Multiscaled Fusion of Deep Convolutional Neural Networks for Screening Atrial Fibrillation From Single Lead Short ECG Recordings

Abstract

Atrial fibrillation (AF) is one of the most common sustained chronic cardiac arrhythmia in elderly population, associated with a high mortality and morbidity in stroke, heart failure, coronary artery disease, systemic thromboembolism, etc. The early detection of AF is necessary for averting the possibility of disability or mortality. However, AF detection remains problematic due to its episodic pattern. In this paper, a multiscaled fusion of deep convolutional neural network (MS-CNN) is proposed to screen out AF recordings from single lead short electrocardiogram (ECG) recordings. The MS-CNN employs the architecture of two-stream convolutional networks with different filter sizes to capture features of different scales. The experimental results show that the proposed MS-CNN achieves 96.99% of classification accuracy on ECG recordings cropped/padded to 5 s. Especially, the best classification accuracy, 98.13%, is obtained on ECG recordings of 20 s. Compared with artificial neural network, shallow single-stream CNN, and VisualGeometry group network, the MS-CNN can achieve the better classification performance. Meanwhile, visualization of the learned features from the MS-CNN demonstrates its superiority in extracting linear separable ECG features without hand-craft feature engineering. The excellent AF screening performance of the MS-CNN can satisfy the most elders for daily monitoring with wearable devices.

Diagnosing Abnormal Electrocardiogram (ECG) via Deep Learning

“…We briefly introduced the most recent automatic detecting schemes such as convolutional neural networks (CNN), recurrent neural networks (RNN) [9,22,34,39], and its variation of longshort-term memory (LSTM) model [18,22], which aims on analyzing different types of cardiac arrhythmias. We presented an investigation of practical examples and applications of deep learning on automatic ECG diagnosis [5,7,16,27,31,36], which consists of a deep learning-based lightweight classifier on ECG data identification, deep belief network (DBN) [1,8,28] on diagnosing cardiac arrhythmia via wearable ECG monitoring devices, and a health cloud platform on automatic ECG detection, data mining and classification. We combined the theoretical concepts of artificial intelligence (AI)-oriented topics such as deep learning, big data health cloud platform to real medical applications, i.e., minishape ECG monitoring devices [9,41], domestic cardiac arrhythmia analyzer [40], automatic ECG diagnosis on © 2019 The Author(s).…”

“…Note that after passing the deep data features through a pooled layer max-pooling, those features will be sequentially loaded into two fully connected layers named as dense, in which purification is performed on the hierarchical data features; in the next step, the classifier function takes responsibility for feature classification on the output of purified data features. Compared with other similar schemes on automatic ECG diagnosis [5,7,16,27,31,36], the invented lightweight algorithm has been released from requiring large set of calculation parameters but still ensures constant accuracy on recognition effects, which is able to realize parallel processing of ECG data despite of limited network resources or running memory on GPU [40].…”

Optimal Length of R-R Interval Segment Window for Lorenz Plot Detection of Paroxysmal Atrial Fibrillation by Machine Learning

“…Their two CNN models corresponding to short-term Fourier and stationary wavelet transforms achieved an accuracy of 98.29% and 98.63%, respectively. Fan et al [15] proposed multiscale CNNs that had two-stream convolutional networks with different filter sizes. They reported 96.99% and 98.13% of classification accuracy for ECG recordings of 5 and 20 s, respectively.…”

Learning Your Heart Actions From Pulse: ECG Waveform Reconstruction From PPG

“…Statistics also reveal that young people, especially athletes, are more prone to sudden cardiac arrests than before [3]. Those life-threatening cardiovascular diseases often happen outside clinics and hospitals, and the patients are recommended by cardiologists to attend a longterm continuous monitoring program [4].…”