id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-024491-f16d1zov	Qiu, Xi	Simultaneous ECG Heartbeat Segmentation and Classification with Feature Fusion and Long Term Context Dependencies	2020-04-17		.txt	text/plain	3465	245	55	To achieve simultaneous segmentation and classification, we present a Faster R-CNN based model that has been customized to handle ECG data. Since deep learning methods can produce feature maps from raw data, heartbeat segmentation can be simultaneously conducted with classification with a single neural network. To achieve simultaneous segmentation and classification, we present a Faster R-CNN [2] based model that has been customized to handle ECG sequences. In our method, we present a modified Faster R-CNN for arrhythmia detection which works in only two steps: preprocessing, and simultaneous heartbeat segmentation and classification. The architecture of our model is shown in Fig. 2 , which takes 1-D ECG sequence as its input and conducts heartbeat segmentation and classification simultaneously. Different from most deep learning methods which compute feature maps for a single heartbeat, our backbone model takes a long ECG sequence as its input.	./cache/cord-024491-f16d1zov.txt	./txt/cord-024491-f16d1zov.txt