Work

Blind ppg to ecg/eeg alignment: synchronization and quality analysis

Year: 2026

Type: conference-paper

Abstract: Multimodal biosignal recordings combining PPG, ECG, and EEG are widely used in mental and physical health research. However, when signals are collected from separate devices, time misalignment between recordings is a common problem. This paper presents a blind synchronization method for aligning PPG signals to ECG and EEG signals. The proposed approach applies cross-correlation on preprocessed signals, where preprocessing consists of PCHIP resampling, band-pass filtering, and rolling Z-score normalization. A peak ratio metric is introduced to estimate the confidence of the detected lag. The method was tested on a dataset of 179 recordings from 50 participants, where PPG was collected via a Polar OH1 sensor at ∼135 Hz and ECG/EEG via a CleverPoint 4 device at 500 Hz and evaluated on a subsample of VitalDB dataset, where PPG and ECG alignment was known. Results show that 143 out of 179 signals were successfully synchronized, with PPG and ECG achieving a confident peak ratio (higher than 3) , in 79% of samples i

Source: European Conference on Modelling and Simulation (ECMS)

DOI: 10.7148/2026-0827

Authors: Gintautas Sedys, Patrikas Armalis, Evelina Aluzaite, Egle Butkeviciute

Institutions: Kaunas University of Technology, Lithuanian University of Health Sciences

Language: English

Topic: Non-Invasive Vital Sign Monitoring

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: bronze

Funder: Lietuvos Mokslo Taryba