The single-fiber action potential (SFAP) can be modeled as a convolution of a biolectrical source (the excitation) and a transfer function, representing the electrical volume conduction. In the Dimitrov-Dimitrova (D-D) convolutional model, the first temporal derivative of the intracellular action potential (IAP) is used as the source. In this model, the ratio between the amplitudes of the second and first phases of the SFAP (which we call the PPR, after peak-to-peak ratio) increases invariably with radial distance. This is not the case of real recorded fibrillation potentials (FPs). Moreover, FPs show a wider PPR range than that which the D-D model can provide. These discrepancies suggest that the D-D model should be revised. Since the volume conduction parameters seem to have no apparent effects on the PPR, we assume that the origin of this difference lies in the excitation source. This paper presents a new analytical description of the IAP based on that expressed in the D-D model. The new approximation is shown to model FPs with a range of PPRs comparable to that observed in a set of real FPs which we used as our test signals.
This paper describes a new method to identify seizures in electroencephalogram (EEG) signals using feature extraction in time frequency distributions (TFDs). Particularly, the method extracts features from the Smoothed Pseudo Wigner-Ville distribution using tracks estimated from the McAulay-Quatieri sinusoidal model. The proposed features are the length, frequency, and energy of the principal track. We evaluate the proposed scheme using several datasets and we compute sensitivity, specificity, F-score, receiver operating characteristics (ROC) curve, and percentile bootstrap confidence to conclude that the proposed scheme generalizes well and is a suitable approach for automatic seizure detection at a moderate cost, also opening the possibility of formulating new criteria to detect, classify or analyze abnormal EEGs.
A single fiber action potential (SFAP) can be modelled as the convolution of a biolectrical source and a filter impulse response. In the Dimitrov-Dimitrova (D-D) convolutional model, the first temporal derivative of the intracellular action potential (IAP) is used as the source, and Tspl is a time parameter related to the duration of the IAP waveform. This paper is centred on the relation between Tspl and the main spike duration (MSD), defined as the time interval between the first and third phases of the SFAP. We show that Tspl essentially determines the MSD parameter. As experimental data, we used fibrillation potentials (FPs) of two different muscles to study the D-D model. We found that Tspl should have a certain statistical variability in order to explain the variability in the MSD of our FPs. In addition, we present a method to estimate the Tspl values corresponding to a given SFAP from its measured MSD.
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