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Morphological classification of ST segment using reference STs set.

Gu-Young Jeong¹, Kee-Ho Yu

¹Division of Mechatronics Engineering, Chonbuk National University, Jeonju, Jeonbuk, Korea. jung902@chonbuk.ac.kr

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|November 16, 2007

Summary

This summary is machine-generated.

This study introduces an algorithm to classify ST segment shapes in electrocardiograms (ECG) using polynomial approximation. This method aids in identifying transient myocardial ischemia by analyzing ST segment morphology.

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Area of Science:

Cardiology
Biomedical Engineering
Signal Processing

Background:

Electrocardiogram (ECG) morphological changes are crucial for diagnosing heart conditions.
Abnormal ST segment changes are particularly important indicators of myocardial ischemia.
Transient ST changes necessitate long-term ECG monitoring for accurate detection.

Purpose of the Study:

To develop and evaluate an algorithm for classifying ST segment shapes in ECG.
To utilize polynomial approximation and a reference ST set for ST shape classification.
To aid physicians in identifying transient ST segment changes indicative of myocardial ischemia.

Main Methods:

Feature point detection, including QRS complex identification based on morphological characteristics.
ST level detection to identify changes in the ST segment.
ST shape classification using polynomial approximation (least squares curve fitting) between the S and T waves.
Classification based on comparing the derived curve's slope with reference ST types.

Main Results:

The developed algorithm successfully detects ST level changes and classifies ST shapes.
The algorithm was applied to ECG data from the European ST database.
Results indicate the algorithm's capability to identify the occurrence and type of ST level changes.

Conclusions:

The developed polynomial approximation algorithm provides a method for classifying ST segment shapes.
This classification aids in understanding ST level changes, crucial for diagnosing myocardial ischemia.
The algorithm demonstrates potential for improving the analysis of transient ECG abnormalities.