Blood Pressure Model Based on Hybrid Feature Convolution Neural Network in Promoting Rehabilitation of Patients with Hypertensive Intracerebral Hemorrhage.

Journal: Computational and mathematical methods in medicine

PMID: 34917162

Abstract

OBJECTIVE: Accurate prediction of the rise of blood pressure is essential for the hypertensive intracerebral hemorrhage. This study uses the hybrid feature convolution neural network to establish the blood pressure model instead of the traditional method of pulse waves.

Authors

Zhixia Zheng

Department of Neurosurgery, Cangzhou Central Hospital, Hebei 061001, China.
Limei Bai

Department of Neurosurgery, Cangzhou Central Hospital, Hebei 061001, China.
Shaoquan Li

Department of Neurosurgery, Cangzhou Central Hospital, Hebei 061001, China.

Keywords

Adult Aged Algorithms Antihypertensive Agents Blood Pressure Computational Biology Female Humans Intracranial Hemorrhage, Hypertensive Machine Learning Male Middle Aged Models, Cardiovascular Neural Networks, Computer Pulse Wave Analysis Recurrence

External Resources

View on PubMed Access via DOI PubMed (34917162)

Blood Pressure Model Based on Hybrid Feature Convolution Neural Network in Promoting Rehabilitation of Patients with Hypertensive Intracerebral Hemorrhage.

Abstract

Authors

Keywords

External Resources

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