Heart rate variability characteristic analysis method based on deep learning
The invention discloses a heart rate variability characteristic analysis method based on deep learning, and relates to the field of computers. Information contained in heart rate variability can be effectively extracted. The method comprises the following steps: acquiring electrocardiogram data thro...
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| creator | HE JIAN YANG QINWEI JIANG SHENGSHENG |
| description | The invention discloses a heart rate variability characteristic analysis method based on deep learning, and relates to the field of computers. Information contained in heart rate variability can be effectively extracted. The method comprises the following steps: acquiring electrocardiogram data through a sensor, and transmitting the electrocardiogram data to an upper computer; preprocessing the ECG signal by applying interpolation and filtering; intercepting the received ECG data by adopting a sliding window, and performing real-time updating and Fourier transform to obtain a frequency spectrum feature map; and analyzing the spectrogram and extracting useful information by using a deep convolutional neural network, including frequency domain feature extraction based on a Resnet18 residual learning method and time-frequency domain feature fusion based on an attention mechanism, and classifying and outputting feature information by using a multi-layer perceptron.
一种基于深度学习的心率变异性特征分析方法涉及计算机领域，能够有效提取心率变异性中包含的信息。本发 |
| format | Patent |
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一种基于深度学习的心率变异性特征分析方法涉及计算机领域，能够有效提取心率变异性中包含的信息。本发</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; DIAGNOSIS ; ELECTRIC DIGITAL DATA PROCESSING ; HUMAN NECESSITIES ; HYGIENE ; IDENTIFICATION ; MEDICAL OR VETERINARY SCIENCE ; PHYSICS ; SURGERY</subject><creationdate>2023</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230905&DB=EPODOC&CC=CN&NR=116702056A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25563,76418</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230905&DB=EPODOC&CC=CN&NR=116702056A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>HE JIAN</creatorcontrib><creatorcontrib>YANG QINWEI</creatorcontrib><creatorcontrib>JIANG SHENGSHENG</creatorcontrib><title>Heart rate variability characteristic analysis method based on deep learning</title><description>The invention discloses a heart rate variability characteristic analysis method based on deep learning, and relates to the field of computers. Information contained in heart rate variability can be effectively extracted. The method comprises the following steps: acquiring electrocardiogram data through a sensor, and transmitting the electrocardiogram data to an upper computer; preprocessing the ECG signal by applying interpolation and filtering; intercepting the received ECG data by adopting a sliding window, and performing real-time updating and Fourier transform to obtain a frequency spectrum feature map; and analyzing the spectrogram and extracting useful information by using a deep convolutional neural network, including frequency domain feature extraction based on a Resnet18 residual learning method and time-frequency domain feature fusion based on an attention mechanism, and classifying and outputting feature information by using a multi-layer perceptron.
一种基于深度学习的心率变异性特征分析方法涉及计算机领域，能够有效提取心率变异性中包含的信息。本发</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>DIAGNOSIS</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>HUMAN NECESSITIES</subject><subject>HYGIENE</subject><subject>IDENTIFICATION</subject><subject>MEDICAL OR VETERINARY SCIENCE</subject><subject>PHYSICS</subject><subject>SURGERY</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNzLEKwjAURuEsDqK-w_UBhFaxzlKUDuLkXm6T3_ZCTEJyEfr2OvgATmf5OEtz68BZKbOC3pyFB_GiM9mJM1tFlqJiiQP7uUihF3SKjgYucBQDOSCR_z6ChHFtFk_2BZtfV2Z7vTzabocUe5TEFgHat_e6bk7Vvjo258M_5gO_WjYD</recordid><startdate>20230905</startdate><enddate>20230905</enddate><creator>HE JIAN</creator><creator>YANG QINWEI</creator><creator>JIANG SHENGSHENG</creator><scope>EVB</scope></search><sort><creationdate>20230905</creationdate><title>Heart rate variability characteristic analysis method based on deep learning</title><author>HE JIAN ; YANG QINWEI ; JIANG SHENGSHENG</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN116702056A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2023</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>DIAGNOSIS</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>HUMAN NECESSITIES</topic><topic>HYGIENE</topic><topic>IDENTIFICATION</topic><topic>MEDICAL OR VETERINARY SCIENCE</topic><topic>PHYSICS</topic><topic>SURGERY</topic><toplevel>online_resources</toplevel><creatorcontrib>HE JIAN</creatorcontrib><creatorcontrib>YANG QINWEI</creatorcontrib><creatorcontrib>JIANG SHENGSHENG</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>HE JIAN</au><au>YANG QINWEI</au><au>JIANG SHENGSHENG</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Heart rate variability characteristic analysis method based on deep learning</title><date>2023-09-05</date><risdate>2023</risdate><abstract>The invention discloses a heart rate variability characteristic analysis method based on deep learning, and relates to the field of computers. Information contained in heart rate variability can be effectively extracted. The method comprises the following steps: acquiring electrocardiogram data through a sensor, and transmitting the electrocardiogram data to an upper computer; preprocessing the ECG signal by applying interpolation and filtering; intercepting the received ECG data by adopting a sliding window, and performing real-time updating and Fourier transform to obtain a frequency spectrum feature map; and analyzing the spectrogram and extracting useful information by using a deep convolutional neural network, including frequency domain feature extraction based on a Resnet18 residual learning method and time-frequency domain feature fusion based on an attention mechanism, and classifying and outputting feature information by using a multi-layer perceptron.
一种基于深度学习的心率变异性特征分析方法涉及计算机领域，能够有效提取心率变异性中包含的信息。本发</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING DIAGNOSIS ELECTRIC DIGITAL DATA PROCESSING HUMAN NECESSITIES HYGIENE IDENTIFICATION MEDICAL OR VETERINARY SCIENCE PHYSICS SURGERY |
| title | Heart rate variability characteristic analysis method based on deep learning |
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