**Training Data Collection and Processing**
ecg-data-collection.ino:
- sample the serial output of the ad8232 (which is just a signal conditioner) for five seconds at ???(200/400?)hz,
- convert serial output to .txt over USB with putty

- convert the .txt file to multiple .tsv files
data generation:
- use the christov r-peaks detector to generate a list of r-peaks
- calculate ibi
- calculate sympathetic and parasympathetic power
- calculate SDNN
- repeat for all samples
- cluster into two clusters with k-means (sympathetic and parasympathetic power)
- calculate distance from baseline centroid for all data points
- if the point is in the cluster for detected anxiety, then multiply by a weight, which is determined by the anxiety cluster centroid's distance from the baseline cluster's centroid
- anxiety index = normalize to a float between 0 and 1 -> {weighted distance + difference between baseline SDNN and SDNN}, where the weighted distance comprises 75% of the index, while the difference between the baseline SDNN and SDNN comprises 25% of the index

todo:
- implement error handling in all programs
- anxiety index gen implementation (ensure that the program can loop through all uploaded .tsv files and generate an index for each of them)
- hardware setup + putty setup + delete all test variables and files
- actual data collection
- change all test variables in all algorithms
- pass the data through to obtain x-y pairs
- convert all tsv files to csv files for easier data management when training?
- implement a load_dataset() function (loads the x-y pairs as numpy arrays)m

issues:
- ad8232 sampling rate
- tflite support for artemis

(e0103.tsv)
Low Frequency Power (Sympathetic): 0.6927324238837926
High Frequency Power (Parasympathetic): 0.0008289976394698316
Parameters: [0.69273242 0.000829  ]

(e0110.tsv)
Low Frequency Power (Sympathetic): 0.6927322301682437 <-
High Frequency Power (Parasympathetic): 0.0008293845888830577 <- extremely subtle differences
Parameters: [0.69273223 0.00082938]

Distance list: [4.3273035749211477e-07, 0.0, 7.15861207908303e-07]
Weighted distance list: [4.327306060073341e-07, 0.0, 7.158616190243757e-07]
Pre-index: 5.569940729620437
Pre-index: 2.0
Pre-index: -17.749491096196778
Anxiety Index (0): 2.227976291848175
Anxiety Index (1): 0.8
Anxiety Index (2): -7.099796438478711

ecg0: [0.6927324238837926, 0.0008289976394698316]
SDNN: 11.04285415915946
ecg1: [0.6927322301682437, 0.0008293845888830577]
SDNN: 14.61279445604929
ecg2: [0.6927325506297615, 0.0008287444625794123]
SDNN: 34.36228626810769