scholarly journals A New Entropy-Based Atrial Fibrillation Detection Method for Scanning Wearable ECG Recordings

Entropy ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. 904 ◽  
Author(s):  
Lina Zhao ◽  
Chengyu Liu ◽  
Shoushui Wei ◽  
Qin Shen ◽  
Fan Zhou ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Time Window ◽  
Characteristic Curve ◽  
Sample Entropy ◽  
Youden Index ◽  
Entropy Measure ◽  
Discrimination Ability ◽  
Fuzzy Function ◽  
Electrocardiogram Ecg

Entropy-based atrial fibrillation (AF) detectors have been applied for short-term electrocardiogram (ECG) analysis. However, existing methods suffer from several limitations. To enhance the performance of entropy-based AF detectors, we have developed a new entropy measure, named EntropyAF, which includes the following improvements: (1) use of a ranged function rather than the Chebyshev function to define vector distance, (2) use of a fuzzy function to determine vector similarity, (3) replacement of the probability estimation with density estimation for entropy calculation, (4) use of a flexible distance threshold parameter, and (5) use of adjusted entropy results for the heart rate effect. EntropyAF was trained using the MIT-BIH Atrial Fibrillation (AF) database, and tested on the clinical wearable long-term AF recordings. Three previous entropy-based AF detectors were used for comparison: sample entropy (SampEn), fuzzy measure entropy (FuzzyMEn) and coefficient of sample entropy (COSEn). For classifying AF and non-AF rhythms in the MIT-BIH AF database, EntropyAF achieved the highest area under receiver operating characteristic curve (AUC) values of 98.15% when using a 30-beat time window, which was higher than COSEn with AUC of 91.86%. SampEn and FuzzyMEn resulted in much lower AUCs of 74.68% and 79.24% respectively. For classifying AF and non-AF rhythms in the clinical wearable AF database, EntropyAF also generated the largest values of Youden index (77.94%), sensitivity (92.77%), specificity (85.17%), accuracy (87.10%), positive predictivity (68.09%) and negative predictivity (97.18%). COSEn had the second-best accuracy of 78.63%, followed by an accuracy of 65.08% in FuzzyMEn and an accuracy of 59.91% in SampEn. The new proposed EntropyAF also generated highest classification accuracy when using a 12-beat time window. In addition, the results from time cost analysis verified the efficiency of the new EntropyAF. This study showed the better discrimination ability for identifying AF when using EntropyAF method, indicating that it would be useful for the practical clinical wearable AF scanning.

scholarly journals A Wearable Electrocardiogram Telemonitoring System for Atrial Fibrillation Detection

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 606 ◽  
Author(s):  
Minggang Shao ◽  
Zhuhuang Zhou ◽  
Guangyu Bin ◽  
Yanping Bai ◽  
Shuicai Wu
Keyword(s):  
Atrial Fibrillation ◽  
Ecg Monitoring ◽  
Web Browser ◽  
Ecg Signals ◽  
Cloud Server ◽  
Android App ◽  
Electrocardiogram Ecg ◽  
The Web ◽  
Ecg Data

In this paper we proposed a wearable electrocardiogram (ECG) telemonitoring system for atrial fibrillation (AF) detection based on a smartphone and cloud computing. A wearable ECG patch was designed to collect ECG signals and send the signals to an Android smartphone via Bluetooth. An Android APP was developed to display the ECG waveforms in real time and transmit every 30 s ECG data to a remote cloud server. A machine learning (CatBoost)-based ECG classification method was proposed to detect AF in the cloud server. In case of detected AF, the cloud server pushed the ECG data and classification results to the web browser of a doctor. Finally, the Android APP displayed the doctor’s diagnosis for the ECG signals. Experimental results showed the proposed CatBoost classifier trained with 17 selected features achieved an overall F1 score of 0.92 on the test set (n = 7270). The proposed wearable ECG monitoring system may potentially be useful for long-term ECG telemonitoring for AF detection.

scholarly journals Anytime ECG Monitoring through the Use of a Low-Cost, User-Friendly, Wearable Device

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6036
Author(s):  
Vincenzo Randazzo ◽  
Jacopo Ferretti ◽  
Eros Pasero
Keyword(s):  
Atrial Fibrillation ◽  
Low Cost ◽  
Portable Devices ◽  
Cardiac Health ◽  
Non Invasive ◽  
Key Factor ◽  
User Friendly ◽  
Electrocardiogram Ecg

