Mapping slow waves and spikes in chronically instrumented conscious dogs: automated on-line electrogram analysis

Abstract

Myoelectric recordings from the gastrointestinal (GI) tract in conscious animals have been limited in duration and site. Recently, we have implanted 24 electrodes and obtained electrograms from these sites simultaneously (200 Hz sampling rate; 1.1 MB/min data stream). An automated electrogram analysis was developed to process this large amount of data. Myoelectrical recordings from the GI tract often consist of slow wave deflections followed by one or more action potentials (=spike deflections) in the same traces. To analyze these signals, a first module separates the signal into one containing only slow waves and a second one containing only spikes. The timings of these waveforms were then detected, in real time, for all 24 electrograms, in a separate slow wave detection module and a separate spike-detection module. Basic statistics such as timing and amplitudes and the number of spikes per slow wave were performed and displayed on-line. In summary, with this online analysis, it is possible to study for long periods of time and under various experimental conditions major components of gastrointestinal motility.

A System and Method for Online High-Resolution Mapping of Gastric Slow-Wave Activity

“…There has been only one previous attempt at developing a system for online spatial analyses of gastrointestinal electrical conduction [22]. The system presented here includes several improvements over their approach, including an event detection method that is relatively insensitive to noise, computationally efficient automated clustering and mapping, and elimination of noisy channels by kurtosis estimation.…”

“…However, this variable threshold method was unsuitable for online use because it was calculated from the complete recorded signals. Instead, the online threshold was calculated by a median absolution deviation method [22] but using a mean calculation method instead of median for computational efficiency (see the Appendix). “Candidate ATs” were identified when the detection signal exceeded an empirically defined (species-specific) threshold.…”

“…The FEVT algorithm may report FPs in such channels, due to the “variable threshold,” causing a response to a lack of true-positive (TP) events in the presence of noise [13]. These FPs do not presented a major problem during off-line analysis, because they can be manually removed given sufficient time, but it is critical to minimize FPs online to provide accurate data, and particularly because FPs distort automated mapping analyses [14], [22]. …”

“…The signals were also convolved with an edge detect kernel ( E t ). The detection signal was calculated as the element-wise product of the edge detected the kernel and the SNEO signal $$F_t=true\{\begin{array}{cc}center{S}_t{E}_t& center\text{if }{S}_t{E}_t\ge 0\\ center0,& center\text{if }{S}_t{E}_t<0\end{array}.$$ An online, variable threshold step was used to derive “candidate ATs” from the detection signal, by an adaptation of the median absolute deviation (MAD) approach [22] $$\text{italicMAD}false(Xfalse)={\text{median}}_{i}false(false|X_i-{\text{mean}}_{\text{j}}false(X_{j}false)false|false)$$ The threshold is determined by the MAD of the detection signal, within a time window, multiplied by a tunable scalar parameter. A window width of 30 s (15 s in each direction), and scalar multiplier of 5.9 was found to be most effective.…”

Telemetry system for slow wave measurement from the small bowel

“…An external decoder receives the RF signal, and sends it to a computer to store the data. The myoelectrical signals are generated from the interstitial cells of Cajal (ICC), which function as an electrical pacemaker and generate spontaneous electrical signals for gastric motility [4,12,22,23]. Figure 1a illustrates an electrical signal that was directly measured from the ICC by using intracellular electrodes.…”