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Article ; Online: Extracting physiologic and clinical data from defibrillators for research purposes to improve treatment for patients in cardiac arrest.

Nordseth, Trond / Eftestøl, Trygve / Aramendi, Elisabete / Kvaløy, Jan Terje / Skogvoll, Eirik

Resuscitation plus

2024 Volume 18, Page(s) 100611

Abstract: Background: A defibrillator should be connected to all patients receiving cardiopulmonary resuscitation (CPR) to allow early defibrillation. The defibrillator will collect signal data such as the electrocardiogram (ECG), thoracic impedance and end-tidal ...

Abstract	Background: A defibrillator should be connected to all patients receiving cardiopulmonary resuscitation (CPR) to allow early defibrillation. The defibrillator will collect signal data such as the electrocardiogram (ECG), thoracic impedance and end-tidal CO Methods: The successful collection of defibrillator files has several challenges. There is no scientific standard on how to store such data, which have resulted in several proprietary industrial solutions. The data needs to be exported to a software environment where signal filtering and classifications of ECG rhythms can be performed. This may be automated using different algorithms and artificial intelligence (AI). The patient can be classified being in ventricular fibrillation or -tachycardia, asystole, pulseless electrical activity or having obtained return of spontaneous circulation. How this dynamic response is time-dependent and related to covariates can be handled in several ways. These include Aalen's linear model, Weibull regression and joint models. Conclusions: The vast amount of signal data from defibrillator represents promising opportunities for the use of AI and statistical analysis to assess patient response to CPR. This may provide an epidemiologic basis to improve resuscitation guidelines and give more individualized care. We suggest that an international working party is initiated to facilitate a discussion on how open formats for defibrillator data can be accomplished, that obligates industrial partners to further develop their current technological solutions.
Language	English
Publishing date	2024-03-20
Publishing country	Netherlands
Document type	Journal Article ; Review
ISSN	2666-5204
ISSN (online)	2666-5204
DOI	10.1016/j.resplu.2024.100611
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Optimizing support vector machines and autoregressive integrated moving average methods for heart rate variability data correction.

Svane, Jakob / Wiktorski, Tomasz / Ørn, Stein / Eftestøl, Trygve Christian

MethodsX

2023 Volume 11, Page(s) 102381

Abstract: Heart rate variability (HRV) is the variation in time between successive heartbeats and can be used as an indirect measure of autonomic nervous system (ANS) activity. During physical exercise, movement of the measuring device can cause artifacts in the ... ...

Abstract	Heart rate variability (HRV) is the variation in time between successive heartbeats and can be used as an indirect measure of autonomic nervous system (ANS) activity. During physical exercise, movement of the measuring device can cause artifacts in the HRV data, severely affecting the analysis of the HRV data. Current methods used for data artifact correction perform insufficiently when HRV is measured during exercise. In this paper we propose the use of autoregressive integrated moving average (ARIMA) and support vector regression (SVR) for HRV data artifact correction. Since both methods are only trained on previous data points, they can be applied not only for correction (i.e., gap filling), but also prediction (i.e., forecasting future values). Our paper describes:•why HRV is difficult to predict and why ARIMA and SVR might be valuable options.•finding the best hyperparameters for using ARIMA and SVR to correct HRV data, including which criterion to use for choosing the best model.•which correction method should be used given the data at hand.
Language	English
Publishing date	2023-09-16
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2830212-6
ISSN	2215-0161
ISSN	2215-0161
DOI	10.1016/j.mex.2023.102381
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Article ; Online: Optimizing support vector machines and autoregressive integrated moving average methods for heart rate variability data correction

Jakob Svane / Tomasz Wiktorski / Stein Ørn / Trygve Christian Eftestøl

MethodsX, Vol 11, Iss , Pp 102381- (2023)

2023

Abstract	Heart rate variability (HRV) is the variation in time between successive heartbeats and can be used as an indirect measure of autonomic nervous system (ANS) activity. During physical exercise, movement of the measuring device can cause artifacts in the HRV data, severely affecting the analysis of the HRV data. Current methods used for data artifact correction perform insufficiently when HRV is measured during exercise. In this paper we propose the use of autoregressive integrated moving average (ARIMA) and support vector regression (SVR) for HRV data artifact correction. Since both methods are only trained on previous data points, they can be applied not only for correction (i.e., gap filling), but also prediction (i.e., forecasting future values). Our paper describes: • why HRV is difficult to predict and why ARIMA and SVR might be valuable options. • finding the best hyperparameters for using ARIMA and SVR to correct HRV data, including which criterion to use for choosing the best model. • which correction method should be used given the data at hand.
Keywords	Optimization of ARIMA and SVR for HRV Data Correction ; Science ; Q
Subject code	310
Language	English
Publishing date	2023-12-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Chest compressions: the good, the bad and the ugly.

