Heart Arrhythmia Classification

Overview

Heart-Arrhythmia-Classification



Instructions to run

  1. Note down the location of the ".edf" file and enter it into the EDF_PATH variable
  2. Run the predict.py file to get the output


Dataset

The original datasets used are the MIT-BIH Arrhythmia Dataset and that are preprocessed based on the methodology described in the paper below in order to end up with samples of a single heartbeat each and normalized amplitudes.

Kachuee, M., Fazeli, S., & Sarrafzadeh, M. (2018). ECG Heartbeat Classification: A Deep Transferable Representation. 2018 IEEE International Conference on Healthcare Informatics (ICHI). https://doi.org/10.1109/ichi.2018.00092 (https://arxiv.org/pdf/1805.00794.pdf)


The process followed is:

  1. Splitting the continuous ECG signal to 10s windows and select a 10s window from an ECG signal.
  2. Normalizing the amplitude values to the range of between zero and one.
  3. Finding the set of all local maximums based on zerocrossings of the first derivative.
  4. Finding the set of ECG R-peak candidates by applying a threshold of 0.9 on the normalized value of the local maximums.
  5. Finding the median of R-R time intervals as the nominal heartbeat period of that window (T).
  6. For each R-peak, selecting a signal part with the length equal to 1.2T.
  7. Padding each selected part with zeros to make its length equal to a predefined fixed length.

MIT-BIH Arrhythmia dataset :

  • Number of Categories: 5
  • Number of Samples: 109446
  • Sampling Frequency: 125Hz
  • Data Source: Physionet’s MIT-BIH Arrhythmia Dataset
  • Classes: [’N’: 0, ‘S’: 1, ‘V’: 2, ‘F’: 3, ‘Q’: 4]


Class distribution in the dataset

  • Before Resampling

  • After Resampling


Model


Figure 1: Model Structure


Results

  • Accuracy: 73%


Figure 2: Accuracy and Loss Plot




Figure 3: Confusion Matrix




Figure 4: Classification Report



The PyTorch implementation for paper "Neural Texture Extraction and Distribution for Controllable Person Image Synthesis" (CVPR2022 Oral)

ArXiv | Get Start Neural-Texture-Extraction-Distribution The PyTorch implementation for our paper "Neural Texture Extraction and Distribution for Cont

Ren Yurui 111 Dec 10, 2022
Implementation of the ivis algorithm as described in the paper Structure-preserving visualisation of high dimensional single-cell datasets.

Implementation of the ivis algorithm as described in the paper Structure-preserving visualisation of high dimensional single-cell datasets.

beringresearch 285 Jan 04, 2023
Production First and Production Ready End-to-End Speech Recognition Toolkit

WeNet 中文版 Discussions | Docs | Papers | Runtime (x86) | Runtime (android) | Pretrained Models We share neural Net together. The main motivation of WeN

2.7k Jan 04, 2023
🌊 Online machine learning in Python

In a nutshell River is a Python library for online machine learning. It is the result of a merger between creme and scikit-multiflow. River's ambition

OnlineML 4k Jan 02, 2023
Flower classification model that classifies flowers in 10 classes made using transfer learning (~85% accuracy).

flower-classification-inceptionV3 Flower classification model that classifies flowers in 10 classes. Training and validation are done using a pre-anot

Ivan R. Mršulja 1 Dec 12, 2021
This repository contains the code for using the H3DS dataset introduced in H3D-Net: Few-Shot High-Fidelity 3D Head Reconstruction

H3DS Dataset This repository contains the code for using the H3DS dataset introduced in H3D-Net: Few-Shot High-Fidelity 3D Head Reconstruction Access

Crisalix 72 Dec 10, 2022
The Illinois repository for Climatehack (https://climatehack.ai/). We won 1st place!

Climatehack This is the repository for Illinois's Climatehack Team. We earned first place on the leaderboard with a final score of 0.87992. An overvie

Jatin Mathur 20 Jun 09, 2022
This is the code for ACL2021 paper A Unified Generative Framework for Aspect-Based Sentiment Analysis

This is the code for ACL2021 paper A Unified Generative Framework for Aspect-Based Sentiment Analysis Install the package in the requirements.txt, the

108 Dec 23, 2022
A Gura parser implementation for Python

Gura Python parser This repository contains the implementation of a Gura (compliant with version 1.0.0) format parser in Python. Installation pip inst

Gura Config Lang 19 Jan 25, 2022
Final project code: Implementing BicycleGAN, for CIS680 FA21 at University of Pennsylvania

680 Final Project: BicycleGAN Haoran Tang Instructions 1. Training To train the network, please run train.py. Change hyper-parameters and folder paths

Haoran Tang 0 Apr 22, 2022
Another pytorch implementation of FCN (Fully Convolutional Networks)

FCN-pytorch-easiest Trying to be the easiest FCN pytorch implementation and just in a get and use fashion Here I use a handbag semantic segmentation f

Y. Dong 158 Dec 21, 2022
Geometric Vector Perceptron --- a rotation-equivariant GNN for learning from biomolecular structure

Geometric Vector Perceptron Code to accompany Learning from Protein Structure with Geometric Vector Perceptrons by B Jing, S Eismann, P Suriana, RJL T

Dror Lab 85 Dec 29, 2022
L-Verse: Bidirectional Generation Between Image and Text

Far beyond learning long-range interactions of natural language, transformers are becoming the de-facto standard for many vision tasks with their power and scalabilty

Kim, Taehoon 102 Dec 21, 2022
Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis Implementation

Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis Implementation This project attempted to implement the paper Putting NeRF on a

254 Dec 27, 2022
A visualisation tool for Deep Reinforcement Learning

DRLVIS - Visualising Deep Reinforcement Learning Created by Marios Sirtmatsis with the support of Alex Bäuerle. DRLVis is an application used for visu

Marios Sirtmatsis 1 Nov 04, 2021
QT Py Media Knob using rotary encoder & neopixel ring

QTPy-Knob QT Py USB Media Knob using rotary encoder & neopixel ring The QTPy-Knob features: Media knob for volume up/down/mute with "qtpy-knob.py" Cir

Tod E. Kurt 56 Dec 30, 2022
CCCL: Contrastive Cascade Graph Learning.

CCGL: Contrastive Cascade Graph Learning This repo provides a reference implementation of Contrastive Cascade Graph Learning (CCGL) framework as descr

Xovee Xu 19 Dec 05, 2022
A custom-designed Spider Robot trained to walk using Deep RL in a PyBullet Simulation

SpiderBot_DeepRL Title: Implementation of Single and Multi-Agent Deep Reinforcement Learning Algorithms for a Walking Spider Robot Authors(s): Arijit

Arijit Dasgupta 9 Jul 28, 2022
CVPR 2021 - Official code repository for the paper: On Self-Contact and Human Pose.

SMPLify-XMC This repo is part of our project: On Self-Contact and Human Pose. [Project Page] [Paper] [MPI Project Page] License Software Copyright Lic

Lea Müller 83 Dec 14, 2022
PyTorch implementation of "Image-to-Image Translation Using Conditional Adversarial Networks".

pix2pix-pytorch PyTorch implementation of Image-to-Image Translation Using Conditional Adversarial Networks. Based on pix2pix by Phillip Isola et al.

mrzhu 383 Dec 17, 2022