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Waveform Similarity Analysis: A Simple Template Comparing Approach for Detecting and Quantifying Noisy Evoked Compound Action Potentials.

Potas JR, de Castro NG, Maddess T, de Souza MN - PLoS ONE (2015)

Bottom Line: Signals were detected and quantified using Waveform Similarity Analysis, which was compared to event detection, latency and magnitude measurements of the same signals performed by a trained observer, a process we called Trained Eye Analysis.Compared to the trained eye, Waveform Similarity Analysis is automatic, objective, does not rely on the observer to identify and/or measure peaks, and can detect small clustered events even when signal-to-noise ratio is poor.Waveform Similarity Analysis provides a simple, reliable and convenient approach to quantify latencies and magnitudes of complex waveforms and therefore serves as a useful tool for studying evoked compound action potentials in neural regeneration studies.

View Article: PubMed Central - PubMed

Affiliation: Department of Neuroscience, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia; Medical School, Australian National University, Canberra, ACT, Australia; Instituto de Ciências Biomédicas, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil.

ABSTRACT
Experimental electrophysiological assessment of evoked responses from regenerating nerves is challenging due to the typical complex response of events dispersed over various latencies and poor signal-to-noise ratio. Our objective was to automate the detection of compound action potential events and derive their latencies and magnitudes using a simple cross-correlation template comparison approach. For this, we developed an algorithm called Waveform Similarity Analysis. To test the algorithm, challenging signals were generated in vivo by stimulating sural and sciatic nerves, whilst recording evoked potentials at the sciatic nerve and tibialis anterior muscle, respectively, in animals recovering from sciatic nerve transection. Our template for the algorithm was generated based on responses evoked from the intact side. We also simulated noisy signals and examined the output of the Waveform Similarity Analysis algorithm with imperfect templates. Signals were detected and quantified using Waveform Similarity Analysis, which was compared to event detection, latency and magnitude measurements of the same signals performed by a trained observer, a process we called Trained Eye Analysis. The Waveform Similarity Analysis algorithm could successfully detect and quantify simple or complex responses from nerve and muscle compound action potentials of intact or regenerated nerves. Incorrectly specifying the template outperformed Trained Eye Analysis for predicting signal amplitude, but produced consistent latency errors for the simulated signals examined. Compared to the trained eye, Waveform Similarity Analysis is automatic, objective, does not rely on the observer to identify and/or measure peaks, and can detect small clustered events even when signal-to-noise ratio is poor. Waveform Similarity Analysis provides a simple, reliable and convenient approach to quantify latencies and magnitudes of complex waveforms and therefore serves as a useful tool for studying evoked compound action potentials in neural regeneration studies.

No MeSH data available.


Related in: MedlinePlus

Effect of WSA event detection threshold levels on the number of identified events.Empirically derived functions were used to determine an ideal detection threshold. A demonstrates the mean number of events detected by WSA from N10avRs evoked from injured sciatic nerves by a 0.7 mA stimulus, as a function of the detection threshold level. The detection threshold level indicates the multiple of standard deviations (refer to red line in Fig 2B2 showing one standard deviation) that the mean (black line, Fig 2B2) of 3 repeated N10avRs had to be greater than, in order to detect an event using WSA. The fuzzy line represents the mean and SEM (6.5 ± 0.6) of the number of events detected by TEA for the same responses. Throughout the study, 2.0× was chosen as the standard detection threshold level for N10avRs, as this level detects a number of events not significantly different to that detected by TEA. B demonstrates the mean number of events detected from M5avRs evoked from injured sciatic nerves by a paired stimulus as a function of background noise level. The paired stimulus was 0.7 mA with an inter-pulse interval of 1 ms. Background noise was calculated from the mean absolute amplitude of the last 17 ms of the sweep, a region where no signal was present. The detection threshold level indicates the threshold based on a multiple of background noise. The grey line indicates the ideal level of event detection, i.e. where the number of events = 1. For in vivo experiments, 12× the background noise level was chosen as the standard detection threshold level for M5avRs.
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pone.0136992.g003: Effect of WSA event detection threshold levels on the number of identified events.Empirically derived functions were used to determine an ideal detection threshold. A demonstrates the mean number of events detected by WSA from N10avRs evoked from injured sciatic nerves by a 0.7 mA stimulus, as a function of the detection threshold level. The detection threshold level indicates the multiple of standard deviations (refer to red line in Fig 2B2 showing one standard deviation) that the mean (black line, Fig 2B2) of 3 repeated N10avRs had to be greater than, in order to detect an event using WSA. The fuzzy line represents the mean and SEM (6.5 ± 0.6) of the number of events detected by TEA for the same responses. Throughout the study, 2.0× was chosen as the standard detection threshold level for N10avRs, as this level detects a number of events not significantly different to that detected by TEA. B demonstrates the mean number of events detected from M5avRs evoked from injured sciatic nerves by a paired stimulus as a function of background noise level. The paired stimulus was 0.7 mA with an inter-pulse interval of 1 ms. Background noise was calculated from the mean absolute amplitude of the last 17 ms of the sweep, a region where no signal was present. The detection threshold level indicates the threshold based on a multiple of background noise. The grey line indicates the ideal level of event detection, i.e. where the number of events = 1. For in vivo experiments, 12× the background noise level was chosen as the standard detection threshold level for M5avRs.

