Time-frequency analysis of ECG signals using an image processing technique

Area of Science:

• Signal Processing
• Biomedical Engineering
• Image Analysis

Background:

• Traditional time-frequency representations (TFR) face limitations in signal analysis.
• Existing kernel-based techniques for TFR may introduce artifacts like aliasing and cross-terms.
• Electrocardiogram (ECG) signal analysis requires accurate time-frequency information.

Purpose of the Study:

• To introduce a new signal processing technique based on the ambiguity function (AF) to overcome TFR limitations.
• To demonstrate the application and potential of AF-based image processing for ECG signal analysis.
• To compare the proposed technique with existing kernel-based methods.

Main Methods:

• Development of a filtering technique applied to the image of the signal's ambiguity function (AF).
• Utilizing the analytic form of the ECG signal to mitigate aliasing and cross-terms.
• Comparative analysis of the proposed AF-based method against established kernel-based TFR techniques.

Main Results:

• The proposed AF image processing technique effectively filters signals, improving TFR quality.
• Application to ECG signals demonstrates significant reduction in aliasing and cross-terms.
• The AF-based approach shows comparable or superior performance to some kernel-based methods in ECG analysis.

Conclusions:

• Data-adaptive time-frequency analysis of ECG signals is achievable using image processing techniques on the AF.
• The proposed AF-based filtering method offers a promising alternative for enhanced signal analysis.
• This approach holds potential for improving the accuracy and reliability of biomedical signal interpretation.