Atrial Fibrillation Detection from Wrist Photoplethysmography Signals Using Smartwatches

Detection of atrial fibrillation (AF) from a wrist watch photoplethysmogram (PPG) signal is important because the wrist watch form factor enables long term continuous monitoring of arrhythmia in an easy and non-invasive manner. We have developed a novel method not only to detect AF from a smart wrist watch PPG signal, but also to determine whether the recorded PPG signal is corrupted by motion artifacts or not. We detect motion and noise artifacts based on the accelerometer signal and variable frequency complex demodulation based time-frequency analysis of the PPG signal. After that, we use the root mean square of successive differences and sample entropy, calculated from the beat-to-beat intervals of the PPG signal, to distinguish AF from normal rhythm. We then use a premature atrial contraction detection algorithm to have more accurate AF identification and to reduce false alarms. Two separate datasets have been used in this study to test the efficacy of the proposed method, which shows a combined sensitivity, specificity and accuracy of 98.18%, 97.43% and 97.54% across the datasets

SiamAF: Learning Shared Information from ECG and PPG Signals for Robust Atrial Fibrillation Detection

Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. It is associated with an increased risk of stroke, heart failure, and other cardiovascular complications, but can be clinically silent. Passive AF monitoring with wearables may help reduce adverse clinical outcomes related to AF. Detecting AF in noisy wearable data poses a significant challenge, leading to the emergence of various deep learning techniques. Previous deep learning models learn from a single modality, either electrocardiogram (ECG) or photoplethysmography (PPG) signals. However, deep learning models often struggle to learn generalizable features and rely on features that are more susceptible to corruption from noise, leading to sub-optimal performances in certain scenarios, especially with low-quality signals. Given the increasing availability of ECG and PPG signal pairs from wearables and bedside monitors, we propose a new approach, SiamAF, leveraging a novel Siamese network architecture and joint learning loss function to learn shared information from both ECG and PPG signals. At inference time, the proposed model is able to predict AF from either PPG or ECG and outperforms baseline methods on three external test sets. It learns medically relevant features as a result of our novel architecture design. The proposed model also achieves comparable performance to traditional learning regimes while requiring much fewer training labels, providing a potential approach to reduce future reliance on manual labeling

Effectiveness of the Chest Strap Electrocardiogram to Detect Atrial Fibrillation

Atrial fibrillation (AF) is a significant cause of cardioembolic strokes. AF is often symptomless and intermittent, making its detection challenging. The aim of this study was to assess the possibility to use a chest strap (Suunto Movesense) to detect AF both by cardiologists and automated algorithms. A single channel electrocardiogram (ECG) from a chest strap of 220 patients (107 AF and 111 sinus rhythm SR with 2 inconclusive rhythms) were analyzed by 2 cardiologists (Doc1 and Doc2) and 2 different algorithms (COSEn and AFE-vidence). A 3-lead Holter served as the gold standard ECG for rhythm analysis. Both cardiologists evaluated the quality of the chest strap ECG to be superior to the quality of the Holter ECG; pPeer reviewe

Detección de fibrilación auricular en señales ECG usando Redes Neuronales para pacientes específicos

