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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.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...

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Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

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Edge-Enabled Heart Rate Estimation from Multisensor PPG Signals.

Xingchi Chen¹, Fa Zhu², Hai Zhao¹

¹School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China.

Journal of Healthcare Engineering

|March 6, 2023

Summary

This summary is machine-generated.

This study introduces an edge-enabled method for accurate, low-latency heart rate (HR) estimation from photoplethysmography (PPG) signals using Internet of Medical Things (IoMT) devices, overcoming bio-artifact interference.

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Area of Science:

Biomedical Engineering
Signal Processing
Edge Computing

Background:

Heart rate (HR) estimation from multisensor photoplethysmography (PPG) signals is often inconsistent due to bio-artifacts (BAs).
Edge computing and Internet of Medical Things (IoMT) devices offer potential for real-time signal processing.

Purpose of the Study:

To propose an edge-enabled method for accurate and low-latency HR estimation from multisensor PPG signals captured by IoMT devices.
To address the challenge of bio-artifact interference in PPG-based HR monitoring.

Main Methods:

Designed a real-world edge network with resource-constrained collection and computing edge nodes.
Developed a self-iteration RR interval calculation method at collection nodes to mitigate BAs and reduce data transmission.
Implemented an unsupervised abnormal detection method within a heart rate pool at computing nodes for average HR estimation.

Main Results:

The proposed method demonstrated superior consistency and accuracy in HR estimation compared to traditional single-PPG signal approaches.
Achieved low-latency processing, obtaining HR from a 30-second PPG signal in just 4.24 seconds.
Significantly reduced data volume transmitted from IoMT devices to computing nodes.

Conclusions:

The edge-enabled method effectively estimates HR with high accuracy and low latency, overcoming BA challenges.
The approach is valuable for real-time applications in IoMT healthcare and fitness management.
The designed edge network architecture optimizes resource utilization for PPG signal processing.