Life-threatening false alarm rejection in ICU: using the rule-based and multi-channel information fusion method

Decision fusion in healthcare and medicine: a narrative review

OBJECTIVE

To provide an overview of the decision fusion (DF) technique and describe the applications of the technique in healthcare and medicine at prevention, diagnosis, treatment and administrative levels.

BACKGROUND

The rapid development of technology over the past 20 years has led to an explosion in data growth in various industries, like healthcare. Big data analysis within the healthcare systems is essential for arriving to a value-based decision over a period of time. Diversity and uncertainty in big data analytics have made it impossible to analyze data by using conventional data mining techniques and thus alternative solutions are required. DF is a form of data fusion techniques that could increase the accuracy of diagnosis and facilitate interpretation, summarization and sharing of information.

METHODS

We conducted a review of articles published between January 1980 and December 2020 from various databases such as Google Scholar, IEEE, PubMed, Science Direct, Scopus and web of science using the keywords decision fusion (DF), information fusion, healthcare, medicine and big data. A total of 141 articles were included in this narrative review.

CONCLUSIONS

Given the importance of big data analysis in reducing costs and improving the quality of healthcare; along with the potential role of DF in big data analysis, it is recommended to know the full potential of this technique including the advantages, challenges and applications of the technique before its use. Future studies should focus on describing the methodology and types of data used for its applications within the healthcare sector.

Weighted Random Forests to Improve Arrhythmia Classification

Construction of an ensemble model is a process of combining many diverse base predictive learners. It arises questions of how to weight each model and how to tune the parameters of the weighting process. The most straightforward approach is simply to average the base models. However, numerous studies have shown that a weighted ensemble can provide superior prediction results to a simple average of models. The main goals of this article are to propose a new weighting algorithm applicable for each tree in the Random Forest model and the comprehensive examination of the optimal parameter tuning. Importantly, the approach is motivated by its flexibility, good performance, stability, and resistance to overfitting. The proposed scheme is examined and evaluated on the Physionet/Computing in Cardiology Challenge 2015 data set. It consists of signals (electrocardiograms and pulsatory waveforms) from intensive care patients which triggered an alarm for five cardiac arrhythmia types (Asystole, Bradycardia, Tachycardia, Ventricular Tachycardia, and Ventricular Fultter/Fibrillation). The classification problem regards whether the alarm should or should not have been generated. It was proved that the proposed weighting approach improved classification accuracy for the three most challenging out of the five investigated arrhythmias comparing to the standard Random Forest model.

Is It Safe for Patients With Cardiac Channelopathies to Undergo Routine Dental Care? Experience From a Single-Center Study

Background Brugada syndrome and long-QT syndrome may account for at least one third of unexplained sudden cardiac deaths. Dental care in patients with cardiac channelopathies is challenging because of the potential risk of life-threatening events. We hypothesized that the use of local dental anesthesia with lidocaine with and without epinephrine is safe and does not result in life-threatening arrhythmias in patients with channelopathies. Methods and Results We performed a randomized, double-blind pilot trial comparing the use of 2% lidocaine without a vasoconstrictor and with 1:100 000 epinephrine in 2 sessions of restorative dental treatment with a washout period of 7 days (crossover trial). Twenty-eight-hour Holter monitoring was performed, and 12-lead electrocardiography, digital sphygmomanometry, and anxiety scale assessments were also conducted at 3 time points. Fifty-six dental procedures were performed in 28 patients (18 women, 10 men) with cardiac channelopathies: 16 (57.1%) had long-QT syndrome, and 12 (42.9%) had Brugada syndrome; 11 (39.3%) of patients had an implantable defibrillator. The mean age was 45.9±15.9 years. The maximum heart rate increased after the use of epinephrine during the anesthesia period from 82.1 to 85.8 beats per minute (P=0.008). In patients with long-QT syndrome, the median corrected QT was higher, from 450.1 to 465.4 ms (P=0.009) at the end of anesthesia in patients in whom epinephrine was used. The other measurements showed no statistically significant differences. No life-threatening arrhythmias occurred during dental treatment. Conclusions The use of local dental anesthesia with lidocaine, regardless of the use of a vasoconstrictor, did not result in life-threatening arrhythmias and appears to be safe in stable patients with cardiac channelopathies. Clinical Trial Registration URL: http://www.clinicaltrials.gov. Unique identifier: NCT03182777.

