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

Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation

The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart's rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of patients' acute and chronic heart conditions. In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes; namely, normal beat, premature ventricular contraction beat, paced beat, right bundle branch block beat, left bundle branch block beat, atrial premature contraction beat, ventricular flutter wave beat, and ventricular escape beat. The one-dimensional ECG time series signals are transformed into 2-D spectrograms through short-time Fourier transform. The 2-D CNN model consisting of four convolutional layers and four pooling layers is designed for extracting robust features from the input spectrograms. Our proposed methodology is evaluated on a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art average classification accuracy of 99.11\%, which is better than those of recently reported results in classifying similar types of arrhythmias. The performance is significant in other indices as well, including sensitivity and specificity, which indicates the success of the proposed method.Comment: 14 pages, 5 figures, accepted for future publication in Remote Sensing MDPI Journa

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

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

Pacemaker Prevention Therapy in Drug–refractory Paroxysmal Atrial Fibrillation: Reliability of Diagnostics and Effectiveness of Prevention Pacing Therapy in Vitatron™ Selection® device

Introduction. Atrial fibrillation (AF), the most common and rising disorder of cardiac rhythm, is quite difficult to control and/or to treat. Non pharmacological therapies for AF may involve the use of dedicated pacing algorithms to detect and prevent atrial arrhythmia that could be a trigger for AF onset. Selection 900E/AF2.0 Vitatron DDDRP pacemaker (1) keeps an atrial arrhythmia diary thus providing detailed onset reports of arrhythmias of interest, (2) provides us data about the number of premature atrial contractions (PACs) and (3) plots heart rate in the 5 minutes preceding the detection of an atrial arrhythmia. Moreover, this device applies four dedicated pacing therapies to reduce the incidence of atrial arrhythmia and AF events. Aim of the Study. To analyze the reliability to record atrial arrhythmias and evaluate effectiveness of its AF preventive pacing therapies. Material and Methods. We enrolled 15 patients (9 males and 6 females, mean age of 71±5 years, NYHA class I–II), with a DDDRP pacemaker implanted for a “bradycardia–tachycardia” syndrome, with advanced atrioventricular conduction disturbances. We compared the number and duration of AF episodes’ stored in the device with a contemporaneous 24h Holter monitoring. After that, we switched on the atrial arrhythmias detecting algorithms, starting from an atrial rate over 180 beats per minute for at least 6 ventricular cycles, and ending with at least 10 ventricular cycles in sinus rhythm. Thereafter, in order to evaluate the possible reduction in PACs number and in number and duration of AF episodes, we tailored all the four pacing preventive algorithms. Patients were followed for 24±8 months (from 20 to 32 months). Results. All 59 atrial arrhythmia episodes occurred in the first part of this trial, were correctly recorded by both systems, with a correlation coefficient (r) of 0.96. During the follow–up, we observed a significant reduction not only in PACs number (from 83±12/day to 2.3±0.8/day) but also in AF episodes (from 46±7/day to 0.12±0.03/day) and AF burden (from 93%±6% to 0.3%±0.06%). An increase in atrial pacing percentages (from 3%±0.5% to 97%±3%) was also contemporaneously observed. Conclusion. In this pacemaker, detection of atrial arrhythmia episodes is highly reliable, thus making available an appropriate monitoring of heart rhythm, mainly suitable in AF asymptomatic patients. Moreover, the significant reduction of atrial arrhythmia episodes indicates that this might represent a suitable therapeutic option for an effective preventive therapy of AF in paced brady–tachy patients

Validity of telemetric-derived measures of heart rate variability: a systematic review

Heart rate variability (HRV) is a widely accepted indirect measure of autonomic function with widespread application across many settings. Although traditionally measured from the 'gold standard' criterion electrocardiography (ECG), the development of wireless telemetric heart rate monitors (HRMs) extends the scope of the HRV measurement. However, the validity of telemetric-derived data against the criterion ECG data is unclear. Thus, the purpose of this study was twofold: (a) to systematically review the validity of telemetric HRM devices to detect inter-beat intervals and aberrant beats; and (b) to determine the accuracy of HRV parameters computed from HRM-derived inter-beat interval time series data against criterion ECG-derived data in healthy adults aged 19 to 62 yrs. A systematic review of research evidence was conducted. Four electronic databases were accessed to obtain relevant articles (PubMed, EMBASE, MEDLINE and SPORTDiscus. Articles published in English between 1996 and 2016 were eligible for inclusion. Outcome measures included temporal and power spectral indices (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology (1996). The review confirmed that modern HRMs (Polar® V800™ and Polar® RS800CX™) accurately detected inter-beat interval time-series data. The HRV parameters computed from the HRM-derived time series data were interchangeable with the ECG-derived data. The accuracy of the automatic in-built manufacturer error detection and the HRV algorithms were not established. Notwithstanding acknowledged limitations (a single reviewer, language bias, and the restricted selection of HRV parameters), we conclude that the modern Polar® HRMs offer a valid useful alternative to the ECG for the acquisition of inter-beat interval time series data, and the HRV parameters computed from Polar® HRM-derived inter-beat interval time series data accurately reflect ECG-derived HRV metrics, when inter-beat interval data are processed and analyzed using identical protocols, validated algorithms and software, particularly under controlled and stable conditions