# PPG-Net 4: Deep-Learning-Based Approach for Classification of Blood Flow Using Non-Invasive Dual Photoplethysmography (PPG) Signals

## TL;DR

PPG-Net 4 is a deep learning model that classifies blood flow patterns using non-invasive PPG signals, offering a promising alternative to traditional diagnostic methods.

## Contribution

PPG-Net 4 introduces a novel dual-sensor PPG setup and deep learning model for accurate non-invasive blood flow classification.

## Key findings

- PPG-Net 4 achieved F1-scores between 0.86 and 0.92 for classifying five blood flow patterns.
- The model showed highest accuracy for pulsatile flow with an F1-score of 0.92.
- The approach offers a non-invasive alternative for cardiovascular disease detection and monitoring.

## Abstract

Cardiovascular disease diagnosis heavily relies on accurate blood flow assessments, traditionally performed using invasive and often uncomfortable methods like catheterization. This research introduces PPG-Net 4, an innovative deep learning approach for non-invasive blood flow pattern classification using dual photoplethysmography (PPG) signals. By leveraging advanced machine learning techniques, the proposed method addresses critical limitations in current diagnostic technologies. The study employed a novel dual-sensor arrangement capturing PPG signals from two body locations, generating a comprehensive dataset from 75 participants. Advanced signal processing techniques, including mel spectrogram generation and mel-frequency cepstral coefficient extraction, enabled sophisticated feature representation. The deep learning model, PPG-Net 4, demonstrated good capability at classifying the following five distinct blood flow patterns: laminar, turbulent, stagnant, pulsatile, and oscillatory. The experimental results revealed strong classification performance, with F1-scores ranging from 0.86 to 0.92 across different flow patterns. The highest accuracy was observed for pulsatile flow (F1-score: 0.92), underscoring the model’s precision and reliability. This approach not only provides a non-invasive alternative to traditional diagnostic methods but also offers a potentially useful technique for early cardiovascular disease detection and continuous monitoring.