Heart rate monitors have been an essential tool in evaluating the health of a person as well as an integral part of diagnosis for cardiac disorders ranging from arrhythmia to conditions like Wellen's syndrome.
This is code that acts as a heart rate analyzer (rather than a monitor). It can determine heartbeats based on two different algorithms: Thresholding and Wavelet transform + Thresholding. I've found the Wavelet method to work the best. The goal here was for anyone to write their own detection method and be able to use it with the HeartRateMonitor class.
Using a virtual environment with a python environmental variable, run the command below to get the necessary packages: pip install -r requirements.txt
This class is a file handler specifically used for a specific kind of csv which contains two strips of data, one being a time array and the other being the signal. Upon initialization, the class checks the dimensions and orientation of the data to ensure it can be properly processed. It then checks for missing and/or non-numeric values and gets rid of them. Lastly, it separates the data int to time and raw_signal attributes to be used by other classes.
The ECGDetectionAlgorithm is meant to be somewhat abstract class which is only differentiated by its find_mean_hr_bpm, find_beats, and plot_graph methods. The core functionality and attributes of the final return which can be easily handled are handled here such as find_voltage_extremes, find_duration, and find_num_beats (which requires the find_beats abstract method, but it must be implemented in extended classes).
The Threshold class is one of the two methods of detection. It takes the raw signal, cleans it using apply_noise_reduction via background subtraction (moving average) as well as band-pass filtering based on literature. The base threshold is set to the maximum of the filtered signal by default and modulated based on the signal itself. The overlap between the threshold and filtered signal is then converted into a binary array using apply_threshold where 1 denotes overlap. The helper method _find_binary_centers locates the rising and falling edges of the binary signal and then calculates the central index between the two, giving a single spike.
The Wavelet class extends Threshold and essentially uses the same thresholding method. The only difference here is the signal that is passed through the thresholding algorithm. As opposed to using a filtered signal, the class uses a wavelet transform of a minimal range of widths. The 1-dimensional average tends to provide a more obvious peak for thresholding, yielding slightly better results.
The HeartRateMonitor class is meant to be the outer-most class to be instantiated by a user. In order to get the necessary information
Using the HeartRateMonitor class, you input the signal filename as well as the detection method. To get an output file, call the write_json() method. To get the metrics with the beats in numpy.ndarray form, call the to_json() method.
from detection_algorithm import Wavelet
from heart_rate_monitor import HeartRateMonitor
def main():
# if you wanted to specify a width for mean hr,
# use the time_interval kwarg. e.g. time_interval=(.13, .16)
# The Wavelet analyzer can be swapped with Threshold
heart_rate_monitor = HeartRateMonitor(
filename="tests/test_data/test_data1.csv",
analyzer=Wavelet)
if heart_rate_monitor is None:
return
# if you do not wish to write the heart rate properties, use to_json()
metrics = heart_rate_monitor.to_json()
heart_rate_monitor.write_json()
if __name__ == "__main__":
main()If you wish to get a plot of the analysis, use the plot_graph method on the analyzer e.g. heart_rate_monitor.analyzer.plot_graph().
Using heart_rate_monitor.analyzer.plot_graph() on a Wavelet analyzer would give you a plot similar to the one below.
