# Atrial Fibrillation Detection from At-Rest PPG Signals Using an SDOF-TF Method

## TL;DR

This paper introduces a new method to detect atrial fibrillation from PPG signals by analyzing motion artifacts and harmonic parameters.

## Contribution

The novel SDOF-TF method improves AF detection accuracy by quantifying motion artifacts and extracting harmonic features.

## Key findings

- The SDOF-TF method achieved 100% accuracy in distinguishing AF from non-AF cases using RM, HRV, and HR standard deviation.
- AF subjects showed diminished RM trends across harmonics compared to non-AF subjects.
- The method effectively removes motion artifacts and noise to extract accurate physiological parameters.

## Abstract

At-rest PPG signals have been explored for detecting atrial fibrillation (AF), yet current signal-processing techniques do not achieve perfect accuracy even under low-motion artifact (MA) conditions. This study evaluates the effectiveness of a single-degree-of-freedom time–frequency (SDOF-TF) method in analyzing at-rest PPG signals for AF detection. The method leverages the influence of MA on the instant parameters of each harmonic, which is identified using an SDOF model in which the tissue–contact–sensor (TCS) stack is treated as an SDOF system. In this model, MA induces baseline drift and time-varying system parameters. The SDOF-TF method enables the quantification and removal of MA and noise, allowing for the accurate extraction of the arterial pulse waveform, heart rate (HR), heart rate variability (HRV), respiration rate (RR), and respiration modulation (RM). Using data from the MIMIC PERform AF dataset, the method achieved 100% accuracy in distinguishing AF from non-AF cases based on three features: (1) RM, (2) HRV derived from instant frequency and instant initial phase, and (3) standard deviation of HR across harmonics. Compared with non-AF, the RM for each harmonic was increased by AF. RM exhibited an increasing trend with harmonic order in non-AF subjects, whereas this trend was diminished in AF subjects.