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Intelligent patient monitoring has continued to enhance and develop in hospitals from the early stage of monitoring systems. So, practical medical monitoring devices to react to patient conditions and also detect unwanted clinical conditions are very important. Aims: Our algorithm uses pulsatile waveforms and simultaneous ECG in order to detect and enhance the determination of the life-threatening arrhythmia alarms in the context of the PhysioNet/Computing in Cardiology 2015 Challenge. Methods: the analysis steps included: In our algorithm, features for training the random forest classifier (RFC) were derived from applying the signal quality assessment (SQI) to both pulsatile signals and ECG signal too. Primarily, preprocessing step was done by applying the band pass filters to multiple sources, such as arterial blood pressure (ABP), photoplethysmogram (PPG) and electrocardiogram (ECG) and then heart beat detection through the adaptive threshold were determined. The SQI approach for the pulsatile signals was applied through the ppgSQI and the jSQI algorithms and also spectral and statistical features were extracted from ECG channel as well. In a next process, the heuristic thresholding of each ABP pulse are estimated with the function of abpfeature and also heart rate (HR) features from the ECG and pulsatile signals in a segment before the alarm was extracted and computed. Also, for assessing regularity of the beats, inter-beat intervals for pulsatile waveforms and also checking the frequency maxims for better suppression of ventricular flutter/fibrillation in the ECG channel were computed. Finally, RFCs were trained with arrhythmia features set for every type of the arrhythmia. Results: our algorithm was trained with the use of 750 records provided by PhysioNet dataset for the challenge of 2015 and according to the types of arrhythmia, our overall scores varied. Our average score for our best performance for all the alarms in terms of true positive were 67% and for true negative were 77% and for false negative were 1.8%.

computing in cardiology

Enhancement of Life:threatening Arrhythmias Discrimination in the Intensive Care Unit with Morphological Features and Interval Features Extraction via Random Forest Classifier