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Zujin Luo

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2 papers

1 author row

JBHI Journal 2025 Journal Article

Design of a Multi-Parameter Fusion Sensor and System for Respiratory Monitoring of Mechanically Ventilated Patients in the ICU

Shuai Ren
Xiaohan Wang
Maolin Cai
Yan Shi
Tao Wang
Zujin Luo

In order to achieve precise respiratory therapy for mechanically ventilated patients, real-time monitoring of the state parameters of inhaled and exhaled gases is required. These parameters are primarily measured by ventilators, with limitations such as insufficient monitoring parameters, circuit leaks, and constraints imposed by distance and obstacles. This paper designs a low-power wireless sensor for multi-parameter monitoring near the patient, which can be used continuously for approximately 60 days. Based on this sensor, an intelligent respiratory monitoring system with a distributed architecture is proposed to achieve intelligent patient-ventilator asynchrony (PVA) perception. Experimental results show that the system can stably and accurately collect and transmit data, with measurement errors for pressure, flow, temperature, humidity, and CO $_{2}$ concentration being $\pm$ 1. 3%, $\pm$ 2. 1%, $\pm$ 0. 6 $^\circ$, $\pm$ 1% RH, $\pm$ 0. 3 mmHg respectively. The proposed sensor and system have the potential to enhance the efficiency and intelligence of medical care significantly.

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JBHI Journal 2025 Journal Article

Improving Patient-Ventilator Synchrony During Pressure Support Ventilation Based on Reinforcement Learning Algorithm

Liming Hao
Xiaohan Wang
Shuai Ren
Yan Shi
Maolin Cai
Tao Wang
Zujin Luo

Mechanical ventilation is an effective treatment for critically ill patients and those with pulmonary diseases. However, patient-ventilator asynchrony (PVA) remains a significant challenge, potentially leading to high mortality. Improving patient-ventilator synchrony poses a complex decision-making problem in clinical practice. Traditional methods rely heavily on clinicians' experience, often resulting in inefficiencies, delayed ventilator adjustments, and resource shortages. This paper proposes a novel approach using a deep reinforcement learning (RL) algorithm based on deep Q-learning (DQN) to enhance patient-ventilator synchrony during pressure support ventilation. The action space and reward function are established from clinical experience, and a pneumatic model of the mechanical ventilation system is constructed to simulate various patient conditions and types of PVAs. Clinical data are used to evaluate the RL algorithm qualitatively and quantitatively. The RL-optimized ventilation strategy reduces the proportion of breaths containing PVAs from 37. 52% to 7. 08%, demonstrating its effectiveness in assisting clinical decision-making, improving synchrony, and enabling intelligent ventilator control, bedside monitoring, and automatic weaning.

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