US 5,778,881 A · Method and apparatus for discriminating P and R waves

Abstract

This is a method and apparatus for the automated discrimination of cardiac events of interest, including P-waves, R-waves, T-waves, and specific arrhythmic sequences, in EGM signals for data storage in an implantable monitor or to control operations of an implantable cardiac stimulator through the use of Hidden Markov Modeling techniques and a reduced set of observations. The number of computations and computation time during a heart cycle is reduced by timing the frames of A-EGM samples to the detection of A-SENSE events by the atrial sense amplifier. The A-EGM sample frame is defined in a window preceding and following each A-SENSE event. The A-EGM sample frames are wavelet transformed, and a number of selected W.T. coefficients for each sample frame are saved in a buffer. Each set of saved W.T. coefficients therefore represents either a P-wave or an intrinsic or paced far field R-wave (including fusion beats) unless noise continuously causes A-SENSE events to occur. When the V-SENSE event occurs, an R-trigger is generated, and each set of saved W.T. coefficients is subjected to the HMM algorithm for a determination as to whether the preceding (and any concurrent) A-SENSE events from which the saved W.T. coefficients were derived are P-waves or R-waves. In addition, when the V-SENSE occurs, the HMM algorithm determines stochastically whether or not the successive sets of saved W.T. coefficients represent P-P sequences, far field R-R sequences or P-R sequences or the like.

Claims18

Dependent claims

A method of identifying P-waves and R-waves in an electrocardiogram using Hidden Markov Modeling (HMM), wherein P-waves and R-waves may each be characterized as a state separated by state transitions in a hidden state sequence and wherein there are discrete probabilities that the states will transition from one to the other in a predetermined order, the method comprising the steps of:

sensing the electrocardiogram from at least one electrode;
continuously sampling the sensed electrocardiogram at a predetermined sampling rate and providing a sample value at each sample time;
detecting an event of interest comprising one of the P-wave or R-wave in the electrocardiogram;
framing a sample data set of sample values as a data frame in response to the detection of an event of interest;
wavelet transforming the data frame of sample values to generate m wavelet transformed coefficients;
selecting a sub-set of wavelet coefficients from among the wavelet coefficients representing an observation vector correlated to each data frame;
applying the HMM algorithm to each observation vector to generate the hidden state sequence;
and
from the hidden state sequence, determining whether the event of interest is a P-wave or an R-wave.

6 more elements

A method of identifying P-waves and far field R-waves in an atrial electrogram using Hidden Markov Modeling (HMM), wherein P-waves and far-field R-waves may each be characterized as a state separated by state transitions in a hidden state sequence and wherein there are discrete probabilities that the states will transition from one to the other in a predetermined order in a cardiac cycle, the method comprising the steps of:

sensing the atrial electrocardiogram from at least one electrode positioned in sensing relation to the patient's atria;
continuously sampling the sensed atrial electrogram at a predetermined sampling rate and providing an atrial sense sample value at each sample time;
detecting an event of interest comprising one of the P-wave or far field R-wave in the atrial electrogram;
framing a sample data set of m atrial sense sample values as a data frame in response to the detection of an event of interest;
wavelet transforming the data frame of m atrial sense sample values to generate m wavelet transformed coefficients;
selecting C.sub.W wavelet coefficients from among the m wavelet coefficients representing an observation vector correlated to each data frame;
retaining the observation vectors correlated to each data frame through a cardiac cycle;
determining the end of a cardiac cycle correlated to the far field R-wave;
upon determining the end of the cardiac cycle, applying the HMM algorithm to each observation vector retained until the end of the cardiac cycle to generate the hidden state sequence;
and
from the hidden state sequence, determining whether the event of interest is a P-wave or an R-wave.

8 more elements

An apparatus for identifying P-waves and R-waves in an electrocardiogram using Hidden Markov Modeling (HMM), wherein P-waves and R-waves may each be characterized as a state separated by state transitions in a hidden state sequence and wherein there are discrete probabilities that the states will transition from one to the other in a predetermined order, the apparatus further comprising:

means for sensing the electrocardiogram from at least one electrode;
means for continuously sampling the sensed electrocardiogram at a predetermined sampling rate and providing a sample value at each sample time;
means for detecting an event of interest comprising one of the P-wave or R-wave in the electrocardiogram;
means for means for framing a sample data set of sample values as a data frame in response to the detection of an event of interest;
means for wavelet transforming the data frame of sample values to generate m wavelet transformed coefficients;
means for selecting a sub-set of wavelet coefficients from among the wavelet coefficients representing an observation vector correlated to each data frame;
means for applying the HMM algorithm to each observation vector to generate the hidden state sequence;
and
from the hidden state sequence, determining whether the event of interest is a P-wave or an R-wave.

6 more elements

An apparatus for identifying P-waves and far field R-waves in an atrial electrogram using Hidden Markov Modeling (HMM), wherein P-waves and R-waves may each be characterized as a state separated by state transitions in a hidden state sequence and wherein there are discrete probabilities that the states will transition from one to the other in a predetermined order in a cardiac cycle, the apparatus comprising:

means for sensing the atrial electrocardiogram from at least one electrode positioned in sensing relation to the patient's atria;
means for continuously sampling the sensed atrial electrogram at a predetermined sampling rate and providing an atrial sense sample value at each sample time;
means for detecting an event of interest comprising one of the P-wave or far field R-wave in the atrial electrogram;
means for framing a sample data set of m atrial sense sample values as a data frame in response to the detection of an event of interest;
means for wavelet transforming the data frame of m atrial sense sample values to generate m wavelet transformed coefficients;
means for selecting C.sub.W wavelet coefficients from among the m wavelet coefficients representing an observation vector correlated to each data frame;
means for retaining the observation vectors correlated to each data frame through a cardiac cycle;
means for determining the end of a cardiac cycle correlated to the far field R-wave;
means operable upon determining the end of the cardiac cycle for applying the HMM algorithm to each observation vector retained until the end of the cardiac cycle to generate the hidden state sequence;
and
means for determining from the hidden state sequence whether the event of interest is a P-wave or an R-wave.

8 more elements

Dependencies are inferred from the claim text.

Method and apparatus for discriminating P and R waves

Life of this patent

Method and apparatus for discriminating P and R waves

Life of this patent

Abstract

Claims18