1. Introduction

Cardiovascular disease is the leading cause of death in worldwide. Considering the fact that a majority of such deaths due to cardiac arrest occur before the patient can get the needed medical care, the patient should be continuously monitored for real time detection of the events of cardiac arrest and timely treatment. In recent years ambulatory ECG monitoring become popular for long term cardiac monitoring because they are convenient to use and serve as an option to the hospitalization. With an AECG device the ECG signal can be recorded or monitored in ambulatory conditions where the patient can perform all the routine activity. However, the effectiveness of AECG can be significantly impaired by motion artifacts which contaminates the signal and that can lead to errors in estimation of cardiac parameters and trigger false alarms. In ambulatory monitoring skin stretching due to body or limb movement or physical activity (PA) is a main cause of motion artifacts in AECG signals. The motion artifact induced due PA has a spectral overlap with ECG signal in 1-10 Hz[2]. Many important cardiac features of ECG signal like P and T wave has significant energy content in this overlapping band of 1-10Hz. So it is very difficult to separate or eliminate motion artifact completely without affecting these cardiac features in AECG[2]. Therefore researchers have developed various techniques to removal of motion artifact from AECG signal. The AECG is superposition of two independent events: the cardiac signal (ECG) and the motion artifact induced due to the PA as shown in fig. 1 is an AECG signal. Since Electronics circuits in AECG devices may themselves add sensor noise in the acquired electrical signal from the electrodes, the AECG signal sample r(n) in digital form can be modeled as sample-wise addition of three different signal components at n^th instance of time.

(1)

Figure 1. Ambulatory ECG signal

Where q(n), s(n) and α(n) are samples of cardiac signal, motion artifact signal and sensor noise, respectively. The mathematical model in (1) used for representing the AECG has been proposed in[3-4].

There are various methods used for AECG signal analysis and to remove artifact, like wavelet based methods are extensively used in pre-processing, denoising and analysis of ECG signals, adaptive filter based methods used for ECG signal processing and to remove motion artifact, neural networks also used for detection of events and pattern classifications in AECG signal analysis, the AECG is analyzed beat-by-beat using a recursive principal component analysis (RPCA) based method in[2].

In this paper wavelet based method used for AECG signal denoising and identification of motion artifact for different types of physical movement using graphical interface tool of MATLAB software and wavelet functions. We have used low cost AECG recorder for data acquisition process at sampling frequency of 500 Hz. AECG signal acquired and stored in pc at Lead –II configuration. We have also taken a video of different physical activity and compared video based analysis with automated analysis for motion artifact accurate verification.

2. Ambulatory ECG Data Acquisition

There are many Wearable AECG recorders available in market. We used self made Wearable ECG recorder that is customized, light weight, compact, battery operated, low power dissipation, low cost device and a person can easily carry with him in a wearable clothes. It can record ECG in digital format at various sampling rate. It consist wireless transceiver, microcontroller on board with software for ECG recording as shown in fig. 2.

Figure 2. AECG monitor for ECG recording

AECG parameter can be displayed on Laptop or PC using LABVIEW software and also it can store in PC. Here AECG recorded for different type of physical activity. We have performed four type of body movement activity of healthy person and recorded AECG for (1) Right arm movement (2) ) Sitting up and down from standstill (3) Twisting of waist and (4) Walking for short duration with slow and faster pace of five healthy people and corresponding video recorded for verification of identified motion artifact.

3. Motion Artifact Identification Using Wavelets

Wavelet analysis widely used in today’s world in science and engineering field especially signal processing, biomedical area, image processing, communication area and other application area. Wavelet is a math function that is used to divide a given function into different component and it is reversible. A wavelet transform is the representation of a function by wavelets for representing functions that have discontinuities and sharp peaks, and for accurately deconstructing and reconstructing finite, non-periodic and/or non-stationary signals like ECG signal. There are wavelet based technique can be used for ECG signal analysis based on Discrete Wavelet Transform (DWT), Multiresolution DWT, Fast Wavelet Transform (FWT), Lifting Wavelets, Multiwavelet Transforms, Stationary Wavelet Transform (SWT), Wavelet Packet Decomposition (WPD), Fractional Wavelet Transform (Fractional WT) and Shrinkage and Threshold Methods[5].

