Detecting atrial fibrillation from short single lead ECGs using statistical and morphological features

Abstract

Most existing algorithms can distinguish the AF rhythm from the normal sinus rhythm when ECG recordings are clean and are obtained with multi-lead systems, while their ability to discriminate against other arrhythmias and noise remains largely unknown. This study proposes an algorithm that classifies a short single lead ECG record from point of care devices into 'Normal', 'AF', 'Other' and 'Noisy' classes and discusses computational approaches to mitigate any unique challenges such as lead inversion, low amplitude signals, noise and artifacts.

Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices

“…Comparison of our RSL_ANN performance against that of other studies in the literature is not a straightforward task due to the many differences among them. Firstly, some studies used data acquired with traditional ECG recorders [ 11 , 12 , 13 , 14 , 15 , 16 , 18 ] and some others with portable devices [ 5 , 6 , 7 , 8 , 9 , 10 , 17 ]. Recorded signals included ECGs [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ] but also photoplethysmograms [ 5 , 6 , 7 , 8 , 9 , 10 ].…”

“…AF diagnosis is typically ECG-based and is usually made by a cardiologist, possibly supported by computerized applications [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ], in hospitals or in clinical facilities. However, traditional medical ECG devices, even when used out-of-the-hospital (such as the Holter ECG recorders), are coupled to a limited amount of people, who are symptomatic or have cryptogenic stroke and, hence, for whom there is an indication for long-term monitoring.…”

“…However, in order to be useful in the preventive diagnosis of AF, they have to be associated with a reliable diagnostic software. As a result, several algorithms for automatic detection of AF have been proposed in the literature [ 5 , 6 , 7 , 8 , 9 ], several of which are based on machine and deep learning approaches [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. Most of them claim very high performances but, when critically analyzed, show some common limitations.…”

“…Most of them claim very high performances but, when critically analyzed, show some common limitations. Firstly, performances of some algorithms for AF identification have been tested only against sinus rhythm [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 13 , 14 , 15 ], without considering the main confounders that are the level of noise affecting ECGs and the presence of other kinds of arrhythmias [ 12 , 16 , 17 , 18 ]. Secondly, most algorithms only rely on HRV to identify AF [ 5 , 6 , 7 , 8 , 9 , 10 , 13 , 14 ], despite high HRV being also associated with many other arrhythmias (not AF-specific) [ 19 , 20 ] and AF being also associated with absence of electrocardiographic P wave and presence of electrocardiographic F waves.…”

Wearable Intelligent System for the Diagnosis of Cardiac Diseases Working in Real Time and with Low Energy Cost

“…Cardiovascular diseases are a serious public health problem in industrialized countries [1,2], since they occupy the first place as cause of death. Half of the deaths in developed countries were due to four chronic diseases: cancer, ischemic heart disease, cardiovascular disease and diabetes mellitus.…”