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Patient monitoring and diagnosis assistance by integratingstatistical and artificial intelligence tools
Abstract
Patient monitoring by automated data collection has created new challenges for health care professionals in their efforts to extract useful information from raw data. New online monitoring devices may generate large amounts of data that must be interpreted quickly and accurately. The use of statistical methods and artificial intelligence tools to summarize and interpret high frequency physiologic data such as the electrocardiogram (EKG) are investigated. The development of a methodology and its associated tools for real-time patient data monitoring and diagnosis was accomplished by using the commercial programming environments MATLAB and G2, a real-time knowledge-based system (KBS) development shell. A KBS was developed that incorporates various statistical methods with a rule-based decision system to detect abnormal situations, provide preliminary interpretation and diagnosis, and to report these findings to the physician
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The noise sensitivities for nine different QRS detection algorithms were measured for a normal, single-channel lead II, synthesized ECG corrupted with five different types of synthesized noise. The noise types were electromyographic interference, 60 Hz powerline interference, baseline drift due to respiration, abrupt baseline shift, and a composite noise constructed from all of the other noise types. The percentage of QRS complexes detected, the number of false positives, and the detection delay were measured. None of the algorithms were able to detect all QRS complexes without any false positives for all of the noise types at the highest noise level. Algorithms based on amplitude and slope had the highest performance for EMG-corrupted ECG. An algorithm using a digital filter had the best performance for the composite noise corrupted data.
The algorithm presented is based on the methods of percent of time
above or below thresholds (PTABT), variability of threshold crossing
intervals (TCI) and peak similarity in autocorrelation function (ACF).
The algorithm offers a way to identify the ventricular fibrillation
(VFib) and nonVFib rhythms with several features of electrocardiogram
(ECG) rhythm and achieves a high sensitivity and specificity