scholarly journals A Prototype Framework Design for Assisting the Detection of Atrial Fibrillation Using a Generic Low-Cost Biomedical Sensor

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 896
Author(s):  
Jesús Pérez-Valero ◽  
Antonio-Javier Garcia-Sanchez ◽  
Manuel Ruiz Marín ◽  
Joan Garcia-Haro
Keyword(s):  
Atrial Fibrillation ◽  
Low Cost ◽  
Portable Document Format ◽  
Joint Photographic Expert Group ◽  
Biomedical Sensors ◽  
Normal Sinus ◽  
Framework Design ◽  
Group Format ◽  
Quantification Analysis ◽  
Sensitivity Specificity

Cardiovascular diseases are the leading cause of death around the world. As a result, low-cost biomedical sensors have been gaining importance in business and research over the last few decades. Their main benefits include their small size, light weight, portability and low power consumption. Despite these advantages, they are not generally used for clinical monitoring mainly because of their low accuracy in data acquisition. In this emerging technological context, this paper contributes by discussing a methodology to help practitioners build a prototype framework based on a low-cost commercial sensor. The resulting application consists of four modules; namely, a digitalization module whose input is an electrocardiograph signal in portable document format (PDF) or joint photographic expert group format (JPEG), a module to further process and filter the digitalized signal, a selectable data calibration module and, finally, a module implementing a classification algorithm to distinguish between individuals with normal sinus rhythms and those with atrial fibrillation. This last module employs our recently published symbolic recurrence quantification analysis (SRQA) algorithm on a time series of RR intervals. Moreover, we show that the algorithm applies to any biomedical low-cost sensor, achieving good results without requiring any calibration of the raw data acquired. In addition, it has been validated with a well-accepted public electrocardiograph (ECG) data base, obtaining 87.65%, 91.84%, and 91.31% in terms of sensitivity, specificity and accuracy, respectively.

scholarly journals CHARGE-AF in a national routine primary care electronic health records database in the Netherlands: validation for 5-year risk of atrial fibrillation and implications for patient selection in atrial fibrillation screening

Open Heart ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. e001459
Author(s):  
Jelle C L Himmelreich ◽  
Wim A M Lucassen ◽  
Ralf E Harskamp ◽  
Claire Aussems ◽  
Henk C P M van Weert ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Primary Care ◽  
High Risk ◽  
The Netherlands ◽  
Patient Selection ◽  
Low Cost ◽  
Calibration Plot ◽  
Primary Care Data ◽  
C Statistic ◽  
Kaplan Meier

AimsTo validate a multivariable risk prediction model (Cohorts for Heart and Aging Research in Genomic Epidemiology model for atrial fibrillation (CHARGE-AF)) for 5-year risk of atrial fibrillation (AF) in routinely collected primary care data and to assess CHARGE-AF’s potential for automated, low-cost selection of patients at high risk for AF based on routine primary care data.MethodsWe included patients aged ≥40 years, free of AF and with complete CHARGE-AF variables at baseline, 1 January 2014, in a representative, nationwide routine primary care database in the Netherlands (Nivel-PCD). We validated CHARGE-AF for 5-year observed AF incidence using the C-statistic for discrimination, and calibration plot and stratified Kaplan-Meier plot for calibration. We compared CHARGE-AF with other predictors and assessed implications of using different CHARGE-AF cut-offs to select high-risk patients.ResultsAmong 111 475 patients free of AF and with complete CHARGE-AF variables at baseline (17.2% of all patients aged ≥40 years and free of AF), mean age was 65.5 years, and 53% were female. Complete CHARGE-AF cases were older and had higher AF incidence and cardiovascular comorbidity rate than incomplete cases. There were 5264 (4.7%) new AF cases during 5-year follow-up among complete cases. CHARGE-AF’s C-statistic for new AF was 0.74 (95% CI 0.73 to 0.74). The calibration plot showed slight risk underestimation in low-risk deciles and overestimation of absolute AF risk in those with highest predicted risk. The Kaplan-Meier plot with categories <2.5%, 2.5%–5% and >5% predicted 5-year risk was highly accurate. CHARGE-AF outperformed CHA2DS2-VASc (Cardiac failure or dysfunction, Hypertension, Age >=75 [Doubled], Diabetes, Stroke [Doubled]-Vascular disease, Age 65-74, and Sex category [Female]) and age alone as predictors for AF. Dichotomisation at cut-offs of 2.5%, 5% and 10% baseline CHARGE-AF risk all showed merits for patient selection in AF screening efforts.ConclusionIn patients with complete baseline CHARGE-AF data through routine Dutch primary care, CHARGE-AF accurately assessed AF risk among older primary care patients, outperformed both CHA2DS2-VASc and age alone as predictors for AF and showed potential for automated, low-cost patient selection in AF screening.

