Ultra Low Power Ambulatory Diagnostic Algorithm for the QRS Complex in EKG

Technology #14748

Pulse-Based Electrocardiogram Signal Processing Detects Accurately the Key QRS Complex from Noise, While Drastically Reducing Size and Power Required

This diagnostic pulse based algorithm isolates the needed QRS complex and filters noise from electrocardiogram signals, while reducing size and power consumption. The QRS complex is the graphical deflections seen on an electrocardiogram, the standard tool used to monitor heart function. Regular monitoring can lead to early detection of potentially fatal cardiovascular signals. Cardiovascular disease, the leading cause of death in the world, caused an estimated 30 percent of all deaths in 2008. By 2030, more than 23 million people worldwide will die annually from cardiovascular disease. Many of these deaths are preventable with treatment and proper detection using signals such as the electrocardiogram. Available electrocardiogram technologies that isolate the needed QRS signal from noise are based on digital signal processors with bulky circuitry and require large power consumption to recognize the QRS signals. This diagnostic tool developed by UF researchers uses integrate and fire pulse train finite state machines to automatically interpret electrocardiogram signals and identify potential health threats, while drastically reducing the size and power consumption needed in continuous ambulatory heart monitoring.

Application

Continuous ambulatory monitoring diagnostic tool for QRS complex detection

Advantages

  • Converts electrocardiogram signals into pulse representations, lowering power consumption
  • Isolates QRS events from noise, enabling clear monitoring and easy analysis of signals
  • Lowers power consumption, extending operational time and battery life
  • Implementable in simple hardware design, reducing manufacturing costs and increasing ease of mobile use

Technology

Integrate and fire sampling encodes a signal in a series of time events rather than as uniformly spaced amplitude values, reducing power consumption by two orders of magnitudes versus existing digital signal processing. It also leads to revolutionary new ways to build ultra low power devices based on finite state automata that recognize events of interest in physiologic monitoring. The technology is fully compatible with existing electrocardiogram designs, replacing existing spacious and power-demanding DSP circuitry, and suffers no loss of performance while providing ultra-low power consumption.