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1.西安交通大学 未来技术学院,陕西 西安 710115
2.西安交通大学 基础医学院生物物理学系医学工程研究所,陕西 西安 710061
[ "吴春生,博士,西安交通大学基础医学院未来技术学院教授,博士生导师,《西北大学学报(自然科学版)》青年编委。担任中国生物医学工程学会生物医学测量分会委员、陕西省医工融合产业技术创新战略联盟专家委员会委员。主要从事生物医学传感与检测技术研究,发表第一或通讯作者SCI论文80余篇。主编/副主编出版学术专著/教材7部。获国家发明专利授权8项、软件著作权7项。曾获教育部自然科学奖二等奖、陕西省自然科学二等奖、陕西高等学校科学技术研究优秀成果一等奖等多项奖励。主持国家重点研发计划课题、国家自然科学基金委与金砖国家科技创新框架计划合作研究项目等10余项科研项目。" ]
赵云迪,男,硕士研究生,从事生物医学电子技术研究,zhaoyd2001@stu.xjtu.edu.cn。
收稿:2025-10-26,
纸质出版:2025-12-25
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吴春生, 赵云迪, 袁嘉娴, 等. 面向低功耗的心脏起搏器信号感知自适应算法及集成电路实现[J]. 西北大学学报(自然科学版), 2025,55(6):1220-1243.
WU Chunsheng, ZHAO Yundi, YUAN Jiaxian, et al. Adaptive algorithm and integrated circuit implementation for signal sensing of low-power pacemakers[J]. Journal of Northwest University (Natural Science Edition), 2025, 55(6): 1220-1243.
吴春生, 赵云迪, 袁嘉娴, 等. 面向低功耗的心脏起搏器信号感知自适应算法及集成电路实现[J]. 西北大学学报(自然科学版), 2025,55(6):1220-1243. DOI: 10.16152/j.cnki.xdxbzr.2025-06-003.
WU Chunsheng, ZHAO Yundi, YUAN Jiaxian, et al. Adaptive algorithm and integrated circuit implementation for signal sensing of low-power pacemakers[J]. Journal of Northwest University (Natural Science Edition), 2025, 55(6): 1220-1243. DOI: 10.16152/j.cnki.xdxbzr.2025-06-003.
心脏起搏器是现代医学治疗心律失常的关键植入式设备,其感知功能的精准性与可靠性直接决定了治疗的成败。为解决传统固定参数感知范式难以应对心内电信号复杂动态特性、易导致感知不良或过感知等临床风险的问题,对自适应感知技术的演进进行系统综述。深入剖析从经典自适应算法到智能自适应算法的架构、原理与交互逻辑,详细论述支撑这些算法实现的专用集成电路关键技术,包括高精度模拟前端设计、数模混合信号协同及超低功耗优化,以此构建了一套涵盖从模型化设计、硬件在环测试到符合医疗器械规范的临床前验证的全流程系统实现与性能评估体系。系统梳理了自适应感知技术的完整技术链条,明确了算法与硬件协同设计的关键。该体系能够有效提升感知系统在复杂生理与噪声环境下的安全性、特异性与个体适应性,并为技术的临床应用提供严谨的验证方法。自适应感知技术是突破传统起搏器性能瓶颈的核心方向,未来技术将向多模态传感融合、个性化自适应及预测性维护等方向发展。
Cardiac pacemakers are key implantable devices in modern medicine for treating arrhythmias. The accuracy and reliability of their sensing function directly determine the success or failure of treatment. To address the issue that traditional fixed-parameter sensing paradigms struggle to cope with the complex dynamic characteristics of intracardiac electrical signals
leading to clinical risks such as under-sensing or oversensing
a systematic review of the evolution of adaptive sensing technology is conducted. The architecture
principles
and interaction logic of adaptive sensing algorithms
from classic to intelligent
are deeply analyzed. Key technologies of application-specific integrated circuits (ASICs) that support the implementation of these algorithms are elaborated
including high-precision analog front-end design
analog-digital mixed signal coordination
and ultra-low power optimization. A comprehensive system implementation and performance evaluation framework is established
covering model-based design
hardware-in-the-loop testing
and preclinical validation in compliance with medical device regulations. A complete technical chain of adaptive sensing technology is systematically reviewed
and the key points of algorithm and hardware co-design are clarified. This system can effectively enhance the safety
specificity
and individual adaptability of the sensing system in complex physiological and noisy environments
and provide rigorous validation methods for clinical application. Adaptive sensing technology is a core direction to break through the performance bottlenecks of traditional pacemakers. Future technologies will develop towards multi-modal sensor fusion
personalized adaptation
and predictive maintenance.
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