研究原始心电信号的预处理方法。
The pretreatment method of the electrocardiosignal has been carried on.
新的生物传感器芯片可实现心电信号的远程采集。
本文主要介绍了心电信号的放大，滤波等。
This paper introduces ECG amplification, filtering and so on.
QRS波检测是心电信号自动分析的基础。
QRS detection is the foundation of automatic ECG analysis system.
用自适应干扰对消技术提取胎儿心电信号。
The FECS were extracted using adaptive interference cancellation technique.
目的寻找适合载人航天需要的心电信号去噪方法。
Objective To find a method for denoising of ECG signals during spaceflight.
进行了心电信号预处理和基本特征点检测的研究。
The ECG preprocessing and characteristic point detection are studied.
心电信号中蕴含着反映心脏功能状态的丰富信息。
ECG signals contain rich information that reflects the functional state of heart.
因此，该网络可提高心电信号的检测率和可靠性。
Therefore the network may improve the detection rate and network reliability.
结果成功采集到心电信号，并计算了心率的大小。
Results the ECG signals were acquired successfully and the heartbeat rate was calculated.
心电噪声抑制是心电信号分析、识别的基础和前提。
Noise reduction in ECG signal is the foundation and precondition of the ECG signal analysis and distinguishing.
心电信号的数据采集是体表标测系统中关键技术之一。
The acquisition of electrocardiological signal is one of the key technologies in body surface potential acquisition system.
最后，用线性组合形态滤波器对心电信号进行了处理。
Lastly, ECG signals are processed by the linear combinations of morphological operators filters.
本文首先采用RLS自适应算法对心电信号进行实时建模。
In this paper, we first build a real-time electrocardiograph signal model using the RLS self-adapting algorithm.
介绍了一种心室晚电位和常规12导联心电信号综合采集电路。
This paper introduces a data acquisition circuit that is able to acquire ventricular late potentials and standard 12-lead electrocardiogram signals.
当前，心电信号的处理仍是生物医学工程研究的重要对象之一。
Currently the processing of Electorcardiosignal is still an important research object in Biomedical Engineering.
针对心电信号及其干扰信号的特点，设计了心电信号检测硬件电路。
Considering the character of ECG signal and its disturbance, the ECG detecting hardware system is designed in this work.
便携式动态心电信号数据采集器是动态心电图分析系统的重要组成设备。
The portable dynamic electrocardiosignal data collector is the important part in the dynamic ECG analysis system.
心电自动分析系统的重要功能是对心电信号进行精确而可靠的分析和识别。
An important function of automatic analysis system is the analysis and distinguishing to ECG signal accurately and reliably.
目的：提出一种新的基于波形特征和SVM的心电信号自动分类实现方法。
Objective: This paper put forward for classifying cardiac arrhythmia signals based on ECG waveform features and support vector machine (SVM).
心电信号的US B传输包括终端usb设备和主机端两方面的协同工作。
From the aspect of ECG transmission based on USB, what we have to do is the collaborative work of USB device and host.
本论文主要内容包括心电信号预处理技术、特征点检测技术以及诊断技术的研究。
The following work has been performed in this research: The ECG preprocessing and characteristic point detection are studied.
实现了心电信号的采集、预处理、简单分析及从心电信号中提取呼吸信号等功能。
It can effective - ly realize the acquisition, pretreatment and basic analysis of ECG and simultaneously derive respiratory signal from ECG.
该装置以ARM为核心，包括低功耗的双路心电信号放大、滤波、抗基线漂移电路。
The system takes ARM as the core and employs signal amplifying, filtering, and anti-baseline drift circuits.
本文用数据分流fcg算法分析心电信号的功率谱图形的形状和参数并给出对照实例。
The paper presents a data flow distributing FCG algorithm, analyses the shape and parameters of ECG power spectrum, and gives a contrast example.
人体心电信号是心血管疾病医学诊断的重要依据，在基础及临床医学上都有重要意义。
Electrocardiosignal (ECG) is an important evidence base for the diagnoses of cardiovascular diseases as well as for the researches in basic and clinical medicine.
介绍了社区远程医疗监护系统的组成，无线心电信号采集器的硬件、软件及无线模块的设计。
We introduce the composition of the community telemedicine monitoring system, and the design of hardware, software and wireless module of wireless ECG signal sampler.
从硬件角度介绍三导心电信号的采集、放大、滤波、模数转换和USB传输的电路原理与构成。
Such aspects get involved in as the acquisition, amplification, filter and AD conversion as well as the principle and structure of USB transmission circuit.
方法：采用软、硬阈值折衷的阈值函数及自适应的阈值策略对心电信号中的不同噪声进行滤除。
Methods: Filtering different noises in ECG signals by adopting threshold function of soft-and-hard threshold tradeoff and self-adapting threshold policies.
用间隔直方图与轨迹图，对处于各种状态下的8条草犬心电信号的R - R间隔波进行分析。
The present paper provides an analysis of heart rate variability (HRV) under different conditions with the histogram and Poincare map on 8 healthy dogs.
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