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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

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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...
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Electrocardiogram Fundamentals01:28

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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
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Instrumentation Amplifier01:25

Instrumentation Amplifier

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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...
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    Area of Science:

    • Biomedical Engineering
    • Analog Signal Processing
    • Wearable Health Technology

    Background:

    • Electrocardiogram (ECG) signal processing is crucial for diagnosing cardiac conditions.
    • Existing ultra-low power ECG processors often struggle with real-time detection and noise suppression.
    • The integration of stochastic resonance offers a novel approach to enhance signal detection in noisy environments.

    Purpose of the Study:

    • To develop an ultra-low power ECG processor for real-time QRS-wave detection.
    • To implement advanced noise suppression and signal enhancement techniques.
    • To validate the processor's performance against established ECG databases and compare it with digital algorithms.

    Main Methods:

    • Designed and implemented an ultra-low power ECG processor using current-mode analog signal processing in 65 nm CMOS technology.
    • Employed a linear filter for out-of-band noise suppression and a nonlinear filter for in-band noise suppression and QRS-wave enhancement via stochastic resonance.
    • Utilized a constant threshold detector for QRS-wave identification on processed ECG signals.

    Main Results:

    • Achieved an average F1 score of 99.88% on the MIT-BIH Arrhythmia database, surpassing previous ultra-low power ECG processors.
    • Demonstrated superior detection performance on noisy ECG recordings from MIT-BIH NST and TELE databases compared to most digital algorithms.
    • The processor boasts a minimal footprint (0.08 mm²) and extremely low power dissipation (2.2 nW at 1V).

    Conclusions:

    • The developed ultra-low power ECG processor enables real-time QRS-wave detection with exceptional accuracy and energy efficiency.
    • The integration of stochastic resonance in an analog processor represents a significant advancement in wearable ECG monitoring.
    • This processor is the first of its kind, offering validated real-time performance with stochastic resonance for noisy ECG data.