About Processing of Exponentially Damped Signals: A Hardware for Biomedical Applications

About Processing of Exponentially Damped Signals: A Hardware for Biomedical Applications

Pasquale Maris, Matteo Cacciola, Filippo Laganà, Diego Pellicanò
DOI: 10.4018/ijmtie.2012040103
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Exponentially damped signals, generally provided by specific sensors, are of great interest because they are used in various applications (e.g., laser detectors, biomedical devices, particle physics, study of vibrations). The aim of this work is to design, build and characterize a small device (the use of the term “board” would be very reductive) which can acquire and process this kind of signals. Within the actual framework of devices for this purpose, the innovation the authors introduce is related to the “intelligence” included within the device, based on inherent requirements associated with their use. The current proposal, opportunely tuned, could be also applied to a number of different applications. The above signals are generally processed using both hardware and software features. But hardware procedure allows to process large data-based signals. One of the most advantage of their work is related to the use of such a board for portable apparatuses.
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1. Introduction

Analysis of biomedical signals (Carr & Brown, 2001), generated by the living being, is of great prominence in science. A signal is, by definition, related to information of various kind that can change over time and/or space. It describes and measure phenomena occurring in time and space, bringing information on the status and operation of the generating source. Particularly, living beings use this information to transfer control and communicate with the surrounding world. Other signals are produced by the interaction between the organism and an external agent (e.g., imaging devices).

A biomedical signal is a means to convey information from biological tissues or organs during their normal or pathological activity. This information typically is not easily acquirable. In some cases, it is necessary to adopt specific procedures for the acquisition and processing, in order to extract useful information from signals for both diagnosis and therapy. Acquiring and analysing these signals allows to:

  • Handle signals (e.g., filter undesired parts);

  • Extract information (e.g., diagnosis, classification of diseases, etc.);

  • Predict the future development through an adequate model (e.g., control of dosage drugs, early detection of alterations);

  • Communicate information to remote stations (e.g., intensive care) or store it for later use (in this sense, compression and data reduction techniques could be required).

Biomedical signals are detected by electrodes by which the difference in potential between two points is measured (Wang & Liu, 2011). Generally, the acquisition and automatic processing of biomedical signals can extract physiological and clinical information with greater precision and accuracy. In this sense, it is particularly important the development of new tools and the use of adequate and non-time-consuming processing techniques. Automation has resulted in significant advances in both diagnosis and therapy. One of the most important problems to the extent of the biomedical signal is represented by the presence of disturbances and noise that contaminate the signal of interest, thus preventing a correct determination of its trend.

For example, the syndrome of obstructive sleep apnea is a breathing disorder more frequent and more severe during the sleep. The diagnosis is made by special tests (Polysomnography, PSG). During the PSG test, clinicians measure different biological activities, as for example the activity of snoring, eye movements (EOG), Electroencephalographic signals, thoracic-abdominal movements, and so forth. During the PSG test, recordings typically have a duration of several hours. Consequently, the medical personnel have to deal with a huge amount of data in order to detect events of interest (apneas) and arrive at a diagnosis. The use of innovative techniques and classification of measured biological variables would allow a reduction of the time and cost of diagnosis. The discrimination between respiration, snoring and respiratory pauses is carried out through technical analysis and classification that extract certain parameters from the signal and allow to find automatically the different recorded events. The acquisition and processing of signals allows controlling devices in various biomedical applications.

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