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Arrhythmia Detection

This repository contains the implementation of a neural network for detecting arrhythmia using the dataset from Kaggle.

1. Model Architecture

Model Architecture

The model architecture is designed to efficiently classify heartbeat signals into different classes, leveraging a combination of convolutional and residual blocks.

2. Residual Inverted Convolution (RIC) Block

RIC Block

The Residual Inverted Convolution (RIC) Block is a key component of the network, enhancing feature extraction through residual connections and inverted convolutions.

3. Results

The model has a total of 19.6K parameters. The performance metrics for each class are as follows:

Class Precision Recall Specificity NPV Accuracy
Class N 0.9887 0.9966 0.9452 0.9829 0.9877
Class S 0.9161 0.7464 0.9982 0.9934 0.9918
Class V 0.9666 0.9606 0.9977 0.9972 0.9952
Class F 0.8897 0.7469 0.9993 0.9981 0.9974
Class Q 0.9925 0.9882 0.9994 0.9991 0.9986

Definitions:

  • Precision: The ratio of true positive predictions to the total predicted positives.
  • Recall: The ratio of true positive predictions to the total actual positives.
  • Specificity: The ratio of true negative predictions to the total actual negatives.
  • NPV (Negative Predictive Value): The ratio of true negative predictions to the total predicted negatives.
  • Accuracy: The ratio of correct predictions (both true positives and true negatives) to the total predictions.

Dataset

The dataset used in this project can be found on Kaggle: Heartbeat Dataset.

4. References

Li, Duo, et al. "Involution: Inverting the inherence of convolution for visual recognition." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021.