The power of ECG in multimodal patient-specific seizure monitoring: Added value to an EEG-based detector using limited channels

Kaat Vandecasteele, Thomas De Cooman, Christos Chatzichristos, Evy Cleeren, Lauren Swinnen, Jaiver Macea Ortiz, Sabine Van Huffel, Matthias Dümpelmann, Andreas Schulze-Bonhage, Maarten De Vos, Wim Van Paesschen, Borbála Hunyadi, Kaat Vandecasteele, Thomas De Cooman, Christos Chatzichristos, Evy Cleeren, Lauren Swinnen, Jaiver Macea Ortiz, Sabine Van Huffel, Matthias Dümpelmann, Andreas Schulze-Bonhage, Maarten De Vos, Wim Van Paesschen, Borbála Hunyadi

Abstract

Objective: Wearable seizure detection devices could provide more reliable seizure documentation outside the hospital compared to seizure self-reporting by patients, which is the current standard. Previously, during the SeizeIT1 project, we studied seizure detection based on behind-the-ear electroencephalography (EEG). However, the obtained sensitivities were too low for practical use, because not all seizures are associated with typical ictal EEG patterns. Therefore, in this paper, we aim to develop a multimodal automated seizure detection algorithm integrating behind-the-ear EEG and electrocardiography (ECG) for detecting focal seizures. In this framework, we quantified the added value of ECG to behind-the-ear EEG.

Methods: This study analyzed three multicenter databases consisting of 135 patients having focal epilepsy and a total of 896 seizures. A patient-specific multimodal automated seizure detection algorithm was developed using behind-the-ear/temporal EEG and single-lead ECG. The EEG and ECG data were processed separately using machine learning methods. A late integration approach was applied for fusing those predictions.

Results: The multimodal algorithm outperformed the EEG-based algorithm in two of three databases, with an increase of 11% and 8% in sensitivity for the same false alarm rate.

Significance: ECG can be of added value to an EEG-based seizure detection algorithm using only behind-the-ear/temporal lobe electrodes for patients with focal epilepsy.

Keywords: ECG; behind-the-ear EEG; epilepsy; multimodal algorithms; reduced electrode montage; seizure detection; wearable sensors.

Conflict of interest statement

None of the authors has any conflict of interest to disclose. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.

© 2021 The Authors. Epilepsia published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.

Figures

FIGURE 1
FIGURE 1
The behind‐the‐ear electroencephalogram (EEG) and electrocardiogram (ECG) are shown during a temporal focal impaired awareness seizure from the right hemisphere. The top panel contains four channels: crosshead (CROSS), left channel, right channel, and ECG. The amplitude of the ECG signal is decreased by a factor 10. The seizure onset is depicted with a vertical black line at 10 s. The bottom left panel shows the extracted heart rate (HR) in beats per minute during the seizure. A heart rate increase from 40 to 80 beats per minute is observed. The bottom right panel shows a close‐up of the behind‐the‐ear EEG channels during 20–30 s after the seizure onset
FIGURE 2
FIGURE 2
An overview of the seizure type (A), localization (B), lateralization (C), and duration (D) for the three different datasets. bi, bilateral; F, frontal; FA, focal aware; F‐BTC, focal to bilateral tonic–clonic; FIA, focal impaired awareness; NC, not clear; O, occipital; P, parietal; T, temporal; UC, unclassified
FIGURE 3
FIGURE 3
A schematic overview of the different steps in the automated seizure detection algorithm. ECG, electrocardiography; EEG, electroencephalography; RF, random forest; SVM, support vector machine
FIGURE 4
FIGURE 4
Sensitivity in relation to false alarm rate (FAR) for the different datasets. The blue graph depicts the unimodal results of electroencephalography (EEG), the orange one the results of electrocardiography (ECG). On those graphs, the sensitivities at an FAR at 0.2 false positives (FP)/h, 0.5 FP/h, and 1 FP/h are depicted with circles. The black symbols indicate the results of the multimodal algorithm at three discrete thresholds (squares, 0.2 FP/h; diamonds, 0.5 FP/h; stars, 1 FP/h). For comparison, the results on the EEG graph with the same FAR are indicated with blue marks. std, standard deviation
FIGURE 5
FIGURE 5
Performance comparison of our proposed electrocardiography‐based seizure detection algorithm. The sensitivity versus false alarm rate (FAR) is plotted together with the standard deviation on the sensitivity (dotted lines). The sensitivity and FAR are shown for two state‐of‐the‐art solutions: Fürbass et al. and De Cooman et al. The dotted lines indicate the standard deviation in two directions: the FAR and sensitivity. FP, false positives
FIGURE 6
FIGURE 6
Percentage of detected seizures at a false alarm rate (FAR) of 1/h is displayed for the four groups: seizures detected with both electroencephalography (EEG) and electrocardiography (ECG), only with EEG, only with ECG, or not detected either with EEG or with ECG, in relation to seizure type (A) and in relation to localization (B). The number of seizures in each group is indicated. F, frontal; FA, focal aware; F‐BTC, focal to bilateral tonic–clonic; FIA, focal impaired awareness; NC, not clear; T, temporal; UC, unclassified

