In this study, I wanted to see the process of classification with respect to ECG Hearbeat data. The dataset being used is the CG heartbeat categorization data The dataset is composed of two collection of heartbeat signals derived from two famous datasets in hearbeat classification.
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.
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]
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.
poetry run python -m heartQuick note: if you want to adjust the parameters, please refer to the utils folder, which contains , constants.py and constants_helper.py