P1880Entropy-based algorithm for atrial fibrillation detection using photoplethysomgraphic signal recorded by a smart watch

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
J Y Chiang ◽  
C M Fu ◽  
Y C Lin ◽  
B W Ku ◽  
S U Hsu ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Characteristic Curve ◽  
Green Light ◽  
Heart Rhythm ◽  
High Sensitivity ◽  
Signal Acquisition ◽  
Light Spectrum ◽  
Ecg Signals ◽  
Smart Watch

Abstract Background Atrial fibrillation (AF) is the most common arrhythmia, and its paroxysmal and short duration nature makes its detection challenging. The most important limitation of current smartwatches is that patients need to touch to the sensor of the watch to record signals when patients feel discomfort. We developed a wearable smart watch and evaluated its accuracy to differentiate AF from sinus rhythm, which can continuously detecting heart rhythm without hand touching the device. Methods and results A wearable smart watch with PPG sensor and electrocardiogram (ECG) recording function was used for signal acquisition. A total 399 patients with a mean age of 67 years old were enrolled in the study, of whom 237 (81.5%) were male, and 101 have been diagnosed with AF. Pulse wave extracted from the green light spectrum of the signal and ECG were recorded for about 10 minutes for each patient. Pulse-to-pulse intervals (PPI) were automatically identified. All ECG signals were verified by two cardiologists. The correlation between R-to-R interval on ECG and PPI were excellent, with a correlation coefficient R >0.99 (p<0.05). An entropy-based algorithm which combined Shannon entropy of successive difference of PPI and sample entropy of PPI was used to discriminate between AF and sinus rhythm. This method had high sensitivity and specificity (96% and 98%, respectively), the area under receiver operating characteristic curve reached 0.98. Conclusions We developed an entropy-based algorithm for AF detection with PPG signal recorded by a wearable smart watch. This algorithm discriminates AF from sinus rhythm accurately. This advance in technology overcomes an important clinical obstacle and can increase the AF detection rate tremendously.

scholarly journals Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pietro Melzi ◽  
Ruben Tolosana ◽  
Alberto Cecconi ◽  
Ancor Sanz-Garcia ◽  
Guillermo J. Ortega ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Normal Sinus Rhythm ◽  
Area Under The Curve ◽  
Heart Rhythm ◽  
Demographic Groups ◽  
Ecg Signals ◽  
Normal Sinus ◽  
Visualization Techniques ◽  
Feature Visualization

AbstractAtrial fibrillation (AF) is an abnormal heart rhythm, asymptomatic in many cases, that causes several health problems and mortality in population. This retrospective study evaluates the ability of different AI-based models to predict future episodes of AF from electrocardiograms (ECGs) recorded during normal sinus rhythm. Patients are divided into two classes according to AF occurrence or sinus rhythm permanence along their several ECGs registry. In the constrained scenario of balancing the age distributions between classes, our best AI model predicts future episodes of AF with area under the curve (AUC) 0.79 (0.72–0.86). Multiple scenarios and age-sex-specific groups of patients are considered, achieving best performance of prediction for males older than 70 years. These results point out the importance of considering different demographic groups in the analysis of AF prediction, showing considerable performance gaps among them. In addition to the demographic analysis, we apply feature visualization techniques to identify the most important portions of the ECG signals in the task of AF prediction, improving this way the interpretability and understanding of the AI models. These results and the simplicity of recording ECGs during check-ups add feasibility to clinical applications of AI-based models.

scholarly journals The Effects of Compression on the Detection of Atrial Fibrillation in ECG Signals

2021 ◽  
Vol 11 (13) ◽  
pp. 5908
Author(s):  
Raquel Cervigón ◽  
Brian McGinley ◽  
Darren Craven ◽  
Martin Glavin ◽  
Edward Jones
Keyword(s):  
Atrial Fibrillation ◽  
Characteristic Curve ◽  
Set Partitioning ◽  
Constrained Systems ◽  
Battery Life ◽  
Detection Accuracy ◽  
Ecg Signal ◽  
Signal Compression ◽  
Ecg Signals ◽  
Cardiac Devices

