An interactive framework for an analysis of ECG signals.

Area of Science:

• Computational intelligence
• Biomedical signal processing
• Data visualization

Background:

• Electrocardiogram (ECG) signal analysis is crucial for diagnosing cardiac conditions.
• Existing analysis methods can be complex and lack user-friendliness.
• High-dimensional ECG data presents challenges for pattern discovery.

Purpose of the Study:

• To develop an interactive and user-friendly environment for ECG signal analysis.
• To leverage self-organizing maps (SOMs) for ECG pattern discovery and data topology visualization.
• To introduce an original method for constructing fuzzy set membership functions tailored for ECG data.

Main Methods:

• Utilized a self-organizing map (SOM) as the core neural architecture for ECG data analysis.
• Employed information granules (fuzzy sets) to describe identified regions within the SOM.
• Developed a novel membership function construction method incorporating class membership.
• Performed comprehensive descriptive modeling of high-dimensional ECG data.

Main Results:

• Successfully created a highly interactive and user-friendly ECG analysis environment.
• Demonstrated the ability of SOMs to discover structure and visualize the topology of ECG patterns.
• Showcased the effectiveness of fuzzy sets in characterizing relationships within ECG data.
• Validated the novel membership function approach for improved ECG data characterization.

Conclusions:

• The developed environment offers an intuitive approach to ECG signal analysis.
• Self-organizing maps and fuzzy sets provide powerful tools for uncovering hidden patterns in ECG data.
• The novel membership function method enhances the descriptive modeling of complex ECG datasets.