Gastric disorders are widely spread among the population of any age. At the moment, the diagnosis is made by using invasive systems that cause several side effects. The present manuscript proposes an innovative non-invasive sensing system for diagnosing gastric dysfunctions. The Electro-GastroGraphy (EGG) technique is used to record myoelectrical signals of stomach activities. Although EGG technique is well known for a long time, several issues concerning the signal processing and the definition of suitable diagnostic criteria are still unresolved. So, EGG is to this day a trial practice. The authors want to overcome the current limitations of the technique and improve its relevance. To this purpose, a smart EGG sensing system has been designed to non-invasively diagnose gastric disorders. In detail, the system records the gastric slow waves by means of skin surface electrodes placed in the epigastric area. Cutaneous myoelectrical signals are so acquired from the body surface in proximity of stomach. Electro-gastrographic record is then processed. According to the diagnostic model designed from the authors, the system estimates specific diagnostic parameters in time and frequency domains. It uses Discrete Wavelet Transform to obtain power spectral density diagrams. The frequency and power of the EGG waveform and the dominant frequency components are so analyzed. The defined diagnostic parameters are put in comparison with the reference values of a normal EGG in order to estimate the presence of gastric pathologies by the analysis of arrhythmias (tachygastria, bradygastria and irregular rhythm). The paper aims to describe the design of the system and of the arrhythmias detection algorithm. Prototype development and experimental data will be presented in future works. Preliminary results show an interesting relevance of the suggested technique so that it can be considered as a promising non-invasive tool for diagnosing gastrointestinal motility disorders.

Design of a non-invasive sensing system for diagnosing gastric disorders

Morello R.
;
de Capua C.
2021

Abstract

Gastric disorders are widely spread among the population of any age. At the moment, the diagnosis is made by using invasive systems that cause several side effects. The present manuscript proposes an innovative non-invasive sensing system for diagnosing gastric dysfunctions. The Electro-GastroGraphy (EGG) technique is used to record myoelectrical signals of stomach activities. Although EGG technique is well known for a long time, several issues concerning the signal processing and the definition of suitable diagnostic criteria are still unresolved. So, EGG is to this day a trial practice. The authors want to overcome the current limitations of the technique and improve its relevance. To this purpose, a smart EGG sensing system has been designed to non-invasively diagnose gastric disorders. In detail, the system records the gastric slow waves by means of skin surface electrodes placed in the epigastric area. Cutaneous myoelectrical signals are so acquired from the body surface in proximity of stomach. Electro-gastrographic record is then processed. According to the diagnostic model designed from the authors, the system estimates specific diagnostic parameters in time and frequency domains. It uses Discrete Wavelet Transform to obtain power spectral density diagrams. The frequency and power of the EGG waveform and the dominant frequency components are so analyzed. The defined diagnostic parameters are put in comparison with the reference values of a normal EGG in order to estimate the presence of gastric pathologies by the analysis of arrhythmias (tachygastria, bradygastria and irregular rhythm). The paper aims to describe the design of the system and of the arrhythmias detection algorithm. Prototype development and experimental data will be presented in future works. Preliminary results show an interesting relevance of the suggested technique so that it can be considered as a promising non-invasive tool for diagnosing gastrointestinal motility disorders.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12318/114237
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