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Electrocardiogram classification method and system

A classification method and classification system technology, applied in medical science, sensors, diagnostic recording/measurement, etc., can solve problems such as noise interference, low accuracy rate, and difficult accurate classification, and achieve the effect of reducing impact and improving accuracy rate

Active Publication Date: 2015-10-14
SUZHOU INST OF NANO TECH & NANO BIONICS CHINESE ACEDEMY OF SCI
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Problems solved by technology

[0004] The real clinical data is very complicated, such as noise interference, QRS wave group is not obvious, and lead off are extremely common, and the physiological differences of different people will also lead to the diversity of ECG data
Existing feature extraction methods are difficult to classify accurately. Even if the most mature R-wave extraction method is tested with clinical data, the accuracy rate will still decline; as for the P-wave, T-wave and other extraction methods in the MIT- The accuracy rate on BIH is very low, and it is even less likely to be used in clinical applications

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  • Electrocardiogram classification method and system
  • Electrocardiogram classification method and system

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Embodiment Construction

[0020] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments.

[0021] refer to figure 1 Shown is a flow chart of a preferred embodiment of the electrocardiogram classification method of the present invention.

[0022] Step S401, filtering and down-sampling the original ECG waveform. Specifically, first filter and down-sample to feq Hz, then skip the first skipN points, take the data of N1 consecutive points in the middle as input data, and only keep the ECG data of 8 basic leads, that is, II , III, V1, V2, V3, V4, V5, V6 leads. In this embodiment, feq is set to 200, skipN is set to 25, and N1 is set to 1900.

[0023] Step S402, performing a translation start point operation on the filtered and down-sampled ECG data. Specifically, the starting point position is selected in the ECG data with a dimension of 8×N1, and the value range is [1, offset]. In the training phase, the starting point is ...

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Abstract

The invention relates to an electrocardiogram classification method. The electrocardiogram classification method comprises the following steps that filtering and downsampling are conducted on an original electrocardiogram waveform; horizontal start point moving operation is conducted on electrocardiogram data obtained after filtering and downsampling are conducted; a convolution neural network is led to recognize and classify the electrocardiogram data. The invention further relates to an electrocardiogram classification system. According to the electrocardiogram classification method and system, the classification accuracy rate can be increased, and the effect of the middle link on the final classification performance can be reduced.

Description

technical field [0001] The invention relates to an electrocardiogram classification method and system. Background technique [0002] The electrocardiogram is a visual time series that records the electrical activity of the human heart. It has been widely used in the clinical examination of heart-related diseases, and a relatively complete electrocardiogram judgment standard has been formed. In recent years, due to the rise of remote monitoring, ECG analysis is developing towards out-of-hospital, sub-healthy groups and long-term monitoring, which has greatly increased the workload of doctors and made the demand for automated diagnosis more and more urgent. [0003] ECG for clinical diagnosis Figure 1 Generally, there are 12 leads, and each lead usually collects 10s of ECG recording data, including 12 to 18 beats. The existing ECG classification methods are mainly aimed at the two-lead ECG data of MIT-BIH (which does not meet the requirements of clinical diagnosis); it is ne...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/0402
Inventor 金林鹏董军
Owner SUZHOU INST OF NANO TECH & NANO BIONICS CHINESE ACEDEMY OF SCI
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