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DNN (Deep Neural Network) based deep bottleneck feature extraction method of heart impact signal

A technology of deep neural network and heart shock signal, which is applied in the direction of neural learning method, biological neural network model, neural architecture, etc., can solve the problems of being easily disturbed by the external environment, so as to improve the performance of cardiac function representation, overcome dependence, The effect of high robustness

Active Publication Date: 2018-07-06
NORTHEASTERN UNIV LIAONING
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Problems solved by technology

Since the signal itself is relatively weak and easily interfered by the external environment, the extraction of conventional waveform characteristic parameters has certain limitations.

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  • DNN (Deep Neural Network) based deep bottleneck feature extraction method of heart impact signal
  • DNN (Deep Neural Network) based deep bottleneck feature extraction method of heart impact signal
  • DNN (Deep Neural Network) based deep bottleneck feature extraction method of heart impact signal

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Embodiment Construction

[0044] The specific implementation manners of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0045] A deep bottleneck feature extraction method for cardiac shock signals based on deep neural network,

[0046] Step 1: Determine the form of the input vector and target vector of the neural network: synchronously detect the ECG signal and the BCG signal of the same subject, and preprocess them respectively to obtain the input vector and target vector of the deep neural network. target vector.

[0047] Step 1.1: Collect synchronous ECG signals and BCG signals of the same subject, and perform signal normalization processing on them respectively;

[0048] Step 1.2: Obtain the position of the R wave of the ECG signal and the position of the J wave of the BCG signal, and divide the ECG and BCG signals into frames based on them, and uniformly acquire 70 sampling points as one frame (the signal sampling rate is 100Hz);

[0049] ...

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Abstract

The invention relates to a DNN based deep bottleneck feature extraction method of a heart impact signal, and relates to the technical field of biological feature extraction. The heart impact signal serves as a feature extraction object, and aimed at the characteristics that the heart impact signal is low in waveform amplitude and easy to be interfered by the outside, a deep bottleneck feature parameter is extracted from the heart impact signal by combining an electrocardio signal synchronously and using the mechanism that DNN digs a deep feature. The feature takes the heart impact signal as aninput vector and the synchronous electrocardio signal as a target vector, training is carried out via the pre-designed 9-layer neural network to obtain the deep bottleneck feature, and cardiodynamicsperformance is effectively combined with an electrophysiological feature. The feature takes the heart impact signal and the electrocardio signal easy to obtain daily as the research object, rely on waveform fluctuation of a routine waveform feature parameter is overcome, the representing performance of the single feature parameter can be improved, and the method serves as a new trial in daily heart function analysis by using the deep learning theories.

Description

technical field [0001] The invention relates to the technical field of biological feature extraction, in particular to a method for extracting deep bottleneck features of heart shock signals based on a deep neural network. Background technique [0002] With the increasing popularity of wearable devices, the field of non-invasive cardiac function assessment has become a research hotspot today. Conventional cardiac function detection methods, including electrocardiogram (ECG), magnetocardiogram, heart sounds, and heart impedance graphs, all require electrodes and other detection equipment attached to the human body surface, which have certain requirements for the monitoring environment, conditions and operators , and caused great inconvenience to the daily life of the subjects. Ballistocardiogram (BCG) indirectly reflects the working state of the heart by detecting the weak vibration transmitted from the heart beat to the body surface. It is an advanced detection method for n...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06N3/04G06N3/08
CPCG06N3/084G06N3/088G06N3/048G06N3/045G06F2218/08
Inventor 蒋芳芳刘星航刘海滨张长帅徐敬傲
Owner NORTHEASTERN UNIV LIAONING
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