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Related Experiment Video

Updated: Jun 7, 2025

Semi-automated Optical Heartbeat Analysis of Small Hearts

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

A non-invasive heart rate prediction method using a convolutional approach.

Ercument Karapinar¹, Ender Sevinc²

¹Elect. Eng. Department, Ankara Science University, Maltepe Mah. Sehit Gonenc Cad. No:5, Ankara, 06570, Turkey.

Medical & Biological Engineering & Computing

|November 14, 2024

Summary

This study uses convolutional neural networks (CNNs) to improve non-invasive heart rate prediction from photoplethysmogram (PPG) signals. The optimized CNN model achieved a 10% lower error rate, offering a more comfortable alternative to traditional methods.

Keywords:

Convolutional neural network (CNN)Heart Rate (HR)Non-invasive blood pressure Photoplethysmography (PPG)

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Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

Area of Science:

Biomedical Engineering
Signal Processing
Artificial Intelligence

Background:

Heart failure is a global health challenge requiring improved monitoring.
Traditional blood pressure cuffs are uncomfortable and invasive.
Photoplethysmogram (PPG) signals offer a non-invasive method for physiological monitoring.

Purpose of the Study:

To develop an accurate and rapid non-invasive heart rate prediction method.
To leverage convolutional neural networks (CNNs) for enhanced PPG signal analysis.
To optimize a CNN model for cuffless heart rate estimation.

Main Methods:

Utilized a k-fold convolutional neural network (CNN) model.
Processed 8-second 1D arrays of PPG data.
Employed k-fold cross-validation to minimize uncertainty.
Optimized the number of convolutional layers for performance.

Main Results:

Achieved a minimum absolute error (MAE) of 6.98 beats per minute (bpm).
Demonstrated a significant 10% improvement over recent studies.
Identified motion artifacts and skin color variations as potential error sources.

Conclusions:

The optimized CNN model provides a highly accurate cuffless heart rate estimation.
This approach offers a cost-effective and comfortable alternative for heart rate monitoring.
The findings support the development of advanced, non-invasive diagnostic devices.