## 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

![HR Boxplot Across Conditions](Plots/Aggregate/HR_boxplot_conditions.png)
![RR Boxplot Across Conditions](Plots/Aggregate/RR_boxplot_conditions.png)
![RMSSD Boxplot Across Conditions](Plots/Aggregate/RMSSD_boxplot_conditions.png)

### Aligned Average Curves

![Aligned Average Heart Rate (HR)](Plots/Aggregate/HR_average_aligned.png)
![Aligned Average RR Interval](Plots/Aggregate/RR_average_aligned.png)
![Aligned Average RMSSD](Plots/Aggregate/RMSSD_average_aligned.png)

## 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.