Using an Adaptive Thresholding Algorithm to Detect CA1 Hippocampal Sharp Wave Ripples

Sharp wave ripples (high frequency events which occur in the CA1 hippocampal region of the brain) are detected using algorithms which automatically threshold the data at certain voltage values. Typically, detection algorithms use the same threshold throughout the entire voltage trace. The problem with this method is that the amplitude distribution of the noise is not constant in time; algorithms that employ constant thresholds may pick up false positives and false negatives when detecting ripples. False positives act to skew the interpretation of what ripples are; false negatives act to reduce the amount of valuable information we have on ripples. Using an algorithm with an adaptive threshold (which is designed to follow the noise level over time) greatly reduces the amount of false positives and may decrease the amount of false negatives. The total ripple "charge" (integral of ripple voltage [z-score] over time in ripple) in an interval of time where very few actual ripples occur is reduced by a factor of approximately four as compared to the case where a constant threshold is used. The total ripple charge in a temporal region where ripples readily occur increases by roughly five percent as compared to the constant threshold case. Using an adaptive threshold to reduce the amount of false positives and false negatives results in more accurate data on ripples which can potentially give us a better idea of how these brain patterns might form.