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Long-term surface ECG is routinely used to diagnose paroxysmal arrhythmias. However, this method only provides information about the heart's electrical activity. To this end, we investigated a novel esophageal catheter that features synchronous esophageal ECG and acceleration measurements, the latter being a record of the heart's mechanical activity. The acceleration data were quantified in a small study and successfully linked to the activity sequences of the heart in all subjects. The acceleration signals were additionally transformed into motion. The extracted cardiac motion was proved to be a valid reference input for an adaptive filter capable of removing relevant baseline wandering in the recorded esophageal ECGs. Taking both capabilities into account, the proposed recorder might be a promising tool for future long-term heart monitoring. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]