scholarly journals AFibNet: an implementation of atrial fibrillation detection with convolutional neural network

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Bambang Tutuko ◽  
Siti Nurmaini ◽  
Alexander Edo Tondas ◽  
Muhammad Naufal Rachmatullah ◽  
Annisa Darmawahyuni ◽  
...  
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Convolutional Neural Network ◽  
Learning System ◽  
Single Frequency ◽  
Feature Maps ◽  
Normal Sinus ◽  
Unseen Data ◽  
Specific Device ◽  
Model Formation

Abstract Background Generalization model capacity of deep learning (DL) approach for atrial fibrillation (AF) detection remains lacking. It can be seen from previous researches, the DL model formation used only a single frequency sampling of the specific device. Besides, each electrocardiogram (ECG) acquisition dataset produces a different length and sampling frequency to ensure sufficient precision of the R–R intervals to determine the heart rate variability (HRV). An accurate HRV is the gold standard for predicting the AF condition; therefore, a current challenge is to determine whether a DL approach can be used to analyze raw ECG data in a broad range of devices. This paper demonstrates powerful results for end-to-end implementation of AF detection based on a convolutional neural network (AFibNet). The method used a single learning system without considering the variety of signal lengths and frequency samplings. For implementation, the AFibNet is processed with a computational cloud-based DL approach. This study utilized a one-dimension convolutional neural networks (1D-CNNs) model for 11,842 subjects. It was trained and validated with 8232 records based on three datasets and tested with 3610 records based on eight datasets. The predicted results, when compared with the diagnosis results indicated by human practitioners, showed a 99.80% accuracy, sensitivity, and specificity. Result Meanwhile, when tested using unseen data, the AF detection reaches 98.94% accuracy, 98.97% sensitivity, and 98.97% specificity at a sample period of 0.02 seconds using the DL Cloud System. To improve the confidence of the AFibNet model, it also validated with 18 arrhythmias condition defined as Non-AF-class. Thus, the data is increased from 11,842 to 26,349 instances for three-class, i.e., Normal sinus (N), AF and Non-AF. The result found 96.36% accuracy, 93.65% sensitivity, and 96.92% specificity. Conclusion These findings demonstrate that the proposed approach can use unknown data to derive feature maps and reliably detect the AF periods. We have found that our cloud-DL system is suitable for practical deployment

scholarly journals Atrial Fibrillation Detection by the Combination of Recurrence Complex Network and Convolution Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Xiaoling Wei ◽  
Jimin Li ◽  
Chenghao Zhang ◽  
Ming Liu ◽  
Peng Xiong ◽  
...  
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Complex Network ◽  
Individual Variation ◽  
Deep Neural Network ◽  
Detection Algorithm ◽  
Convolution Neural Network ◽  
Electrocardiogram Signal ◽  
Sensitivity Specificity ◽  
Atrial Fibrillation Detection

