ECG Heartbeat Categorization Dataset

  • by user1
  • 21 February, 2022

Segmented and Preprocessed ECG Signals for Heartbeat Classification

Tagshealth, classification, health conditions, deep learning, heart conditionsand 1 more

Context

ECG Heartbeat Categorization Dataset

Abstract

This dataset is composed of two collections of heartbeat signals derived from two famous datasets in heartbeat classification, the MIT-BIH Arrhythmia Dataset and The PTB Diagnostic ECG Database. The number of samples in both collections is large enough for training a deep neural network.

This dataset has been used in exploring heartbeat classification using deep neural network architectures, and observing some of the capabilities of transfer learning on it. The signals correspond to electrocardiogram (ECG) shapes of heartbeats for the normal case and the cases affected by different arrhythmias and myocardial infarction. These signals are preprocessed and segmented, with each segment corresponding to a heartbeat.

Content

Arrhythmia Dataset

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

The PTB Diagnostic ECG Database

  • Number of Samples: 14552
  • Number of Categories: 2
  • Sampling Frequency: 125Hz
  • Data Source: Physionet’s PTB Diagnostic Database

Remark: All the samples are cropped, downsampled and padded with zeroes if necessary to the fixed dimension of 188.

Data Files

This dataset consists of a series of CSV files. Each of these CSV files contain a matrix, with each row representing an example in that portion of the dataset. The final element of each row denotes the class to which that example belongs.

Acknowledgements

Mohammad Kachuee, Shayan Fazeli, and Majid Sarrafzadeh. “ECG Heartbeat Classification: A Deep Transferable Representation.” arXiv preprint arXiv:1805.00794 (2018).

Inspiration

Can you identify myocardial infarction?

Size: 101205 KB Price: Free Author: Shayan Fazeli Data source: kaggle.com

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