Eftestøl, Trygve

Resuscitation

2012 Volume 83, Issue 2, Page(s) 143–144

MeSH term(s)	Heart Arrest/therapy ; Heart Massage/methods ; Humans ; Thorax ; Treatment Outcome
Language	English
Publishing date	2012-02
Publishing country	Ireland
Document type	Editorial
ZDB-ID	189901-6
ISSN	1873-1570 ; 0300-9572
ISSN (online)	1873-1570
ISSN	0300-9572
DOI	10.1016/j.resuscitation.2011.12.022
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Article ; Online: Machine learning model to predict evolution of pulseless electrical activity during in-hospital cardiac arrest.

Urteaga, Jon / Elola, Andoni / Norvik, Anders / Unneland, Eirik / Eftestøl, Trygve C / Bhardwaj, Abhishek / Buckler, David / Abella, Benjamin S / Skogvoll, Eirik / Aramendi, Elisabete

Resuscitation plus

2024 Volume 17, Page(s) 100598

Abstract: Background: During pulseless electrical activity (PEA) the cardiac mechanical and electrical functions are dissociated, a phenomenon occurring in 25-42% of in-hospital cardiac arrest (IHCA) cases. Accurate evaluation of the likelihood of a PEA patient ... ...

Abstract	Background: During pulseless electrical activity (PEA) the cardiac mechanical and electrical functions are dissociated, a phenomenon occurring in 25-42% of in-hospital cardiac arrest (IHCA) cases. Accurate evaluation of the likelihood of a PEA patient transitioning to return of spontaneous circulation (ROSC) may be vital for the successful resuscitation. The aim: We sought to develop a model to automatically discriminate between PEA rhythms with favorable and unfavorable evolution to ROSC. Methods: A dataset of 190 patients, 120 with ROSC, were acquired with defibrillators from different vendors in three hospitals. The ECG and the transthoracic impedance (TTI) signal were processed to compute 16 waveform features. Logistic regression models where designed integrating both automated features and characteristics annotated in the QRS to identify PEAs with better prognosis leading to ROSC. Cross validation techniques were applied, both patient-specific and stratified, to evaluate the performance of the algorithm. Results: The best model consisted in a three feature algorithm that exhibited median (interquartile range) Area Under the Curve/Balanced accuracy/Sensitivity/Specificity of 80.3(9.9)/75.6(8.0)/ 77.4(15.2)/72.3(16.4) %, respectively. Conclusions: Information hidden in the waveforms of the ECG and TTI signals, along with QRS complex features, can predict the progression of PEA. Automated methods as the one proposed in this study, could contribute to assist in the targeted treatment of PEA in IHCA.
Language	English
Publishing date	2024-03-08
Publishing country	Netherlands
Document type	Journal Article
ISSN	2666-5204
ISSN (online)	2666-5204
DOI	10.1016/j.resplu.2024.100598
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Active Learning Based Domain Adaptation for Tissue Segmentation of Histopathological Images

Fuster, Saul / Khoraminia, Farbod / Eftestøl, Trygve / Zuiverloon, Tahlita C. M. / Engan, Kjersti

2023

Abstract: Accurate segmentation of tissue in histopathological images can be very beneficial for defining regions of interest (ROI) for streamline of diagnostic and prognostic tasks. Still, adapting to different domains is essential for histopathology image ... ...