References

    1. Forsgren L, Beghi E, Oun A, Sillanpää M. The epidemiology of epilepsy in Europe—a systematic review. Eur J Neurol. 2005;12:245–53.
    1. French JA. Refractory epilepsy: clinical overview. Epilepsia. 2007;48:3–7.
    1. Elger CE, Mormann F. Seizure prediction and documentation—two important problems. Lancet Neurol. 2013;12:531–2.
    1. Blum DE, Eskola J, Bortz JJ, Fisher RS. Patient awareness of seizures. Neurology. 1996;47:260–4.
    1. Fisher RS, Blum DE, DiVentura B, Vannest J, Hixson JD, Moss R, et al. Seizure diaries for clinical research and practice: limitations and future prospects. Epilepsy Behav. 2012;24:304–10.
    1. Hoppe C, Poepel A, Elger CE. Epilepsy: accuracy of patient seizure counts. Arch Neurol. 2007;64:1595–9.
    1. Poochikian‐Sarkissian S, Tai P, del Campo M , Andrade DM, Carlen PL, Valiante T, et al. Patient awareness of seizures as documented in the epilepsy monitoring unit. Can J Neurosci Nurs. 2009;31:22–3.
    1. Tatum WO, Winters L, Gieron M, Passaro EA, Benbadis S, Ferreira J, et al. Outpatient seizure identification: results of 502 patients using computer‐assisted ambulatory EEG. J Clin Neurophysiol. 2001;18:14–9.
    1. Gu Y, Cleeren E, Dan J, Claes K, Van Paesschen W, Van Huffel S, et al. Comparison between scalp EEG and behind‐the‐ear EEG for development of a wearable seizure detection system for patients with focal epilepsy. Sensors. 2017;18:1–19.
    1. Vandecasteele K, De Cooman T, Dan J, Cleeren E, Van Huffel S, Hunyadi B, et al. Visual seizure annotation and automated seizure detection using behind‐the‐ear electroencephalographic channels. Epilepsia. 2020;61:766–75.
    1. Devinsky O, Kelley K, Porter RJ, Theodore WH. Clinical and electroencephalographic features of simple partial seizures. Neurology. 1988;38:1347–52.
    1. Foldvary N, Klem G, Hammel J, Bingaman W, Najm I, Lüders H. The localizing value of ictal EEG in focal epilepsy. Neurology. 2001;57:2022–8.
    1. Walczak TS, Radtke RA, Lewis DV. Accuracy and interobserver reliability of scalp ictal EEG. Neurology. 1992;42:2279–85.
    1. Spencer SS, Williamson PD, Bridgers SL, Mattson RH, Cicchetti DV, Spencer DD. Reliability and accuracy of localization by scalp ictal EEG. Neurology. 1985;35:1567–75.
    1. Pavei J, Heinzen RG, Novakova B, Walz R, Serra AJ, Reuber M, et al. Early seizure detection based on cardiac autonomic regulation dynamics. Front Physiol. 2017;8:765.
    1. De Cooman T, Varon C, Hunyadi B, Van Paesschen W, Lagae L, Van Huffel S. Online automated seizure detection in temporal lobe epilepsy patients using single‐lead ECG. Int J Neural Syst. 2017;27:1750022.
    1. Jeppesen J, Fuglsang‐Frederiksen A, Johansen P, Christensen J, Wüstenhagen S, Tankisi H, et al. O‐45 automated seizure detection for epilepsy patients using wearable ECG‐device. Neurophysiol Clin. 2019;130:36.