Although Atrial Fibrillation (AF) is the most frequent cause of cardioembolic stroke, the arrhythmia remains underdiagnosed, as it is often asymptomatic or intermittent. Automated detection of AF in ECG signals is important for patients with implantable cardiac devices, pacemakers or Holter systems. Such resource-constrained systems often operate by transmitting signals to a central server where diagnostic decisions are made. In this context, ECG signal compression is being increasingly investigated and employed to increase battery life, and hence the storage and transmission efficiency of these devices. At the same time, the diagnostic accuracy of AF detection must be preserved. This paper investigates the effects of ECG signal compression on an entropy-based AF detection algorithm that monitors R-R interval regularity. The compression and AF detection algorithms were applied to signals from the MIT-BIH AF database. The accuracy of AF detection on reconstructed signals is evaluated under varying degrees of compression using the state-of-the-art Set Partitioning In Hierarchical Trees (SPIHT) compression algorithm. Results demonstrate that compression ratios (CR) of up to 90 can be obtained while maintaining a detection accuracy, expressed in terms of the area under the receiver operating characteristic curve, of at least 0.9. This highlights the potential for significant energy savings on devices that transmit/store ECG signals for AF detection applications, while preserving the diagnostic integrity of the signals, and hence the detection performance.

scholarly journals Kardia Mobile and ISTEL HR applicability in clinical practice: A comparison of Kardia Mobile, ISTEL HR and standard 12-lead electrocardiogram records in 98 consecutive patients of a tertiary cardiovascular care centre

2021 ◽  
Author(s):  
Bartosz Krzowski ◽  
Kamila Skoczylas ◽  
Gabriela Osak ◽  
Natalia Żurawska ◽  
Michał Peller ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Clinical Practice ◽  
Cardiovascular Diseases ◽  
Sinus Rhythm ◽  
Sensitivity And Specificity ◽  
Heart Rhythm ◽  
Everyday Practice ◽  
Portable Devices ◽  
Care Centre ◽  
Cardiovascular Care

Abstract Aims Mobile, portable ECG-recorders allow the assessment of heart rhythm in out-of-hospital conditions and may prove useful for monitoring patients with cardiovascular diseases. However, the effectiveness of these portable devices has not been tested in everyday practice. Methods and results A group of 98 consecutive cardiology patients (62 males [63%], mean age 69 ± 12.9 years) were included in an academic care centre. For each patient, a standard 12-lead electrocardiogram (SE), as well as a Kardia Mobile 6L (KM) and Istel (IS) HR-2000 ECG were performed. Two groups of experienced physycians analyzed obtained recordings. After analyzing ECG tracings from SE, KM, and IS, quality was marked as good in 82%, 80%, and 72% of patients, respectively (p &lt; 0.001). There were no significant differences between devices in terms of detecting sinus rhythm (SE [60%, n = 59], KM [58%, n = 56], and IS [61%, n = 60]; SE vs KM p = 0.53; SE vs IS p = 0.76) and atrial fibrillation (SE [22%, n = 22], KM [22%, n = 21], and IS [18%, n = 18]; (SE vs KM p = 0.65; SE vs IS = 0.1). KM had a sensitivity of 88.1% and a specificity of 89.7% for diagnosing sinus rhythm. IS showed 91.5% and 84.6% sensitivity and specificity, respectively. The sensitivity of KM in detecting atrial fibrillation was higher than IS (86.4% vs. 77.3%), but their specificity was comparable (97.4% vs. 98.7%). Conclusion Novel, portable devices are useful in showing sinus rhythm and detecting atrial fibrillation in clinical practice. However, ECG measurements concerning conduction and repolarisation should be clarified with a standard 12-lead electrocardiogram.

scholarly journals Relationship between serum angiopoietin-like 4 levels and recurrence of atrial fibrillation

2021 ◽  
Vol 49 (2) ◽  
pp. 030006052098839
Author(s):  
Zhongping Ning ◽  
Xinming Li ◽  
Xi Zhu ◽  
Jun Luo ◽  
Yingbiao Wu
Keyword(s):  
Atrial Fibrillation ◽  
Catheter Ablation ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
High Sensitivity ◽  
Peroxisome Proliferator ◽  
C Reactive Protein ◽  
Peroxisome Proliferator Activated Receptor ◽  
Reactive Protein ◽  
New Onset