Abstract	Accurate segmentation of tissue in histopathological images can be very beneficial for defining regions of interest (ROI) for streamline of diagnostic and prognostic tasks. Still, adapting to different domains is essential for histopathology image analysis, as the visual characteristics of tissues can vary significantly across datasets. Yet, acquiring sufficient annotated data in the medical domain is cumbersome and time-consuming. The labeling effort can be significantly reduced by leveraging active learning, which enables the selective annotation of the most informative samples. Our proposed method allows for fine-tuning a pre-trained deep neural network using a small set of labeled data from the target domain, while also actively selecting the most informative samples to label next. We demonstrate that our approach performs with significantly fewer labeled samples compared to traditional supervised learning approaches for similar F1-scores, using barely a 59\% of the training set. We also investigate the distribution of class balance to establish annotation guidelines.
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition
Subject code	004 ; 006
Publishing date	2023-03-09
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Nested Multiple Instance Learning with Attention Mechanisms

Fuster, Saul / Eftestøl, Trygve / Engan, Kjersti

2021

Abstract: Multiple instance learning (MIL) is a type of weakly supervised learning where multiple instances of data with unknown labels are sorted into bags. Since knowledge about the individual instances is incomplete, labels are assigned to the bags containing ... ...

Abstract	Multiple instance learning (MIL) is a type of weakly supervised learning where multiple instances of data with unknown labels are sorted into bags. Since knowledge about the individual instances is incomplete, labels are assigned to the bags containing the instances. While this method fits diverse applications were labelled data is scarce, it lacks depth for solving more complex scenarios where associations between sets of instances have to be made, like finding relevant regions of interest in an image or detecting events in a set of time-series signals. Nested MIL considers labelled bags within bags, where only the outermost bag is labelled and inner-bags and instances are represented as latent labels. In addition, we propose using an attention mechanism to add interpretability, providing awareness into the impact of each instance to the weak bag label. Experiments in classical image datasets show that our proposed model provides high accuracy performance as well as spotting relevant instances on image regions. Comment: Submitted to ICASSP 2022
Keywords	Computer Science - Machine Learning ; Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2021-11-01
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Texture-based probability mapping for automatic scar assessment in late gadolinium-enhanced cardiovascular magnetic resonance images.

Frøysa, Vidar / Berg, Gøran J / Eftestøl, Trygve / Woie, Leik / Ørn, Stein

European journal of radiology open

2021 Volume 8, Page(s) 100387

Abstract: Purpose: To evaluate a novel texture-based probability mapping (TPM) method for scar size estimation in LGE-CMRI.: Methods: This retrospective proof-of-concept study included chronic myocardial scars from 52 patients. The TPM was compared with three ... ...

Abstract	Purpose: To evaluate a novel texture-based probability mapping (TPM) method for scar size estimation in LGE-CMRI. Methods: This retrospective proof-of-concept study included chronic myocardial scars from 52 patients. The TPM was compared with three signal intensity-based methods: manual segmentation, full-width-half-maximum (FWHM), and 5-standard deviation (5-SD). TPM is generated using machine learning techniques, expressing the probability of scarring in pixels. The probability is derived by comparing the texture of the 3 × 3 pixel matrix surrounding each pixel with reference dictionaries from patients with established myocardial scars. The Sørensen-Dice coefficient was used to find the optimal TPM range. A non-parametric test was used to test the correlation between infarct size and remodeling parameters. Bland-Altman plots were performed to assess agreement among the methods. Results: The study included 52 patients (76.9% male; median age 64.5 years (54, 72.5)). A TPM range of 0.328-1.0 was found to be the optimal probability interval to predict scar size compared to manual segmentation, median dice (25th and 75th percentiles)): 0.69(0.42-0.81). There was no significant difference in the scar size between TPM and 5-SD. However, both 5-SD and TPM yielded larger scar sizes compared with FWHM (p < 0.001 and p = 0.002). There were strong correlations between scar size measured by TPM, and left ventricular ejection fraction (LVEF, r = -0.76, p < 0.001), left ventricular end-diastolic volume index (r = 0.73, p < 0.001), and left ventricular end-systolic volume index (r = 0.75, p < 0.001). Conclusion: The TPM method is comparable with current SI-based methods, both for the scar size assessment and the relationship with left ventricular remodeling when applied on LGE-CMRI.
Language	English
Publishing date	2021-12-03
Publishing country	England
Document type	Journal Article
ZDB-ID	2810314-2
ISSN	2352-0477
ISSN	2352-0477
DOI	10.1016/j.ejro.2021.100387
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Article ; Online: Continuous assessment of ventricular fibrillation prognostic status during CPR: Implications for resuscitation.

