Development and Implementation of Cardiac Event Detectors in Digital CMOS

Sammanfattning: This doctoral dissertation presents the development and digital hardware realization of cardiac event detectors. Implantable medical appliances, as the cardiac pacemaker, have progressed from a life sustaining device to a device that considerably improves life quality for all ages. The number of electronic devices and household appliances in everyday live has an ongoing exponential growth. These devices contaminate their environment with electronic, magnetic or electromagnetic radiation. Pacemaker patients exposed to this environment may suffer due to malfunction of the pacemaker. Thus, the next generation of pacemakers require a low-power consuming event detector that provides reliable detection performance. In this thesis two papers that present an artificial neural network based event detector for R-wave detection are merged to an extended manuscript. The neural network functions as a whitening filter prior to a matched filter. It is shown how the neural network responds to sudden changes in the input sequence. An algorithm that determines the initial template for matched filtering is proposed, and a continuous update of the filter impulse response is implemented in order to track long-term changes in signal morphology. Furthermore, an updated threshold function is proposed which addresses amplitude variations in the electrogram. Noise suppression and classification performance under ``real-life situation'' are explored by analyzing recordings from databases of electrograms and noise. Finally, the suitability for pacemaker application is discussed. Four papers that present a low-power digital hardware implementation of a wavelet based event detector are merged and extended in the second part of this thesis. The theory of the wavelet filterbank is presented, and it is shown how the architecture was modified to achieve an area and power efficient silicon implementation. An algorithm is presented that determines automatically a threshold level during the initialization phase. A second operation mode is proposed to shut down major parts of the hardware, if the patient is at rest or in a ``low-noise'' environment. Power analysis on RTL-level shows that leakage power is the dominant factor in the total power figure. An estimate for leakage reduction is presented if sleep transistors are introduced between the supply rails and the logic that is shut-off in low-noise operation mode. The R-wave detector has been implemented in 0.13$,mu$m low-leakage CMOS technology. The design has been routed, and, thereafter, sleep transistors are introduced in the layout. Detection performance is evaluated by means of databases containing electrograms to which five types of exogenic and endogenic interference are added. The results show that reliable detection is obtained at moderate and low SNRs.