Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

5.5K
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...
5.5K
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

1.4K
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...
1.4K
Time and frequency -Domain Interpretation of PI Control01:27

Time and frequency -Domain Interpretation of PI Control

403
Proportional-Integral (PI) controllers are essential in many control systems to improve stability and performance. They are commonly used in everyday devices like thermostats to enhance system damping and reduce steady-state error. When the zero in the controller's transfer function is optimally placed, the system benefits significantly in terms of stability and accuracy.
Acting as a low-pass filter, the PI controller slows the system's response and extends settling times. This requires...
403
Passive Filters01:27

Passive Filters

962
Passive filters are utilized to shape the frequency spectrum of signals across a diverse array of applications. These filters, using only passive elements like resistors (R), inductors (L), and capacitors (C), are capable of selectively allowing or blocking certain frequency ranges without the need for external power sources.
Low-Pass Filters
Low-pass filters are designed to transmit signals with frequencies lower than the cutoff frequency, ωc, and attenuate those above it. The cutoff...
962
Time and frequency -Domain Interpretation of Phase-lead Control01:24

Time and frequency -Domain Interpretation of Phase-lead Control

435
Phase-lead controllers are commonly used in various control systems to enhance response speed and stability. Adjusting the brightness on a television screen offers a practical example of phase-lead control. When contrast is enhanced, a phase-lead controller is employed. Mathematically, phase-lead control is identified when the first parameter is smaller than the second.
The design of phase-lead control involves the strategic placement of poles and zeros to balance steady-state error and system...
435
Time and frequency -Domain Interpretation of Phase-lag Control01:21

Time and frequency -Domain Interpretation of Phase-lag Control

396
Phase-lag controllers are widely used in control systems to improve stability and reduce steady-state errors. A dimmer switch controlling the brightness of a light bulb serves as a practical example of phase-lag control, gradually adjusting the bulb's brightness. Mathematically, phase-lag control or low-pass filtering is represented when the factor 'a' is less than 1.
Phase-lag controllers do not place a pole at zero, but instead influence the steady-state error by amplifying any...
396

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Fr<sup>n</sup>OBSA: fractional order-based spectral analysis for arrhythmia detection.

Physical and engineering sciences in medicine·2025
Same author

FHESA: fourier decomposition and hilbert transform based EEG signal analysis for Alzheimer's disease detection.

Physical and engineering sciences in medicine·2025
Same author

Integrated wearable PPG: a multi-vital sign monitoring based on group sparse mode decomposition framework in remote health care using PPG signal.

Physical and engineering sciences in medicine·2025
Same author

Optimized deep neural network models for blood pressure classification using Fourier analysis-based time-frequency spectrogram of photoplethysmography signal.

Biomedical engineering letters·2023
Same author

Blood pressure estimation and classification using a reference signal-less photoplethysmography signal: a deep learning framework.

Physical and engineering sciences in medicine·2023
Same author

Efficiency Enhancement in Organic Solar Cells by Use of Cobalt Phthalocyanine (CoPc) Thin Films.

Journal of nanoscience and nanotechnology·2019

Related Experiment Video

Updated: Jan 20, 2026

Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice
04:45

Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice

Published on: May 5, 2022

2.9K

Time-frequency localization using three-tap biorthogonal wavelet filter bank for electrocardiogram compressions.

Ashish Kumar1, Rama Komaragiri1, Manjeet Kumar1

  • 1Department of Electronics and Communication Engineering, Bennett University, Greater Noida, U. P. 210310 India.

Biomedical Engineering Letters
|August 29, 2019
PubMed
Summary

This study introduces an advanced wavelet filter bank for compressing Electrocardiogram (ECG) signals, achieving significant data reduction while maintaining signal integrity for remote medical use.

Keywords:
Biorthogonal wavelet transformElectrocardiogramElectrocardiogram compressionWavelet filter bank

More Related Videos

Brain Banking: Making the Most of your Research Specimens
08:12

Brain Banking: Making the Most of your Research Specimens

Published on: July 24, 2009

10.4K
Simultaneous Recordings of Cortical Local Field Potentials, Electrocardiogram, Electromyogram, and Breathing Rhythm from a Freely Moving Rat
10:07

Simultaneous Recordings of Cortical Local Field Potentials, Electrocardiogram, Electromyogram, and Breathing Rhythm from a Freely Moving Rat

Published on: April 2, 2018

11.4K

Related Experiment Videos

Last Updated: Jan 20, 2026

Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice
04:45

Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice

Published on: May 5, 2022

2.9K
Brain Banking: Making the Most of your Research Specimens
08:12

Brain Banking: Making the Most of your Research Specimens

Published on: July 24, 2009

10.4K
Simultaneous Recordings of Cortical Local Field Potentials, Electrocardiogram, Electromyogram, and Breathing Rhythm from a Freely Moving Rat
10:07

Simultaneous Recordings of Cortical Local Field Potentials, Electrocardiogram, Electromyogram, and Breathing Rhythm from a Freely Moving Rat

Published on: April 2, 2018

11.4K

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Data Compression

Background:

  • Electrocardiogram (ECG) signal compression is crucial for efficient data transmission and storage in telemedicine.
  • Existing wavelet-based methods often face limitations in achieving high compression ratios without compromising signal quality.

Purpose of the Study:

  • To propose a novel joint time-frequency localized three-band biorthogonal wavelet filter bank for ECG signal compression.
  • To enhance compression efficiency using adaptive thresholding and modified run-length encoding.
  • To evaluate the proposed method's performance against existing techniques.

Main Methods:

  • Development of a three-band biorthogonal wavelet filter bank with joint time-frequency localization.
  • Implementation of adaptive thresholding for noise reduction and data simplification.
  • Application of modified run-length encoding for efficient data representation.
  • Performance evaluation using metrics such as compression ratio (CR), maximum absolute error (EMA), quality score (Qs), and compression time (CT).

Main Results:

  • The proposed three-band wavelet filter bank demonstrates superior performance compared to a two-band wavelet filter bank.
  • Achieved maximum data volume reduction while ensuring high-quality signal reconstruction.
  • The method enables lossless data transmission of medical signals, crucial for remote therapeutic applications.

Conclusions:

  • The developed joint time-frequency localized three-band biorthogonal wavelet filter bank offers an effective solution for ECG signal compression.
  • The combination of adaptive thresholding and modified run-length encoding significantly improves compression efficiency and data integrity.
  • This approach facilitates reliable remote transmission of vital medical signals for improved healthcare delivery.