computing the Fourier spectrum of overlapping segments of data and then averaging these spectra to obtain the final result. The Fourier transform is typically computed from data that have been detrended and had their mean value removed. Older versions of AcqKnowledge detrend the entire waveform at once instead of detrending each segment independently of the others. Newer versions allow choice between these alternatives. Better estimates are probably obtained by detrending independently. This issue is described in more detail in Application Note 246 R-R Interval Processing Using BIOPAC’s HRV Algorithm Implementation.
That application note also performs comparisons between results obtained by the HRV analysis in AcqKnowledge and those obtained by other software packages. The ratios of powers computed across different parts of the spectrum agree across these different packages even though the absolute power levels do not. AcqKnowledge computes FFTs using the Cooley and Tukey algorithm applied to the raw time series data. No scaling is performed on the coefficients. In addition, this spectrum-based HRV analysis routine in AcqKnowledge allows you to transparently apply various windowing functions on the segments prior to FFT computation. Algorithms utilized by other software packages may use different (or no) windowing and may scale coefficients leading to disagreement over fine details of the spectrum and absolute power levels.
Learn a technique—Trans-radial Electrical Bioimpedance Velocimetry (TREV)—that is more user-friendly and less invasive than the transthoracic method. TREV measures blood movement in the radial and ulnar arteries of the forearm via bioimpedance.
– Presentation and discussion of TREV
– Derived Indices Relevant to TREV
– Hardware setup
– Participant setup
– Recognizing good quality and bad quality data
– Demonstration with the grip anticipation test
– Review of results from other tests
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