    1. Vandecasteele K, De Cooman T, Gu Y, Cleeren E, Claes K, Van Paesschen W, et al. Automated epileptic seizure detection based on wearable ECG and PPG in a hospital environment. Sensors. 2017;17:2338.
    1. Eggleston KS, Olin BD, Fisher RS. Ictal tachycardia: the head‐heart connection. Seizure. 2014;23:496–505.
    1. Zijlmans M, Flanagan D, Gotman J. Heart rate changes and ECG abnormalities during epileptic seizures: prevalence and definition of an objective clinical sign. Epilepsia. 2002;43:847–54.
    1. Chen W, Guo CL, Zhang PS, Liu C, Qiao H, Zhang JG, et al. Heart rate changes in partial seizures: analysis of influencing factors among refractory patients. BMC Neurol. 2014;14:135.
    1. Fürbass F, Kampusch S, Kaniusas E, Koren J, Pirker S, Hopfengärtner R, et al. Automatic multimodal detection for long‐term seizure documentation in epilepsy. Clin Neurophysiol. 2017;128:1466–72.
    1. Qaraqe M, Ismail M, Serpedin E, Zulfi H. Epileptic seizure onset detection based on EEG and ECG data fusion. Epilepsy Behav. 2016;58:48–60.
    1. Cogan D, Birjandtalab J, Nourani M, Harvey J, Nagaraddi V. Multi‐biosignal analysis for epileptic seizure monitoring. Int J Neural Syst. 2017;27:1650031.
    1. Beniczky S, Ryvlin P. Standards for testing and clinical validation of seizure detection devices. Epilepsia. 2018;59:9–13.
    1. Klatt J, Feldwisch‐Drentrup H, Ihle M, Navarro V, Neufang M, Teixeira C, et al. The EPILEPSIAE database: an extensive electroencephalography database of epilepsy patients. Epilepsia. 2012;53:1669–76.
    1. Ihle M, Feldwisch‐Drentrup H, Teixeira CA, Witon A, Schelter B, Timmer J, et al. EPILEPSIAE—a European epilepsy database. Comput Methods Programs Biomed. 2012;106:127–38.
    1. Seeck M, Koessler L, Bast T, Leijten F, Michel C, Baumgartner C, et al. The standardized EEG electrode array of the IFCN. Clin Neurophysiol. 2017;128:2070–7.
    1. Li C, Zheng C, Tai C. Detection of ECG characteristic points using wavelet transforms. IEEE Trans Biomed Eng. 1995;42:21–8.
    1. Varon C, Caicedo A, Testelmans D, Buyse B, Van Huffel S. A novel algorithm for the automatic detection of sleep apnea from single‐lead ECG. IEEE Trans Biomed Eng. 2015;62:2269–78.
    1. De Cooman T, Goovaerts G, Varon C, Widjaja D, Willemen T, Van Huffel S. Heart beat detection in multimodal data using automatic relevant signal detection. Physiol Meas. 2015;36:1691–704.
    1. Jeppesen J, Beniczky S, Johansen P, Sidenius P, Fuglsang‐Frederiksen A. Detection of epileptic seizures with a modified heart rate variability algorithm based on Lorenz plot. Seizure. 2015;24:1–7.
    1. Doyle OM, Temko A, Marnane W, Lightbody G, Boylan GB. Heart rate based automatic seizure detection in the newborn. Med Eng Phys. 2010;32:829–39.
    1. Orphanidou C, Bonnici T, Charlton P, Clifton D, Vallance D, Tarassenko L. Signal‐quality indices for the electrocardiogram and photoplethysmogram: derivation and applications to wireless monitoring. IEEE J Biomed Health Inform. 2015;19:832–8.
