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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...

Instrumentation Amplifier

Instrumentation Amplifier

An electrocardiography (ECG) machine is an essential piece of medical equipment used to monitor the electrical activity of the heart. It operates by detecting small electrical changes on the skin that result from the depolarization of the heart muscle during each heartbeat. However, these signals are in the microvolt range and can be easily overwhelmed by noise or interference.
To overcome this challenge, an ECG machine utilizes an instrumentation amplifier. This specialized amplifier is...

Correlation between ECG and Cardiac Cycle

Correlation between ECG and Cardiac Cycle

The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...

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Updated: May 18, 2026

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

[Study on motion artifact reduction based on periodic component analysis using ECG as a case].

Kui Xiang¹, Qiao Luo, Jing Chen

¹School of Automation, Wuhan University of Technology, Wuhan 430070, China. xkarcher@126.com

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi

|September 29, 2012

Summary

This summary is machine-generated.

This study introduces periodic component analysis to reduce motion artifacts in wearable physiological signals. The method effectively separates physiological data from noise, improving signal quality for wearable devices.

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Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

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Area of Science:

Biomedical Engineering
Signal Processing
Wearable Technology

Context:

Motion artifacts significantly interfere with ambulatory physiological signals, especially in wearable systems using dry electrodes.
Existing methods struggle to effectively remove these artifacts due to their instantaneous nature and the inherent periodicity of physiological signals.

Purpose:

To present a novel method, periodic component analysis (PCA), for reducing motion artifacts in physiological signals.
To demonstrate PCA's effectiveness in separating physiological signals from motion artifacts using multi-resolution analysis and time-domain signal separation.

Summary:

The proposed method transforms single-channel signals into multi-channel signals via multi-resolution analysis, enabling PCA to distinguish physiological signals from motion artifacts.
A case study using electrocardiogram (ECG) signals shows PCA outperforms empirical mode decomposition and adaptive filtering.
PCA effectively handles frequency aliasing and recovers corrupted ECG waveform features.

Impact:

Provides a robust solution for motion artifact reduction in wearable physiological monitoring.
Enhances the accuracy and reliability of data acquired from wearable sensors.
Offers a versatile method applicable to various physiological signal processing tasks, including ECG analysis.