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Enhanced premature ventricular contraction pulse detection and classification using deep convolutional neural

Remya Raj¹, Ushus S Kumar², Vivek Maik³

¹Department of Biomedical Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, India. remyar1@srmist.edu.in.

Physical and Engineering Sciences in Medicine

|September 18, 2023

Summary

This study introduces a deep convolutional neural network (CNN) for detecting fetal cardiac arrhythmia using photoplethysmography (PPG) and arterial blood pressure (ABP) signals. The novel method achieves high accuracy in classifying premature ventricular contractions (PMVCs).

Keywords:

Arterial blood pressure (ABP)Convolutional neural network (CNN)Low-rank optimization Photoplethysmography (PPG)Premature ventricular contraction (PMVC)Wavelet transform

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Area of Science:

Biomedical Engineering
Cardiology
Artificial Intelligence in Medicine

Background:

Accurate monitoring systems are crucial for preventing cardiac disorders.
Fetal cardiac arrhythmia, specifically premature ventricular contractions (PMVCs), requires precise detection methods.

Purpose of the Study:

To develop and evaluate a deep convolutional neural network (CNN) for classifying fetal cardiac arrhythmia (PMVCs).
To utilize photoplethysmography (PPG) and arterial blood pressure (ABP) signals for enhanced detection accuracy.

Main Methods:

A deep convolutional neural network (CNN) architecture was employed.
Transfer learning was utilized, with weights from the Icentia 11k ECG dataset.
The CNN was fine-tuned for improved classification of PMVCs using PPG and ABP data.

Main Results:

The proposed CNN method demonstrated high accuracy in detecting and classifying PMVCs.
Classification accuracy for Normal, P1, and P2 types of PMVCs reached 99.9%, 99.8%, and 99.5%, respectively.

Conclusions:

The developed CNN framework effectively detects and classifies fetal cardiac arrhythmia (PMVCs).
The integration of PPG and ABP signals with CNNs offers a promising approach for non-invasive fetal cardiac monitoring.