Kubios HRV用户指南：蓝牙技术在心率变异性分析中的应用

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"Kubios_HRV用户指南，版本3.0.2，详细介绍了心率变异性(HRV)分析软件的使用方法，包括系统要求、安装、卸载、软件功能、输入数据格式、用户界面、结果保存以及偏好设置等。" 本文主要探讨的是蓝牙技术在Kubios HRV软件中的应用，尽管标题中并未直接提及蓝牙，但从提供的内容来看，该软件可能用于通过蓝牙设备收集和分析心率变异性数据。心率变异性是衡量心脏自主神经系统的功能状态，常用于评估健康、疲劳和训练状态。 Kubios HRV分为标准版和高级版，适用于专业运动员和健康研究人员。软件的主要特性包括： 1. 关于Kubios HRV: 这是一款专门用于分析心率变异性的心电图(ECG)处理软件，提供基础的HRV分析和更高级的功能。 2. 系统要求: 软件运行可能需要特定的操作系统、处理器、内存和硬盘空间，确保用户了解这些要求以顺利安装和运行。 3. 安装与卸载: 用户需要按照指南步骤进行软件安装，同时提供了卸载程序的路径和方法。 4. 用户界面: 包括RR间隔系列选项、数据浏览器、结果视图和菜单及工具栏按钮，设计直观，便于用户操作和理解分析结果。 5. 输入数据格式: 软件可以处理多种数据格式，可能包括通过蓝牙从心率监测器或其他生理信号记录设备传输的数据。 6. 结果保存: 提供ASCII文本文件、报告表、Matlab MAT文件（仅限高级版）和"SPSS友好"批处理文件（也仅限高级版）等多种保存方式，方便用户进一步分析或与其他软件兼容。 7. 设置偏好: 用户可以根据个人需求调整软件的默认设置，如分析参数、数据显示等。 8. 样例运行: 通过两个样例运行，指导用户如何进行静态分析和动态分析，帮助他们熟悉软件操作。在实际应用中，蓝牙技术在Kubios HRV中可能扮演了关键角色，允许无线连接心率带或其他可穿戴设备，实时或事后获取和分析心率数据。这种无线连接不仅提高了数据收集的便利性，还减少了数据传输过程中的错误可能性。因此，蓝牙技术在健康监测和运动表现评估领域具有广泛的实用性。

2.1. Heart beat period and QRS detection 10

rhythms are characteristic for HRV signals and have been related to, e.g., humoral factors such as the

thermoregulatory processes and renin-angiotensin system [5].

Heart rate variability (HRV) is a commonly used tool when trying to assess the functioning of cardiac

autonomic regulation. It has been used in multitude of studies, related to cardiovascular research and

different human wellbeing applications, as an indirect tool to evaluate the functioning and balance of the

autonomic nervous system (ANS) [51].

One of the main clinical scenarios where HRV has been found valuable include the risk stratification

of sudden cardiac death after acute myocardial infarction [51, 1, 22, 42]. In addition, decreased HRV

is generally accepted to provide an early warning sign of diabetic cardiovascular autonomic neuropathy

[51, 1], the most significant decrease in HRV being found within the first 5-10 years of diabetes [55, 48].

Besides these two main clinical scenarios, HRV has been studied with relation to several cardiovascular

diseases, renal failure, physical exercise, occupational and psychosocial stress, gender, age, drugs,

alcohol, smoking and sleep [54, 27, 51, 43, 2, 1].

The term HRV refers, in general, to changes in heart beat interval which is a reciprocal of the heart

rate. This is also the case here. The starting point for HRV analysis is the ECG recording from which

the HRV time series can be extracted. In the formulation of the HRV time series, a fundamental issue is

the determination of heart beat period.

2.1 Heart beat period and QRS detection

The aim in HRV analysis is to exam-

ine the sinus rhythm modulated by the

autonomic nervous system. Therefore,

one should technically detect the occur-

rence times of the SA-node action po-

tentials. This is, however, practically

impossible and, thus, the fiducial points

for the heart beat is usually determined

from the ECG recording. The near-

est observable activity in the ECG com-

pared to SA-node firing is the P-wave

resulting from atrial depolarization (see

Fig. 2.2) and, thus, the heart beat pe-

riod is generally defined as the time

difference between two successive P-

waves. The signal-to-noise ratio of the

P-wave is, however, clearly lower than

that of the strong QRS complex which

results primarily from ventricular depo-

larization. Therefore, the heart beat

period is commonly evaluated as the

time difference between the easily de-

tectable QRS complexes.

A typical QRS detector consists of a

preprocessing part followed by a deci-

sion rule. Several different QRS de-

tectors have been proposed within last

decades [52, 36, 37, 20, 14].

Figure 2.2: Electrophysiology of the heart (redrawn from

[28]). The different waveforms for each of the specialized

cells found in the heart are shown. The latency shown ap-

proximates that normally found in the healthy heart.

The accuracy of the R-wave occurrence time estimates is often required to be 1–2 ms and, thus, the

sampling frequency of the ECG should be at least 500–1000 Hz [51]. If the sampling frequency of the

ECG is less than 500 Hz, the errors in R-wave occurrence times can cause critical distortion to HRV

analysis results, especially to spectrum estimates [32]. The distortion of the spectrum is even bigger

if the overall variability in heart rate is small [40]. The estimation accuracy can however be improved

by interpolating the QRS complex e.g. by using a cubic spline interpolation [11] or some model based

approach [6]. Kubios HRV uses interpolation to improve the detection accuracy.

Kubios HRV (ver. 3.0.2)

2.2. Derivation of HRV time series 11

2.2 Derivation of HRV time series

After the QRS complex occurrence times have been estimated, the HRV time series can be derived. The

inter-beat intervals or RR intervals are obtained as differences between successive R-wave occurrence

times. That is, the n’th RR interval is obtained as the difference between the R-wave occurrence times

= t

− t

n−1

. In some context, normal-to-normal (NN) may also be used when referring to these

intervals indicating strictly intervals between successive QRS complexes resulting from SA-node depo-

larization [51]. In practice, the NN and RR intervals appear to be the same and, thus, the term RR is

preferred here.

The time series constructed from all available RR intervals is, clearly, not equidistantly sampled, but

has to be presented as a function of time, i.e. as values (t

,RR

). This fact has to be taken into account

before frequency-domain analysis. In general, three different approaches have been used to get around

this issue [51]. The simplest approach that have been adopted in, e.g., [4] is to assume equidistant

sampling and calculate the spectrum directly from the RR interval tachogram (RR intervals as a function

of beat number), see the left panel of Fig. 2.3. This assumption can, however, cause distortion into the

spectrum [30]. This distortion becomes substantial when the variability is large in comparison with the

mean level. Furthermore, the spectrum can not be considered to be a function of frequency but rather

of cycles per beat [12]. Another common approach, adopted in this software, is to use interpolation

methods for converting the non-equidistantly sampled RR interval time series (also called the interval

function) to equidistantly sampled [51], see the right panel of Fig. 2.3. One choice for the interpolation

method is the cubic spline interpolation [30]. After interpolation, regular spectrum estimation methods

can be applied. The third general approach is to apply methodology, which are designed for analysing

non-equidistantly sampled data. Such a method is for example the Lomb-Scargle periodogram, which

computes the periodogram spectrum estimate for non-equidistantly sampled data [13].

Derived RR intervals

RR interval tachogram

RR interval series (with two possible ways of interpolation)

12345

Figure 2.3: Derivation of two HRV signals from ECG: the interval tachogram (middle panel) and interpo-

lated RR interval series (bottom panel).

Kubios HRV (ver. 3.0.2)

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