Detection of Small Bowel Slow-Wave Frequencies From Noninvasive Biomagnetic Measurements

Abstract

We report a novel method for identifying the small intestine electrical activity slow wave frequencies from noninvasive biomagnetic measurements. Superconducting QUantum Interference Device (SQUID) magnetometer measurements are pre-processed to remove baseline drift and high frequency noise. Subsequently, the underlying source signals are separated using the well-known SOBI algorithm. A simple classification scheme identifies and assigns some of the SOBI components to a section of small bowel. Slow wave frequencies were clearly identified in 10 out of 12 test subjects to within 0.09-0.25 cycles per minute. The method is sensitive at the 40.3-55.9% level, while false positive rates were 0-8.6%. This technique could potentially be used to help diagnose gastrointestinal ailments and obviate some exploratory surgeries.

Noninvasive Biomagnetic Detection of Isolated Ischemic Bowel Segments

“…Since respiration and other artifactual signals can dominate enteric signatures in the MENG, we used enteric SOBI components reconstructed to the sensor array to identify enteric slow waves, as outlined in figure 1 [6]. A typical bandpass filtered MENG signal during baseline is shown in figure 1a.1.…”

“…In order to identify segments of ischemic bowel, our studies utilized CWT-filtered and SOBI-separated MENG signals. We found that Second Order Blind Identification (SOBI), a blind source separation technique, allowed the separation and identification of enteric slow wave signals from noisy, artifact-contaminated SQUID measurements [6], and to identify ischemic and normal intestinal slow wave components from raw or filtered SQUID data. We select SOBI components to retain or to eliminate based on frequency content and signal periodicity, and reconstruct the noise-reduced SQUID signal at each sensor location.…”

“…For SQUID signals, in addition to filtering, we also applied the Second-Order Blind Identification (SOBI) signal processing algorithm to reduce the interfering noise signals [6, 23]. After applying SOBI, primarily sinusoidal signals in the normal enteric frequency range (9 – 20 cpm) and artifact-related components were identified [6]. Enteric SOBI signals were reconstructed onto the sensor array, allowing us to localize enteric sources.…”

“…ICCs are electrically coupled to circular and longitudinal muscle layers by gap junctions [1–4]. The small intestine exhibits a stepwise gradient in slow wave frequency from about 12 cycles per minute (cpm) in the normal human duodenum to between 9 and 11 cpm in the jejunum and around 8 cpm in the ileum [5, 6]. Disease states such as ischemia and motility disorders of the small bowel are known to alter the normal enteric slow wave activity [7, 8].…”

“…Previous studies by several research groups have demonstrated that MENG overcomes many of the limitations of EENG [16–18]. MENG can differentiate physiological from non-physiological electrical activity [6], detect characteristics of slow wave propagation [19], identify uncoupling of the electrical syncytium[20] and distinguish spiking activity [21]. Studies in our laboratory have been the first to demonstrate the ability of MENG to detect changes in the slow wave frequency after the onset of global ischemia in animal models [11, 22].…”