Minimum data length required for reliable resting frequency-domain ultra-short-term heart rate variability analysis

Area of Science:

• Cardiology and Autonomic Neuroscience
• Biomedical Signal Processing

Background:

• Heart rate variability (HRV) analysis is vital for assessing autonomic nervous system function.
• Current consensus on minimum data length for reliable ultra-short-term frequency-domain HRV analysis is lacking.

Purpose of the Study:

• To identify critical factors influencing minimum data length requirements for HRV analysis.
• To establish minimum data lengths for reliable ultra-short-term frequency-domain HRV estimation.

Main Methods:

• Simulated and real inter-beat interval (IBI) data from ECG recordings were analyzed.
• Spectral analysis parameters and signal properties were examined to determine minimum data lengths.
• High frequency (HF), low frequency (LF), and very low frequency (VLF) components were assessed using limits of agreement (LoA) and intraclass correlation coefficient (ICC).

Main Results:

• Minimum data length is influenced by signal properties and spectral analysis parameters.
• Reliable HF, LF, and VLF estimations were achieved at approximately 200s, 220s, and 1180s, respectively.
• While ICC > 0.90 was met earlier for LF, reliable estimation required longer durations due to wide LoA.

Conclusions:

• Provides crucial methodological insights for selecting recording durations in ultra-short-term HRV analysis.
• Findings are applicable to clinical, wearable device, and research settings.
• Optimized data lengths enhance the reliability of autonomic function assessment using HRV.