Every year cardiovascular diseases kill the highest number of people worldwide. Among these, pathologies characterized by sporadic symptoms, such as atrial fibrillation, are difficult to be detected as state-of-the-art solutions, e.g., 12-leads electrocardiogram (ECG) or Holter devices, often fail to tackle these kinds of pathologies. Many portable devices have already been proposed, both in literature and in the market. Unfortunately, they all miss relevant features: they are either not wearable or wireless and their usage over a long-term period is often unsuitable. In addition, the quality of recordings is another key factor to perform reliable diagnosis. The ECG WATCH is a device designed for targeting all these issues. It is inexpensive, wearable (size of a watch), and can be used without the need for any medical expertise about positioning or usage. It is non-invasive, it records single-lead ECG in just 10 s, anytime, anywhere, without the need to physically travel to hospitals or cardiologists. It can acquire any of the three peripheral leads; results can be shared with physicians by simply tapping a smartphone app. The ECG WATCH quality has been tested on 30 people and has successfully compared with an electrocardiograph and an ECG simulator, both certified. The app embeds an algorithm for automatically detecting atrial fibrillation, which has been successfully tested with an official ECG simulator on different severity of atrial fibrillation. In this sense, the ECG WATCH is a promising device for anytime cardiac health monitoring.

scholarly journals Automatic Detection of Atrial Fibrillation Based on CNN-LSTM and Shortcut Connection

Healthcare ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 139
Author(s):  
Yongjie Ping ◽  
Chao Chen ◽  
Lu Wu ◽  
Yinglong Wang ◽  
Minglei Shu
Keyword(s):  
Atrial Fibrillation ◽  
Neural Networks ◽  
Short Term Memory ◽  
Segment Length ◽  
Practical Applications ◽  
Multi Scale ◽  
Long Short Term Memory ◽  
Electrocardiogram Ecg ◽  
Fold Cross Validation

Atrial fibrillation (AF) is one of the most common persistent arrhythmias, which has a close connection to a large number of cardiovascular diseases. However, if spotted early, the diagnosis of AF can improve the effectiveness of clinical treatment and effectively prevent serious complications. In this paper, a combination of an 8-layer convolutional neural network (CNN) with a shortcut connection and 1-layer long short-term memory (LSTM), named 8CSL, was proposed for the Electrocardiogram (ECG) classification task. Compared with recurrent neural networks (RNN) and multi-scale convolution neural networks (MCNN), not only can 8CSL extract features skillfully, but also deal with long-term dependency between data. In particular, 8CSL includes eight shortcut connections that can improve the speed of the data transmission and processing as a result of the shortcut connections. The model was evaluated on the base of the test set of the Computing in Cardiology Challenge 2017 dataset with the F1 score. The ECG recordings were cropped or padded to the same length. After 10-fold cross-validation, the average test F1 score was 84.89%, 89.55%, and 85.64% when the segment length was 5, 10, 20 s, respectively. The experiment results demonstrate excellent performance with potential practical applications.

scholarly journals Recent Research for Unobtrusive Atrial Fibrillation Detection Methods Based on Cardiac Dynamics Signals: A Survey

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3814
Author(s):  
Fangfang Jiang ◽  
Yihan Zhou ◽  
Tianyi Ling ◽  
Yanbing Zhang ◽  
Ziyu Zhu
Keyword(s):  
Atrial Fibrillation ◽  
Detection Methods ◽  
Ecg Signals ◽  
Non Invasive ◽  
Cardiac Dynamics ◽  
Feature Extraction And Selection ◽  
Future Work ◽  
Electrocardiogram Ecg ◽  
Term Monitoring

Atrial fibrillation (AF) is the most common cardiac arrhythmia. It tends to cause multiple cardiac conditions, such as cerebral artery blockage, stroke, and heart failure. The morbidity and mortality of AF have been progressively increasing over the past few decades, which has raised widespread concern about unobtrusive AF detection in routine life. The up-to-date non-invasive AF detection methods include electrocardiogram (ECG) signals and cardiac dynamics signals, such as the ballistocardiogram (BCG) signal, the seismocardiogram (SCG) signal and the photoplethysmogram (PPG) signal. Cardiac dynamics signals can be collected by cushions, mattresses, fabrics, or even cameras, which is more suitable for long-term monitoring. Therefore, methods for AF detection by cardiac dynamics signals bring about extensive attention for recent research. This paper reviews the current unobtrusive AF detection methods based on the three cardiac dynamics signals, summarized as data acquisition and preprocessing, feature extraction and selection, classification and diagnosis. In addition, the drawbacks and limitations of the existing methods are analyzed, and the challenges in future work are discussed.

