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Pulse rhythm

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.
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Related Experiment Video

Updated: May 23, 2025

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function

Published on: December 11, 2019

Enhancing Cardiopulmonary Resuscitation Quality Using a Smartwatch: Neural Network Approach for Algorithm Development

Gaurav Rao¹, David W Savage², Gabrielle Erickson²

¹Department of Mathematics and Computing, Faculty of Science, Saint Mary's University, Halifax, NS, Canada.

JMIR Mhealth and Uhealth

|May 5, 2025

Summary

This summary is machine-generated.

This study developed a neural network model using smartwatch data to accurately assess cardiopulmonary resuscitation (CPR) quality in real-time. The model predicts compression depth and rate, aiming to improve survival rates from sudden cardiac arrest.

Keywords:

CPR feedback CPR performance cardiac arrest cardiopulmonary resuscitation chest compression compressions data collection efficacy emergency mobile phone monitoring network model neural network smart health smartwatch sudden cardiac arrest wearables

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Last Updated: May 23, 2025

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Area of Science:

Biomedical Engineering
Artificial Intelligence in Healthcare
Emergency Medicine Technology

Background:

Sudden cardiac arrest (SCA) is a leading cause of mortality.
High-quality cardiopulmonary resuscitation (CPR) is critical for improving SCA survival rates.
Real-time monitoring of CPR quality (100-120 compressions/min, 50-60 mm depth) is challenging during emergencies.

Purpose of the Study:

To introduce a novel neural network model for predicting and assessing CPR quality.
To utilize accelerometer data from smartwatches for CPR quality assessment.
To develop a system for real-time feedback on CPR performance.

Main Methods:

Collected accelerometer data from 83 participants performing CPR on mannequins using smartwatches.
Aligned smartwatch data with gold-standard mannequin data.
Trained 1226 neural network models using 5-second intervals of compression data, optimizing hyperparameters and dataset configurations.

Main Results:

The optimal neural network model accurately predicted compression count (±0.8 deviation) and average depth (±3.8 mm) within 5-second intervals.
The model demonstrated superior accuracy in CPR quality assessment compared to existing methods.
A large, diverse dataset enhanced the model's robustness and reliability.

Conclusions:

Validated the efficacy of a neural network model for predicting CPR metrics from smartwatch accelerometer data.
The model shows significant promise for real-time CPR feedback, potentially improving SCA survival rates.
Future research will focus on deploying the model directly onto smartwatches for immediate, real-time application.