Apple Watch Heart Rate Variability and Sleep State Data from a Polysomnography Study

Martin G. Frasch's dataset accompanies a manuscript under review in MethodsX. It contains Jupyter notebooks and computed datasets resulting from applying a NeuroKit2-based pipeline to an Apple Watch polysomnography dataset. The pipeline computes 124 heart rate variability measures, including dynamic, time-series-specific complexity estimates.

Use Cases

• Studying dynamic relationships between sleep state architecture and multi-dimensional HRV metrics based on the described methodology.
• Validating automated HRV computation pipelines based on the 124 measures generated by the NeuroKit2 adaptation.
• Reproducing the analysis of Apple Watch heart rate data in relation to polysomnography as presented in the associated manuscript.

Strengths

• Pipeline computes 124 distinct heart rate variability measures.
• Methodology includes dynamic, time-series-specific complexity estimation with a user-definable time window.
• Dataset is associated with a manuscript under peer review in MethodsX.

Limitations

• Row count and dataset size are unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The underlying Apple Watch polysomnography data is published elsewhere and not included.

Provenance

Source

Martin G. Frasch

Collection Method

Application of a NeuroKit2-based computational pipeline to an existing Apple Watch polysomnography dataset.