Datasets Structure

These datasets were created from data gathered following a specific protocol described in Rodrigues et al., 2018.

In this study were performed the following tests:

• Baseline: sit comfortably for 10 min.

• 2-Choice Reaction Time Task (CRTT): selective-attention task, whereas participants identified either the large, global letter or the small, local letters of a hierarchically organized visual object. Their response time and correct/incorrect/missed answers were recorded.

• Trier Social Stress Test (TSST): acute psychosocial stress paradigm.

The order of the tasks was the following: Baseline -> CRTT1 -> TSST -> CRTT2

Along the protocol, various phsycological scales assessments were performed and their results are saved in the created dataset.

A dataset was created per each Air Trafic Controlers (ATC).

Each dataset is stored in a structured format. Below is an example of the file format:

• File Type: .json
• Columns:

Column Name	Description
Test_phase	The phase of the experiment (e.g., baseline, CRTT1, TSST, CRTT2).
ECG	Raw ECG signal, 1 lead & Fs = 500 HZ.
VAS	Visual Analogue Scale rating.
STAI_6items	State-Trait Anxiety Inventory (6-item version) score.
Accuracy	Percentage of correct responses in a cognitive task.
Reaction_time	Response time in seconds.
RT_std	Standard deviation of reaction times.
Cognitive_performance	Composite score of cognitive task performance.

ECG Waveform Features

Column Name	Description
p_wave_duration	Duration of the P wave (ms).
pr_interval	Time from the start of the P wave to the start of the QRS complex (ms).
pr_segment	Time between the end of the P wave and the start of the QRS complex (ms).
qrs_duration	Duration of the QRS complex (ms).
qt_interval	Time between the start of the Q wave and the end of the T wave (ms).
st_segment	Segment between the QRS complex and the T wave (ms).
st_interval	Time between the J point and the end of the T wave (ms).
t_wave_duration	Duration of the T wave (ms).
tp_segment	Time from the end of the T wave to the start of the next P wave (ms).
rr_interval	Time between two consecutive R-peaks (ms).

Heart Rate Variability (HRV) Metrics

Column Name	Description
mean_nni	Mean of normal-to-normal (NN) intervals (ms).
sdnn	Standard deviation of NN intervals (ms).
sdsd	Standard deviation of successive differences between NN intervals (ms).
nni_50	Number of pairs of successive NN intervals differing by more than 50 ms.
pnni_50	Percentage of NN50 count divided by the total number of NN intervals.
nni_20	Number of pairs of successive NN intervals differing by more than 20 ms.
pnni_20	Percentage of NN20 count divided by the total number of NN intervals.
rmssd	Root mean square of successive differences (ms).
median_nni	Median of NN intervals (ms).
range_nni	Range of NN intervals (max-min) (ms).
cvsd	Coefficient of variation of successive differences.
cvnni	Coefficient of variation of NN intervals.

Heart Rate Metrics

Column Name	Description
mean_hr	Mean heart rate (beats per minute).
max_hr	Maximum heart rate recorded.
min_hr	Minimum heart rate recorded.
std_hr	Standard deviation of heart rate.

Frequency-Domain HRV Metrics

Column Name	Description
lf	Low-frequency power (ms²).
hf	High-frequency power (ms²).
lf_hf_ratio	Ratio of LF to HF power.
lfnu	Low-frequency power in normalized units.
hfnu	High-frequency power in normalized units.
total_power	Total spectral power of HRV (ms²).
vlf	Very low-frequency power (ms²).

Running the Code

To execute the algorithm, ensure you are in the root directory of the repository. The script should be run using the following command:

python -m code.algorithms.cognition.model_personalization

Upon execution, you will be prompted to select a file containing the dataset.

⚠ Attention: The input dataset must follow the required structure, though no example is currently provided, as the data used for testing is private.

Output Details:

• Running the command will generate a model based on the provided dataset.
• Users must enter a unique code to the model when prompted.
• The model will be saved as a .pklfile in the personalised_models folder within the working directory. - The - The file will follow this naming convention: personalised_models/model_<user_code>.pkl