ECG Arrhythmia Classification and Interpretation using Convolutional Networks for Intelligent IoT Healthcare System
ICyberPhyS 2024, pp. 47-62
Editorial summary
ECG fragments are classified with a CNN containing additional convolution and batch-normalisation layers. Evaluation on MIT-BIH examines recognition of arrhythmia types and attempts to explain decisions through clinical features. Overlapping pathologies remain a limitation of those explanations.
Bibliographic reference
Use the publisher’s record for the citation format required by your journal or organisation.
Oleksii Kovalchuk, Olexander Barmak, Pavlo Radiuk, Iurii Krak. "ECG Arrhythmia Classification and Interpretation using Convolutional Networks for Intelligent IoT Healthcare System". ICyberPhyS 2024, pp. 47-62, 2024.
Test a method against your business task.
Describe the decision your system needs to support and the data you have. We assess whether a method fits, define a prototype and agree how to measure its quality, response time and operating cost.