Accurate estimation of entropy in very short physiological time series: the problem of atrial fibrillation detection in implanted ventricular devices

2011 ◽  
Vol 300 (1) ◽  
pp. H319-H325 ◽  
Author(s):  
Douglas E. Lake ◽  
J. Randall Moorman
Keyword(s):  
Atrial Fibrillation ◽  
Time Series ◽  
Sinus Rhythm ◽  
Normal Sinus Rhythm ◽  
Sample Entropy ◽  
Heart Beat ◽  
Entropy Measure ◽  
Accurate Estimation ◽  
High Entropy ◽  
Entropy Estimation

Entropy estimation is useful but difficult in short time series. For example, automated detection of atrial fibrillation (AF) in very short heart beat interval time series would be useful in patients with cardiac implantable electronic devices that record only from the ventricle. Such devices require efficient algorithms, and the clinical situation demands accuracy. Toward these ends, we optimized the sample entropy measure, which reports the probability that short templates will match with others within the series. We developed general methods for the rational selection of the template length m and the tolerance matching r. The major innovation was to allow r to vary so that sufficient matches are found for confident entropy estimation, with conversion of the final probability to a density by dividing by the matching region volume, 2 r m. The optimized sample entropy estimate and the mean heart beat interval each contributed to accurate detection of AF in as few as 12 heartbeats. The final algorithm, called the coefficient of sample entropy (COSEn), was developed using the canonical MIT-BIH database and validated in a new and much larger set of consecutive Holter monitor recordings from the University of Virginia. In patients over the age of 40 yr old, COSEn has high degrees of accuracy in distinguishing AF from normal sinus rhythm in 12-beat calculations performed hourly. The most common errors are atrial or ventricular ectopy, which increase entropy despite sinus rhythm, and atrial flutter, which can have low or high entropy states depending on dynamics of atrioventricular conduction.

Electrocardiographic characteristics of 5-year atrial tachy-arrhythmia free patients after catheter ablation for atrial fibrillation

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
J.W Park ◽  
H.T Yu ◽  
T.H Kim ◽  
J.S Uhm ◽  
J.Y Kim ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Catheter Ablation ◽  
De Novo ◽  
Multivariate Regression Analysis ◽  
P Wave ◽  
Youden Index ◽  
Pericardial Fat ◽  
Pr Interval ◽  
Electrocardiogram Ecg ◽  
Fat Volume

Abstract Background Predictors are not well known for long-lasting sinus rhythm after atrial fibrillation catheter ablation (AFCA). Purpose We investigated the pre-procedural clinical factors associated with the patients with 5-year long-term atrial fibrillation (AF) free after AFCA. Methods Among 2,085 patients who underwent de novo AFCA, 934 patients (58±11 years, 73.1% male, 66.0% paroxysmal AF), who underwent guidelines-based rhythm follow-up longer than 5-years, were included in this study. We compared 340 patients who never showed AF recurrence for 5-years and 594 patients with AF/ atrial tachycardia (AT) recurrence at least a single episode. Cut-off values of pericardial fat volume and electrocardiogram (ECG) parameters were obtained by Youden index. Results 1. The patients with AF-free for 5-years (AF-Free-5yrs) had higher proportion of paroxysmal AF (p<0.001), lower body mass index (p=0.020), and lower proportion of pre-existing congestive heart failure (p=0.035), and smaller LA size (p<0.001) than those who experienced recurrence. 2. The patients with AF-Free-5yrs showed lower pericardial fat volume (105.9±47.5 vs. 121.2±53.cm3, p<0.001) and shorter PR interval (181.5±29.6 vs. 190.5±33.1ms, p<0.001) and P wave duration in lead II (PWDII, 121.1±18.0 vs. 129.4±18.8ms, p<0.001) rather than those of counter-part. 3. Multivariate regression analysis revealed that paroxysmal AF at the procedure, lower pericardial fat volume (OR 0.995, 95% CI [0.991–0.998], p=0.004), short PR interval (OR 0.994, 95% CI [0.988–1.000], p=0.038), and PWDII (OR 0.986, 95% CI [0.978–0.995], p=0.002) were independently associated with the AF-Free-5yrs. 3. Among the patients with persistent AF, PWDII was significantly shorter in AF-Free-5yrs group. (123.7±22.6 vs. 133.5±19.5, p<0.001), and PWDII <127.1ms was independently associated with AF-Free-5yrs (OR 3.114, 95% CI [1.819–5.331], p<0.001). Conclusions Pericardial fat volume (<113.7cm3) and PR interval (<196 ms) in pre-procedural ECG was independently associated with an excellent 5-year rhythm outcome after AFCA, and PWDII (<127.1 ms) predicted super-responders especially after persistent AF ablation. Funding Acknowledgement Type of funding source: None

Seizure Forecasting Using Long-Term Electroencephalography and Electrocardiogram Data

