Sleep Stage Assessment Using Power Spectral Indices of Heart Rate Variability With a Simple Algorithm

Abstract

<jats:p>Clinical researchers do not typically assess sleep with polysomnography (PSG) but rather with observation. However, methods relying on observation have limited reliability and are not suitable for assessing sleep depth and cycles. The purpose of this methodological study was to compare a sleep analysis method based on power spectral indices of heart rate variability (HRV) data to PSG. PSG and electrocardiography data were collected synchronously from 10 healthy women (ages 20–61 years) over 23 nights in a laboratory setting. HRV was analyzed for each 60-s epoch and calculated at 3 frequency band powers (very low frequency [VLF]-hi: 0.016–0.04 Hz; low frequency [LF]: 0.04–0.15 Hz; and high frequency [HF]: 0.15–0.4 Hz). Using HF/(VLF-hi + LF + HF) value, VLF-hi, and heart rate (HR) as indices, an algorithm to categorize sleep into 3 states (shallow sleep corresponding to Stages 1 &amp; 2, deep sleep corresponding to Stages 3 &amp; 4, and rapid eye movement [REM] sleep) was created. Movement epochs and time of sleep onset and wake-up were determined using VLF-hi and HR. The minute-by-minute agreement rate with the sleep stages as identified by PSG and HRV data ranged from 32 to 72% with an average of 56%. Longer wake after sleep onset (WASO) resulted in lower agreement rates. The mean differences between the 2 methods were 2 min for the time of sleep onset and 6 min for the time of wake-up. These results indicate that distinguishing WASO from shallow sleep segments is difficult using this HRV method. The algorithm's usefulness is thus limited in its current form, and it requires additional modification.</jats:p>

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Unconstrained Sleep Stage Estimation Based on Respiratory Dynamics and Body Movement

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