TY - GEN
T1 - Automatic cleaning and labelling process for electrogastrogram
AU - Espinosa-Espejel, Karina I.
AU - Contreras-Uribe, Tania J.
AU - Tovar-Corona, Blanca
AU - Garay-Jimenez, Laura I.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - It is well known that recording electrical signals from the human body is a challenging task due to their nature and its vulnerability to noise effects. This is also the case of the electrogastrographic signal, the electrical activity of the digestive system that can be recorded using surface electrodes on the external wall of the abdomen. This signal is especially difficult to record due to the different signals present in the abdomen that are larger in amplitude and sometimes completely mask the signal of interest. Therefore, it is very important to clean the recordings before any information can be extracted. This paper describes a methodology to segment the signals with useful information, eliminating those segments masked with artefacts that make the signal useless. A database with 50 recordings from healthy volunteers and diabetes type II patients were analyzed during three stages: fasting, after drinking water and after eating a caloric meal. The recordings are first filtered with an adaptive high-pass filter, then it is analyzed in one second windows to look for attenuations, saturation and drastic changes. A file with labels is generated that contains only the information of useful windows in order to extract the 2.5 minutes segments that provide information. Then, the Discrete Wavelet Transform and the Fast Fourier Transform are used to calculate the percentage of prevalence in the gastric pacemaker (normagastry, taquygastry and bradygastry). This automatic procedure was tested comparing the segmentation made by an expert by visual inspection. It was found that the automatic segmentation, made by the proposed algorithms, recovers 20% more segments than the expert and saves considerable the time spent in the labelling. The prevalence obtained after segmentation is congruent with the previous works reported.
AB - It is well known that recording electrical signals from the human body is a challenging task due to their nature and its vulnerability to noise effects. This is also the case of the electrogastrographic signal, the electrical activity of the digestive system that can be recorded using surface electrodes on the external wall of the abdomen. This signal is especially difficult to record due to the different signals present in the abdomen that are larger in amplitude and sometimes completely mask the signal of interest. Therefore, it is very important to clean the recordings before any information can be extracted. This paper describes a methodology to segment the signals with useful information, eliminating those segments masked with artefacts that make the signal useless. A database with 50 recordings from healthy volunteers and diabetes type II patients were analyzed during three stages: fasting, after drinking water and after eating a caloric meal. The recordings are first filtered with an adaptive high-pass filter, then it is analyzed in one second windows to look for attenuations, saturation and drastic changes. A file with labels is generated that contains only the information of useful windows in order to extract the 2.5 minutes segments that provide information. Then, the Discrete Wavelet Transform and the Fast Fourier Transform are used to calculate the percentage of prevalence in the gastric pacemaker (normagastry, taquygastry and bradygastry). This automatic procedure was tested comparing the segmentation made by an expert by visual inspection. It was found that the automatic segmentation, made by the proposed algorithms, recovers 20% more segments than the expert and saves considerable the time spent in the labelling. The prevalence obtained after segmentation is congruent with the previous works reported.
KW - Bradygastry
KW - Diabetes Mellitus
KW - Discrete Wavelet Transform
KW - Gastric pacemaker
KW - Normagastry
KW - Pacemaker prevalence
KW - Taquygastry
UR - http://www.scopus.com/inward/record.url?scp=85063907122&partnerID=8YFLogxK
U2 - 10.1109/ROPEC.2018.8661473
DO - 10.1109/ROPEC.2018.8661473
M3 - Contribución a la conferencia
AN - SCOPUS:85063907122
T3 - 2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018
BT - 2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018
Y2 - 14 November 2018 through 16 November 2018
ER -