ARTIFICIAL INTELLIGENCE SELF-LEARNING-BASED STATIC ELECTROCARDIOGRAPHY ANALYSIS METHOD AND APPARATUS

ARTIFICIAL INTELLIGENCE SELF-LEARNING-BASED STATIC ELECTROCARDIOGRAPHY ANALYSIS METHOD AND APPARATUS

An artificial intelligence self-learning-based static electrocardiography analysis method and apparatus, said method comprising data preprocessing, heartbeat detection, heartbeat classification based on a depth learning method, heartbeat verification, heartbeat waveform feature detection, measuremen...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser: LV, Youchao, CAO, Jun, ZHAO, Pengfei, LIU, Chang, ZANG, Kaifeng, WANG, Erbin
Format: Patent
Sprache:eng ; fre ; ger
Schlagworte:
DIAGNOSIS
HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA
HUMAN NECESSITIES
HYGIENE
IDENTIFICATION
INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS
MEDICAL OR VETERINARY SCIENCE
PHYSICS
SURGERY
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Beschreibung
Zusammenfassung:An artificial intelligence self-learning-based static electrocardiography analysis method and apparatus, said method comprising data preprocessing, heartbeat detection, heartbeat classification based on a depth learning method, heartbeat verification, heartbeat waveform feature detection, measurement and analysis of electrocardiography events, and finally automatic output of reporting data, realizing an automated static electrocardiograph analysis method having a complete and rapid flow. The static electrocardiography analysis method can also record modification information of an automatic analysis result, collect modified data, and feed same back to the depth learning model to continue training, thereby continuously making improvements and improving the accuracy of the automatic analysis method.