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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

Latency profiles derived by WSA and TEA.The latency to the peak of the first event of N10avRs (A1, intact side; A2, regenerating side) and the latency to the peak of M5avRs (B1, intact side; B2, regenerating side) were measured using the temporal location of best fit between the event and its respective template signals (WSA), and compared with the latency of the peak of each respective event (TEA). There was no significant difference in the latencies derived from WSA and TEA. Inter-pulse interval = time interval between the conditioning and testing pulses (refer to Fig 4). Abbreviations: WSA, waveform similarity analysis; TEA, trained eye analysis.
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pone.0136992.g006: Latency profiles derived by WSA and TEA.The latency to the peak of the first event of N10avRs (A1, intact side; A2, regenerating side) and the latency to the peak of M5avRs (B1, intact side; B2, regenerating side) were measured using the temporal location of best fit between the event and its respective template signals (WSA), and compared with the latency of the peak of each respective event (TEA). There was no significant difference in the latencies derived from WSA and TEA. Inter-pulse interval = time interval between the conditioning and testing pulses (refer to Fig 4). Abbreviations: WSA, waveform similarity analysis; TEA, trained eye analysis.

Mentions: Latency profiles were produced by quantifying the same CNAP latencies to varying stimulation intensities using WSA and TEA; the intact side received 3 stimulation intensities and the regenerating side received 7 (Fig 6A1 and 6A2). The reduced number of stimulation intensities on the left side was due to limitations in the data set, but this did not impede our ability to evaluate the WSA algorithm and compare it to TEA. There was no significant difference between the two methods used to quantify latency of CNAPs as demonstrated by their latency profiles (Fig 6A1 and 6A2), nor was there a significant difference in the mean absolute difference in latency across all stimuli between the intact (0.08 ± 0.03 ms) and the regenerating sides (0.08 ± 0.01 ms).


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)

Latency profiles derived by WSA and TEA.The latency to the peak of the first event of N10avRs (A1, intact side; A2, regenerating side) and the latency to the peak of M5avRs (B1, intact side; B2, regenerating side) were measured using the temporal location of best fit between the event and its respective template signals (WSA), and compared with the latency of the peak of each respective event (TEA). There was no significant difference in the latencies derived from WSA and TEA. Inter-pulse interval = time interval between the conditioning and testing pulses (refer to Fig 4). Abbreviations: WSA, waveform similarity analysis; TEA, trained eye analysis.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4556619&req=5

pone.0136992.g006: Latency profiles derived by WSA and TEA.The latency to the peak of the first event of N10avRs (A1, intact side; A2, regenerating side) and the latency to the peak of M5avRs (B1, intact side; B2, regenerating side) were measured using the temporal location of best fit between the event and its respective template signals (WSA), and compared with the latency of the peak of each respective event (TEA). There was no significant difference in the latencies derived from WSA and TEA. Inter-pulse interval = time interval between the conditioning and testing pulses (refer to Fig 4). Abbreviations: WSA, waveform similarity analysis; TEA, trained eye analysis.
Mentions: Latency profiles were produced by quantifying the same CNAP latencies to varying stimulation intensities using WSA and TEA; the intact side received 3 stimulation intensities and the regenerating side received 7 (Fig 6A1 and 6A2). The reduced number of stimulation intensities on the left side was due to limitations in the data set, but this did not impede our ability to evaluate the WSA algorithm and compare it to TEA. There was no significant difference between the two methods used to quantify latency of CNAPs as demonstrated by their latency profiles (Fig 6A1 and 6A2), nor was there a significant difference in the mean absolute difference in latency across all stimuli between the intact (0.08 ± 0.03 ms) and the regenerating sides (0.08 ± 0.01 ms).

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