Limits...
Analysis of Electromyographic Signals from Rats' Stomaches for Detection and Classification of Motility

View Article: PubMed Central

ABSTRACT

Ppp: This paper presents the analysis of the electromyographic signals from rat stomachs to identify and classify contractions. The results were validated with both visual identification and an ultrasonic system to guarantee the reference. Some parameters were defined and associated to the energy of the signal in frequency domain and grouped in a vector. The parameters were statistically analyzed and according to the results, an artificial neuronal network was designed to use the vectors as inputs to classify the electrical signals related to the contraction conditions. A first approach classification was performed with and without contraction classes (CR and NCR), then the same database were subdivided in four classes: with induced contraction (ICR), spontaneous contraction (SCR), without contraction due a post mortem condition (PMR) or under physiological conditions (PNCR). In a two-class classifier, performance was 86%, 93% and 91% of detections for each electrogastromyografic (EGMG) signal from each of three pairs of electrodes considered. Because in the four-class classifier, enough data was not collected for the first pair, then a three-class classifier with 82% of performance was used. For the other two EGMG signals electrode pairs, performance was of 76% and 86% respectively. Based in the results, the analysis of vectors could be used as a contraction detector in motility studies due to different stimuli in a rat model.

No MeSH data available.


Related in: MedlinePlus

Mean + SEM of P vector of the EGMG from the pair of electrodes e2. Energy data of ICR: induced contraction records, SCR: spontaneous contraction records, PNCR: physiological non contraction records, PMR: post mortem records were normalized. The x-axis shows the elements of the P vector.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3675526&req=5

f4-sensors-08-02974: Mean + SEM of P vector of the EGMG from the pair of electrodes e2. Energy data of ICR: induced contraction records, SCR: spontaneous contraction records, PNCR: physiological non contraction records, PMR: post mortem records were normalized. The x-axis shows the elements of the P vector.

Mentions: In the second pair of electrodes e2, the parameter p4 presented the biggest difference between contraction conditions (Figure 4). In this pair, the parameter magnitudes were higher for records with than without contractions. Again, considering the P vector, contraction condition could be identified.


Analysis of Electromyographic Signals from Rats' Stomaches for Detection and Classification of Motility
Mean + SEM of P vector of the EGMG from the pair of electrodes e2. Energy data of ICR: induced contraction records, SCR: spontaneous contraction records, PNCR: physiological non contraction records, PMR: post mortem records were normalized. The x-axis shows the elements of the P vector.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3675526&req=5

f4-sensors-08-02974: Mean + SEM of P vector of the EGMG from the pair of electrodes e2. Energy data of ICR: induced contraction records, SCR: spontaneous contraction records, PNCR: physiological non contraction records, PMR: post mortem records were normalized. The x-axis shows the elements of the P vector.
Mentions: In the second pair of electrodes e2, the parameter p4 presented the biggest difference between contraction conditions (Figure 4). In this pair, the parameter magnitudes were higher for records with than without contractions. Again, considering the P vector, contraction condition could be identified.

View Article: PubMed Central

ABSTRACT

Ppp: This paper presents the analysis of the electromyographic signals from rat stomachs to identify and classify contractions. The results were validated with both visual identification and an ultrasonic system to guarantee the reference. Some parameters were defined and associated to the energy of the signal in frequency domain and grouped in a vector. The parameters were statistically analyzed and according to the results, an artificial neuronal network was designed to use the vectors as inputs to classify the electrical signals related to the contraction conditions. A first approach classification was performed with and without contraction classes (CR and NCR), then the same database were subdivided in four classes: with induced contraction (ICR), spontaneous contraction (SCR), without contraction due a post mortem condition (PMR) or under physiological conditions (PNCR). In a two-class classifier, performance was 86%, 93% and 91% of detections for each electrogastromyografic (EGMG) signal from each of three pairs of electrodes considered. Because in the four-class classifier, enough data was not collected for the first pair, then a three-class classifier with 82% of performance was used. For the other two EGMG signals electrode pairs, performance was of 76% and 86% respectively. Based in the results, the analysis of vectors could be used as a contraction detector in motility studies due to different stimuli in a rat model.

No MeSH data available.


Related in: MedlinePlus