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Towards better heartbeat segmentation with deep learning classification.

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  • 1Computing Department, Federal University of Ouro Preto, Ouro Prêto, MG, 35400-000, Brazil.

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Reducing false positive alarms in medical equipment is crucial. This study introduces a convolutional neural network (CNN) to validate heartbeats, improving the accuracy of existing algorithms like Pan-Tompkins.

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare

Background:

  • False alarms in medical equipment reduce diagnostic confidence.
  • Minimizing false positives is essential for reliable patient monitoring.

Purpose of the Study:

  • To develop a real-time validation method for heartbeat detection algorithms.
  • To improve the accuracy of heartbeat segmentation using convolutional neural networks (CNNs).

Main Methods:

  • A seven-layer CNN was designed to classify heartbeat patterns.
  • The CNN approach was evaluated on the MIT-BIH and CYBHi databases.
  • Performance was compared against the Pan-Tompkins algorithm.

Main Results:

  • The proposed CNN method enhanced the positive prediction rate of the Pan-Tompkins algorithm.
  • The CNN approach slightly decreased sensitivity while significantly improving precision.
  • Feasibility was demonstrated on diverse heartbeat signal databases.

Conclusions:

  • CNNs offer a feasible and effective solution for validating heartbeat detection.
  • This approach can enhance the reliability of automated cardiac monitoring systems.
  • Real-time embedded hardware implementation of CNNs is viable.