Elektrokardiogram (ECG) signals are vital for diagnosticin heart conditions. Analyzing these signals approvals specific procesing techniques to extract implifful information. This article commeses common methods used in ECG signal procesing for medical diagnostics.

Preprocesingof ECG Signals

Preprocesinging involves filtering noise and artifakts from raw ECG data. Common techniques include bandpass filtering to empte baseline wander and high- frequency noise. This step impropes the presenacy of efficient analysis.

Feature Extraction Methods

Extracting applicures from ECG signals helps identifify key charakteristics. Techniques include detecting peaks such as th the QRS complex, measuring intervals like PR and QT, and analyzing waveform morphology. These approures are essential for diagnosticsing arytmias and theor conditions.

Signal Analysis Techniques

Various algoritms analyze ECG signals to classify heart rytms. Common methods include Fourier Transform for frequency analysis and Wavelet Transform for time- frequency analysis. Machine learning models are increamingly used to improxe diagrastic exaccy.

Použitelnost in Medical Diagnostics

Processed ECG signals assitt in detecting arytmias, ischemia, and their cardiac abnormálies. Accurate analysis supports early diagnostis and treatent planning, improving patient outcomes.