ECG analysis
One method to obtain heart rate variability (HRV) data is through electrocardiogram (ECG) recordings. ECG recordings are collected through a lead II configuration using Physio16 and NetStation. From there, ECG recordings are exported to a txt file using MatLab. In order to pre-process data prior to analysis, recordings are then filtered and artifacts are corrected manually in AcqKnowledge. After pre-processing, data sets are now ready for analysis in Kubios. The features used in Kubios will vary based on the particular study. For a 5 minute analysis, a 5 minute sample will be taken from the data set, automatic artifact correction will be applied (in case of any potential error during pre-processing), and the FFT settings will be adjusted to 512 points, a window size of 150 s, and an overlap of 50%. For one study, all data sets will be saved to an SPSS batch file for statistical analysis. Before analysis, data should be checked for a normal distribution using histograms, and transformed through a natural log (LN) transformation.
RR analysis
For alternative recordings devices, such as finger monitors or chest straps, RR intervals are recorded as opposed to the raw waveform. After recording, the RR intervals can be emailed as txt files and then uploaded to Box from email. Since AcqKnowledge has no option to import RR intervals without an ECG waveform, there are no pre-processing steps for this form of data. Instead, RR data can be imported straight to Kubios where analysis will be the same procedure as with an ECG recording (select sample length, turn on automatic artifact correction, adjust FFT settings according to the recording length, etc.). Since only the RR intervals are recorded with these devices, there will be no ECG waveform. Instead, analysis is based on the tachogram. As with ECG, all data sets from the same study should be saved to an SPSS batch file for statistical analysis. Before analysis, data should be checked for a normal distribution using histograms, and transformed through a natural log (LN) transformation.
For an in-depth explanation of these methods, proceed to the HRV tutorials section