    1. Johnson AE, Behar J, Andreotti F, Clifford GD, Oster J. Multimodal heart beat detection using signal quality indices. Physiol Meas. 2015;36:1665–77.
    1. Ganeshapillai G, Liu JF, Guttag J. Reconstruction of ECG signals in presence of corruption. Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:3764–7.
    1. Citi L, Klerman E, Brown E, Barbieri R. Point process heart rate variability assessment during sleep deprivation. Comput Cardiol. 2010;37:721–4.
    1. Osorio I. Automated seizure detection using EKG. Int J Neural Syst. 2014;24:1450001.
    1. Osorio I, Manly BF. Is seizure detection based on EKG clinically relevant? Clin Neurophysiol. 2014;125:1946–51.
    1. Ungureanu C, Bui V, Roosmalen W, Aarts RM, Arends JB, Verhoeven R, et al. A wearable monitoring system for nocturnal epileptic seizures. ISMICT. Proceedings of the 8th International Symposium on Medical Information and Communication Technology (ISMICT'14), April 2‐4, 2014, Firenze, Italy. Piscataway, NJ: Institute of Electrical and Electronics Engineers; 2014. p. 1–5.
    1. Deviaene M, Testelmans D, Borzee P, Buyse B, Van Huffel S, Varon C. Feature selection algorithm based on random forest applied to sleep apnea detection. Annu Int Conf IEEE Eng Med Biol Soc. 2019;2019:2580–3.
    1. Sim I. Mobile devices and health. N Engl J Med. 2019;381:956–68.
    1. Kurada AV, Srinivasan T, Hammond S, Ulate‐Campos A, Bidwell J. Seizure detection devices for use in antiseizure medication clinical trials: a systematic review. Seizure. 2019;66:6169.
    1. Verdru J, Van Paesschen W. Wearable seizure detection devices in refractory epilepsy. Acta Neurol Belg. 2020;120:1271–81.
    1. Duun‐Henriksen J, Baud M, Richardson MP, Cook M, Kouvas G, Heasman JM, et al. A new era in electroencephalographic monitoring? Subscalp devices for ultra–long‐term recordings. Epilepsia. 2020;61:1805–17.
    1. Scheffer IE, Berkovic S, Capovilla G, Connolly MB, French J, Guilhoto L, et al. ILAE classification of the epilepsies: position paper of the ILAE Commission for Classification and Terminology. Epilepsia. 2017;58:512–21.
    1. De Cooman T, Vandecasteele K, Varon C, Hunyadi B, Cleeren E, Van Paesschen W, et al. Personalizing heart rate‐based seizure detection using supervised SVM transfer learning. Front Neurol. 2020;11:145.
    1. Becker T, Vandecasteele K, Chatzichristos C, Van Paesschen W, Valkenborg D, Van Huffel S, et al. Classification with a deferral option and low‐trust filtering for automated seizure detection. Sensors. 2021;21:1046.
    1. Phan H, Chén OY, Koch P, Lu Z, McLoughlin I, Mertins A, et al. Towards more accurate automatic sleep staging via deep transfer learning. IEEE Trans Biomed Eng. 2021;68:1787–98.

Source: PubMed

Sponsoren und Mitarbeiter

Krankheiten

Drogeninterventionen

3
Abonnieren