Overview

• Data source: SingleRecordings/<id>/{hr.csv, rr.csv, timestamps.csv}
• Scripts:
  • plot_meditation_data.py: per-recording plots (raw + moving averages + segment boxplots)
  • aggregate_segments_analysis.py: aggregate metrics/plots across recordings with exactly 4 marks (conditions)

Results

Condition	HR (bpm)	RR (ms)	RMSSD (ms)
Breathing Scene 1	72.61	843.33	52.70
Spring Scene	72.33	860.78	56.95
Summer Scene	72.50	862.00	41.15
Autumn Scene	72.89	850.44	41.89
Breathing Scene 2	73.78	839.94	42.59

Plots

Aggregate Boxplots

Aligned Average Curves

Outlier Detection

Scripts filter any RR values outside of the range [300, 2000] ms. Values outside of this range are replaced by interpolated values. For current samples no outliers were detected.

How calculations are done

• HR and RR loading

  • Read timestamp (ms) → convert to datetime; set as index.
  • HR column: hr (beats per minute). RR column: rr_ms (milliseconds).

• Moving averages (per recording)

  • Time-based rolling means over irregular timestamps using windows: 5 s, 10 s, 30 s, 60 s.
  • X-axis is seconds from each recording’s start.

• Mark handling (per recording)

  • Vertical lines at marks from timestamps.csv.
  • Segment boxplots use values in: each interval between consecutive marks and the final interval (last mark → end).

• RMSSD (HRV)

  • Definition: RMSSD = sqrt(mean(diff(RR_ms)^2)) using successive RR intervals (ms).
  • Aggregate per-condition RMSSD: computed over all RR samples within each condition’s time window (recording-level); requires ≥2 RR samples.
  • Aligned time-series RMSSD: first compute a 30 s time-based rolling RMSSD per recording, then align and average across recordings (see below).

• Aggregate metrics across recordings (exactly 4 marks)

  • Conditions (English labels):
    • Breathing Scene 1 (pre-first mark)
    • Spring Scene (1st–2nd mark)
    • Summer Scene (2nd–3rd mark)
    • Autumn Scene (3rd–4th mark)
    • Breathing Scene 2 (post-last mark)
  • Per recording: compute medians for HR and RR within each condition; compute RMSSD within each condition.
  • Summary CSV aggregates these per-recording values; boxplots show distributions. The mean of each condition is marked by a black dot on the boxplots.

• Aligned average curves (HR, RR, RMSSD)

  • Only recordings with exactly 4 marks are used.
  • For each recording, durations of the five segments are measured; median segment proportions across recordings define a normalized 0–1 time axis with aligned boundaries.
  • Each recording’s series is piecewise-linearly time-normalized to this axis, interpolated to a common grid, and then averaged (mean ± 1 SD). For RMSSD, a 30 s rolling RMSSD is used before alignment.