In this paper, R wave peak interval independent atrial fibrillation detection algorithm is proposed based on the analysis of the synchronization feature of the electrocardiogram signal by a deep neural network. Firstly, the synchronization feature of each heartbeat of the electrocardiogram signal is constructed by a Recurrence Complex Network. Then, a convolution neural network is used to detect atrial fibrillation by analyzing the eigenvalues of the Recurrence Complex Network. Finally, a voting algorithm is developed to improve the performance of the beat-wise atrial fibrillation detection. The MIT-BIH atrial fibrillation database is used to evaluate the performance of the proposed method. Experimental results show that the sensitivity, specificity, and accuracy of the algorithm can achieve 94.28%, 94.91%, and 94.59%, respectively. Remarkably, the proposed method was more effective than the traditional algorithms to the problem of individual variation in the atrial fibrillation detection.

scholarly journals TASK-1 current is inhibited by phosphorylation during human and canine chronic atrial fibrillation

2015 ◽  
Vol 308 (2) ◽  
pp. H126-H134 ◽  
Author(s):  
Erin Harleton ◽  
Alessandra Besana ◽  
Parag Chandra ◽  
Peter Danilo ◽  
Tove S. Rosen ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Normal Sinus Rhythm ◽  
Human Subjects ◽  
Phosphorylation Site ◽  
Chronic Atrial Fibrillation ◽  
Resting Membrane Potential ◽  
Atrial Remodeling ◽  
Protein Levels ◽  
Atrial Tissue ◽  
Normal Sinus

Atrial fibrillation (AF) is a common arrhythmia with significant morbidities and only partially adequate therapeutic options. AF is associated with atrial remodeling processes, including changes in the expression and function of ion channels and signaling pathways. TWIK protein-related acid-sensitive K+ channel (TASK)-1, a two-pore domain K+ channel, has been shown to contribute to action potential repolarization as well as to the maintenance of resting membrane potential in isolated myocytes, and TASK-1 inhibition has been associated with the induction of perioperative AF. However, the role of TASK-1 in chronic AF is unknown. The present study investigated the function, expression, and phosphorylation of TASK-1 in chronic AF in atrial tissue from chronically paced canines and in human subjects. TASK-1 current was present in atrial myocytes isolated from human and canine hearts in normal sinus rhythm but was absent in myocytes from humans with AF and in canines after the induction of AF by chronic tachypacing. The addition of phosphatase to the patch pipette rescued TASK-1 current from myocytes isolated from AF hearts, indicating that the change in current is phosphorylation dependent. Western blot analysis showed that total TASK-1 protein levels either did not change or increased slightly in AF, despite the absence of current. In studies of perioperative AF, we have shown that phosphorylation of TASK-1 at Thr383 inhibits the channel. However, phosphorylation at this site was unchanged in atrial tissue from humans with AF or in canines with chronic pacing-induced AF. We conclude that phosphorylation-dependent inhibition of TASK-1 is associated with AF, but the phosphorylation site responsible for this inhibition remains to be identified.

scholarly journals Disease Classification and Biomarker Discovery Using ECG Data

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Rong Huang ◽  
Yingchun Zhou
Keyword(s):  
Biomarker Discovery ◽  
Heart Diseases ◽  
Low Cost ◽  
Recent Decade ◽  
Extraction Methods ◽  
Disease Classification ◽  
Stepwise Discriminant Analysis ◽  
Heavy Tailed ◽  
Sensitivity Specificity ◽  
Sample Quantiles

In the recent decade, disease classification and biomarker discovery have become increasingly important in modern biological and medical research. ECGs are comparatively low-cost and noninvasive in screening and diagnosing heart diseases. With the development of personal ECG monitors, large amounts of ECGs are recorded and stored; therefore, fast and efficient algorithms are called for to analyze the data and make diagnosis. In this paper, an efficient and easy-to-interpret procedure of cardiac disease classification is developed through novel feature extraction methods and comparison of classifiers. Motivated by the observation that the distributions of various measures on ECGs of the diseased group are often skewed, heavy-tailed, or multimodal, we characterize the distributions by sample quantiles which outperform sample means. Three classifiers are compared in application both to all features and to dimension-reduced features by PCA: stepwise discriminant analysis (SDA), SVM, and LASSO logistic regression. It is found that SDA applied to dimension-reduced features by PCA is the most stable and effective procedure, with sensitivity, specificity, and accuracy being 89.68%, 84.62%, and 88.52%, respectively.

