Objective: Point of care ECG devices can improve the early detection of atrial fibrillation (AF). The efficiency of such devices depends on the capability of automatic AF detection against normal sinus rhythm and other arrhythmias from a short single lead ECG record in the presence of noise and artifacts. The objective of this study was to develop an algorithm that classifies a short single lead ECG record into 'Normal', 'AF', 'Other' and 'Noisy' classes, and identify the challenges in developing such algorithms and potential mitigation steps.
Approach: Rule-based identification was used to detect lead inversion and records too noisy to be of immediate use. A set of statistical and morphological features describing the rhythm was then extracted, and support vector machine classifiers were used to classify records into three classes: 'Normal', 'AF' or 'Other'. The algorithm was trained and tested using 12 186 short single lead ECGs recorded on a point of care device made available via the Computing in Cardiology Challenge 2017.
Main results: The algorithm achieved a sensitivity of 77.5%, a specificity of 97.9% and an accuracy of 96.1% in the detection of AF from a non-AF rhythm in a five-fold cross validation. It achieved F1 measures of 89%, 78% and 67% for 'Normal', 'AF' and 'Other' classes, respectively, when evaluated with a hidden test set. The overall challenge score was 78%.
Significance: 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.