ECG noise reduction

ECG noise reduction using multiplier free FIR digital filters

Abstract

The electrocardiogram (ECG) is the recording of the heart’s electrical potential versus time. Three types of predominant noise that commonly contaminate the signal are baseline wander (BW) noise, electromyographic (EMG) interference, and 50 or 60 Hz power line interference. Among them power line interference is a significant source of noise. Cables carrying ECG signals from the patients to the monitoring equipment are susceptible to electromagnetic interference (EMI) from the 50/60Hz power line noise. Many methods were proposed in the past for the removal of power line interference in the ECG. They can be categorized into non-adaptive and adaptive filtering. The non-adaptive filtering approach employs a sharp notch filter and is easy to implement at low cost. However its performance depends on the frequency stability of the power line. Adaptive filtering, on the other hand, is able to effectively remove time-varying power line frequency, but requires considerable computational power which is not suitable for portable battery powered ECG devices. To lower power consumption we opt for non-adaptive filtering based upon a FIR filter with a linear phase property to obtain the noise reduction without introducing the phase distortion. The main advantage of using an FIR filter is that it minimizes waveform distortion. In our design , we use the notch filter with a pole/zero canceling method, the comb notch filter with a pole/zero canceling method and the equiripple notch filter with the usage of Parks-McClellan algorithm. By the comparison of the transition bandwidth, stop band attenuation (A_stop) of these filters, we can learn the frequency response of these filters. Furthermore we use the mean square error (M.S.E.) to estimate the embedded 50/60 Hz noise. We compute the M.S.E. with respect to the order, N, these filters and plot a response between M.S.E. and N. The results show that the proposed filter effectively removes the power line noise from the ECG signal.

Keywords - ECG Signals. Noise Reduction. Notch Filter

1. Introduction

Electrocardiogram (ECG or EKG) is a diagnostic tool that measures and records the electrical activity of the heart in exquisite detail. Interpretation of these details allows diagnosis of a wide range of heart conditions. These conditions can vary from minor to life threatening.

An ECG is generated by a nerve impulse stimulus to a heart. The current is diffused around the surface of the body surface. The current at the body surface will build on the voltage drop, which is a couple of µV to mV with an impulse variation. Usually, this is very small amplitude of impulse, which requires a couple of thousand times of amplification.

A typical ECG tracing of a normal heartbeat (or cardiac cycle) consists of a P wave, a QRS complex and a T wave. A small U wave is normally visible in 50 to 75% of ECGs. The baseline voltage of the electrocardiogram is known as the isoelectric line. Typically the isoelectric line is measured as the portion of the tracing following the T wave and preceding the next P wave. The electrical activity of the heart can be recorded at the surface of the body using an electrocardiogram. Therefore the electro-cardio-gram (EKG) is simply a voltmeter that uses up to 12 different leads (electrodes) placed on designated areas of the body. Figure 1 shows the typical ECG trace. The electrical activity of the heart is generally sensed by monitoring electrodes placed on the skin surface. The electrical signal is very small (normally 0.0001 to 0.003 volt). These signals are within the frequency range of 0.05 to 100 Hertz (Hz.) or cycles per second.

Unfortunately, other artifactual signals of similar frequency and often larger amplitude reach the skin surface and mix with the ECG signals. Artifactual signals arise from several internal and external sources. Means Electro-cardio-graphic signals (ECG) may be corrupted by various kinds of noise. 

Typical examples are:

1. Power line interference

2. Electrode contact noise.

3. Motion artifacts.

4. Muscle contraction.

5. Base line drift.

6. Instrumentation noise generated by electronic devices.

7. Electrosurgical noise.