Medical informatics intersects computer, information and health science. This involves optimizing the information, equipment, and methods needed to obtain, store, retrieve, and utilize information in the healthcare sectors and telemedicine applications.  This study has been carried out from the preliminary study which have already found the best order for wavelets, now the performance of that chosen order will be analyzed in this study.

Basically, Medical information is composed of physiological data which have become an essential part in both diagnosis and therapeutic phase. The physiological data obtained from the human body are either in the form of images or signals that provides diagnostic information. This data can be stored, updated, retrieved and transmitted from one place to another. Transmission of the signals within healthcare sectors or organizations, has difficulty in storage as well as archival of data. Therefore, compression techniques have its significance while enormous quantities of data transfer are considered. If an EGG signal is sampled at 180 samples per second (180 Hz) and each sample is coded on 12 bits, then 24 hours of information will amount to 22.8 MB. This figure increases at a rapid rate in accordance with the length of the recording. So, compression presents a low-cost alternative to the repeated updating and increment of storage capacities and lines of communication. It would make possible the use of a compression technique that maximizes the quantity of information attained for online healthcare access. Compression is crucial in telemedicine application for transmission of images or signals so as to compare or analyze particular results in detail. Digital data sharing (telemedicine) in biomedical research revolutionizes certain methods and allows researchers to quickly and remotely analyze preliminary examinations. The advantage of this compression technique is efficient and economical transmission by decreasing the size of data that is to be stored and communicated on daily basis. This paper concentrates only on EGG signal compression which is almost similar for all type of Bio-signal compression.

Many people around the world are suffering from arrhythmias such as peptic ulcer disease, gastroparesis, indigestion, gastritis, gastric emptying, nausea, motion sickness, chronic mesenteric ischemia etc. In order to achieve a non-invasive stomach diagnosis, Electrogastrogram technique will be helpful and can be acquired easily. So, the acquired EGG signals are tested with various wavelets transforms such as Biorthogonal, coiflet, Daubechies, Haar, reverse Biorthogonal and symlet wavelet transforms using MATLAB software for finding the best and efficient performance in compression technique. The objective of this study is to identify the best performance wavelet for compression in telemedicine.

EGG Signal Compression for Telemedicine Using Various Wavelet Transform

This paper discusses the recording and compression analysis of an Electrogastrogram (EGG), a non-invasive instrument that visually portrays the electrical activity of the stomach in order to diagnose stomach illnesses. The EGG signal's compression is important in the diagnosis, prognosis, and survival analysis of all stomach-related disorders, especially in telemedicine applications where the patient is geographically isolated. Over the years, several signal compression algorithms have been presented. Excessive cost, signal degradation, and a low compression ratio are just a few of the drawbacks that result in an inefficient signal at the receiver's end. Three important benefits of EGG compression in the digital domain for telemedicine applications are, effective & economic utilization of storage data, reduced data transmission rate and efficient transmission bandwidth conversation. Various wavelet transformations such as biorthogonal, coiflet, daubechies, haar, reverse biorthogonal, and symlet wavelet transforms are applied to EGG signals and examined using MATLAB software in this paper. The wavelet's performance was evaluated in order to select the best wavelet for telemedicine. This is accomplished by a quantitative analysis of the recovery ratio, percent root mean square difference (PRD), and compression ratio (CR) measurements. The findings of this study in terms of determining the optimal signal compression performance can undoubtedly become a valuable asset in the telemedicine area for the transmission of quantitative biological signals.

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