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Electrocardio-signal emotion recognition method based on deep learning

A technology of electrocardiogram signal and emotion recognition, which is applied in the field of biomedicine and deep learning, can solve problems such as difficulties in emotion recognition, and achieve the effect of strong learning ability, good expression ability, and good recognition results

Inactive Publication Date: 2018-02-27
TIANJIN UNIV
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Problems solved by technology

Due to some of the above limitations, accurate emotion recognition is difficult

Method used

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  • Electrocardio-signal emotion recognition method based on deep learning
  • Electrocardio-signal emotion recognition method based on deep learning

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Embodiment approach

[0026] The best implementation method of the present invention is as follows:

[0027] The present invention proposes a method based on deep learning, through learning a large number of electrocardiographic signals under various emotions, thereby extracting more effective features to improve the recognition rate of electrocardiographic signals under various emotions. Specifically, it mainly includes the following stages: data collection stage, data preprocessing stage, model training stage, and model use stage. The specific implementation method of the above-mentioned stages is as follows:

[0028] 1. Data Collection Phase

[0029] The data collection phase mainly includes the following steps:

[0030] 1) Preliminary preparation: According to needs, collect pictures, audio or video data that can induce emotions such as happiness, anger, sadness and joy. Such as the International Emotional Picture System and the International Emotional Digital Sound System, as well as the Ch...

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Abstract

The invention relates to an electrocardio-signal emotion recognition method based on deep learning. The electrocardio-signal emotion recognition method comprises the following steps that under variousemotion picture inducing conditions, electrocardio data of a testee under different emotions is acquired through the chest or the wrist, the acquired electrocardio data is segmented into electrocardio signals fixed in length, and corresponding labels are made for the electrocardio data obtained under different emotions; the acquired electrocardio data is preprocessed, baseline drift removal basedon wavelet transformation is firstly performed, and noise removal is performed; data standardization is performed, normalization processing is conducted on noise-removed electrocardio data; a model training stage is performed.

Description

technical field [0001] The invention relates to the fields of biomedicine and deep learning, in particular to a deep learning-based ECG emotion recognition method. Background technique [0002] Emotion is a general term for a series of subjective cognitive experiences, and it is a psychological and physiological state produced by a variety of feelings, thoughts and behaviors. Emotions serve an important purpose in our lives, and it plays an important role in the communication between people. With the rapid development of artificial intelligence, in order for machines to serve humans better, it is necessary to enable machines to understand human emotions and build a bridge between humans and machines. In real life, emotion recognition has been widely used in many fields such as education, medical care, and business. [0003] Emotion recognition methods are mainly divided into two categories: recognition based on non-physiological signals and recognition based on physiologic...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/16A61B5/0402G06K9/00G06K9/62G06N3/04G06N3/08
CPCA61B5/165G06N3/084A61B5/7203A61B5/7253A61B5/7267A61B5/318G06N3/048G06F2218/06G06F2218/12G06F18/24G06F18/214
Inventor 李鸿杨庞彦伟
Owner TIANJIN UNIV
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