Atrial-Fibrillation-using-ML

This project uses ML to detect if a person has Atrial Fibrillation or irregular heartbeats.

The project takes an ECG signal of heart as input which is converted to relevant features using HeartPy module such as,

BPM — heart rate (BPM), is calculated as the average beat-beat interval across the entire analysed signal (segment).

IBI — interbeat interval.

SDNN — standard deviation of RR intervals.

SDSD — standard deviation of successive differences

RMSSD — root mean square of successive differences.

PNN20 — proportion of successive differences above 20ms.

PNN50 — proportion of successive differences above 50ms.

HR_MAD — median absolute deviation of RR intervals.

SD1 — standard deviation perpendicular to identity line (Poincaré parameters[7]).

SD2 — standard deviation a long identtiy line.

S — area of ellipse described by SD1 and SD2.

SD1/SD2 — ratio.

BREATHING RATE — that is the frequency with which the heart beats is strongly influenced by.

The above features are used as input to predict whether the person has a Normal reading 'N' or an AF episode in his reading 'AF'

The accuracy of the model is 92% and f_score is 0.67

Algorithms used:

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• KNN classifier

• ANOVA based feature selection method

• L1 regularizer

Dataset link : https://drive.google.com/file/d/1gML6OVGEJ8d_7sjyxyISbXvjdAwvrkZ1/view?usp=sharing