Health monitoring of implantable biological electronic systems by statistical pattern recognition techniques

A wavelet-packet energy decomposition, and time frequency analysis based approach has been developed to monitor system-level damage in implantable biological electronic systems such as pacemakers and defibrillators. The approach focuses is on the pre-failure space and methodologies for quantification of failure in electronic equipment. Presented methodologies are applicable at the system-level for identification of impending failures to trigger repair or replacement significantly prior to failure. Leading indicators of shock-damage have been developed to correlate with the damage initiation and progression in under variety of stresses in electronic system. Currently, the built-in stress test (BIST) which is extensively used for diagnostics or identification of failure, is focused on reactive failure detection and provides limited insight into reliability and residual life. Statistical pattern recognition techniques including, wavelet packet energy decomposition, and time-frequency (TFA) techniques have been investigated for system identification, condition monitoring, and fault detection and diagnosis in electronic systems. Reduced reliance on system analysis helps avoid errors which otherwise may render the process of fault detection and diagnosis quite complex and dependent on the skill of the analyst. Explicit finite element models have been developed and various kinds of failure modes have been simulated such as solder ball cracking, package fall off and solder ball failure. The above damage monitoring approach is not based on electrical continuity and hence can be applied to any biological system irrespective of the interconnections. The damage index developed provides parametric damage progression data, thus removing the limitation of current failure testing, where the damage progression can not be monitored.