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Premature ventricular contraction identification method and system

A technology for ventricular premature beats and identification methods, applied in the field of medical electronics, can solve problems such as long training time, large computational load of neural network algorithms, and difficulty in real-time detection, achieving high accuracy, excellent performance, and improved classification performance.

Inactive Publication Date: 2017-05-31
SUZHOU INST OF NANO TECH & NANO BIONICS CHINESE ACEDEMY OF SCI
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  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

In the above research methods, the traditional neural network algorithm has a large amount of computation and a long training time, and it is difficult to realize real-time detection.

Method used

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  • Premature ventricular contraction identification method and system

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no. 1 example

[0047] figure 1 is a flow chart of the method for identifying premature ventricular beats according to the first embodiment of the present invention.

[0048] refer to figure 1 , in step S1, receive an electrocardiogram signal.

[0049]In this embodiment, the electrocardiogram signal may be collected from the Chinese Cardiovascular Disease Database (CCDD for short), but the present invention is not limited thereto.

[0050] In step S2, the electrocardiogram signal is preprocessed to obtain the preprocessed electrocardiogram signal.

[0051] Generally, the received ECG signal will contain noise such as power frequency, myoelectricity, and baseline drift. The power frequency noise will affect the small turning point in the electrocardiogram signal, so that the characteristics of the electrocardiogram signal will change and affect the diagnosis of the disease by the electrocardiogram signal, and its frequency is fixed at 50 Hz. Baseline drift is generally caused by human brea...

no. 2 example

[0077] figure 2 is a flow chart of the method for identifying premature ventricular beats according to the first embodiment of the present invention.

[0078] refer to figure 2 , in step S1, receive an electrocardiogram signal.

[0079] In this embodiment, the electrocardiogram signal may be collected from the Chinese Cardiovascular Disease Database (CCDD for short), but the present invention is not limited thereto.

[0080] In step S2, the electrocardiogram signal is preprocessed to obtain the preprocessed electrocardiogram signal.

[0081] Generally, the received ECG signal will contain noises such as power frequency, base current, and baseline drift. The power frequency noise will affect the small turning point in the electrocardiogram signal, so that the characteristics of the electrocardiogram signal will change and affect the diagnosis of the disease by the electrocardiogram signal, and its frequency is fixed at 50 Hz. Baseline drift is generally caused by human br...

no. 3 example

[0123] image 3 It is a block diagram of a premature ventricular beat recognition system according to the third embodiment of the present invention.

[0124] refer to image 3 , The premature ventricular beat recognition system according to the third embodiment of the present invention includes a receiving module 10 , a preprocessing module 11 , a classification module 12 , an identification module 13 and a re-identification module 14 .

[0125] The receiving module 10 is configured to receive electrocardiogram signals.

[0126] The preprocessing module 11 is configured to preprocess the electrocardiogram signal to obtain a preprocessed electrocardiogram signal.

[0127] The classification module 12 is configured to classify the preprocessed ECG signal by using several classification models to obtain several original probability values, wherein the classification model is a lead neural convolutional network classification model.

[0128] The identification module 13 is conf...

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Abstract

The invention provides a premature ventricular contraction identification method and system. The method includes the steps that electrocardiogram signals are received; the electrocardiogram signals are preprocessed; the preprocessed electrocardiogram signals are classified through several classification models to obtain several original probability values; fusion decision-making is conducted on the original probability values based on a preset fusion decision-making rule to obtain a comprehensive probability value after fusion decision-making, and whether the preprocessed electrocardiogram signals are electrocardiogram signals indicating premature ventricular contraction or to-be-determined electrocardiogram signals indicating premature ventricular contraction is identified according to the comprehensive probability value; if the preprocessed electrocardiogram signals are to-be-determined electrocardiogram signals indicating premature ventricular contraction, extraction is conducted, and identification is conducted again according to the characteristic parameters of the preprocessed electrocardiogram signals. By means of the premature ventricular contraction identification method and system, higher PVC identification sensitivity, better PVC identification performance and higher PVC identification accuracy can be achieved.

Description

technical field [0001] The invention relates to the technical field of medical electronics, in particular to a premature ventricular beat recognition method and a premature ventricular beat recognition system. Background technique [0002] Premature Ventricular Contraction (PVC) is an abnormal heartbeat originating from the ventricle that occurs early, and is also one of the common clinical arrhythmias. Therefore, it is of great clinical significance to be able to correctly detect and automatically identify PVC. [0003] At present, there are many methods for PVC automatic classification or PVC classification, such as support vector machine, neural network, wavelet transform, template matching and other methods. In the above research methods, the traditional neural network algorithm has a large amount of computation and a long training time, making it difficult to achieve real-time detection. As for the wavelet transform method, how to choose an appropriate wavelet base is...

Claims

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Application Information

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IPC IPC(8): A61B5/0402A61B5/0472G06F19/00A61B5/366
Inventor 周飞燕董军
Owner SUZHOU INST OF NANO TECH & NANO BIONICS CHINESE ACEDEMY OF SCI
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