Mentions: The WSA algorithm determined the cross-correlation sequence between the response being quantified and its relevant template signal (Fig 1A or 1B). A plot containing cross-correlation peaks corresponding to the time instants of greatest similarity was produced by varying the time-lag between a nerve or muscle response and its template waveforms (Fig 2). As there were three N10avR and one M5avR generated per stimulus, the application of WSA to detect and quantify nerve and muscle CAPs were different. For nerve responses, this sequence was generated from the three N10avRs (Fig 2, row A) to produce a mean and standard deviation cross-correlation function (Fig 2, row B). Event detection and quantification for nerve responses were derived from cross-correlation function peaks where the mean was greater than 2 times the standard deviation. This detection threshold level was chosen as it detected a similar number of events as that detected by the trained eye (see Fig 3A).


Waveform Similarity Analysis: A Simple Template Comparing Approach for Detecting and Quantifying Noisy Evoked Compound Action Potentials.

Potas JR, de Castro NG, Maddess T, de Souza MN - PLoS ONE (2015)

Effect of WSA event detection threshold levels on the number of identified events.Empirically derived functions were used to determine an ideal detection threshold. A demonstrates the mean number of events detected by WSA from N10avRs evoked from injured sciatic nerves by a 0.7 mA stimulus, as a function of the detection threshold level. The detection threshold level indicates the multiple of standard deviations (refer to red line in Fig 2B2 showing one standard deviation) that the mean (black line, Fig 2B2) of 3 repeated N10avRs had to be greater than, in order to detect an event using WSA. The fuzzy line represents the mean and SEM (6.5 ± 0.6) of the number of events detected by TEA for the same responses. Throughout the study, 2.0× was chosen as the standard detection threshold level for N10avRs, as this level detects a number of events not significantly different to that detected by TEA. B demonstrates the mean number of events detected from M5avRs evoked from injured sciatic nerves by a paired stimulus as a function of background noise level. The paired stimulus was 0.7 mA with an inter-pulse interval of 1 ms. Background noise was calculated from the mean absolute amplitude of the last 17 ms of the sweep, a region where no signal was present. The detection threshold level indicates the threshold based on a multiple of background noise. The grey line indicates the ideal level of event detection, i.e. where the number of events = 1. For in vivo experiments, 12× the background noise level was chosen as the standard detection threshold level for M5avRs.
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pone.0136992.g003: Effect of WSA event detection threshold levels on the number of identified events.Empirically derived functions were used to determine an ideal detection threshold. A demonstrates the mean number of events detected by WSA from N10avRs evoked from injured sciatic nerves by a 0.7 mA stimulus, as a function of the detection threshold level. The detection threshold level indicates the multiple of standard deviations (refer to red line in Fig 2B2 showing one standard deviation) that the mean (black line, Fig 2B2) of 3 repeated N10avRs had to be greater than, in order to detect an event using WSA. The fuzzy line represents the mean and SEM (6.5 ± 0.6) of the number of events detected by TEA for the same responses. Throughout the study, 2.0× was chosen as the standard detection threshold level for N10avRs, as this level detects a number of events not significantly different to that detected by TEA. B demonstrates the mean number of events detected from M5avRs evoked from injured sciatic nerves by a paired stimulus as a function of background noise level. The paired stimulus was 0.7 mA with an inter-pulse interval of 1 ms. Background noise was calculated from the mean absolute amplitude of the last 17 ms of the sweep, a region where no signal was present. The detection threshold level indicates the threshold based on a multiple of background noise. The grey line indicates the ideal level of event detection, i.e. where the number of events = 1. For in vivo experiments, 12× the background noise level was chosen as the standard detection threshold level for M5avRs.
Mentions: The WSA algorithm determined the cross-correlation sequence between the response being quantified and its relevant template signal (Fig 1A or 1B). A plot containing cross-correlation peaks corresponding to the time instants of greatest similarity was produced by varying the time-lag between a nerve or muscle response and its template waveforms (Fig 2). As there were three N10avR and one M5avR generated per stimulus, the application of WSA to detect and quantify nerve and muscle CAPs were different. For nerve responses, this sequence was generated from the three N10avRs (Fig 2, row A) to produce a mean and standard deviation cross-correlation function (Fig 2, row B). Event detection and quantification for nerve responses were derived from cross-correlation function peaks where the mean was greater than 2 times the standard deviation. This detection threshold level was chosen as it detected a similar number of events as that detected by the trained eye (see Fig 3A).

