Atrial Fibrillation Identification With PPG Signals Using a Combination of Time-Frequency Analysis and Deep Learning

Abstract

Atrial fibrillation (AF) is the most common persistent arrhythmia and is likely to cause strokes and damage to heart function in patients. Electrocardiogram (ECG) is the gold standard for detecting AF. However, ECGs have short boards with short monitoring cycles and problems with gathering. It is also difficult to detect a burst AF through ECG. In contrast, photoplethysmography (PPG) is easy to perform and suitable for long-term monitoring. In this study, we propose a method that combines timefrequency analysis with deep learning and identifies AF based on PPG. The advantage of the method is that there is no need for the noise filtering and feature extraction of PPG, and it has a high generalization capability. The data for the experiment came from three publicly accessible databases. The first part of the experimental method uses data augmentation to convert the 10 s PPG segment into a time-frequency chromatograph by means of time-frequency analysis. The second part inputs the chromatograph into a hybrid framework that combines a convolutional neural network (CNN) and long short-term memory (LSTM) for AF/nonAF classification. The experimental results show that the method has a high classification accuracy, sensitivity, specificity, and F1 score, which are equal to 98.21%, 98.00%, 98.07% and 98.13%, respectively. The area under the receiver operating characteristic curve (AUC) is 0.9959. The model we propose not only aids doctors in diagnosing AF but also provides a method for identifying AF through portable wearable devices. INDEX TERMS Atrial fibrillation, photoplethysmography (PPG), time-frequency analysis, convolutional neural networks (CNN), long short-term memory (LSTM).

Photoplethysmogram Analysis and Applications: An Integrative Review

“…In addition, studies for predicting various parameters or diagnosing diseases have been conducted using PPG. In addition to basic heart rate estimation, PPG is used for blood pressure estimation ( Poon and Zhang, 2005 ; Muehlsteff et al, 2006 ; He et al, 2014 ; Nabeel et al, 2017 ; Wang et al, 2018 ; El Hajj and Kyriacou, 2020 ), vascular aging assessment ( Takazawa et al, 1998 ; Bortolotto et al, 2000 ; Millasseau et al, 2003 ; Baek et al, 2007 ; Jubadi and Sahak, 2009 ; Wang et al, 2009 ; Yousef et al, 2012 ; Dall’Olio et al, 2020 ; Korkalainen et al, 2020 ), arterial fibrillation prediction ( Poh et al, 2018 ; Kwon et al, 2019 ; Aschbacher et al, 2020 ; Cheng et al, 2020 ; Pereira et al, 2020 ), diabetes prediction ( Shan et al, 2016 ; Tang et al, 2017 ; Poh et al, 2018 ; Eerikäinen et al, 2019 ; Guo et al, 2019 ; Kwon et al, 2019 ; Proesmans et al, 2019 ; Yang et al, 2019 ; Aschbacher et al, 2020 ; Cheng et al, 2020 ; Pereira et al, 2020 ), peripheral vascular disease assessment ( Allen and Murray, 1993 ; Alnaeb et al, 2007 ; Bentham et al, 2018 ; Allen et al, 2021 ), surgical and postoperative pain assessment ( Ahonen et al, 2007 ; Struys et al, 2007 ; Kallio et al, 2008 ; Hasanin et al, 2017 ; Yang et al, 2018 ; Seok et al, 2019 ), heterogeneous bio-signal (e.g., ECG) reconstruction ( Zhu et al, 2021 ), hemodynamic parameter estimation such as cardiac output ( McCombie et al, 2005 ; Wang et al, 2009 , Wang et al, 2010 , 2014 ; Lee et al, 2013 ) or stroke volume ( Liu et al, 2020a , b ...…”

“…In particular, attempts to find meaningful information from PPG using various deep learning models are continuously increasing. Representative applications of PPG analysis using deep learning include heart rate estimation ( Biswas et al, 2019 ; Reiss et al, 2019 ; Panwar et al, 2020 ; Chang et al, 2021 ; Mehrgardt et al, 2021 ), cuff-less blood pressure estimation ( Panwar et al, 2020 ; El-Hajj and Kyriacou, 2021a , b ; Schrumpf et al, 2021a , b ; Tazarv and Levorato, 2021 ), and arterial fibrillation prediction ( Poh et al, 2018 ; Kwon et al, 2019 ; Aschbacher et al, 2020 ; Cheng et al, 2020 ; Pereira et al, 2020 ). In addition, PPG-based deep learning models are being used for respiratory rate estimation ( Ravichandran et al, 2019 ), sleep monitoring ( Korkalainen et al, 2020 ), diabetes ( Avram et al, 2019 ), vascular aging estimation ( Dall’Olio et al, 2020 ), and peripheral arterial disease classification ( Allen et al, 2021 ).…”

Evaluation of Objective Distortion Measures for Automatic Quality Assessment of Processed PPG Signals for Real-Time Health Monitoring Devices

“…It includes normal sinus rhythm (NSR), premature atrial/ventricular contractions (PAC/PVCs), atrial fibrillation (AF) and the other signal patterns. The MIMIC database is widely used for evaluating the performance blood pressure estimation [65], [66], and detection of PAC/PVCs [69], [70], [71] and AF events [14], [67], [68]. The PPG signals contain various types of extrasystolic beats and other pathological patterns, and various signal corruptions and artifacts.…”

“…Nowadays, wearables or portable electronic devices have attracted huge attention and strong potential in providing better quality of care with reduced overall costs by enabling noninvasive and unobtrusive measurement of important vital signs, including the pulse rate (PR) and respiration rate (RR) from the photoplethysmogram (PPG) signal in real-time. Further, extracted PPG parameters of wearables or smart devices have been used for determining the nature of a disease or disorder, distinguishing disease from other possible risk factors and enabling dynamic clinical settings to monitor health and fitness of an individual or to control function of drug-delivery/therapy device based on the measured biomarkers or clinical indexes [1]- [14]. Most wearable health monitoring devices are designed to continuously sense, process, interpret and transmit the PPG signal [8]- [10] which may undergo different kinds of waveform distortions during signal acquisition, denoising, compression and transmission.…”

Cluster consistency: Simple yet effect robust learning algorithm on large-scale photoplethysmography for atrial fibrillation detection in the presence of real-world label noise

“…Besides ECG, with the advancement of convenient and lower-cost, photoplethysmography (PPG) has been ubiquitous in many wearable devices to detect beat-to-beat blood volume changes and thus become an important indicator for monitoring cardiovascular conditions, including AF. It has been shown that deep learning (DL) algorithms achieve better results than traditional machine learning algorithms [3,4,5,6,7] in AF detection with PPG signal.…”