Identification of ECG signal pattern changes to reduce the incidence of Ventricular Tachycardia false alarms

Abstract

The paper focuses on the reduction of the false alarms in the Intensive Care Units (ICU). Five alarm types were analyzed in this study: Asystole, Extreme Bradycardia, Extreme Tachycardia, Ventricular Tachycardia and Ventricular Flutter/Fibrillation. Most of the analyzed alarm types rely on the quality of the heart rate estimation. The false alarm reduction algorithms analyzed in this paper use the quality estimate of the arterial blood pressure signal from which the heart rate is estimated and additionally the results of heart beat detection in two ECG signals are analyzed before making the final decision about the true or false alarm type.The most attention in this paper is focused on the correct detection of Ventricular Tachycardia alarms. The decision about the true or false alarm is made according to RR interval variation and changes of QRS complex shape features. A subset of sample entries data of the Physionet/CinC Challenge 2015 is used to test the proposed algorithm modifications. The false alarm detection according to the RR interval variation gave 49% TPR, 49% TNR (score 34.82) for the Phase I Entries data set and 46% TPR, 51% TNR (score 34.59) for the Phase II Entries data set. The VT alarm detection algorithm based on the features related to the the ECG waveform shape has increased the VT score for Phase I Entries data set to 41.98.

Extraction and investigation of biosignal features for visual discomfort evaluation

“…The research results, presented in this chapter are published in three papers (Abromavičius 2017;Serackis et al 2015) and announced at international "CINC" (Nice, 2015), "eSTREAM" (Vilnius, 2017) and national "Science -Future of Lithuania" (Vilnius, 2017) scientific conferences.…”

“…• trys publikacijos atspausdintos tarptautinių konferencijų straipsnių rinkiniuose, cituojamuose ISI Proceedings duomenų bazėje , Serackis et al 2015 Pagrindiniai disertacijos rezultatai paskelbti aštuoniuose mokslinėse konferencijose:…”

Intensive Care Unit False Alarm Identification Based on Convolution Neural Network

“…The rule-based decision-making system focuses on accurate heart rate estimation using multi-modal signals, and many algorithms and decision rules are fine-tuned via approaches including invalid data or noise detection [12]- [14], signal filtering [15], signal quality indices(SQIs) or assessment calculation(SQA) [15]- [18], pulse detection [15], [19], QRS detection [12], [14], [17], [18], [20], [21], spec-tral features extraction [16], heartbeat estimation [12], [14], [17], and multi-rules fusion [12], [14], [18], [20], etc. For example, in the challenge, Plesinger and colleagues [14] established a model based on a series of decision rules and obtained a final score of 81.39 on the hidden testing set and ranked 1st in the competition.…”

ICU False Alarm Identification Based on Convolution Neural Network

“…In the decision-making process, according to the characteristics of the signal, many heart rate estimating algorithms and decision rules are fine-tuned. These improvements include invalid data or noise detection [4,5,6], signal filtering [7], signal quality indices(SQIs) or assessment(SQA) [7,8,9,10], pulse detection [7,11], QRS detection [4,6,9,10,12,13], spectral features [8], heartbeat estimation [4,6,9], and multi-rules fusion [4,6,10,12], etc. For example, in the challenge, Plesinger and colleagues [6] established a model based on a series of decision rules and obtained a final score of 81.39 on the hidden testing set of the competition and ranked 1st in the competition.…”