# Measuring Einthoven II ECG and reference EMG simultaneously to be able to benchmark noise reduction algorithms

This database contains ECG and EMG recordings of 15 participants. The data was recorded with the [Attys](https://www.attys.tech/)
device using both channels:
  - The first channel records from right wrist to left hip (ECG+EMG).
  - The second channel records from right wrist to right upper arm or right shoulder (EMG only).

No filtering has been applied [Attys](https://www.attys.tech/) to any of the data. Video while recording may be present if consent given.

## Data Format

The recordings are available in .tsv (tab separated) format with the following structure

`samplingNumber      ECG+EMG      EMGonly      checkDigitRow
`

## Data Information

  * The sampling frequency is 250 Hz.

  * Each recording is approximately 120 seconds long.

  * The signal is in V (raw data straight from the ADC)

  * The video recordings are synchronised with the data recording.

  * There are 1 clean (no movement) and 3 types of forearm movements for each participant:

    0. __clean.tsv__: recording with relaxed arm and minimal EMG pollution
    1. __stress1.tsv__: voluntary arm muscle contraction at approximately 0-15, 25-35, 45-55, 65-75, 85-95, 105-115 seconds of the recording.
    2. __stress2.tsv__: voluntary arm muscle contraction at approximately 25-35, 45-55, 65-75, 85-95, 105-115 seconds of the recording.
    3. __stress3.tsv__: voluntary arm muscle contraction during the 120 seconds of the recording.


## Instructional example python script of how to use the dataset

The python3 script `view_data.py` shows all recordings from one
participant. By default it plots data from the 1st participant. You
can provide the participant number as a parameter, for example to plot
participant 3 write: `python view_data.py -p 3`. If an `-f` is
provided after the subject number the result is filtered, for example
`python view_data.py -p 3 -f`. You can also provide the last sample
plotted with `-n` or `-t` for the last time to plot a shorter bit:

```
python view_data.py -p participantNo -n maxSampleNo -t maxTimeSec -f -h
```