TY - GEN
T1 - Early signs of critical slowing down in heart surface electrograms of ventricular fibrillation victims
AU - Nannes, Berend
AU - Quax, Rick
AU - Ashikaga, Hiroshi
AU - Hocini, Mélèze
AU - Dubois, Remi
AU - Bernus, Olivier
AU - Haïssaguerre, Michel
N1 - Funding Information:
This work was partly supported by the Fondation Leducq Transatlantic Network of Excellence 16CVD02. RQ thanks EU Horizon 2020 project TO AITION (848146) for support.
Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - Ventricular fibrillation (VF) is a dangerous type of cardiac arrhythmia which, without intervention, almost always results in sudden death. Implantable automatic defibrillators are among the most successful devices to prevent sudden death by automatically applying a shock to the heart when fibrillation occurs. However, the electric shock is very painful and could lead to dangerous situations when a patient is, for example, driving or biking. An early warning signal for VF could reduce the risk in such situations or, in the future, reduce the need for defibrillation altogether. Here, we test for the presence of critical slowing down (CSD), which has proven to be an early warning indicator for critical transitions in a range of different systems. CSD is characterized by a buildup of autocorrelation; we therefore study the residuals of heart surface electrocardiograms (ECGs) of patients that suffered VF to investigate if we can measure positive trends in autocorrelation. We consider several methods to extract these residuals from the original signals. For three out of four VF victims, we find a significant amount of positive autocorrelation trends in the residuals, which might be explained by CSD. We show that these positive trends may not be measurable from the original body surface ECGs, but only from certain areas around the heart surface. We argue that additional experimental studies involving heart surface ECG data of subjects that did not suffer VF are required to quantify the prediction accuracy of the promising results we get from the data of VF victims.
AB - Ventricular fibrillation (VF) is a dangerous type of cardiac arrhythmia which, without intervention, almost always results in sudden death. Implantable automatic defibrillators are among the most successful devices to prevent sudden death by automatically applying a shock to the heart when fibrillation occurs. However, the electric shock is very painful and could lead to dangerous situations when a patient is, for example, driving or biking. An early warning signal for VF could reduce the risk in such situations or, in the future, reduce the need for defibrillation altogether. Here, we test for the presence of critical slowing down (CSD), which has proven to be an early warning indicator for critical transitions in a range of different systems. CSD is characterized by a buildup of autocorrelation; we therefore study the residuals of heart surface electrocardiograms (ECGs) of patients that suffered VF to investigate if we can measure positive trends in autocorrelation. We consider several methods to extract these residuals from the original signals. For three out of four VF victims, we find a significant amount of positive autocorrelation trends in the residuals, which might be explained by CSD. We show that these positive trends may not be measurable from the original body surface ECGs, but only from certain areas around the heart surface. We argue that additional experimental studies involving heart surface ECG data of subjects that did not suffer VF are required to quantify the prediction accuracy of the promising results we get from the data of VF victims.
KW - Critical slowing down
KW - Critical transition
KW - Early warning signal
KW - Ventricular fibrillation
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U2 - 10.1007/978-3-030-50423-6_25
DO - 10.1007/978-3-030-50423-6_25
M3 - Conference contribution
AN - SCOPUS:85087276948
SN - 9783030504229
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 334
EP - 347
BT - Computational Science – ICCS 2020 - 20th International Conference, Proceedings
A2 - Krzhizhanovskaya, Valeria V.
A2 - Závodszky, Gábor
A2 - Lees, Michael H.
A2 - Sloot, Peter M.A.
A2 - Sloot, Peter M.A.
A2 - Sloot, Peter M.A.
A2 - Dongarra, Jack J.
A2 - Brissos, Sérgio
A2 - Teixeira, João
PB - Springer Science and Business Media Deutschland GmbH
T2 - 20th International Conference on Computational Science, ICCS 2020
Y2 - 3 June 2020 through 5 June 2020
ER -