2021 ◽  
pp. 2150039
Author(s):  
Wenjuan Xiong ◽  
Ewan S. Nurse ◽  
Elisabeth Lambert ◽  
Mark J. Cook ◽  
Tatiana Kameneva
Keyword(s):  
Characteristic Curve ◽  
Area Under The Curve ◽  
Patient Specific ◽  
Continuous Eeg ◽  
Electrographic Seizures ◽  
Electrocardiogram Ecg ◽  
The Relationship ◽  
Ecg Data

Electroencephalography (EEG) has been used to forecast seizures with varying success. There is an increasing interest to use electrocardiogram (ECG) to help with seizure forecasting. The neural and cardiovascular systems may exhibit critical slowing, which is measured by an increase in variance and autocorrelation of the system, when change from a normal state to an ictal state. To forecast seizures, the variance and autocorrelation of long-term continuous EEG and ECG data from 16 patients were used for analysis. The average period of recordings was 161.9 h, with an average of 9 electrographic seizures in an individual patient. The relationship between seizure onset times and phases of variance and autocorrelation in EEG and ECG data was investigated. The results of forecasting models using critical slowing features, seizure circadian features, and combined critical slowing and circadian features were compared using the receiver-operating characteristic curve. The results demonstrated that the best forecaster was patient-specific and the average area under the curve (AUC) of the best forecaster across patients was 0.68. In 50% of patients, circadian forecasters had the best performance. Critical slowing forecaster performed best in 19% of patients. Combined forecaster achieved the best performance in 31% of patients. The results of this study may help to advance the field of seizure forecasting and lead to the improved quality of life of people who suffer from epilepsy.

The Youden Index in the Generalized Receiver Operating Characteristic Curve Context

2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Pablo Martínez-Camblor ◽  
Juan Carlos Pardo-Fernández
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Interval Estimation ◽  
Youden Index ◽  
Discrimination Ability ◽  
Real World Application ◽  
Operating Characteristic Curve ◽  
Two Populations ◽  
Receiver Operating

Abstract The receiver operating characteristic (ROC) curve and their associated summary indices, such as the Youden index, are statistical tools commonly used to analyze the discrimination ability of a (bio)marker to distinguish between two populations. This paper presents the concept of Youden index in the context of the generalized ROC (gROC) curve for non-monotone relationships. The interval estimation of the Youden index and the associated cutoff points in a parametric (binormal) and a non-parametric setting is considered. Monte Carlo simulations and a real-world application illustrate the proposed methodology.

scholarly journals Long-term Arrhythmia Monitoring in Cryptogenic Stroke: Who, How, and for How Long?

2016 ◽  
Vol 10 (1) ◽  
pp. 89-93 ◽  
Author(s):  
Mayra Montalvo ◽  
Rushna Ali ◽  
Brian Silver ◽  
Muhib Khan
Keyword(s):  
Atrial Fibrillation ◽  
Oral Anticoagulants ◽  
Cryptogenic Stroke ◽  
Significant Risk ◽  
Hospital Setting ◽  
Persistent Atrial Fibrillation ◽  
Ischemic Attack ◽  
New Generation ◽  
Electrocardiogram Ecg

Cryptogenic stroke and transient ischemic attack (TIA) account for approximately one-third of stroke patients [1]. Paroxys-mal atrial fibrillation (PAF) has been suggested as a major etiology of these cryptogenic strokes [2, 3]. PAF can be difficult to diagnose because it is intermittent, often brief, and asymptomatic. PAF might be more prevalent than persistent atrial fibrillation in stroke and TIA patients, especially in younger populations [4, 5]. In patients with atrial fibrillation, anticoagulation provides significant risk reduction [6]. A new generation of oral anticoagulants has been approved for non-valvular atrial fibrillation, providing a variety of therapeutic options for patients with atrial fibrillation and risk of stroke [7].Prior practice included an admission electrocardiogram (ECG) and continuous telemetry monitoring while in hospital [8]. However, this approach can lead to under-detection of brief asymptomatic events, which can occur at variable intervals, often outside of the hospital setting. Technological advancements have led to devices that can monitor cardiac rhythms outside of the hospital for longer durations resulting in higher yield of detection of atrial fibrillation events.Moreover, recent studies show that the normal monitoring time for arrhythmias may be shorter than ideal in order to detect atrial fibrillation, and increasing this interval could significantly improve detection of atrial fibrillation in these patients [9, 10].The aim of this study is to review the literature in order to define what subgroup of patients, with what methodologies, and for how long monitoring for atrial fibrillation should occur in patients presenting with cryptogenic stroke.

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