CVAI Catalog

·

View Catalog

MEAE (Multi-Encoder Autoencoder for PPG Heart Rate)

PPG / wearable

Filter catalog by Modality:
PPG / Wearable

Heart rate estimation

Filter catalog by Disease / Trait:
Cardiac Function & Hemodynamics

Regression

Filter catalog by Task Type:
Regression

Hybrid

Filter catalog by Architecture:
Hybrid / Multi-branch

PyTorch

Filter catalog by Framework:
PyTorch

Self-supervised multi-encoder autoencoder (MEAE) that separates heartbeat-related source signals from noisy photoplethysmogram (PPG) via blind source separation, improving heart-rate detection without requiring any pre-processing or manual data selection. Trained entirely on PPG signals from a large open polysomnography database (with no cleaning or curation), the model is then applied to a noisy real-world PPG dataset collected during daily activities of 9 subjects and a surgical dataset of 4,681 patients; the extracted heartbeat-related source signal significantly improves heart-rate detection accuracy compared with using the raw PPG signal directly.

memory Specifications

category

Architecture

Hybrid

Self-supervised multi-encoder autoencoder that performs blind source separation on PPG signals, isolating a heartbeat-related source channel from noise/motion-artifact-related channels

code

Framework

PyTorch

calendar_month

Added to catalog

2026-08-14

description Publication

database Training & evaluation data

Daily-Activity Noisy PPG Dataset (MEAE evaluation)

test

public 9 subjects

Surgical PPG Dataset (MEAE evaluation)

test

public 4,681 subjects

science Capabilities & performance

Heartbeat-related source signal isolated from noisy PPG, used to improve heart rate detection accuracy

Regression Heart rate estimation