Gopu G., Neelaveni R., Porkumaran K. Analysis of Electrogastrogram for the Investigation of Digestive System Disorders using wavelet Transforms // International Journal of Engineering and Technology. 2009. Volume 2. Number 4. December. 43–49.

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Авторы: Gopu G. / Neelaveni R. / PorkumaranK.


Analysis of Electrogastrogram for the Investigation of Digestive System Disorders using wavelet Transforms

G.Gopu

Dept.of EEE, PSG College of Technology, Coimbatore-641004, Tamilnadu, INDIA gopugovindasamy@gmail.com

R.Neelaveni

Dept.of EEE, PSG College of Technology, Coimbatore-641004, Tamilnadu, INDIA

K.Porkumaran

Dept.of BME,Sri Ramakrishna Engineering College, Coimbatore-641022, Tamilnadu, INDIA porkumaran@gmail.com


Abstract

In human being, the Digestive system is a one of the important system. Universally many people have the digestive system problems due to junk food, adulteration, etc,. A preliminary investigation method proposed is called Electrogastrography. Electrogastrogram [EGG] is a recording of a electrical activity due to the motility of the stomach using surface electrodes cutaneously. This procedure is adopted more than one hundred and eighty subjects which include normal subjects and subjects with Gastric disorders. In the acquisition of EGG, the analog signal’s numeric values are recorded as .bio file format using the data scope. Wavelet transform is applied to the EGG signal to find the deviation in frequency and power from the power spectral estimate plot. This paper performs analysis that includes the Principle Component Analysis [PCA], denoising of the signal with wavelets namely Daubechies [db4], haar and power spectrum estimation. The signal is reconstructed with a data obtained through data scope. This signal undergoes principle component analysis and then the interference and noise in the EGG signal are removed to obtain de-noised signal. The denoised EGG signal is plotted for power spectral density estimation with Welch power spectral Density Estimation. The power variation is found and frequency is detected as 0.06 to 0.075Hz for ulcer patient, 0.02 to 0.04 Hz for dyspepsia patient and 0.046 to 0.058 Hz for normal.


1. Introduction

Gastric Electrical Activity [GEA] due to the stomach motility is measured cutaneously using the surface electrodes (Ag/Agcl) is called Electrogastrography [1, 2]. Before 75years W.C Alvarez is published about the EGG but the first recording of gastro electrical activity is performed in dogs by C.F. Code and J.A.Marleh in the year 1974 [3] and in human this procedure is recorded in 1978 [4] by R.L.Telander et al. But the progress in this field has been very slow, especially compared with other cutaneous electrophysiological measurements because of its difficulty in data acquisition, lack of understanding, etc., Due to the advancement in quantitative analysis of the EGG, more and more physicians and biomedical researchers interested in this field. The abnormality arises due to recurrent nausea, vomiting, Dyspepsia, Stomach ulcer, Cyclic vomiting syndrome, Tachygastria, Bradygastria, etc which results in improper digestion of food by the stomach. If the EGG is abnormal, it confirms that the problem probably is with the stomach's muscles or the nerves that control the muscles. This paper mainly deals with the denoising of EGG using wavelets Daubechies wavelet Transform. The denoising of the EGG by db wavelet is found good compare to Haar and other wavelets. The EGG for a subjects with Bradygastria and with Tachygastria is obtained by the Continuous Wavelet Transform [CWT].The denoised EGG signal’s frequency and power in db is obtained with Welch power spectral Density Estimation. The EGG can be considered as an experimental procedure since its exact role in the diagnosis of digestive disorders of the stomach has not been defined yet.

2. Electrogastrogram

An EGG is similar to an electrocardiogram of the heart. It is a recording of the electrical signals that travel through the muscles of the stomach and control the muscle’s contraction [14,15,16]. EGG used when there is a suspicion that the muscles of the stomach or the nerves controlling the muscles are not working normally. EGG done by placing the electrode cutaneously over the stomach and the electrical signals coming from the stomach’s muscles are sensed by the electrode and recorded on a computer for analysis by lying patient quietly. In normal individuals the EGG is a regular electrical rhythm generated by the muscles of the stomach and the power (voltage) of the electrical current increases after the meal. In patients with abnormalities of the muscles or nerves of the stomach, the rhythm often is irregular or there is no post-meal increase in electric power. EGG will not have any side effects and it is painless study.

3. Electrodes Positioning

The electrical signals are generally produced in the mid-corpus of the stomach where the electrical activity takes place.

Fig. 1. Electrodes Positioning for Recording EGG

Fig. 1. Electrodes Positioning for Recording EGG

The positioning of the Ag/Agcl electrodes for tapping of these signals is as follows as shown Fig.1. Two electrodes A and B are placed in the fundus and the mid corpus of the stomach. The third electrode C is placed as ground at the end of the stomach region for patient safety [5, 6, 7].

4. Role of Wavelet Analysis

Wavelet analysis, analyze the signal with short duration finite energy function. They transform the signal into another representation of a signal with more useful form. This Transformation is called wavelet Transform[17]. In this paper, the wavelet is mainly used for denoising the EGG signal acquired from the different subject. The Daubechies wavelet is choosed for denoising because its performance is good when compare to the Haar wavelet. The Haar wavelet algorithm has the advantage of being simple to compute and easier to understand. The Daubechies D4 algorithm has a slightly higher computational overhead and is conceptually more complex. As the matrix forms of the Daubechies D4 algorithm above show, there is overlap between iterations in the Daubechies D4 transform step. This overlap allows the Daubechies D4 algorithm to pick up detail that is missed by the Haar wavelet algorithm. The CWT is developed as an alternative approach to the Fourier transform [FT] to reduce the difficulty in extracting information from the signals. Here with CWT, the gastric electrical dysrhythmia such as tachygastria(fast frequency waves from 3.75-10.0cpm) and bradygastria( slow frequency waves from 1.0-2.5 cpm) is detected from EGG.