Bottom Line: Signals were detected and quantified using Waveform Similarity Analysis, which was compared to event detection, latency and magnitude measurements of the same signals performed by a trained observer, a process we called Trained Eye Analysis.Compared to the trained eye, Waveform Similarity Analysis is automatic, objective, does not rely on the observer to identify and/or measure peaks, and can detect small clustered events even when signal-to-noise ratio is poor.Waveform Similarity Analysis provides a simple, reliable and convenient approach to quantify latencies and magnitudes of complex waveforms and therefore serves as a useful tool for studying evoked compound action potentials in neural regeneration studies.

View Article: PubMed Central - PubMed

Affiliation: Department of Neuroscience, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia; Medical School, Australian National University, Canberra, ACT, Australia; Instituto de Ciências Biomédicas, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil.

ABSTRACT
Experimental electrophysiological assessment of evoked responses from regenerating nerves is challenging due to the typical complex response of events dispersed over various latencies and poor signal-to-noise ratio. Our objective was to automate the detection of compound action potential events and derive their latencies and magnitudes using a simple cross-correlation template comparison approach. For this, we developed an algorithm called Waveform Similarity Analysis. To test the algorithm, challenging signals were generated in vivo by stimulating sural and sciatic nerves, whilst recording evoked potentials at the sciatic nerve and tibialis anterior muscle, respectively, in animals recovering from sciatic nerve transection. Our template for the algorithm was generated based on responses evoked from the intact side. We also simulated noisy signals and examined the output of the Waveform Similarity Analysis algorithm with imperfect templates. Signals were detected and quantified using Waveform Similarity Analysis, which was compared to event detection, latency and magnitude measurements of the same signals performed by a trained observer, a process we called Trained Eye Analysis. The Waveform Similarity Analysis algorithm could successfully detect and quantify simple or complex responses from nerve and muscle compound action potentials of intact or regenerated nerves. Incorrectly specifying the template outperformed Trained Eye Analysis for predicting signal amplitude, but produced consistent latency errors for the simulated signals examined. Compared to the trained eye, Waveform Similarity Analysis is automatic, objective, does not rely on the observer to identify and/or measure peaks, and can detect small clustered events even when signal-to-noise ratio is poor. Waveform Similarity Analysis provides a simple, reliable and convenient approach to quantify latencies and magnitudes of complex waveforms and therefore serves as a useful tool for studying evoked compound action potentials in neural regeneration studies.

No MeSH data available.


Related in: MedlinePlus