Chinese Academy of Sciences

Xiaomao Fan

Ye Li

Qihang Yao

Yunpeng Cai

Yujie Yang

Liyan Yin

Fen Miao

Furu Xiang

The present disclosure provides an atrial fibrillation signal recognition method, apparatus and device. The method comprises: obtaining an electrocardiogram signal to be recognized; inputting the electrocardiogram signal to be recognized to a pre-established atrial fibrillation signal recognition model, and outputting an atrial fibrillation signal recognition result, where the atrial fibrillation signal recognition model is established in the following way: obtaining a specified number of electrocardiogram sample signals and corresponding identifier information; balancing, according to the number of normal signals, atrial fibrillation signals by means of SMOTE; establishing a network structure of multiple convolutional neural networks, each of the convolutional neural networks being provided with a specific receptive field for recognizing the atrial fibrillation signals of a corresponding granularity; and inputting the normal signals and the balanced atrial fibrillation signals to the network structure for training to generate an atrial fibrillation signal recognition model.

Atrial fibrillation signal recognition method, apparatus and device

Provided are an electrocardiogram signal parallel analysis apparatus, a mobile terminal incorporating the apparatus, and related methods. The apparatus includes an integrated memory, a central processing unit and a graphic processing unit. The integrated memory includes a first memory and a second memory for being used by the central processing unit and the graphic processing unit respectively, and the central processing unit may access the second memory. The central processing unit performs primary noise reduction on a received electrocardiogram original signal to obtain a primary electrocardiogram signal, and performs abnormal heartbeat classification preliminary screening on characteristic data extracted from the graphic processing unit to obtain suspected abnormal heartbeat data. The graphic processing unit performs characteristic extraction on the primary electrocardiogram signal to obtain characteristic data, performs secondary noise reduction on the primary electrocardiogram signal to obtain a secondary electrocardiogram signal, and processes the suspected abnormal heartbeat data and the secondary electrocardiogram signal by applying a template matching classification mode to obtain final abnormal heartbeat data.

ECG signal parallel analysis apparatus, method and mobile terminal

The present disclosure provides a GPU-based parallel electrocardiogram signal analysis method, comprising: performing a filtering process of electrocardiogram signals through a long interval artifact removal and a short interval artifact removal; performing a QRS detection of the filtering-processed electrocardiogram signals through an R-wave position extraction, a QRS complex start and end positions extraction and a QRS complex width extraction; performing an abnormal waveform classification of the QRS-detected electrocardiogram signals through template creation; wherein at least one of the long interval artifact removal, the short interval artifact removal, the R-wave position extraction, the QRS complex width extraction and the creation template is performed by a multiple threads at a GPU device side in parallel, any thread being read through its unique index number to process corresponding data. By executing one or more steps of the electrocardiogram signal analysis at GPU in parallel, the present disclosure increases the analysis speed of the electrocardiogram signals.

GPU-based parallel electrocardiogram signal analysis method, computer readable storage medium and device

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Guangdong, China

Xiaomao Fan