Digital to Analog Transformation and Reconstruction of ECG Data

The advantage of using cardiac biometrics over existing methods is that heart signatures are more difficult to forge compared to other biometric devices. Iris scanners can be fooled by contact lenses and sunglasses, and a segment of the population does not have readable fingerprints due to age or working conditions. Previous electrocardiographic signals employed a single template and compared that template with new test templates by means of cross-correlation or linear-discriminant analysis.The benefit of this technology over competing cardiac biometric methods is that it is more reliable with a significant reduction in error rates. The benefit of this technology is that it creates a probabilistic model of the electrocardiographic features of a person instead of a single signal template of the average heartbeat. The probabilistic model described as Gaussian mixture model allows various modes of the feature distribution, in contrast to a template model that only characterizes a mean waveform. Another advantage is that the model uses both physiological and anatomical characterization of the heart, unlike other methods that mainly use only physiological characterization of the heart. By combining features from different leads, the heart of the person is better characterized in terms of anatomical orientation because each lead represents a different projection of the electrical vector of the heart. Thus, employing multiple electrocardiographic leads provides a better performance in subject verification or identification.

Microphones and stethoscopes are regularly used by physicians to detect sounds when monitoring physiological conditions. These monitors are coupled directly to a person's body and measure in certain bandwidths either by listening or by recording the signals. The physiological processes such as respiration and cardiac activity are reflected in a different frequency bandwidth from 0.01 Hz to 500 Hz. This technology can monitor physiological conditions in the entire bandwidth range. Signals can also be wirelessly transmitted, using Bluetooth, to other recording devices at any other location.

The EFI imaging platform consists of a sensor array, processing equipment, and an output device. By registering voltage differences at multiple points within the sensor array, the EFI system can calculate the electrical potential at points removed from the sensor. Using techniques similar to computed tomography, the electrical potential data can be assembled into a three-dimension map of the magnitude and direction of electric fields. Since objects interact with electric fields differently based on their shape and dielectric properties, this electric field data can then be used to understand shape, internal structure, and dielectric properties (e.g., impedance, resistance) of objects in three dimensions. The EFI sensor can be used on its own to see electric fields or image electric fieldemitting objects near the sensor (e.g., to evaluate leakage from poorly shielded wires or casings). For evaluation of objects that do not produce an electric field, NASA has developed generator that emits a low-current, human-safe electrostatic field for snapshot evaluation of objects. Additionally, an alternative EFI system optimized to evaluate electric fields at significant distances (greater than 1 mile) is being developed for weather-related applications.

The technology utilizes four cutting-edge sensor technologies to enable minimally- or non-invasive analysis of various biological samples, including saliva, breath, and blood. The combination of technologies and sample pathways have unique advantages that collectively provides a powerful analytical capability. The four key technology components include the following: (1) the carbon nanotube (CNT) array designed for the detection of volatile molecules in exhaled breath; (2) a breath condenser surface to isolate nonvolatile breath compounds in exhaled breath; (3) the miniaturized differential mobility spectrometer (DMS) -like device for the detection of volatile and non-volatile molecules in condensed breath and saliva; and (4) the miniaturized circular disk (CD)-based centrifugal microfluidics device that can detect analytes in any liquid sample as well as perform blood cell counts. As an integrated system, the device has two ports for sample entry a mouthpiece for sampling of breath and a port for CD insertion. The breath analysis pathway consists of a CNT array followed by a condenser surface separating liquid and gas phase breath. The exhaled breath condensate is then analyzed via a DMS-like device and the separated gas breath can be analyzed by both CNT sensor array again and by DMS detectors.

Each of two analog signals (channels A and B) is converted to a digital bit stream by phase correcting it and comparing it to an average of itself at a sampling clock rate f. The hard-limited conversions of A and B are bitwise compared to measure the level of similarity between the two by the OBDC. This similarity measurement X is equal to the maximum possible Hamming distance (N bits in disagreement) minus the measured number of bits in disagreement. The OBDC functions are embedded into a field programmable gate array (FPGA). The OBDC is made up of two shift registers containing the current sample values (of length N) from each of the two input channels (A and B). During each sample clock, a new sample from each A and B input is clocked into the input linear shift register for each respective channel; this input shifts the current values in the linear shift register. Once the inputs have been clocked in, the correlation routine can start. Once the correlation value has been calculated, this result is forwarded to compare with the max correlation value register. If the X value is greater than the current max correlation value, then the max correlation value becomes X, and the shift counter register is latched and put into the best correlation index register, providing the index of the current best correlation. This index is the number of sample clock periods difference between the two input signals and thus, for sample clock rate f, indicates the delay between the signals A and B. This is an early-stage technology requiring additional development. Glenn welcomes co-development opportunities.