Atrial Fibrillation (AF) is the most common cardiac arrhythmia worldwide. It is associated with reduced quality of life and increases the risk of stroke and myocardial infarction. Unfortunately, many cases of AF are asymptomatic and undiagnosed, which increases the risk for the patients. Due to its paroxysmal nature, the detection of AF requires the evaluation, by a cardiologist, of long-term ECG signals. In Colombia, it is difficult to have access to an early diagnosis of AF because of the associated costs to the detection and the geographical distribution of cardiologists. This work is part of a macro project that aims to develop a specific-patient portable device for the detection of AF. This device will be based on a Convolutional Neural Network (CNN). We aim to find a suitable CNN model, which later could be implemented in hardware. Diverse techniques were applied to improve the answer regarding accuracy, sensitivity, specificity, and precision. The final model achieves an accuracy of , a specificity of , a sensitivity of  and a precision of . During the development of the model, the computational cost and memory resources were taking into account in order to obtain an efficient hardware model in a future implementation of the device.La fibrilación auricular (FA) es la arritmia cardíaca más común en todo el mundo. Se asocia con una reducción de la calidad de vida y aumenta el riesgo de accidente cerebrovascular e infarto de miocardio. Desafortunadamente muchos casos de FA son asintomáticos, lo cual aumenta el riesgo para los pacientes. Debido a su naturaleza paroxística, la detección de la FA requiere la evaluación, por parte de un cardiologo, de señales ECG de larda duración. En Colombia, es difícil dificil tener dianóstico temprano de la FA debido a los costos asociados a la detección de la FA y la distribución geográfica de los cardiólogos. Este trabajo es parte de un proyecto macro que tiene como objetivo desarrollar un dispositivo portátil para pacientes específicos que permita detectar la FA, el cual estará basado en una red neuronal convolucional (CNN). Nuestro objetivo es encontrar un modelo CNN adecuado, que luego se pueda implementar en hardware. Se aplicaron diversas técnicas para mejorar la respuesta con respecto a la exactitud, la sensibilidad, la especificidad y la precisión. El modelo final alcanza una exactitud del 97,44%, una especificidad del 97,76%, una sensibilidad del 96,97% y una precisión del 96,80%. Durante el desarrollo del modelo, el costo computacional y los recursos de memoria se tuvieron en cuenta para obtener un modelo de hardware eficiente en una futura  implementación del dispositivo

Wrist-worn device combining PPG and ECG can be reliably used for atrial fibrillation detection in an outpatient setting

Aims: The aim was to validate the performance of a monitoring system consisting of a wrist-worn device and a data management cloud service intended to be used by medical professionals in detecting atrial fibrillation (AF). Methods: Thirty adult patients diagnosed with AF alone or AF with concomitant flutter were recruited. Continuous photoplethysmogram (PPG) and intermittent 30 s Lead I electrocardiogram (ECG) recordings were collected over 48 h. The ECG was measured four times a day at prescheduled times, when notified due to irregular rhythm detected by PPG, and when self-initiated based on symptoms. Three-channel Holter ECG was used as the reference. Results: The subjects recorded a total of 1,415 h of continuous PPG data and 3.8 h of intermittent ECG data over the study period. The PPG data were analyzed by the system’s algorithm in 5-min segments. The segments containing adequate amounts, at least ~30 s, of adequate quality PPG data for rhythm assessment algorithm, were included. After rejecting 46% of the 5-min segments, the remaining data were compared with annotated Holter ECG yielding AF detection sensitivity and specificity of 95.6 and 99.2%, respectively. The ECG analysis algorithm labeled 10% of the 30-s ECG records as inadequate quality and these were excluded from the analysis. The ECG AF detection sensitivity and specificity were 97.7 and 89.8%, respectively. The usability of the system was found to be good by both the study subjects and the participating cardiologists. Conclusion: The system comprising of a wrist device and a data management service was validated to be suitable for use in patient monitoring and in the detection of AF in an ambulatory setting. Clinical Trial Registration: ClinicalTrials.gov/, NCT05008601.publishedVersionPeer reviewe

BayesBeat: A Bayesian Deep Learning Approach for Atrial Fibrillation Detection from Noisy Photoplethysmography Data

The increasing popularity of smartwatches as affordable and longitudinal monitoring devices enables us to capture photoplethysmography (PPG) sensor data for detecting Atrial Fibrillation (AF) in real-time. A significant challenge in AF detection from PPG signals comes from the inherent noise in the smartwatch PPG signals. In this paper, we propose a novel deep learning based approach, BayesBeat that leverages the power of Bayesian deep learning to accurately infer AF risks from noisy PPG signals, and at the same time provide the uncertainty estimate of the prediction. Bayesbeat is efficient, robust, flexible, and highly scalable which makes it particularly suitable for deployment in commercially available wearable devices. Extensive experiments on a recently published large dataset reveal that our proposed method BayesBeat substantially outperforms the existing state-of-the-art methods.Comment: 8 pages, 5 figure