Objective To investigate the association between serum angiopoietin-like 4 (ANGPTL4) levels and recurrence of atrial fibrillation (AF) after catheter ablation. Methods This retrospective study recruited patients with AF undergoing catheter ablation and they were divided into two groups (new-onset AF group and recurrent AF group). Demographic, clinical, and laboratory parameters were collected. Results A total of 192 patients with AF were included, including 69 patients with recurrence of AF. Serum ANGPTL4 levels were lower in patients with recurrent AF than in those with new-onset AF. Serum ANGPTL4 levels were positively correlated with superoxide dismutase and peroxisome proliferator-activated receptor γ, and negatively correlated with the CHA2DS2-VASC score, left atrial diameter, and levels of brain natriuretic peptide, malondialdehyde, high-sensitivity C-reactive protein, and interleukin-6. The receiver operating characteristic curve showed that the best cut-off for recurrent AF was serum ANGPTL4 levels  < 19.735 ng/mL, with a sensitivity and specificity of 63.9% and 74.5%, respectively. Serum ANGPTL4 levels were significantly associated with recurrence and new onset of AF (odds ratio, 2.241; 95% confidence interval, 1.081–4.648). Conclusions Serum ANGPTL4 levels are lower in patients with recurrent AF than in those with new-onset AF, and are associated with cardiac hypertrophy, oxidative stress, and inflammation.

scholarly journals A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yong-Soo Baek ◽  
Sang-Chul Lee ◽  
Wonik Choi ◽  
Dae-Hyeok Kim
Keyword(s):  
Atrial Fibrillation ◽  
Deep Learning ◽  
Sinus Rhythm ◽  
Learning Algorithm ◽  
Normal Sinus Rhythm ◽  
Characteristic Curve ◽  
External Validation ◽  
Digital Data ◽  
Internal Validation ◽  
Optimal Interval

AbstractAtrial fibrillation (AF) is the most prevalent arrhythmia and is associated with increased morbidity and mortality. Its early detection is challenging because of the low detection yield of conventional methods. We aimed to develop a deep learning-based algorithm to identify AF during normal sinus rhythm (NSR) using 12-lead electrocardiogram (ECG) findings. We developed a new deep neural network to detect subtle differences in paroxysmal AF (PAF) during NSR using digital data from standard 12-lead ECGs. Raw digital data of 2,412 12-lead ECGs were analyzed. The artificial intelligence (AI) model showed that the optimal interval to detect subtle changes in PAF was within 0.24 s before the QRS complex in the 12-lead ECG. We allocated the enrolled ECGs to the training, internal validation, and testing datasets in a 7:1:2 ratio. Regarding AF identification, the AI-based algorithm showed the following values in the internal and external validation datasets: area under the receiver operating characteristic curve, 0.79 and 0.75; recall, 82% and 77%; specificity, 78% and 72%; F1 score, 75% and 74%; and overall accuracy, 72.8% and 71.2%, respectively. The deep learning-based algorithm using 12-lead ECG demonstrated high accuracy for detecting AF during NSR.

scholarly journals Novel rhythm conversion approach in unsuccessful electric cardioversion of persistent atrial fibrillation

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
O V Pyataeva ◽  
S A Zenin ◽  
O V Kononenko ◽  
I M Felikov ◽  
A V Fedoseenko
Keyword(s):  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Dynamic Assessment ◽  
Heart Rhythm ◽  
Persistent Atrial Fibrillation ◽  
Electrical Cardioversion ◽  
Class Iii ◽  
Small Pilot Study ◽  
Heart Pathology ◽  
Intravenous Medication

Abstract   The average effectiveness of electrical cardioversion in persistent atrial fibrillation (AF) is considered about 90%. The success is limited by arrhythmia longevity, essential heart pathology, excessive body mass, concomitant deceases. A novel developed in Russia class III intravenous medication “Refralon” (4-nitro-N-(1RS)-1(4-fluorophenyl)-2-(1-ethylpiperidin-4-ethyl) benzamide hydrochloride) seems to be promising in sinus rhythm restoration in such patients. The aim of the study was to assess the effectiveness of a novel class III intravenous medication “Refralon” in conversion to sinus rhythm in patients with permanent AF in whom electrical cardioversion was unsuccessful. Materials and methods 19 patients were included: 16 male and 3 female aged from 45 to 68 years old (59,9±5,84 (M±SD)). Left atrial size was 47±3,2 × 59±2,8 mm, BMI 38,5±7,0 kg/m2, arrhythmia duration from 2 to 21 months (6,7±4,99). Refralon was injected according to the approved manual in ICU; heart rhythm and rate, blood pressure, SpO2 were monitored. Dynamic assessment of QT and QRS duration was performed. Results In 17 of 19 patients (89,4%) sinus rhythm was restored. In 7 patients (41%) sinus rhythm was restored before 10 min, in 4 patients (24%) before one hour, in 4 patients (24%) before two hours, in 2 patients (11%) before six hours. In two patients sinus rhythm was not restored. In both target dose was not infused due to non-sustained ventricular tachycardia in one case, and QT prolongation in another. Conclusion In a small pilot study Refralon was highly effective in patients with persistent atrial fibrillation when electrical cardioversion was ineffective. All the patients had significantly increased BMI. The results may suggest the indication for Refralon usage in obese patients. FUNDunding Acknowledgement Type of funding sources: None.

