Key result
Dual PPG signals and a CNN achieve ~87% accuracy in distinguishing abnormal blood flow patterns.
Why the study?
Can a dual PPG-based deep learning system accurately detect blood flow abnormalities?
Can a dual PPG-based deep learning system accurately detect blood flow abnormalities?
A novel dual PPG-based deep learning system demonstrates promising accuracy (87.3%) for non-invasive detection of blood flow abnormalities.
This paper presents a novel system for predicting nonuniform blood flow using dual Photoplethysmography (PPG) signals and deep learning. The hardware comprises two PPG sensors strategically placed on the finger and thumb muscle, connected to an ESP32 node MCU board for data acquisition. The captured signals are transmitted via Wi-Fi to a computer for analysis. A Convolutional Neural Network (CNN) classifier processes the dual PPG data to categorize blood flow as normal or abnormal, providing a confidence level for each prediction. The model, trained on a dataset of 960 samples from 22 individuals, achieved an accuracy of 87.3% in distinguishing between normal and abnormal blood flow patterns. Additional performance metrics, including precision (84.8%), recall (90.3%), and F1-score (87.5%), demonstrate the system's effectiveness. This approach offers a promising non-invasive method for early detection of blood flow abnormalities, potentially improving cardiovascular health monitoring.
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Samant et al. (2024) studied Blood flow abnormalities (n=22). Dual PPG sensors with Convolutional Neural Network (CNN) classifier was evaluated on Accuracy in distinguishing between normal and abnormal blood flow patterns. A novel system using dual PPG signals and a Convolutional Neural Network achieved an accuracy of 87.3% in distinguishing between normal and abnormal blood flow patterns.
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