Abstract: The present disclosure provides an atrial fibrillation signal recognition method, apparatus and device.

Abstract: The present disclosure provides an atrial fibrillation signal recognition method, apparatus and device.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.