MESSAI.iO

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70% confidence

An important diagnostic method for diagnosing abnormalities in the human heart is the electrocardiogram (ECG). A large number of heart patients increase the assignment of physicians.

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Abstract

An important diagnostic method for diagnosing abnormalities in the human heart is the electrocardiogram (ECG). A large number of heart patients increase the assignment of physicians. To reduce their assignment, an automatic computer detection system is needed. In this study, a computer system for classifying ECG signals is presented. The MIT-BIH, ECG arrhythmia database is used for analysis. After the ECG signal is noisy in the preprocessing stage, the data feature is extracted. In the feature extraction step, the decision tree is used and the support vector machine (SVM) is constructed to classify the ECG signal into two categories. It is normal or abnormal. The results show that the system classifies the given ECG signal with 90% sensitivity.

Key findings

  • The results show that the system classifies the given ECG signal with 90% sensitivity.

Keywords

PreprocessorArtificial intelligenceSupport vector machinePattern recognition (psychology)Feature extractionDecision tree

Identifiers

DOI
10.33140/amlai.02.01.09
Journal
Advances in Machine Learning & Artificial Intelligence
Year
2021