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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...

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A Paralleled Multi-Task Learning-Based Framework for Single-Lead ECG Fine-Grained Noise Localization, Denoising and

Yating Hu¹, Qing Liu², Zheng Zhou¹

¹School of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China.

Sensors (Basel, Switzerland)

|December 11, 2025

Summary

This summary is machine-generated.

This study introduces a novel multi-task learning framework for electrocardiogram (ECG) preprocessing. The method enhances noise reduction and quality assessment for wearable ECG monitoring, improving diagnostic reliability.

Keywords:

ECG quality assessment denoising multi-task learning transformer

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Area of Science:

Biomedical Engineering
Signal Processing
Artificial Intelligence

Background:

Wearable electrocardiogram (ECG) devices are crucial for personalized healthcare but susceptible to transient noise during daily activities.
This noise complicates signal classification, denoising, and can reduce diagnostic accuracy.
Existing methods struggle with diverse noise types and maintaining waveform fidelity.

Purpose of the Study:

To develop an advanced ECG preprocessing framework using multi-task learning.
To improve the accuracy of ECG signal quality assessment and denoising.
To enable robust, real-time ECG monitoring in wearable devices.

Main Methods:

A Transformer-based multi-task learning model was developed, incorporating a fine-grained noise localization task.
The model was trained using weak supervision and pathological ECG data, optimizing with three task-specific loss functions.
Intra-class awareness was integrated to handle varied noise within quality categories, enabling adaptive denoising.

Main Results:

The framework achieved state-of-the-art performance in ECG denoising and quality assessment, with F1-scores up to 98.49% and classification accuracy over 95.68%.
Significant signal-to-noise ratio (SNR) improvement from -1.95 ± 3.83 dB to 12.20 ± 2.51 dB was observed under severe noise, preserving waveform fidelity.
The model demonstrated effective compression via pruning and quantization for edge computing deployment.

Conclusions:

The proposed method offers an efficient and clinically relevant solution for large-scale, real-time ECG monitoring.
It preserves diagnostically important ECG waveforms and provides interpretable noise localization.
The framework enhances the reliability and applicability of wearable ECG devices in personalized healthcare.