Pasko-Emiliano/ECG-Cardiac-Insuffisance-Dataset

About this Dataset

Context

Cardiac Insufficiency Classification Dataset

Abstract

This dataset comprises heartbeat signals structured similarly to well-known ECG heartbeat datasets, designed to classify the degree of cardiac insufficiency. The signals represent electrocardiogram (ECG) waveforms corresponding to various levels of cardiac insufficiency, ranging from no insufficiency to severe stages, including a possible insufficiency class. Each heartbeat signal is preprocessed and segmented for classification purposes.

This dataset is suitable for training and evaluating deep learning models to assess cardiac insufficiency severity, offering an opportunity to explore predictive modeling for clinical cardiac function assessment.

Content

Cardiac Insufficiency Dataset

• —Number of Samples: 109 446
• —Number of Categories: 5
• —Sampling Frequency: 125Hz
• —Classes:
• —0: No Cardiac Insufficiency
• —1: Mild Cardiac Insufficiency
• —2: Moderate Cardiac Insufficiency
• —3: Severe Cardiac Insufficiency
• —4: Possible Cardiac Insufficiency

Data Files

This dataset is provided as CSV files, where each row represents an individual heartbeat segment. The last column in each row indicates the cardiac insufficiency class label for that heartbeat.

Acknowledgements

This dataset builds upon methodologies and structures inspired by established ECG databases such as the MIT-BIH Arrhythmia Dataset and PTB Diagnostic ECG Database.

Inspiration

Can your model accurately distinguish between different degrees of cardiac insufficiency based on ECG heartbeat signals?