Asynchronous ECG time sampling: Saving bits with Golomb-Rice encoding

We present a technique for online compression of ECG signals using the Golomb-Rice encoding algorithm. This is facilitated by a novel time encoding asynchronous analog-to-digital converter targeted for low-power, implantable, long-term bio-medical sensing applications. In contrast to capturing the actual signal (voltage) values the asynchronous time encoder captures and encodes the time information at which predefined changes occur in the signal thereby minimizing the sensor's energy use and the number of bits we store to represent the information by not capturing unnecessary samples. The time encoder transforms the ECG signal data to pure time information that has a geometric distribution such that the Golomb-Rice encoding algorithm can be used to further compress the data. An overall online compression rate of about 6 times is achievable without the usual computations associated with most compression methods.

fMRI-Compatible Electromagnetic Haptic Interface

A new haptic interface device is suggested, which can be used for functional magnetic resonance imaging (fMRI) studies. The basic component of this 1 DOF haptic device are two coils that produce a Lorentz force induced by the large static magnetic field of the MR scanner. A MR-compatible optical angular encoder and a optical force sensor enable the implementation of different control architectures for haptic interactions. The challenge was to provide a large torque, and not to affect image quality by the currents applied in the device. The haptic device was tested in a 3T MR scanner. With a current of up to 1A and a distance of 1m to the focal point of the MR-scanner it was possible to generate torques of up to 4 Nm. Within these boundaries image quality was not affected.