LabVIEW-based ECG signal acquisition and analysis and design program (1).

Introduction

Bio-medical electrical signals, such as ECG, blood pressure signal, EEG, etc, are all characteristics of the pathological features to ECG, for example, typically to ECG to record heart resulting shengwudianliu, clinicians can use electrocardiograms to patients to assess cardiac conditions, and make further diagnosis.

But for some home or medical instrument manufacturer, you need to develop a specific signal processing algorithms and deployed to embedded processors, the complete medical electrical characteristics of extraction. Usually the entire ECG monitoring product development process, from ECG data acquisition, ECG analysis, human-computer display, file storage, and several parts, through NI provide graphical system design platform, you can overwrite the data acquisition, signal read, ECG analysis and report generation, and a series of product development processes, complete set of system development, increase development efficiency. But in the entire development process, signal analysis section is often focused on, is the core technology of software manufacturers. This article will focus on the ECG data acquisition and analysis discussion describes how LabVIEW efficient implementation of the ECG signal acquisition and analysis algorithm development.

Figure 1 a typical single-cycle ECG waveform

ECG data acquisition

Generally speaking, the ECG signal is passed on several electrodes (leads) perception shengwudianliu, and through data acquisition device will lead generation analog signals into digital signals for computer analysis.

Leads generated by analog signals are often relatively weak, amplitude in mV, need dynamic signal acquisition devices acquisition or through the front after acquisition preamplifier. Whether it is independent of the ECG leads or integrated medical-ECG devices, to pass through the NI data acquisition devices.

Through 30 years of development, the United States national instrument (NI) in test and measurement fields laid the leadership from a portable USB device to high-precision synchronous sampling equipment, PXIe may realize from 8-bit to 24-bit resolution, and the 48kHz to 2GHz sampling rate.

At the same time NI device will gain error, offset error, do not determine noise and other error value after comprehensive consideration, offers absolute precision value, to ensure the accuracy of the final measurement. Generally speaking, the ECG signal frequency in hundreds of cycles per second or so, you can pass around 1k to 5k sampling rate for sampling, in addition, the accuracy difference depending on the application, you can choose 14 ~ 16bit sampling precision, basically any platform for NI data acquisition devices are able to meet the needs of sampling ECG. According to the application, select the appropriate devices, such as in portable devices, select USB data acquisition, in application of telemedicine in select Wireless acquisition, etc.

Figure 2 from the NI USB-to-wireless data collection solution

Regardless of NI hardware platform, to pass through the same kind of programming platforms — NI LabVIEW graphical programming software for development.

Since its inception in 1986, the LabVIEW graphical development platform has been working to simplify the programming complexity, in all involved data acquisition and control fields, LabVIEW graphical programming has become the standard development tools. For medical electronic equipment development team, the LabVIEW provides hardware I/O introduced algorithm design, seamlessly combining from data acquisition, analysis, data storage, and human-computer interaction, and so on all aspects of the process, at the same time different NI hardware available through code reuse, publish to different commercial, built the embedded platform, simplifying the complexity of the prototype system.

Through the NI LabVIEW and ECG signal acquisition equipment that can quickly be acquisition and display.

Figure 1 shows a typical ECG waveform cycle. Of course, the process, the ECG is the introduction of noise and artifacts of the pollution, noise and artifacts in our interest to the band, and ECG itself have similar characteristics. In order to have noise of ECG to extract useful information, we require the original ECG signal is processed.

From a functional point of view, the ECG's processing can be roughly divided into two phases: the preconditioning and feature extraction (as shown in Figure 3).

The preprocessing phase the elimination and reduction of the original ECG signal noise, and feature extraction phase extraction from ECG diagnostic information.

Figure 3 typical ECG signal processing flowchart

Through LabVIEW signal processing in function, users can easily create for the two phases of signal processing applications, including the Elimination of baseline drift, purge, noise, QRS complex wave detection, fetal heart rate testing.

Next, you will focus on using LabVIEW for typical ECG signal processing methods.

ECG signal preprocessing

ECG signal preprocessing can help users remove the pollution from the ECG.

Broadly speaking, the ECG signal pollution can be divided into the following categories:

• Power cable interference

• Electrode separation or contact noise

• Patient electrode during the move to introduce artifacts

• Electromyography (EMG) noise

• Baseline drift

These noise interference, power cord and baseline drift is the most important, can strongly influence the ECG analysis.

In addition to these two types of noise, and other noise as possible is the broadband and complex stochastic processes, also makes the ECG signal distortion. Power line noise is at 60 Hz (50 Hz) as the center of narrow-band noise, bandwidth is less than 1Hz. Typically, the ECG signal acquisition hardware or software notch filter to remove the power cordInterference. However, the baseline drift and other broadband noise through the hardware it is hard to suppress. While the software design is becoming more powerful and practical ECG signal processing methods. Users can use the following methods to eliminate baseline drift and other broadband noise.

Elimination of baseline drift

Baseline drift produces usually comes from breathing frequency in Hz 0.15 to 0.3, either by using a high pass digital filter.

Users can also use the wavelet transform by eliminating the ECG's tendency to eliminate the baseline drift.

1. digital filter method

Through LabVIEW graphical and interactive way to efficiently design and realization of finite impulse response (FIR) or infinite impulse response (IIR) filter.

For example, a user can use the Classical Filter Design Express VI design FIR high-pass filter Kaiser window remove baseline drift. Figure 4 shows the use of high-pass filter to eliminate the instance of the baseline drift.

Figure 4 design and using the high pass filter to eliminate baseline drift