An Adaptive Organization Index to Characterize Atrial Fibrillation using Wrist:Type Photoplethysmographic Signals

Abstract

The performance of photoplethysmography (PPG)-based wearable monitors to diagnose atrial fibrillation (AF) remains unknown to date. This study aims at assessing the performance of new indices quantifying the level of organization in PPG signals to diagnose AF. A database made of 18 adult patients undergoing catheter ablation of various cardiac arrhythmias was used. PPG signals were recorded using a wrist-type sensor. A 12-lead ECG was used as gold standard. ECGs were annotated by experts and selected segments were divided into 4 categories: sinus rhythm (SR), regularly paced rhythm (RPR), irregularly paced rhythm (IPR) and AF. The level of organization of the various PPG signals was measured using an adaptive organization index (AOI), defined as the ratio of the power of the fundamental frequency and the first harmonic to the total power of the PPG signal, computed with adaptive band-pass filters. A total of 2806/803/852/287 10-second epochs were considered for AF/SR/RPR/IPR classes. The following mean AOI values were measured: 0.45±0.11 for AF, 0.73±0.19 for SR, 0.78±0.20 for RPR and 0.61±0.19 for IPR classes. Importantly, the AF AOI was significantly smaller than that of the other categories (p<0.001), indicating a higher degree of disorganization.

Wrist:Located Optical Device for Atrial Fibrillation Screening: a Clinical Study on Twenty Patients

“…The results of the present study (see additional material in [13]) constitute to the best of our knowledge a first clinical evidence of reliable AF detection using PPG sensors at the wrist. The high values of sensitivity (99%) showed in Table 1 demonstrates the potential of a wrist-located AF classifier using PPG technology.…”

“…The analysis of the recorded data has pointed at the fact that the addition of features based on variation of waveform morphologies (as presented in [13]) might be necessary to achieve improved algorithm performances: typical data segments depicted in Figure 1 illustrate this challenge. We observed that for a large number of AF epochs, pathological PPG waveform morphologies were recorded: see by instance Figure 1 D) and F).…”

“…Adding to these facts the expected high patient acceptance of such a device, we expect PPG-based wrist-located devices to revolutionize the screening of AF in large populations. Nevertheless, further work is still required to improve achieved performances, in particular concerning the PPG preprocessing during AF, the cardiac beat detection from PPG atypical waveforms, and the extraction of features from time series of interbeat intervals [13].…”

A life-threatening arrhythmia detection method based on pulse rate variability analysis and decision tree

“…The power spectrum is calculated using the autoregressive (AR) model for the PRV signal, from which the features are extracted according to the frequency range to reflect the stability of cardiovascular activity within the human body and to obtain information about the variability of the cardiovascular system (Fallet et al, 2019). 1) LF_HF: the ratio of low frequency (LF) to high frequency (HF), which can reflect a balanced state of sympathetic and parasympathetic tone.…”

An evaluation of the accelerometer output as a motion artifact signal during photoplethysmograph signal processing control

“…PPG sensors are fallible during movements due to the induced noise caused by motion; this leads to inaccurate readings of the PPG signal and causes false diagnosis [4]. The ANC is one of the several techniques for providing accurate PPG signals [5,6]. It depends on active filtering and utilizes the adaptive filter.…”