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Electrocardiogram

Electrocardiogram

An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and the T...

Classification of Signals

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...

Basic Discrete Time Signals

Basic Discrete Time Signals

The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is the...

Deconvolution

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
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Discrete-time Fourier transform

Discrete-time Fourier transform

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Electrocardiogram Fundamentals

Electrocardiogram Fundamentals

Introduction
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
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Electrocardiogram signals de-noising using lifting-based discrete wavelet transform.

Ergun Erçelebi¹

¹Department of Electrical and Electronics Engineering, University of Gaziantep, 27310-Gaziantep, Turkey. ercelebi@gantep.edu.tr

Computers in Biology and Medicine

|July 22, 2004

Summary

This study presents a novel wavelet transform technique for denoising electrocardiogram (ECG) signals from nonstationary noise. The method significantly outperforms traditional median filtering, improving signal quality for analysis.

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Area of Science:

Biomedical Signal Processing
Digital Signal Processing
Wavelet Theory

Background:

Electrocardiogram (ECG) signals are crucial for diagnosing cardiac conditions.
ECG signals are often corrupted by nonstationary noises, hindering accurate analysis.
Existing denoising methods may not effectively handle complex, time-varying noise artifacts.

Purpose of the Study:

To introduce an effective denoising technique for ECG signals corrupted by nonstationary noises.
To evaluate the performance of a novel method based on second-generation wavelet transform and level-dependent threshold estimation.
To compare the proposed method against traditional nonlinear filtering techniques like the median filter.

Main Methods:

Utilized a second-generation wavelet transform constructed via a lifting scheme for ECG signal decomposition.
Applied a level-dependent threshold estimator to remove noise from wavelet coefficients.
Investigated various wavelet filters (Haar, DB4, DB6, Filter(9-7), Cubic B-splines) and decomposition depths.
Introduced muscle artifact, electrode motion artifact, and white noise for performance evaluation.

Main Results:

The proposed lifting-based wavelet transform method demonstrated superior denoising performance compared to the median filter.
Signal-to-noise ratio (SNR) and visual inspection confirmed the effectiveness of the technique across different noise types.
Performance was evaluated across multiple wavelet filter types and decomposition levels.

Conclusions:

The developed second-generation wavelet transform technique offers an effective and faster alternative for ECG signal denoising.
The method shows consistent superiority over median filtering in removing nonstationary noise artifacts.
This approach enhances ECG signal quality, aiding in more reliable cardiac diagnostics.