scholarly journals Method for auxiliary use of thermography in diagnosing inflammation in the coxofemoral joint in dogs

2018 ◽  
Vol 39 (4) ◽  
pp. 1565
Author(s):  
Fernanda Lúcia Passos Fukahori ◽  
Daniela Maria Bastos de Souza ◽  
Eduardo Alberto Tudury ◽  
George Chaves Jimenez ◽  
José Ferreira da Silva Neto ◽  
...  
Keyword(s):  
Thermal Emission ◽  
Low Cost ◽  
Joint Inflammation ◽  
Domestic Animals ◽  
The Body ◽  
Joint Diseases ◽  
Infrared Images ◽  
Infrared Thermometry ◽  
Non Invasive ◽  
Sensitivity Specificity

Joint diseases are relatively common in domestic animals, such as dogs. The involved inflammation produces thermal emission, which can be imaged using specific sensors that allow capturing of infrared images. Given that there have been few reports on the use of thermography in the diagnosis of inflammation associated with diseases of the hip joint in dogs, we here propose a method for identification of inflammatory foci in dogs by using infrared thermometry. The present study aimed to find non-invasive and low-cost resources that couldfacilitate a clinical diagnosis in cases withinflammation in the coxofemoral joint of dogs.To this end, we developed a system in whichthe Flir Systems TG165 thermograph is coupled to a black PVC cannula with a 30-cm focus-to-animal distance.External effects of the environment on the temperature of the animalswere compared with the body temperature as measured by a conventional thermometer.Thirty-one dogs with and without inflammation in the coxofemoral joint underwent clinical evaluation.We verified that the temperature registered by the thermograph inthe animals with joint inflammation was significantlydifferentfrom that incontrol animals without inflammation, in the lateral projection.The method showed a sensitivity of 80%, specificity of 87.5%, and accuracy of 83.87%. This standardized method of diagnosis of inflammatory foci in the coxofemoral articulation of dogs by way of thermography showed sensitivity, specificity, and satisfactory accuracy.

scholarly journals An Atrial Fibrillation with Normal Limits using Signal Processing- A Review

2018 ◽  
Vol 7 (2.24) ◽  
pp. 453
Author(s):  
S. Sathish ◽  
K Mohanasundaram
Keyword(s):  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Normal Sinus Rhythm ◽  
P Wave ◽  
Wave Duration ◽  
Normal Limits ◽  
Normal Sinus ◽  
P Wave Duration ◽  
Standard Lead ◽  
Wave Morphology

Atrial fibrillation is an irregular heartbeat (arrhythmia) that can lead to the stroke, blood clots, heart failure and other heart related complications. This causes the symptoms like rapid and irregular heartbeat, fluttering, shortness of breath etc. In India for every around 4000 people eight of them are suffering from Atrial Fibrillation. P-wave Morphology.  Abnormality of P-wave (Atrial ECG components) seen during sinus rhythm are associated with Atrial fibrillation. P-wave duration is the best predictor of preoperative atrial fibrillation. but the small amplitudes of atrial ECG and its gradual increase from isometric line create difficulties in defining the onset of P wave in the Standard Lead Limb system (SLL).Studies shows that prolonged P-wave have duration in patients (PAF) In this Study, a Modified Lead Limb (MLL) which solves the practical difficulties in analyzing the P-ta interval for both in healthy subjects and Atrial Fibrillation patients. P-Ta wave interval and P-wave duration can be estimated with following proposed steps which is applicable for both filtered and unfiltered atrial ECG components which follows as the clinical database trials. For the same the p-wave fibrillated signals that escalates the diagnosis follows by providing minimal energy to recurrent into a normal sinus rhythm.  

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