(1) The Hilbert–Huang transform shows high ability to analyze time-changing properties of frequency components of heart rate variability from blood pressure signals. Thus, it can identify instantaneous frequency shifts and provide new insight into the nature these kinds of data.

The study compares the Hilbert-Huang transform with the power spectrum and continuous wavelet transform in analysis of a five-minute blood pressure signal. Power spectrum does not identify slow oscillations, whereas continuous wavelet transform has lower time resolution than the Hilbert-Huang transform.

(2) Knai’s second paper includes the first report of glucose oscillations in the 50–100 s range. Four pigs under general anaesthesia were included in the experiments, which explored high-resolution blood glucose time-series sampled using a novel continuous intravascular sensor. In all time series, the researches found several interesting oscillatory components, especially in the 5000–10000 s, 500–1000 s, and 50–100 s regions. The origin of these oscillations and their role in overall blood glucose regulation remains unknown.

(3) Cardiac surgery does not lead to loss of oscillatory components in circulatory signals, contrary to the researchers’ hypothesis. Loss of complexity is seen as a consequence of several diseases and aging, and oscillatory components of circulatory signals are linked to the system’s overall complexity. Heart rate variability is known to decrease after cardiac surgery. However, oscillatory components did not represent any reduction in the overall complexity.

To study this, Knai and co-workers extracted three time series from recordings of systolic blood pressure, heart rate and amplitude of the electrocardiogram’s R‐wave in eight patients undergoing cardiac surgery.

(4) Oscillations from ECG signals in 20 healthy individuals were analyzed, and the researchers found slow oscillation in this data. They corresponded to known oscillations in blood pressure and heart rate.