Cooperative classification of clean and deformed capnogram segments using a voting approach: A trade-off between specificity and sensitivity

El-Badawy, Ismail M.; Omar, Zaid; Singh, Om Prakash

doi:10.1109/embc48229.2022.9871051

Cited by 3 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Notwithstanding these crucial differences in the data, we share a common objective with the work of El Badawy et al; we both aim to develop a classi er to differentiate between normal and abnormal capnography segments. The latest model created by this group discusses the delicate balance between speci city and recall, and their most successful model in terms of recall achieved a rate of 94%, against a precision of 80.8% [19]. In comparison, our model improves upon this performance by gaining 4% in recall and 2.2% in precision.…”

Section: Discussionmentioning

confidence: 83%

“…Another remarkable observation is that much of the existing research in capnography classi cation relies on analysis of very short time intervals. Often segments only include 15 seconds, which on average captures just three full respiratory cycles, or the input includes only a single breath [18][19][20][21][22][23]. However, certain abnormal respiratory patterns are more clearly observable over extended periods, such as apnea episodes which can last more than 60 seconds.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

A Machine Learning Algorithm for Detecting Abnormal Patterns in Continuous Capnography and Pulse Oximetry Monitoring

Spijkerboer,

Overdyk,

Dahan

2023

Preprint

View full text Add to dashboard Cite

Purpose: Continuous capnography monitors patient ventilation but can be susceptible to artifact, resulting in alarm fatigue. Development of smart algorithms may facilitate accurate detection of abnormal ventilation, allowing intervention before patient deterioration. The objective of this analysis was to use machine learning (ML) to classify combined waveforms of continuous capnography and pulse oximetry as normal or abnormal. Methods: This analysis used data collected during the observational, prospective PRODIGY trial, in which patients receiving parenteral opioids underwent continuous capnography and pulse oximetry monitoring while on the general care floor [1]. Abnormal ventilation segments in the data stream were reviewed by nine experts and inter-rater agreement was assessed. Abnormal segments were defined as the time series 60sec before and 30sec after an abnormal pattern was detected. Normal segments (90sec continuous monitoring) were randomly sampled and filtered to discard sequences with missing values. Five ML models were trained on extracted features and optimized towards an Fβ score with β=2. Results: The inter-rater agreement was high (>87%), allowing 7,858 sequences (2,944 abnormal) to be used for model development. Data were divided into 80% training and 20% test sequences. The XGBoost model had the highest Fβ score of 0.94 (with β=2), showcasing an impressive recall of 0.98 against a precision of 0.83. Conclusions: This study presents a promising advancement in respiratory monitoring, focusing on reducing false alarms and enhancing accuracy of alarm systems. Our algorithm reliably distinguishes normal from abnormal waveforms. More research is needed to define patterns to distinguish abnormal ventilation from artifacts. Trial Registration: clinicaltrials.gov: NCT02811302, registered June 23, 2016

show abstract

Section: Discussionmentioning

confidence: 83%

Section: Discussionmentioning

confidence: 99%

A Machine Learning Algorithm for Detecting Abnormal Patterns in Continuous Capnography and Pulse Oximetry Monitoring

Spijkerboer,

Overdyk,

Dahan

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The reason why SMS had the highest sensitivity in this study may be because oversampling and undersampling techniques were selected in order of high sensitivity. However, there is a trade-off between sensitivity and specificity [24,25]. When sensitivity is high, the model is good at finding positive samples without missing them, but this makes it easier for the model to predict positive ones and may mispredict negative ones as positive [24].…”

Section: Zeng Et Al (2016) Proposed a Preprocessing Technique Combini...mentioning

confidence: 99%

Improving Diagnostic Sensitivity for Imbalanced Musculoskeletal Disorder Data: A Sensitivity-Based Multi-Sampling Technique for Osteoarthritis Prediction

Kim

2023

Preprint

View full text Add to dashboard Cite

BackgroundMedical datasets containing musculoskeletal disorders may have data imbalances due to the incidence of the disease, which may limit the predictive ability, such as the sensitivity, of musculoskeletal diagnostic prediction models built from these data. This study aimed to increase the sensitivity performance of osteoarthritis (OA) prediction when building a model by adjusting an OA imbalanced dataset using a sensitivity-based multi-sampling (SMS) technique.MethodsOA Data were obtained from the Korea National Health and Nutrition Examination Survey (KNHANES). SMS technique combining oversampling and undersampling was applied to the imbalanced OA data, and the RandomForest algorithm was used for machine learning modeling. Model performance was evaluated based on accuracy, sensitivity, and specificity and compared with other hybrid sampling techniques.ResultIn the SMS technique, ADASYN, Borderline-SMOTE, SMOTE oversampling and ENN undersampling techniques were combined and applied. The OA prediction model using the SMS technique showed the highest sensitivity (82.20) but the lowest specificity (82.26) and accuracy (82.26) compared to other hybrid models.ConclusionSMS technique offers a potential solution for improving sensitivity performance for prediction models built on medical data imbalances due to low-incidence diseases. Nonetheless, caution is warranted due to the concern that while improving sensitivity, it may decrease specificity with a trade-off.

show abstract

A machine learning algorithm for detecting abnormal patterns in continuous capnography and pulse oximetry monitoring

Spijkerboer,

Overdyk,

Dahan

2024

J Clin Monit Comput

View full text Add to dashboard Cite

Continuous capnography monitors patient ventilation but can be susceptible to artifact, resulting in alarm fatigue. Development of smart algorithms may facilitate accurate detection of abnormal ventilation, allowing intervention before patient deterioration. The objective of this analysis was to use machine learning (ML) to classify combined waveforms of continuous capnography and pulse oximetry as normal or abnormal. We used data collected during the observational, prospective PRODIGY trial, in which patients receiving parenteral opioids underwent continuous capnography and pulse oximetry monitoring while on the general care floor [1]. Abnormal ventilation segments in the data stream were reviewed by nine experts and inter-rater agreement was assessed. Abnormal segments were defined as the time series 60s before and 30s after an abnormal pattern was detected. Normal segments (90s continuous monitoring) were randomly sampled and filtered to discard sequences with missing values. Five ML models were trained on extracted features and optimized towards an Fβ score with β = 2. The results show a high inter-rater agreement (> 87%), allowing 7,858 sequences (2,944 abnormal) to be used for model development. Data were divided into 80% training and 20% test sequences. The XGBoost model had the highest Fβ score of 0.94 (with β = 2), showcasing an impressive recall of 0.98 against a precision of 0.83. This study presents a promising advancement in respiratory monitoring, focusing on reducing false alarms and enhancing accuracy of alarm systems. Our algorithm reliably distinguishes normal from abnormal waveforms. More research is needed to define patterns to distinguish abnormal ventilation from artifacts.

show abstract

Cooperative classification of clean and deformed capnogram segments using a voting approach: A trade-off between specificity and sensitivity

Cited by 3 publications

References 21 publications

A Machine Learning Algorithm for Detecting Abnormal Patterns in Continuous Capnography and Pulse Oximetry Monitoring

A Machine Learning Algorithm for Detecting Abnormal Patterns in Continuous Capnography and Pulse Oximetry Monitoring

Improving Diagnostic Sensitivity for Imbalanced Musculoskeletal Disorder Data: A Sensitivity-Based Multi-Sampling Technique for Osteoarthritis Prediction

A machine learning algorithm for detecting abnormal patterns in continuous capnography and pulse oximetry monitoring

Contact Info

Product

Resources

About