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Electrocardiogram Fundamentals01:28

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.
Parts of an ECG
An ECG utilizes electrodes on the skin to...
Holter Monitor: 24-Hour Monitoring01:23

Holter Monitor: 24-Hour Monitoring

Holter monitoring is a continuous electrocardiography (ECG) recording that tracks the heart's electrical activity over an extended period, generally 24 to 48 hours. This noninvasive diagnostic tool detects irregular heart rhythms that may not be captured during a standard ECG performed in a clinical setting.DeviceThe Holter monitor is a portable, small device connected to several electrodes on the patient's chest. These electrodes detect the heart's electrical signals and transmit them to the...
Electrocardiogram01:29

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...

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Related Experiment Video

Updated: May 30, 2026

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
05:03

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function

Published on: December 11, 2019

Development and evaluation of multilead wavelet-based ECG delineation algorithms for embedded wireless sensor nodes.

Francisco Rincón1, Joaquin Recas, Nadia Khaled

  • 1School of Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland. francisco.rincon@fdi.ucm.es

IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society
|August 11, 2011
PubMed
Summary
This summary is machine-generated.

This study evaluates digital wavelet transform (DWT) algorithms for wearable electrocardiogram (ECG) devices. Optimized algorithms achieve high accuracy comparable to single-lead methods, with low energy consumption for long-lasting monitoring.

Related Experiment Videos

Last Updated: May 30, 2026

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
05:03

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function

Published on: December 11, 2019

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Wearable Technology

Background:

  • Electrocardiogram (ECG) signal analysis is crucial for cardiac monitoring.
  • Wearable sensor platforms require efficient algorithms due to limited resources.
  • Digital Wavelet Transform (DWT) offers potential for ECG delineation.

Purpose of the Study:

  • To evaluate multilead DWT-based ECG delineation algorithms optimized for wearable platforms.
  • To assess the performance, resource usage, and energy consumption of embedded ECG delineation.
  • To provide design guidelines for energy-efficient wearable ECG monitoring systems.

Main Methods:

  • Optimization and porting of multilead DWT-based ECG delineation algorithms to a commercial wearable sensor.
  • Investigation of root-mean squared (RMS)-based multilead delineation followed by an online single-lead algorithm.
  • Analysis of delineation accuracy, execution time, memory usage, and energy consumption.

Main Results:

  • RMS-based multilead delineation performed equivalently to state-of-the-art single-lead delineation on the QT database.
  • Algorithm implementations met Common Standards for Electrocardiography (CSE) tolerances within a fraction of a sample duration.
  • Comprehensive energy consumption evaluation identified dominant energy-draining functionalities.

Conclusions:

  • Optimized DWT-based multilead ECG delineation is feasible and accurate for wearable platforms.
  • The proposed methods offer a balance of accuracy and efficiency for embedded systems.
  • Insights into energy consumption guide the design of long-lasting wearable ECG monitoring solutions.