Reducing False Arrhythmia Alarms Using Different Methods of Probability and Class Assignment in Random Forest Learning Methods

The literature indicates that 90% of clinical alarms in intensive care units might be false. This high percentage negatively impacts both patients and clinical staff. In patients, false alarms significantly increase stress levels, which is especially dangerous for cardiac patients. In clinical staff, alarm overload might lead to desensitization and could result in true alarms being ignored. In this work, we applied the random forest method to reduce false arrhythmia alarms and specifically explored different methods of probability and class assignment, as these affect the classification accuracy of the ensemble classifiers. Due to the complex nature of the problem, i.e., five types of arrhythmia and several methods to determine probability and the alarm class, a synthetic measure based on the ranks was proposed. The novelty of this contribution is the design of a synthetic measure that helps to leverage classification results in an ensemble model that indicates a decision path leading to the best result in terms of the area under the curve (AUC) measure or the global accuracy (score). The results of the research are promising. The best performance in terms of the AUC was 100% accuracy for extreme tachycardia, whereas the poorest results were for ventricular tachycardia at 87%. Similarly, in terms of the accuracy, the best results were observed for extreme tachycardia (91%), whereas ventricular tachycardia alarms were the most difficult to detect, with an accuracy of only 51%.

Game Theoretic Approach for Systematic Feature Selection; Application in False Alarm Detection in Intensive Care Units

Intensive Care Units (ICUs) are equipped with many sophisticated sensors and monitoring devices to provide the highest quality of care for critically ill patients. However, these devices might generate false alarms that reduce standard of care and result in desensitization of caregivers to alarms. Therefore, reducing the number of false alarms is of great importance. Many approaches such as signal processing and machine learning, and designing more accurate sensors have been developed for this purpose. However, the significant intrinsic correlation among the extracted features from different sensors has been mostly overlooked. A majority of current data mining techniques fail to capture such correlation among the collected signals from different sensors that limits their alarm recognition capabilities. Here, we propose a novel information-theoretic predictive modeling technique based on the idea of coalition game theory to enhance the accuracy of false alarm detection in ICUs by accounting for the synergistic power of signal attributes in the feature selection stage. This approach brings together techniques from information theory and game theory to account for inter-features mutual information in determining the most correlated predictors with respect to false alarm by calculating Banzhaf power of each feature. The numerical results show that the proposed method can enhance classification accuracy and improve the area under the ROC (receiver operating characteristic) curve compared to other feature selection techniques, when integrated in classifiers such as Bayes-Net that consider inter-features dependencies.

Electrocardiogram failure in the operating room - bench testing to prevent bed-side disaster

Electrocardiogram (ECG) false alarms are common in electrically-hostile peri-operative environments. Newer integrated monitoring, with sophisticated hardware and software, has the potential to minimise artefacts. However, monitoring issues continue to occur, with the potential for critical incidents and unnecessary and harmful interventions. We describe the root cause analysis of a series of apparent ECG flatline asystolic events that appeared in the operating room shortly after the introduction of new intra-operative monitoring systems. Clinical events and biomedical laboratory testing revealed complete loss of ECG signal with increasing resistance. The new ECG systems had incorporated both software and hardware changes to improve the fidelity of signal acquisition and display, but had become much more sensitive to impedance changes. After we alerted the manufacturer, they added software and hardware updates that resulted in resolution of all incidents of ECG loss-of-signal.

False alarm reduction in critical care

High false alarm rates in the ICU decrease quality of care by slowing staff response times while increasing patient delirium through noise pollution. The 2015 PhysioNet/Computing in Cardiology Challenge provides a set of 1250 multi-parameter ICU data segments associated with critical arrhythmia alarms, and challenges the general research community to address the issue of false alarm suppression using all available signals. Each data segment was 5 minutes long (for real time analysis), ending at the time of the alarm. For retrospective analysis, we provided a further 30 seconds of data after the alarm was triggered. A total of 750 data segments were made available for training and 500 were held back for testing. Each alarm was reviewed by expert annotators, at least two of whom agreed that the alarm was either true or false. Challenge participants were invited to submit a complete, working algorithm to distinguish true from false alarms, and received a score based on their program's performance on the hidden test set. This score was based on the percentage of alarms correct, but with a penalty that weights the suppression of true alarms five times more heavily than acceptance of false alarms. We provided three example entries based on well-known, open source signal processing algorithms, to serve as a basis for comparison and as a starting point for participants to develop their own code. A total of 38 teams submitted a total of 215 entries in this year's Challenge. This editorial reviews the background issues for this challenge, the design of the challenge itself, the key achievements, and the follow-up research generated as a result of the Challenge, published in the concurrent special issue of Physiological Measurement. Additionally we make some recommendations for future changes in the field of patient monitoring as a result of the Challenge.