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As we know, many biological systems such as neurons or the heart can exhibit chaotic behavior. Conventional methods for parameter estimation in models of these systems have some limitations caused by sensitivity to initial conditions. In this paper, a novel cost function is proposed to overcome those limitations by building a statistical model on the distribution of the real system attractor in state space. This cost function is defined by the use of a likelihood score in a Gaussian mixture model (GMM) which is fitted to the observed attractor generated by the real system. Using that learned GMM, a similarity score can be defined by the computed likelihood score of the model time series. We have applied the proposed method to the parameter estimation of two important biological systems, a neuron and a cardiac pacemaker, which show chaotic behavior. Some simulated experiments are given to verify the usefulness of the proposed approach in clean and noisy conditions. The results show the adequacy of the proposed cost function. [ABSTRACT FROM AUTHOR]

This paper presents a digital hardware implementation of a novel wavelet-based event detector suitable for the next generation of cardiac pacemakers. Significant power savings are achieved by introducing a second operation mode that shuts down 2/3 of the hardware for long time periods when the pacemaker patient is not exposed to noise, while not degrading performance. Due to a 0.13-μm CMOS technology and the low clock frequency of 1 kHz, leakage power becomes the dominating power source. By introducing sleep transistors in the power-supply rails, leakage power of the hardware being shut off is reduced by 97 %. Power estimation on RTL-level shows that the overall power consumption is reduced by 67% with a dual operation mode. Under these conditions, the detector is expected to operate in the sub-μW region. Detection performance is evaluated by means of databases containing electrograms to which five types of exogenic and endogenic interferences are added. The results show that reliable detection is obtained at moderate and low signal to noise-ratios (SNRs). Average detection performance in terms of detected events and false alarms for 25-dB SNR is PD = 0.98 and PFA = 0.014, respectively. [ABSTRACT FROM AUTHOR]