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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...

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A Real-Time Wearable Electromyography Measurement System for Small Animals
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Miniaturized Wearable System for Multimodal EEG/ECG/EMG Sensing and Real-Time Physiological Monitoring.

Yunxiang Zhang1,2,3, Xueyang Meng1, Chengbang Lu2,3

  • 1School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, China.

Micromachines
|June 26, 2026
PubMed
Summary

We developed a low-cost, compact wearable device for simultaneous electroencephalography (EEG), electromyography (EMG), and electrocardiography (ECG) sensing. This platform enables real-time physiological state awareness for advanced human-computer interaction applications.

Keywords:
biosensing and diagnosticsmicro devicemultimodalreal-time monitoringseizure detection

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Published on: July 22, 2022

Area of Science:

  • Biomedical Engineering
  • Wearable Technology
  • Human-Computer Interaction

Background:

  • Existing electrophysiological sensing platforms are often limited by single-modality sensing, high costs, or bulky form factors.
  • Real-time physiological state awareness is crucial for advanced wearable computing and human-state-aware interactions.
  • There is a need for integrated, low-cost, and compact wearable solutions for multi-modal physiological signal acquisition.

Purpose of the Study:

  • To present a novel, compact, and low-cost wearable platform for simultaneous electroencephalography (EEG), electromyography (EMG), and electrocardiography (ECG) acquisition.
  • To validate the system's performance in capturing physiological signals and its potential for real-time applications.
  • To demonstrate the system's compatibility with advanced signal processing pipelines for applications like seizure detection.

Main Methods:

  • Development of a single-board platform integrating analog front-end, microcontroller, and Bluetooth for multi-modal sensing.
  • Experimental validation on three healthy subjects, including benchmarking ECG against a clinical-grade recorder.
  • Application of a Convolutional Neural Network (CNN) pipeline for epileptic seizure detection using recorded EEG data.

Main Results:

  • The platform successfully acquired recognizable ECG waveforms (P-QRS-T morphology), enabled reliable heart rate estimation, and captured stable resting-state EEG spectral features.
  • EMG activation was clearly distinguished from baseline in both time-domain and time-frequency analyses.
  • The CNN pipeline achieved 86.60% mean accuracy for five-class epileptic state classification on the Bonn EEG benchmark.

Conclusions:

  • The proposed wearable platform offers a scalable, affordable solution for simultaneous multi-modal physiological sensing.
  • It provides a foundation for real-time human-state-aware interactions and has potential in clinical monitoring, rehabilitation, and brain-computer interfaces.
  • The system's compact size, low cost, and wireless capabilities facilitate everyday deployment and diverse application scenarios.