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Related Concept Videos

Pulse rhythm01:30

Pulse rhythm

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.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac muscle...
Assessing Blood pressure using a doppler ultrasound01:19

Assessing Blood pressure using a doppler ultrasound

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.
Pre-Procedural Guidelines for Doppler Ultrasound Blood Pressure Assessment:
Preparation of Equipment:
Location and Orientation of the Heart01:13

Location and Orientation of the Heart

The human heart, despite its modest size and weight, is an organ of remarkable strength and endurance. Roughly the size of a fist, the heart weighs between 250 and 350 grams and is nestled within the mediastinum, the medial cavity of the thorax. It extends obliquely for about 12 to 14 cm, resting on the superior surface of the diaphragm. The heart is positioned anterior to the vertebral column and posterior to the sternum, with two-thirds of its mass lying to the left of the midsternal line.

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Related Experiment Video

Updated: May 28, 2026

Semi-automated Optical Heartbeat Analysis of Small Hearts
12:10

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

Deep Learning-Based Heartbeat Detection from 3D Seismocardiography for Robust Heart Rate Monitoring.

Sobuz Rana1, Jukka A Lipponen1, Mika P Tarvainen1

  • 1Department of Technical Physics, University of Eastern Finland, 70211 Kuopio, Finland.

Sensors (Basel, Switzerland)
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning model using seismocardiography (SCG) for accurate heart rate (HR) monitoring. The AI-powered approach effectively detects heartbeats and estimates HR from chest vibrations, showing promise for wearable health tech.

Keywords:
accelerometersbeat detectiondeep learningheart rate monitoringseismocardiography

Related Experiment Videos

Last Updated: May 28, 2026

Semi-automated Optical Heartbeat Analysis of Small Hearts
12:10

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

Area of Science:

  • Biomedical Engineering
  • Cardiovascular Physiology
  • Artificial Intelligence in Healthcare

Background:

  • Accurate heart rate (HR) monitoring is crucial for cardiac health assessment.
  • Seismocardiography (SCG), utilizing chest vibrations via accelerometers, offers a non-invasive method for HR monitoring.
  • Existing methods may lack accuracy or convenience for continuous or widespread use.

Purpose of the Study:

  • To develop and validate a deep learning model for precise heartbeat detection and HR estimation using 3D seismocardiography signals.
  • To evaluate the model's performance in both controlled resting conditions and real-world nocturnal scenarios.
  • To assess the potential of SCG-based deep learning for integration into consumer wearable devices.

Main Methods:

  • A deep learning model was developed for analyzing 3D seismocardiography (SCG) signals.
  • The model was trained on a large dataset (6600 subjects) of resting SCG recordings.
  • Performance was evaluated on an independent cohort (947 subjects) and nocturnal recordings from wearable devices.

Main Results:

  • The model demonstrated high accuracy in heartbeat detection (PPV: 0.979, sensitivity: 0.916, F1-score: 0.946) for short-term resting SCG.
  • Resting HR estimation showed excellent accuracy (MAE: 0.27 bpm, RMSE: 1.02 bpm, correlation: 0.996).
  • Nocturnal HR monitoring using Apple Watch data yielded strong results (MAE: 1.10 bpm, RMSE: 1.88 bpm, correlation: 0.982).

Conclusions:

  • The proposed deep learning model provides robust and accurate heart rate monitoring using seismocardiography.
  • The SCG-based approach is effective in both resting and nocturnal conditions.
  • This technology holds significant potential for enhancing non-invasive cardiac monitoring with consumer-grade wearables.