Coult, Jason / Kwok, Heemun / Eftestøl, Trygve / Bhandari, Shiv / Blackwood, Jennifer / Sotoodehnia, Nona / Kudenchuk, Peter J / Rea, Thomas D

Resuscitation

2022 Volume 179, Page(s) 152–162

Abstract: Background: Ventricular fibrillation (VF) waveform measures reflect myocardial physiologic status. Continuous assessment of VF prognosis using such measures could guide resuscitation, but has not been possible due to CPR artifact in the ECG. A recently- ... ...

Abstract	Background: Ventricular fibrillation (VF) waveform measures reflect myocardial physiologic status. Continuous assessment of VF prognosis using such measures could guide resuscitation, but has not been possible due to CPR artifact in the ECG. A recently-validated VF measure (termed VitalityScore), which estimates the probability (0-100%) of return-of-rhythm (ROR) after shock, can assess VF during CPR, suggesting potential for continuous application during resuscitation. Objective: We evaluated VF using VitalityScore to characterize VF prognostic status continuously during resuscitation. Methods: We characterized VF using VitalityScore during 60 seconds of CPR and 10 seconds of subsequent pre-shock CPR interruption in patients with out-of-hospital VF arrest. VitalityScore utility was quantified using area under the receiver operating characteristic curve (AUC). VitalityScore trends over time were estimated using mixed-effects models, and associations between trends and ROR were evaluated using logistic models. A sensitivity analysis characterized VF during protracted (100-second) periods of CPR. Results: We evaluated 724 VF episodes among 434 patients. After an initial decline from 0-8 seconds following VF onset, VitalityScore increased slightly during CPR from 8-60 seconds (slope: 0.18%/min). During the first 10 seconds of subsequent pre-shock CPR interruption, VitalityScore declined (slope: -14%/min). VitalityScore predicted ROR throughout CPR with AUCs 0.73-0.75. Individual VitalityScore trends during 8-60 seconds of CPR were marginally associated with subsequent ROR (adjusted odds ratio for interquartile slope change (OR) = 1.10, p = 0.21), and became significant with protracted (100 seconds) CPR duration (OR = 1.28, p = 0.006). Conclusion: VF prognostic status can be continuously evaluated during resuscitation, a development that could translate to patient-specific resuscitation strategies.
MeSH term(s)	Cardiopulmonary Resuscitation ; Electric Countershock ; Electrocardiography ; Humans ; Prognosis ; Ventricular Fibrillation/complications ; Ventricular Fibrillation/diagnosis ; Ventricular Fibrillation/therapy
Language	English
Publishing date	2022-08-27
Publishing country	Ireland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	189901-6
ISSN	1873-1570 ; 0300-9572
ISSN (online)	1873-1570
ISSN	0300-9572
DOI	10.1016/j.resuscitation.2022.08.015
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Zs.A 1048: Show issues

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Book ; Online: 3D Masked Modelling Advances Lesion Classification in Axial T2w Prostate MRI

Fernandez-Quilez, Alvaro / Andersen, Christoffer Gabrielsen / Eftestøl, Trygve / Kjosavik, Svein Reidar / Oppedal, Ketil

2022

Abstract: Masked Image Modelling (MIM) has been shown to be an efficient self-supervised learning (SSL) pre-training paradigm when paired with transformer architectures and in the presence of a large amount of unlabelled natural images. The combination of the ... ...

Abstract	Masked Image Modelling (MIM) has been shown to be an efficient self-supervised learning (SSL) pre-training paradigm when paired with transformer architectures and in the presence of a large amount of unlabelled natural images. The combination of the difficulties in accessing and obtaining large amounts of labeled data and the availability of unlabelled data in the medical imaging domain makes MIM an interesting approach to advance deep learning (DL) applications based on 3D medical imaging data. Nevertheless, SSL and, in particular, MIM applications with medical imaging data are rather scarce and there is still uncertainty. around the potential of such a learning paradigm in the medical domain. We study MIM in the context of Prostate Cancer (PCa) lesion classification with T2 weighted (T2w) axial magnetic resonance imaging (MRI) data. In particular, we explore the effect of using MIM when coupled with convolutional neural networks (CNNs) under different conditions such as different masking strategies, obtaining better results in terms of AUC than other pre-training strategies like ImageNet weight initialization. Comment: To be published in the Northern Lights Conference Proceedings 2023
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2022-12-29
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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