A Review of Atrial Fibrillation Detection Methods as a Service

Atrial Fibrillation (AF) is a common heart arrhythmia that often goes undetected, and even if it is detected, managing the condition may be challenging. In this paper, we review how the RR interval and Electrocardiogram (ECG) signals, incorporated into a monitoring system, can be useful to track AF events. Were such an automated system to be implemented, it could be used to help manage AF and thereby reduce patient morbidity and mortality. The main impetus behind the idea of developing a service is that a greater data volume analyzed can lead to better patient outcomes. Based on the literature review, which we present herein, we introduce the methods that can be used to detect AF efficiently and automatically via the RR interval and ECG signals. A cardiovascular disease monitoring service that incorporates one or multiple of these detection methods could extend event observation to all times, and could therefore become useful to establish any AF occurrence. The development of an automated and efficient method that monitors AF in real time would likely become a key component for meeting public health goals regarding the reduction of fatalities caused by the disease. Yet, at present, significant technological and regulatory obstacles remain, which prevent the development of any proposed system. Establishment of the scientific foundation for monitoring is important to provide effective service to patients and healthcare professionals

Electrocardiographic patch devices and contemporary wireless cardiac monitoring.

Cardiac electrophysiologic derangements often coexist with disorders of the circulatory system. Capturing and diagnosing arrhythmias and conduction system disease may lead to a change in diagnosis, clinical management and patient outcomes. Standard 12-lead electrocardiogram (ECG), Holter monitors and event recorders have served as useful diagnostic tools over the last few decades. However, their shortcomings are only recently being addressed by emerging technologies. With advances in device miniaturization and wireless technologies, and changing consumer expectations, wearable “on-body” ECG patch devices have evolved to meet contemporary needs. These devices are unobtrusive and easy to use, leading to increased device wear time and diagnostic yield. While becoming the standard for detecting arrhythmias and conduction system disorders in the outpatient setting where continuous ECG monitoring in the short to medium term (days to weeks) is indicated, these cardiac devices and related digital mobile health technologies are reshaping the clinician-patient interface with important implications for future healthcare delivery

Clasification Of Arrhythmic ECG Data Using Machine Learning Techniques

In this paper we proposed a automated Artificial Neural Network (ANN) based classification system for cardiac arrhythmia using multi-channel ECG recordings. In this study, we are mainly interested in producing high confident arrhythmia classification results to be applicable in diagnostic decision support systems. Neural network model with back propagation algorithm is used to classify arrhythmia cases into normal and abnormal classes. Networks models are trained and tested for MIT-BIH arrhythmia. The differen structures of ANN have been trained by mixture of arrhythmic and non arrhythmic data patient. The classification performance is evaluated using measures; sensitivity, specificity, classification accuracy, mean squared error (MSE), receiver operating characteristics (ROC) and area under curve (AUC). Our experimental results gives 96.77% accuracy on MIT-BIH database and 96.21% on database prepared by including NSR database also

Arrhythmias After Tetralogy of Fallot Repair

Tetralogy of Fallot is the most common cyanotic congenital heart disease, with a good outcome after total surgical correction. In spite of a low perioperative mortality and a good quality of life, late sudden death remains a significant clinical problem, mainly related to episodes of sustained ventricular tachycardia and ventricular fibrillation. Fibro-fatty substitution around infundibular resection, intraventricular septal scar, and patchy myocardial fibrosis, may provide anatomical substrates of abnormal depolarization and repolarization causing reentrant ventricular arrhythmias. Several non-invasive indices based on classical examination such as ECG, signal-averaging ECG, and echocardiography have been proposed to identify patients at high risk of sudden death, with hopeful results. In the last years other more sophisticated invasive and non-invasive tools, such as heart rate variability, electroanatomic mapping and cardiac magnetic resonance added a relevant contribution to risk stratification. Even if each method per se is affected by some limitations, a comprehensive multifactorial clinical and investigative examination can provide an accurate risk evaluation for every patien

Clinical evaluation of a wireless ECG sensor system for arrhythmia diagnostic purposes

