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Related Concept Videos

Pulse rhythm01:30

Pulse rhythm

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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
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To obtain accurate blood pressure measurements in clinical settings, especially when traditional methods are insufficient, healthcare professionals utilize the Doppler ultrasound technique. This method uses high-frequency sound waves to detect blood flow within the arteries, which is crucial for patients with conditions that complicate circulatory system assessment.
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Factors Influencing Heart Rate01:30

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The heart rate, or pulse rate, is a vital indicator of cardiovascular health. It reflects the number of times the heart beats per minute. Various physiological and environmental factors influence heart rate, increasing or decreasing cardiac output. Understanding these factors is crucial for assessing heart function and identifying potential health issues.
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Holter Monitor: 24-Hour Monitoring01:23

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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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Related Experiment Video

Updated: Jan 15, 2026

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students
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Real-Time Heartbeat Classification on Distributed Edge Devices: A Performance and Resource Utilization Study.

Eko Sakti Pramukantoro1, Kasyful Amron1, Putri Annisa Kamila2

  • 1Faculty of Computer Science, Universitas Brawijaya, Malang 65145, Indonesia.

Sensors (Basel, Switzerland)
|October 16, 2025
PubMed
Summary

This study introduces a real-time system for early heart disease detection using wearable sensors and machine learning. It achieves 99% accuracy in heartbeat classification with fast inference times, enabling immediate diagnosis.

Keywords:
Jetson NanoLSTMheartbeat classificationreal-time inferencestream processing

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Area of Science:

  • Cardiology and Health Technology
  • Machine Learning and Distributed Computing

Background:

  • Early heart disease detection is vital for prevention.
  • Wearable devices and machine learning offer potential for automated heartbeat detection.
  • Existing heartbeat classification systems often use batch processing, causing delays.

Purpose of the Study:

  • To develop a real-time heartbeat classification inference system using distributed stream processing.
  • To enable continuous, immediate, and scalable analysis of ECG data for early diagnosis.
  • To address the limitations of batch processing in current systems.

Main Methods:

  • Implemented a real-time system with distributed stream processing and Flask framework.
  • Integrated Polar H10 sensors via Bluetooth and Web Bluetooth API for ECG data acquisition.
  • Utilized LSTM-512, LSTM-256, and FCN models with RR-interval, morphology, and wavelet features.

Main Results:

  • Achieved 99% accuracy in heartbeat classification with optimal Wavelet features and LSTM-Sequential architecture.
  • Demonstrated real-time performance with an inference time of 0.12 seconds.
  • Showcased efficient resource utilization on Jetson Orin devices (24.75% CPU, 0.34% GPU, 293 MB memory).

Conclusions:

  • Real-time heartbeat classification is feasible on distributed edge devices using optimized features and models.
  • The developed distributed architecture ensures resilience and scalability for continuous ECG monitoring.
  • This system enhances early diagnosis capabilities for heart disease through immediate data analysis.