October 5, 2020SensorsOpen Access

Sleep Stage Estimation from Bed Leg Ballistocardiogram Sensors

View Full Paper

Ask AI

Bookmark

Why the study?

Although ballistocardiograms cause less burden to users than electrocardiograms, they remain limited in clinical use due to noise sensitivity.

Does a novel bed leg ballistocardiogram (BCG) algorithm accurately detect heart rate variability and predict sleep stages compared to PPG and conventional algorithms?

Structured PICO

Does a novel bed leg ballistocardiogram (BCG) algorithm accurately detect heart rate variability and predict sleep stages compared to PPG and conventional algorithms?

Population

36 subjects (99 datasets) for BCG measurement validation and 25 subjects (100 datasets) for sleep stage model validation

Intervention

Novel bed leg ballistocardiogram (BCG) measurement algorithm and mathematical model for predicting sleep stages

Comparator

Photoplethysmography (PPG) for ground truth heart rate variability; conventional five-stage sleep classification algorithms

Outcome

Accuracy of inter-beat interval detection and sleep stage predictionsurrogate

A novel bed leg BCG sensor algorithm provides accurate beat-to-beat interval extraction and sleep stage prediction, offering a less burdensome alternative to clinical ECGs for sleep monitoring.

Implication

BCG-based sleep staging may reduce monitoring burden; leaves open prospective validation before clinical use.

Abstract

Ballistocardiogram (BCG) is a graphical representation of the subtle oscillations in body movements caused by cardiovascular activity. Although BCGs cause less burden to the user, electrocardiograms (ECGs) are still commonly used in the clinical scene due to BCG sensors' noise sensitivity. In this paper, a robust method for sleep time BCG measurement and a mathematical model for predicting sleep stages using BCG are described. The novel BCG measurement algorithm can be described in three steps: preprocessing, creation of heartbeat signal template, and template matching for heart rate variability detection. The effectiveness of this algorithm was validated with 99 datasets from 36 subjects, with photoplethysmography (PPG) to compute ground truth heart rate variability (HRV). On average, 86.9% of the inter-beat intervals were detected and the mean error was 8.5ms. This shows that our method successfully extracted beat-to-beat intervals from BCG during sleep, making its usability comparable to those of clinical ECGs. Consequently, compared to other conventional BCG systems, even more accurate sleep heart rate monitoring with a smaller burden to the patient is available. Moreover, the accuracy of the sleep stages mathematical model, validated with 100 datasets from 25 subjects, is 80%, which is higher than conventional five-stage sleep classification algorithms (max: 69%). Although, in this paper, we applied the mathematical model to heart rate interval features from BCG, theoretically, this sleep stage prediction algorithm can also be applied to ECG-extracted heart rate intervals.