We use the wavelet toolbox of matlab software and wavelet functions to analyze the AECG signal with motion artifact. Wavelet Toolbox provides functions and an application for developing wavelet-based algorithms for the analysis, synthesis, denoising, and compression of signals and images in 1-D and 2-D[7]. We performed a multilevel, a level 8 decomposition of the AECG signal with motion artifact using the biorthogonal wavelet. There are many wavelet families like Daubachies, Coiflets, Symlet, Biorthogonal, Meyer, Mexican Hat, Morlet etc available. In this paper biorthogonal family wavelets is used, biorthogonal wavelet transform has frequently been used in numerous image and signal processing applications, because it makes possible multiresolution analysis and does not produce redundant information[6]. The graphical interface tools feature a de-noising option with a predefined thresholding strategy. This makes it very easy to remove noise from a signal. Level 8 a Multilevel 1-D decomposition which contain approximation and detail component of low pass filter and high pass filter respectively, that is used to reconstruct the signal. Reconstructed signal have following approximation and detail component

(2)

While motion artifact signal identified by from

(3)

Here X1 is reconstructed signal while M1 is identified motion artifact. As shown in fig. 3 identified motion artifact superimposed on AECG signal, for slow hand movement. Here fig. 3(a) contain ambulatory ECG signal with motion artifact due to continuously right hand movement at slower pace while fig. 3 (b) indicates identified motion artifact and its peaks while fig. 3(c) indicates AECG signal with separated motion artifact. In fig. 4 histogram of fast hand movement shown, which also shows slower and faster movement have different motion artifact peak interval density. We have identified motion artifact in most common physical movement as per shown in table 1 and corresponding motion artifact is identified for all. Table 1 and 2 shows parameters of motion artifact for hand movement of five people, they are mean value of peaks of motion artifact, peak interval, percentage of standard deviation of peak interval , valley and valley interval of motion artifact in persons 1,2,3,4 and 5 of hand movements at faster pace while table 2 shows for slow physical hand movement. Here number of peaks and valley in motion artifact is increases in fast movement compared to slow physical movement while peak interval and valley decrease of person 1,2,3,4, and 5 as per table 3 and 4. Table 5 shows comparison of video based physical movement analysis with automated analysis for verification of motion artifact identification. We verified also for slow and fast physical movement for four type of activity of one person. We find that peak interval of motion artifact for fast and slow physical movement of one person with all physical activity by automated analysis and from video analysis that is nearly same that justify that accurately motion artifact is identified. Table 6 shows physical movement interval by automated and video based analysis that also indicate slow movement have higher interval cycle/s than fast movement interval cycle/s for all four physical activity and it also indicate hand and walking movement lower interval cycle/s than waist and seat to stand interval cycle/s.

Figure 3. Identification of motion artifact for right hand up down slow movement

Table 1. Fast hand movement

Table 2. Slow hand movement

Table 3. Motion artifact interval parameter mean value of five persons

Table 4. Motion artifact interval parameter mean value of five persons

Table 5. Comparison of automated analysis with video based analysis for slow hand movement for 60 second

Table 6. Comparison of automated analysis with video based analysis for all physical movement

Figure 4. Histogram of motion artifact peak interval of right hand slow movement

4. Conclusions

In this paper we have recorded Ambulatory ECG signal of five healthy people with four type of physical activity like right hand movement, sitting to stand movement, waist and walking movement with faster and slower pace. We denoised AECG signal and identified motion artifact of different four physical activity using wavelet tool box of matlab software. We have also recorded videos of different physical activity to analyze and verify motion artifact in AECG signal is accurately identified for all physical activity. So the AECG signal of the patient should be continuously monitored for real time detection of the events of cardiac arrest and timely treatment of a person having cardiac abnormalities with motion artifact signal removed by wavelet methods can be used to diagnosis cardiac disorder by expert.

ACKNOLEDGEMENTS

The authors would like to thank Charutar Vidyamandal (CVM), Vallabh Vidyanagar, India and Sophisticated Instrumentation Centre for Advanced Research and Testing (SICART), Vallabh Vidyanagar, India, Birla Vishvakarma Mahavidylaya Engineering College, Vallabh Vidyanagar, India for their support, guidance and research facility provided during my work.