Anomaly Detection in ECG using Deep Learning

Electrocardiogram (ECG) signals play a very important role in the detection of heart irregularities. Early detection of abnormalities is essential for better patient care and improved medical outcomes. Recent years have witnessed a surge in the application of deep learning techniques, such as autoencoders, long short-term memory (LSTM) networks, and convolutional neural networks (CNNs), for automated ECG signal detection. Our research study utilizes a large-scale ECG database encompassing various types of cardiac diseases. We individually trained and employed autoencoders, LSTM networks, and CNNs to identify abnormal ECG patterns. Autoencoders enhance the dataset by revealing subtle features that might otherwise go unnoticed due to attention limitations. LSTM networks effectively model the temporal relationships between individual elements within the dataset. CNNs, on the other hand, focus primarily on spatial features and waveform graphs. The performance of these algorithms is evaluated based on accuracy, precision, and F1-score, ensuring high scores in sensitivity, predictive value, specificity, and overall classification accuracy, with minimal false positives or negatives. Beyond accuracy, we also consider practical aspects such as generalization approaches and computational efficiency. Understanding the strengths and limitations of each deep learning algorithm is crucial for ECG detection. Comparative studies of this nature hold immense value for the healthcare sector and academia, guiding the selection of the most effective deep learning algorithm for abnormality detection. Moreover, it illustrates how the combined approach may enhance performance, leading to improved diagnostics and treatment of patients.

• Publication year

  2024

• Venue

  International Conference on Computing for Sustainable Global Development

• Publication date

  2024-02-28

• Fields of study

  Not labeled

• Identifiers
  DOI 10.23919/INDIACom61295.2024.10498800
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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