Falling-Edge, Variable Threshold (FEVT) Method for the Automated Detection of Gastric Slow Wave Events in High-Resolution Serosal Electrode Recordings

Department of Physics, Vanderbilt University, Nashville, TN, USA.
Annals of Biomedical Engineering (Impact Factor: 3.23). 12/2009; 38(4):1511-29. DOI: 10.1007/s10439-009-9870-3
Source: PubMed


High resolution (HR) multi-electrode mapping is increasingly being used to evaluate gastrointestinal slow wave behaviors. To create the HR activation time (AT) maps from gastric serosal electrode recordings that quantify slow wave propagation, it is first necessary to identify the AT of each individual slow wave event. Identifying these ATs has been a time consuming task, because there has previously been no reliable automated detection method. We have developed an automated AT detection method termed falling-edge, variable threshold (FEVT) detection. It computes a detection signal transform to accentuate the high 'energy' content of the falling edges in the serosal recording, and uses a running median estimator of the noise to set the time-varying detection threshold. The FEVT method was optimized, validated, and compared to other potential algorithms using in vivo HR recordings from a porcine model. FEVT properly detects ATs in a wide range of waveforms, making its performance substantially superior to the other methods, especially for low signal-to-noise ratio (SNR) recordings. The algorithm offered a substantial time savings (>100 times) over manual-marking whilst achieving a highly satisfactory sensitivity (0.92) and positive-prediction value (0.89).

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Available from: Gregory O'Grady,
    • "A novel approach to assessing signal fidelity online was developed by assessing a moving estimate of signal " kurtosis " [25]. A kurtosis-based approach was chosen based on the observation that signals recording TP events generally show a small range with periodic slow-wave deflections, whereas channels recording FPs tend to show a more consistent variation around a mean value, representing random noise or ventilator signal [13]. Additional information on the kurtosis method is provided in the Appendix. "
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    ABSTRACT: High-resolution (HR) mapping employs multi-electrode arrays to achieve spatially-detailed analyses of propagating bioelectrical events. A major current limitation is that spatial analyses must currently be performed 'offline' (after experiments), compromising timely recording feedback and restricting experimental interventions. These problems motivated development of a system and method for 'online' HR mapping. HR gastric recordings were acquired and streamed to a novel software client. Algorithms were devised to filter data, identify slow wave events, eliminate corrupt channels, and cluster activation events. A graphical user interface animated data and plotted electrograms and maps. Results were compared against offline methods. The online system analyzed 256-channel serosal recordings with no unexpected system terminations with a mean delay 18 s. Activation time marking sensitivity was 0.92; positive predictive value was 0.93. Abnormal slow wave patterns including conduction blocks, ectopic pacemaking, and colliding wave fronts were reliably identified. Compared to traditional analysis methods, online mapping had comparable results with equivalent coverage of 90% of electrodes, average RMS errors of less than 1 s, and CC of activation maps of 0.99. Accurate slow wave mapping was achieved in near real-time, enabling monitoring of recording quality and experimental interventions targeted to dysrhythmic onset. This work also advances the translation of HR mapping toward real-time clinical application.
    IEEE transactions on bio-medical engineering 05/2014; 61(11). DOI:10.1109/TBME.2014.2325829 · 2.35 Impact Factor
    • "The η defines the threshold which the signal must surpass in order to be considered as a slow wave event, Tr defines the minimum period between FEVT marks, ρ defines the time width of the moving average filter applied to the detection signal transform, and τHW defines the time window for which the time-varying threshold is computed.12 Additionally, the signal transform used for the FEVT algorithm was also optimized between 4 different methods: negative derivative, amplitude-sensitive differentiator, non-linear energy operator, and fourth-order differential energy operator.12 The negative derivative method computes the first-derivative of the signal and considers only those values which correspond to a negative deflection in the signal. "
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    ABSTRACT: BACKGROUNDAIMS: Small intestine motility is governed by an electrical slow wave activity, and abnormal slow wave events have been associated with intestinal dysmotility. High-resolution (HR) techniques are necessary to analyze slow wave propagation, but progress has been limited by few available electrode options and laborious manual analysis. This study presents novel methods for in vivo HR mapping of small intestine slow wave activity. Recordings were obtained from along the porcine small intestine using flexible printed circuit board arrays (256 electrodes; 4 mm spacing). Filtering options were compared, and analysis was automated through adaptations of the falling-edge variable-threshold (FEVT) algorithm and graphical visualization tools. A Savitzky-Golay filter was chosen with polynomial-order 9 and window size 1.7 seconds, which maintained 94% of slow wave amplitude, 57% of gradient and achieved a noise correction ratio of 0.083. Optimized FEVT parameters achieved 87% sensitivity and 90% positive-predictive value. Automated activation mapping and animation successfully revealed slow wave propagation patterns, and frequency, velocity, and amplitude were calculated and compared at 5 locations along the intestine (16.4 ± 0.3 cpm, 13.4 ± 1.7 mm/sec, and 43 ± 6 µV, respectively, in the proximal jejunum). The methods developed and validated here will greatly assist small intestine HR mapping, and will enable experimental and translational work to evaluate small intestine motility in health and disease.
    Journal of neurogastroenterology and motility 04/2013; 19(2):179-91. DOI:10.5056/jnm.2013.19.2.179 · 2.30 Impact Factor
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    • "Wired data was down-sampled to 30 Hz and pre-processed with moving median and Savitzky–Golay filters to control drift and high-frequency noises (Paskaranandavadivel et al 2011). Individual slow wave events were automatically detected with the falling-edge variable threshold (FEVT) method, followed by manual review and correction (Erickson et al 2010). Slow wave events were marked at the point of maximal negative gradient (the activation time), which corresponds to the arrival of the wavefront directly under the electrode (O'Grady 2012). "
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    ABSTRACT: Stomach contractions are initiated and coordinated by an underlying electrical activity (slow waves), and electrical dysrhythmias accompany motility diseases. Electrical recordings taken directly from the stomach provide the most valuable data, but face technical constraints. Serosal or mucosal electrodes have cables that traverse the abdominal wall, or a natural orifice, causing discomfort and possible infection, and restricting mobility. These problems motivated the development of a wireless system. The bidirectional telemetric system constitutes a front-end transponder, a back-end receiver and a graphical userinter face. The front-end module conditions the analogue signals, then digitizes and loads the data into a radio for transmission. Data receipt at the backend is acknowledged via a transceiver function. The system was validated in a bench-top study, then validated in vivo using serosal electrodes connected simultaneously to a commercial wired system. The front-end module was 35 × 35 × 27 mm3 and weighed 20 g. Bench-top tests demonstrated reliable communication within a distance range of 30 m, power consumption of 13.5 mW, and 124 h operation when utilizing a 560 mAh, 3 V battery. In vivo,slow wave frequencies were recorded identically with the wireless and wired reference systems (2.4 cycles min−1), automated activation time detection was modestly better for the wireless system (5% versus 14% FP rate), and signal amplitudes were modestly higher via the wireless system (462 versus 3 86μV; p<0.001). This telemetric system for slow wave acquisition is reliable,power efficient, readily portable and potentially implantable. The device will enable chronic monitoring and evaluation of slow wave patterns in animals and patients.0967-3334/
    Physiological Measurement 05/2012; 33(6):N29-37. DOI:10.1088/0967-3334/33/6/N29 · 1.81 Impact Factor
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