https://github.com/r13i/ecg-classify

Classifying Heartbeat Arrhythmia using novel features (Auto-regressive coefficients & RR inter-beat distance)
https://github.com/r13i/ecg-classify

auto-regressive-coefficients beats ecg heartbeat heartbeats-classification machine-learning python qrs rr-interval-features signal-processing veb-beats waves-autoregressive-features

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Classifying Heartbeat Arrhythmia using novel features (Auto-regressive coefficients & RR inter-beat distance)

• Host: GitHub
• URL: https://github.com/r13i/ecg-classify
• Owner: r13i
• License: mit
• Created: 2018-09-15T09:35:44.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2020-05-01T16:29:37.000Z (over 4 years ago)
• Last Synced: 2024-04-24T12:15:15.943Z (8 months ago)
• Topics: auto-regressive-coefficients, beats, ecg, heartbeat, heartbeats-classification, machine-learning, python, qrs, rr-interval-features, signal-processing, veb-beats, waves-autoregressive-features
• Language: Jupyter Notebook
• Homepage:
• Size: 1.53 MB
• Stars: 30
• Watchers: 4
• Forks: 7
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# ecg-classify

Classifying Heartbeat Arrhythmia using novel features (AR coefficients + RR distance).

Full explanation available in [this notebook](./ecg_prototyping.ipynb) (You can explore it on your browser!).

### Introduction

In this project, I implement a heartbeat arrhythmia classification algorithm to separate normal heartbeats (N) from Ventricular Ectopic Beats (VEB). This work is based on the paper _"Heartbeats classification using QRS and T waves autoregressive features and RR interval features", Adnane M, Belouchrani A._ (see references).

The features used to train the model are:

- Auto-regressive coefficients of the QRS complex and T wave of the electrocardiogram.

- RR interbeat distance.

Normal Beats: ![Normal Beats](./img/img_N.png)

VEB Beats: ![VEB Beats](./img/img_VEB.png)

### Procedure

#### Pre-processing

- A 3rd order high-pass Butterworth filter is used to eliminate DC componant and baseline wander.

- A 3rd order band-reject Butterworth filter is used to eliminate the 60 Hz AC interference.

- A 4th order low-pass Butterworth filter is used to high-freq artifacts like EMG noise.

#### Features Extraction

- We use the Levinson algorithm to extract AR coefficients from ECG signal.

- A basic substraction gives the RR interdistance.

#### Training Day

SVM classifiers give good results for now. I'll stick to them while trying to improve hyper-parameters before going to anything else (e.g. Neural Nets).

### Result

For the time being (2017), results are reaching those obtained by state-of-the-art methods with accuracy metric 97.02% (overall), and 98.86% for subject specific scheme (See references).

### Dataset

- [MIT/BIH Arrhythmia Database](https://www.physionet.org/physiobank/database/mitdb/)

### References

- [Adnane M, Belouchrani A. Heartbeats classification using QRS and T waves autoregressive features and RR interval features. Expert Systems. 2017;34:e12219.](https://doi.org/10.1111/exsy.12219)