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

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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: VEB Beats:

• 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.

• We use the Levinson algorithm to extract AR coefficients from ECG signal.
• A basic substraction gives the RR interdistance.

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).

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).

• MIT/BIH Arrhythmia Database

• Adnane M, Belouchrani A. Heartbeats classification using QRS and T waves autoregressive features and RR interval features. Expert Systems. 2017;34:e12219.