Detection of the P and T waves in an ECG
Computers and Biomedical Research
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
An application of one-class support vector machines in content-based image retrieval
Expert Systems with Applications: An International Journal
Recognition of semiconductor defect patterns using spatial filtering and spectral clustering
Expert Systems with Applications: An International Journal
QRS complexes detection for ECG signal: The Difference Operation Method
Computer Methods and Programs in Biomedicine
Modelling ECG signals with hidden Markov models
Artificial Intelligence in Medicine
Computer Methods and Programs in Biomedicine
Feature selection algorithm for ECG signals using Range-Overlaps Method
Expert Systems with Applications: An International Journal
ECG arrhythmia classification based on optimum-path forest
Expert Systems with Applications: An International Journal
Computer Methods and Programs in Biomedicine
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This work proposes a method of analyzing ECG signal to diagnose cardiac arrhythmias utilizing the cluster analysis (CA) method. The proposed method can accurately classify and distinguish the difference between normal heartbeats (NORM) and abnormal heartbeats. Abnormal heartbeats may include the following: left bundle branch block (LBBB), right bundle branch block (RBBB), ventricular premature contractions (VPC), and atrial premature contractions (APC). Analysis of ECG signal consists of three major stages: (i) detecting the QRS waveform; (ii) selecting qualitative features; and (iii) determining heartbeat case. The ECG signals in the MIT-BIH arrhythmia database are adopted as reference data for accomplishing the first two stages, and cluster analysis is used to determine patient heartbeat case. In the experiments, the sensitivity is 95.59%, 91.32%, 90.50%, 94.51%, and 93.77% for heartbeat case NORM, LBBB, RBBB, VPC, and APC, respectively. The total classification accuracy (TCA) was about 94.30%.