In a clinical study, a novel wireless electrocardiogram (ECG) recorder has been evaluated with regard to its ability to perform arrhythmia diagnostics. As the ECG recorder will detect a "non-standard" ECG signal, it has been necessary to compare those signals to "standard" ECG recording signals in order to evaluate the arrhythmia detection ability of the new system. Simultaneous recording of ECG signals from both the new wireless ECG recorder and a conventional Holter recorder was compared by two independent cardiology specialists with regard to signal quality for performing arrhythmia diagnosis. In addition, calculated R-R intervals from the two systems were correlated. A total number of 16 patients participated in the study. It can be considered that recorded ECG signals obtained from the wireless ECG system had an acceptable quality for arrhythmia diagnosis. Some of the patients used the wireless sensor while doing physical sport activities, and the quality of the recorded ECG signals made it possible to perform arrhythmia diagnostics even under such conditions. Consequently, this makes possible improvements in correlating arrhythmias to physical activities

Prohormones in the early diagnosis of cardiac syncope

Background--The early detection of cardiac syncope is challenging. We aimed to evaluate the diagnostic value of 4 novel prohormones, quantifying different neurohumoral pathways, possibly involved in the pathophysiological features of cardiac syncope: midregional-pro-A-type natriuretic peptide (MRproANP), C-terminal proendothelin 1, copeptin, and midregionalproadrenomedullin. Methods and Results--We prospectively enrolled unselected patients presenting with syncope to the emergency department (ED) in a diagnostic multicenter study. ED probability of cardiac syncope was quantified by the treating ED physician using a visual analogue scale. Prohormones were measured in a blinded manner. Two independent cardiologists adjudicated the final diagnosis on the basis of all clinical information, including 1-year follow-up. Among 689 patients, cardiac syncope was the adjudicated final diagnosis in 125 (18%). Plasma concentrations of MRproANP, C-terminal proendothelin 1, copeptin, and midregional-proadrenomedullin were all significantly higher in patients with cardiac syncope compared with patients with other causes (P < 0.001). The diagnostic accuracies for cardiac syncope, as quantified by the area under the curve, were 0.80 (95% confidence interval [CI], 0.76-0.84), 0.69 (95% CI, 0.64-0.74), 0.58 (95% CI, 0.52-0.63), and 0.68 (95% CI, 0.63-0.73), respectively. In conjunction with the ED probability (0.86; 95% CI, 0.82-0.90), MRproANP, but not the other prohormone, improved the area under the curve to 0.90 (95% CI, 0.87-0.93), which was significantly higher than for the ED probability alone (P=0.003). An algorithm to rule out cardiac syncope combining an MRproANP level of < 77 pmol/L and an ED probability of < 20% had a sensitivity and a negative predictive value of 99%. Conclusions--The use of MRproANP significantly improves the early detection of cardiac syncope among unselected patients presenting to the ED with syncope

A Novel Neural Network based Classification for ECG Signals

Cardiac Arrhythmia represents heart abnormalities. This problem is faced by people, irrespective of age. Even the physicians feel difficulty in diagnosing the abnormal behavior of heart accurately. Accurate detection of cardiac abnormalities helps to provide right treatment. Classification plays an important role in predicting abnormal behaviors of heart and it helps the physician to treat the patients who are having cardiac arrhythmia. Extracted features from ECG (Electrocardiogram) signals are used for classification. It is possible to extract multiple features from ECG signal regardless of the features used for classification. Classification performed using all the extracted features leads to misclassification of abnormalities. So feature selection is an important concept in classifying the normal and abnormal behavior of heart. MIT BIH Arrhythmia dataset is used in our proposed work where the classification is done in MATLAB using Fitting Neural Network. DOI: 10.17762/ijritcc2321-8169.150314

Detection of atrial fibrillation episodes in long-term heart rhythm signals using a support vector machine

Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficients very sensitive to beat-to-beat heart changes, taken from the fetal heart rate analysis in perinatal medicine. Effectiveness of the proposed classifier has been verified on the MIT-BIH Atrial Fibrillation Database. Designing of the LSVM classifier using very large number of feature vectors requires extreme computational efforts. Therefore, an original approach has been proposed to determine a training set of the smallest possible size that still would guarantee a high quality of AF detection. It enables to obtain satisfactory results using only 1.39% of all heartbeats as the training data. Post-processing stage based on aggregation of classified heartbeats into AF episodes has been applied to provide more reliable information on patient risk. Results obtained during the testing phase showed the sensitivity of 98.94%, positive predictive value of 98.39%, and classification accuracy of 98.86%.Web of Science203art. no. 76