Hewlett-packard Company

Hewlett-packard Corporation

James M Lindauer

Richard D Pering

Don Goodnature

John M Heumann

Earl C Herleikson

Eric D Helfenbein

Michael C Higgins

During sampling, a finite impulse response (FIR) filter separates a composite signal into two components, non-cardiac physiological phenomena and cardiac artifacts. The length of the filter window dynamically varies to produce an output signal in which the cardiac frequency and any associated harmonics are suppressed with a fixed delay. The FIR filter is applied to the patient signal and lags the patient signal by at least one interval between triggering events. When the present interval between triggering events passes by the filter window, the window length is gradually adjusted to have the same length as the next period such that the window length is centered between two adjacent triggering events. The difference between the present interval and the next interval is evenly distributed or subtracted across the filter window between centerings.

Real-time artifact removal from waveforms using a dynamic filter having

During sampling, a finite impulse response (FIR) filter separates a composite signal into two components, non-cardiac physiological phenomena and cardiac artifacts. The length of the filter window dynamically varies to produce an output signal in which the cardiac frequency and any associated harmonics are suppressed. The FIR filter is applied to the patient signal and lags the patient signal by at least one interval between triggering events. When the present interval between triggering events passes by the filter window, the window length is gradually adjusted to have the same length as the next period such that the triggering events are synchronized with the endpoints of the filter window at the start of each interval. The difference between the present interval and the next interval is evenly distributed or subtracted across the filter window between synchronizations.

Dynamic filter for real-time artifact removal from waveforms

A high-accuracy QRS detector detects 'activity' on the ECG waveform. Activity results from a steep slope of one sense followed shortly thereafter by a steep slope of the opposite sense. Low-pass filtering activity and multiplying by 2.5 yields a noise threshold. An activity peak which rises above this threshold may be a QRS complex. The detector waits three-fourths the average R to R interval after detecting a peak to allow for greater peaks to be detected. If none are, the peak is confirmed if it is either at least half the magnitude of the previous peak, or if it occurs at least three-fourths the average R to R interval after the last detected peak. This high-accuracy detector may be coupled with a fast QRS detector by providing it with the peak values of the previous three detections. The fast detector will detect if the slope is greater than three-fourths the average of the previous three detections, greater than the noise threshold, and more than 160 ms has elapsed since its last detection.

QRS detector for defibrillator/monitor

A computer system comprises a computer, a monitor, a keyboard, a mouse, and an instrumentation system. The instrumentation system is coupled to a patient to gather data about a patient over time. The data is arranged in a flowsheet, only a portion of which can be displayed at one time. Indicators are used to indicate the presence of data off-display meeting a preselected criterion. This criterion can be the existence of a value entry or the existence of a value entry exceeding a threshold, for example. Upon activation of an indicator, the display scrolls to the next flowsheet neighborhood containing an entry meeting the criterion. In this way, a physician or other operator can navigate efficiently and rapidly through a large flowsheet. The invention allows an operator to skip large quantities of empty cells and uninteresting data when the flowsheet is sparsely filled.

Guided data presentation

A method for displaying the joint variation of two or more dependent numerical variables v.sub.1 and v.sub.2 with respect to a third, independent numerical variable v.sub.3. For each of a sequence of numerical values of v.sub.3, the coordinate pairs (v.sub.1 (v.sub.3), v.sub.2 (v.sub.3)) are displayed on a two-dimensional Cartesian graph of v.sub.1 versus v.sub.2. A cursor or other indicator is provided on this graph that identifies the numerical value of the third variable v.sub.3 at any of the original sequence of such values. The cursor position is continuously interpolated between two consecutive numerical values of v.sub.3, corresponding to continuous variation of v.sub.3 between these two consecutive numerical values. The joint variation of v.sub.1 and v.sub.2 is also displayed by provision of two univariate graphs that exhibit v.sub.1 and v.sub.2 separately as functions of the third variable v.sub.3, with a suitable cursor or other movable indicator associated with each graph. The joint variation of v.sub.1 and v.sub.2 is also displayed as a numerical table of the values of v.sub.1, v.sub.2 and v.sub.3, with a cursor indicating the current choice of value of the variable v.sub.3. The graph of v.sub.1 (v.sub.3) versus v.sub.2 (v.sub.3) may be provided with an overlay showing normal and/or abnormal ranges of the coordinate pair (v.sub.1, v.sub.2).

Display of multiple variable relationships

Apparatus for classifying beats in EKG signals from two sets of electrodes in which the respective signals are applied to different channels and the classification is derived from a simultaneous analysis of the signals provided by the two channels based on the quality of the channels and physiological factors of the signals.

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San Francisco, CA, United States of America

James M Lindauer