scholarly journals Pharmacological cardioversion of atrial fibrillation paroxysm in elderly and senile patients at prehospital stage

2021 ◽  
Vol 2 (3) ◽  
pp. 26-32
Author(s):  
Andrei V. Syrov ◽  
◽  
Keyword(s):  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Heart Rhythm ◽  
Day Hospital ◽  
Drug Of Choice ◽  
Wide Range ◽  
Rhythm Disorder ◽  
Pharmacological Cardioversion ◽  
High Level

Atrial fibrillation (AF) is the most common heart rhythm disorder requiring treatment and is associated with an increased rate of hospitalization and death. When choosing the tactics of restoring and maintaining sinus rhythm in most patients with AF paroxysm without pronounced organic heart damage, the drug of choice is propafenone, which has a high level of safety and efficacy. The use of the drug within the framework of a «pill in a pocket» by the patient himself, intravenously in a day hospital of an outpatient medical institution or by an ambulance team allows stopping AF paroxysm in most patients at the prehospital stage. Propafenone is also the drug of choice for inpatient sinus rhythm restoration and for long-term antiarrhythmic therapy in a wide range of patients with AF.

scholarly journals Characteristics of symptom burden in atrial fibrillation with concomitant heart failure

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Ran Heo ◽  
Myung-Jin Cha ◽  
Tae-Hoon Kim ◽  
Jung Myung Lee ◽  
Junbeom Park ◽  
...  
Keyword(s):  
Heart Failure ◽  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Left Atrial ◽  
Symptom Control ◽  
Symptom Burden ◽  
Heart Rhythm ◽  
Comparison Study ◽  
Reduced Ejection Fraction ◽  
Atypical Symptoms

Abstract Background Symptom burden is an important factor in determining the treatment of atrial fibrillation (AF). AF is frequently accompanied by heart failure (HF). This study investigated the characteristics of AF symptoms with concomitant HF. Methods A total of 4885 patients with AF were consecutively enrolled through a prospective observational registry (the Comparison Study of Drugs for Symptom Control and Complication Prevention of Atrial Fibrillation [CODE-AF] registry). Clinically diagnosed HF was divided into three categories (preserved, mid-range, and reduced ejection fraction [EF]). Symptom severity was assessed using the European Heart Rhythm Association (EHRA) classification. Results The presence of AF-related symptoms was comparable irrespective of concomitant HF. Patients with HF with reduced EF demonstrated severe (EHRA classes 3 and 4) and atypical symptoms. HF with preserved EF was also associated with atypical symptoms. Female sex and AF type were associated with the presence of symptoms in AF without HF, and non-maintenance of sinus rhythm and increased left atrial pressure (E/e′ ≥ 15) were factors related to the presence of symptoms in AF with HF. Conclusion AF with concomitant HF presented with more severe and atypical symptoms than AF without HF. Maintaining the sinus rhythm and reducing the E/e’ ratio are important factors for reducing symptoms in AF with concomitant HF.

scholarly journals An Automatic System for Atrial Fibrillation by Using a CNN-LSTM Model

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Fengying Ma ◽  
Jingyao Zhang ◽  
Wei Chen ◽  
Wei Liang ◽  
Wenjia Yang
Keyword(s):  
Atrial Fibrillation ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Sequence Data ◽  
Detection System ◽  
Heart Rhythm ◽  
Classification Performance ◽  
Ecg Signals ◽  
Electrocardiogram Ecg

Atrial fibrillation (AF) is a common abnormal heart rhythm disease. Therefore, the development of an AF detection system is of great significance to detect critical illnesses. In this paper, we proposed an automatic recognition method named CNN-LSTM to automatically detect the AF heartbeats based on deep learning. The model combines convolutional neural networks (CNN) to extract local correlation features and uses long short-term memory networks (LSTM) to capture the front-to-back dependencies of electrocardiogram (ECG) sequence data. The CNN-LSTM is feeded by processed data to automatically detect AF signals. Our study uses the MIT-BIH Atrial Fibrillation Database to verify the validity of the model. We achieved a high classification accuracy for the heartbeat data of the test set, with an overall classification accuracy rate of 97.21%, sensitivity of 97.34%, and specificity of 97.08%. The experimental results show that our model can robustly detect the onset of AF through ECG signals and achieve stable classification performance, thereby providing a suitable candidate for the automatic classification of AF.

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