Concept

BAM (Bio-signal Analysis and Monitoring) provides a new approach for detecting and analyzing complex neurophysiological conditions. Single parameter biometrics do not adequately characterize complex neurophysiological conditions like stress or chronic pain. Many discrete biometric parameters, however, do correlate with certain specific aspects of several complex biological conditions. The BAM hypothesis is that multiple parameters describing different aspects of a complex biological system can be synergistically processed to identify and accurately characterize important neurophysiological conditions like stress, chronic pain, or hidden cardiovascular (CV) issues.

BAM has two principal components, sensing and analysis:

Biometric Sensing - The collection of multiparameter biometric data (EEG, ECG, blood pressure, etc.) that reflect different perspectives on central and autonomic nervous system activity and status.

Bio-Signal Analysis (BSA) - The processing of multiparameter biometric data from biologic sensors to identify, discriminate, and characterize specific neurologic states. BSA offers new methods, not available to previous generations of researchers, to mine and correlate important information from a collection of biologic signals.

The initial focus of BAM development is a system to noninvasively monitor and analyze certain cardiovascular conditions not otherwise detectable by conventional methods.   The heart is controlled by the sympathetic and parasympathetic components of the autonomic nervous system, and the electro-cardiogram (ECG) is an excellent reflection of the autonomic nervous system for our starting point.

The ECG is a well known complex bio-signal that quite accurately shows the critical sequences of bioelectrical activity in each heartbeat. The ECG signal is comprised of at least six well known waveform components, and numerous ways have beem developed to characterize individual ECG signal components in relation to specifc aspects of heart function.

When a heart has been damaged the ECG clearly reflects how it has been affected. Prior to the onset of major biological damage, the ECG may look fairly normal to most observers. Our current BAM studies are attempting to determine if concurrent multiparameter ECG analysis can identify hidden precursor conditions that lead to potentially damaging CV events. The detection of precusor conditions can allow preemptive treatment before permanent CV damage occurs.

Recent studies have shown that subtle changes in certain ECG signal components may be precursors to damaging cardiac events like atrial fibrillation. Such changes are often reflected in the timing and temporal variability of a specific ECG signal component, such as heart rate variability (HRV). CV researchers have tended to focus on the relationship of a single ECG signal component parameter to one specific type of cardiac problem, and few have studied how multiple CV parameters may interact to more accurately describe several different CV conditions.

NeuroTraq’s BAM approach employs modern and proprietary multivariate analysis methods to find and correlate multiple ECG parameters for greater specificity and accuracy. This should open the doors for a new CV research paradigm leading to more cost effective prevention and treatment methods for CV diseases.