Inspiration
In recent days, it is seen that a lot of people are suffering from heart related diseases. One of them is arrhythmia. The problem is that the symptoms do not always appear and the physician may be mistaken in the diagnosis. Therefore, patients need continuous monitoring through real-time ECG analysis to detect arrhythmias in a timely manner and prevent an eventual incident that threatens the patient's life.
What it does
Machine learning model that detects different heart beats based on ECG signal
How we built it
We used Python and libraries supported for building our project
Challenges we ran into
Deciding best ML algorithm for our use case
Accomplishments that we're proud of
Our model is giving accuracy of more than 98%
What we learned
We learned data analysis, machine learning
What's next for ECG classifier
We are planning to implement in the form of IoT device
Built With
- biosppy
- matplotlib
- numpy
- pandas
- python
- scikit-learn
- wfdb
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