5. Materials and Method

EGG data is recorded with the electrogastrography setup among the patients suffering from stomach disorders such as Nausea, Dyspepsia, Vomiting, ulcer etc, at biomedical department of our institution and gastroenterology department of a reputed hospital. More than hundred numbers, includes patients, normal subjects in both male and female category of different age groups are participated in this recording [8,10]. The electrodes are positioned as described in the section 3 of this paper to record the data after lightly abrade the skin with abrasive pads and the gel is applied to the electrode contact area. The output of the electrode is given to the instrumentation amplifier of the recording setup. The amplified signal undergoes preprocessing and then the data are acquired via data scope and the same is stored as data base in the computer for further analysis. In this paper, EGG data are reconstructed as an original EGG signal and the same is included in the PCA as a preprocessing step to get actual EGG signal. Denoising of the half an hour recorded EGG signal is performed with wavelet denoising technique to find the frequency and power spectrum. The Fig.2 represents the denoising of a EGG signal with Haar wavelet and the Fig.3 represents the denoising of a EGG signal with db4 wavelet. As a finding the denoising by db4 is better than Haar wavelet.

Fig. 2. Denoising of EGG signal with Haar wavelet transforms

Fig. 2. Denoising of EGG signal with Haar wavelet transforms

Fig. 3. Denoising of EGG signal with db4 wavelet transforms

Fig. 3. Denoising of EGG signal with db4 wavelet transforms

6. Results and Discussion

The denoising of the EGG signal using Wavelet transform increases the reduction of noise signal which is under or above EGG frequency limit. From the database the data analyzed for detecting frequency and power of the EGG signal of the normal subjects and the patients with digestive system disorders after wavelet denoising using Welch power spectral Density Estimation as shown in Fig.4, which shows the range of frequencies and power for the dyspepsia patient with frequency 0.02344 Hz and power 48.42 db. Like this the frequency and power can be found for the other digestive disorder subjects.

In this study, we have used the EGG setup to record myoelectrical activity for the patients suffering from Dyspepsia, Stomach ulcer, tachygastria, bradygastria and normal subjects [9, 11]. The condition of bradygastria and tachygastria is represented with CWT in Fig.5 and Fig.6 respectively. It is observed that the slow frequency wave for bradygastria and fast frequency wave for tachygastria. The frequency and power for the EGG signals are obtained with the help of MATLAB software and the same is compared between normal individual and stomach disorder patients as shown in Fig.8. It is observed from bar graph result if the frequency gets increased the power get decreased for the disorder subjects. Power comparison is shown in ‘A’ and the frequency comparison is shown in ‘B’ of Fig.7 respectively.

Fig.4. Welch power spectral Density Estimation for disorder subjects

Fig. 4. Welch power spectral Density Estimation for disorder subjects
Fig.5. CWT of EGG for a subject with Bradygastria
Fig. 5. CWT of EGG for a subject with Bradygastria
Fig.6. CWT of EGG for a subject with Tachygastria
Fig. 6. CWT of EGG for a subject with Tachygastria
Fig.7. Power comparison and frequency comparison of EGG signals
Fig. 7. Power comparison and frequency comparison of EGG signals 

7. Conclusion

In this paper, we have succeeded in acquiring the EGG [12, 13] from normal subjects and abnormal subjects who are suffering from different stomach disorders under the monitor and guidance of the gastroenterologist from the reputed hospital and formed a database for EGG signals. The signal analysis is performed using MATLAB software in reconstructing the original EGG signal from the database. The denoising of the same is performed using wavelet transform. The Welch power spectral Density Estimation method is adapted to the denoised EGG signal to find the power and frequency. The power and frequency of the EGG are compared and it is observed that when the power of the signal increased corresponding frequency is decreased for digestive disorder patient when compare to normal subjects. This fact may very much support the previous research work done in the field. The future work in this project would be to acquire EGG from patients suffering from different diseases to form a strong database. The signal analysis can be extended further as challenge to the researcher to find best analysis techniques which should detect a minute changes in the frequency in the spectral analysis to a greater extent to study the intricate behaviour of the EGG signals which would help to diagnose the diseases more efficiently and precisely because the diagnosis of digestive disorders with EGG has not been defined yet. A database for each disease is desired to be created so that sufficient data will be available to diagnose a disease.

Acknowledgement

The authors acknowledge their indebtedness to the following medical experts Dr L Venkatakrishnan, M.D., D.M.,D.N.B., Head of Gastroenterology Dept., Dr.J.Krishnaveni, M.D.,D.N.B., Gastroenterologist from PSG Hospitals,Coimbatore,Dr.T.S. Chandrasekar,M.D.,D.M,Interventional Gastroenter -ologist and for their support and for permitting us to use the facilities at the hospitals for live testing of the recording setup and sharing valuable patient database with us. We are also acknowledging the Management of Sri Ramakrishna Engineering College, Coimbatore for their support in acquiring data from the different patients at the department of biomedical Engineering.

References

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