Graph Fractional Hilbert Transform, Analytic Signal, and Its Application to ECG Classification

Key Points

• The study aims to explore the effectiveness of the graph fractional Hilbert transform combined with the analytic signal in classifying ECG signals.
• Applied graph fractional Hilbert transform to ECG data for feature extraction.
• Utilized analytic signals to enhance classification accuracy.
• Conducted performance evaluation using various classification algorithms.
• Achieved an accuracy rate of 95% in ECG classification, indicating strong performance.
• Showed that the combination of techniques significantly outperformed traditional methods.
• P<0.01 in comparative analysis against baseline classification algorithms.