Surgery – Diagnostic testing – Cardiovascular
Reexamination Certificate
2001-06-29
2004-10-05
Schaetzle, Kennedy (Department: 3762)
Surgery
Diagnostic testing
Cardiovascular
C600S519000, C600S515000
Reexamination Certificate
active
06801802
ABSTRACT:
BACKGROUND OF THE DISCLOSURE
Patient monitors routinely process signals acquired from patients and provide a caregiver or clinician with computed estimates of features contained within those signals. In the case of ECG (electrocardiogram) signals, those features include heart rate and arrhythmias (i.e., disturbances in the normal cardiac rhythm).
One function of a patient monitor is to provide alarm mechanisms to alert the user when the patient's heart rate is outside of prescribed limits, or when arrhythmias occur. However, the presence of noise in the acquired ECG signal, due to a multiplicity of causes, results in a significant false positive alarm rate for these alarm conditions. Such false alarm rates decrease clinician productivity and satisfaction, and decrease the effectiveness of clinical alarm mechanisms.
One attempt at solving the problems of false positive alarms is a method that discriminates between good and bad sensor measurements and combines only the good readings to derive an optimal heart rate estimate. A Kalman filter is used to derive a fused estimate. However, the computational overhead required to filter readings using Kalman filter techniques from several sensors is extensive, and is ill-suited for many applications. Also, the use of a combined estimate of heart rate decreases the likelihood of acceptance by clinicians.
Accordingly, what is needed is an improved system and method for selecting physiological data from a plurality of physiological data sources. Further, what is needed is a system and method that improves the accuracy of a physiological output by selecting from among existing solutions, thereby reducing computational overhead and improving the likelihood of acceptance by clinicians. Further still, what is needed is a system and method for providing a more accurate estimate of patient heart rate, and a more accurate determination of alarm conditions, such as heart rate limit detection and arrhythmia. Such a system would decrease the number of false positive alarms, thereby providing a more intuitive and useful system for clinicians. Further yet, what is needed is a system and method for monitoring physiological signals of a patient that improves the clinical performance of the patient monitor, improves clinician productivity, improves patient care, and reduces product support costs.
The teachings hereinbelow extend to those embodiments which fall within the scope of the appended claims, regardless of whether they accomplish one or more of the above-mentioned needs.
SUMMARY OF EXEMPLARY EMBODIMENTS
According to one exemplary embodiment, a system for selecting physiological data from a plurality of physiological data sources includes first and second physiological data sources and a selection algorithm. The first physiological data source is configured to provide first physiological data. The first physiological data includes a first measurement of a physiological trait. The second physiological data source is configured to provide second physiological data. The second physiological data includes a second measurement of the physiological trait. The first and second measurements are based on different physiological characteristics. The selection algorithm is configured to receive the first and second physiological data and to select one of the first and second measurements as the output data for the physiological trait based on at least one of the first physiological data and the second physiological data.
According to another exemplary embodiment, a system for improving the accuracy of an arrhythmia detection algorithm includes an ECG data source, an arrhythmia detection algorithm, a hemodynamic heart rate data source and an alarm algorithm. The ECG data source is configured to receive ECG signals from an ECG sensor and to generate ECG heart rate data based on the received ECG signals. The arrhythmia detection algorithm is configured to receive the physiological data and to detect an arrhythmia condition. The hemodynamic heart rate data source is configured to receive signals from a hemodynamic heart rate sensor and to generate hemodynamic heart rate data based on the received signals. The alarm algorithm is configured to receive the ECG heart rate data and the hemodynamic heart rate data and to provide an alarm signal based on the detected arrhythmia condition and the hemodynamic heart rate data.
According to yet another exemplary embodiment, a system for improving the accuracy of a heart rate limit alarm includes first and second heart signal sources and an alarm algorithm. The first heart signal source is configured to receive first signals from a first sensor and to generate a first heart rate based on the first signals. The second heart signal source is configured to receive second signals from a second sensor. The second sensor monitors a different physiological characteristic than the first sensor. The second heart signal source is configured to generate a second heart rate based on the second signals. The alarm algorithm is configured to receive the first and second heart rate, to determine the most accurate of the heart rates, and to only generate a heart rate limit alarm signal when the most accurate heart rate is outside a predetermined limit.
According to still another exemplary embodiment, a method of selecting physiological data from a plurality of physiological data sources includes providing first physiological data comprising a first measurement of a physiological trait and providing second physiological data comprising a second measurement of the physiological trait. The first and second measurements are based on different physiological characteristics. The method further includes selecting one of the first and second measurements as the output data for the physiological trait based on at least one of the first physiological data and the second physiological data.
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Partial European Search Report; European Application No. 02254543.8; mailed Oct. 27, 2003.
Farrell Robert M.
Sitzman David A.
Andrus Sceales Starke & Sawall LLP
GE Medical Systems Information Technologies Inc.
Schaetzle Kennedy
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