Prediction of paroxysmal Atrial Fibrillation: A machine learning based approach using combined feature vector and mixture of expert classification on HRV signal

2018 ◽  
Vol 165 ◽  
pp. 53-67 ◽  
Author(s):  
Elias Ebrahimzadeh ◽  
Maede Kalantari ◽  
Mohammadamin Joulani ◽  
Reza Shahrokhi Shahraki ◽  
Farahnaz Fayaz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Paroxysmal Atrial Fibrillation ◽  
Feature Vector ◽  
Expert Classification ◽  
Combined Feature

Diagnosis of Atrial Fibrillation Using Machine Learning with Wearable Devices after Cardiac Surgery (Preprint)

2021 ◽  
Author(s):  
Daisuke Hiraoka ◽  
Tomohiko Inui ◽  
Eiryo Kawakami ◽  
Megumi Oya ◽  
Ayumu Tsuji ◽  
...  
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Cardiac Surgery ◽  
Early Detection ◽  
Pulse Rate ◽  
Paroxysmal Atrial Fibrillation ◽  
Wearable Device ◽  
Strongly Correlated ◽  
Cross Correlation Analysis ◽  
Data Output

BACKGROUND Some attempts have been made to detect atrial fibrillation with a wearable device equipped with photoelectric volumetric pulse wave technology, and it is expected to be applied under real clinical conditions. OBJECTIVE This study is the second part of a two-phase study aimed at developing a method for immediate detection of paroxysmal atrial fibrillation (AF) using a wearable device with built-in PPG. The objective of this study is to develop an algorithm to immediately diagnose atrial fibrillation by wearing an Apple Watch equipped with a photoplethysmography (PPG) sensor on patients undergoing cardiac surgery and using machine learning of the pulse data output from the device. METHODS A total of 80 subjects who underwent cardiac surgery at a single institution between June 2020 and March 2021 were monitored for postoperative atrial fibrillation using telemetry monitored ECG and Apple Watch. Atrial fibrillation was diagnosed by qualified physicians from telemetry-monitored ECGs and 12-lead ECGs; a diagnostic algorithm was developed using machine learning on pulse rate data output from the Apple Watch. RESULTS One of the 80 patients was excluded from the analysis due to redness of the Apple Watch wearer. 27 (34.2%) of the 79 patients developed AF, and 199 events of AF, including brief AF, were observed. 18 events of AF lasting longer than 1 hour were observed, and Cross-correlation analysis (CCF) showed that pulse rate measured by Apple Watch was strongly correlated (CCF 0.6-0.8) with 8 events and very strongly correlated (CCF >0.8) with 3 events. The diagnostic accuracy by machine learning was 0.7952 (sensitivity 0.6312, specificity 0.8605 at the point closest to the top-left) for the AUC of the ROC curve. CONCLUSIONS We were able to safely monitor pulse rate in patients after cardiac surgery by wearing an Apple Watch. Although the pulse rate from the PPG sensor does not follow the heart rate of the telemetry monitoring ECG in some parts, which may reduce the accuracy of the diagnosis of atrial fibrillation by machine learning, we have shown the possibility of clinical application of early detection of atrial fibrillation using only the pulse rate collected by the PPG sensor. CLINICALTRIAL The use of wristband type continuous pulse measurement device with artificial intelligence for early detection of paroxysmal atrial fibrillation Clinical Research Protocol No. jRCTs032200032 https://jrct.niph.go.jp/latest-detail/jRCTs032200032

The Role of HbA1c on Mortality in Patients with Medical History of Ischemic Stroke and Paroxysmal Atrial Fibrillation

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 2406-PUB
Author(s):  
KONSTANTINA KANELLOPOULOU ◽  
IOANNIS L. MATSOUKIS ◽  
ASIMINA GANOTOPOULOU ◽  
THEODORA ATHANASOPOULOU ◽  
CHRYSOULA TRIANTAFILLOPOULOU ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Ischemic Stroke ◽  
Medical History ◽  
Paroxysmal Atrial Fibrillation ◽  
History Of
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