QRS Complex ALgorithm

This repository holds the code for an QRS complex detection algorithms based on finding the rate of change of the acquired signal and add it as a feature column. Once this feature is appende to the dataset, a binary encoding of the rate of change represents a detected QRS complex. Following this, a machine learning algorithm is trained on this mathematical model.

The Datasets

The signal acquired is a time-domain electrocardiagram with the y-axis being the voltage meassured in the ECG. This signal has been retreieve at a laboratory using an MP36/35 unit, performing an electrocardiogram. There are two dataset (one for training, another for testing).

Training Signal

Mathematical Model

The mathematical model follows two functions:

Rate of Change Function

• Take the first and second derivative of the signal
• Add the absolute values of these two results.
• Transform the signal from time domain to frequency domain.
• Apply a low pass filter
• return the signal to the time domain

Function Results

QRS Detection

• Randomly select 4 windows (500 ms [approximate QRS coomplex length]) in the dataset and calcualtes their maximum values from this
• Finds the mean value from the these and stores it as threshold parameter.
• Iterates through the data and binary encodes and stores the indexes in which a QRS complex was detected.

Function Results

Training the Machine Learning Model

Before applying the both the Rate of Change function and the QRS one, we clean the training dataset and create a preprocessing pipeline using sklearn. Pass the dataset into the pipeline and train a machine learning model, in this case a support vector classifier model, using the dataset.

Testing the Model on Other Datasets

Finally, once we have a trained model, we perform a prediction by using our second subject's dataset.

Before Prediction

After Prediction

Model Performance Metrics:

Training Outputs

Support Vector Classifier - Training	mse	MAE	RMSE	R2
Result	0.000083	0.000083	0.009129	0.998111

Testing Outputs

Support Vector Classifier - Testing	mse	MAE	RMSE	R2
Result	0.000313	0.000313	0.017678	0.992211