Identification of Ictal Tachycardia in Focal Motor- and Non-Motor Seizures by Means of a Wearable PPG Sensor
Open accessAbstract
Photoplethysmography (PPG) as an additional biosignal for a seizure detector has been underutilized so far, which is possibly due to its susceptibility to motion artifacts. We investigated 62 focal seizures from 28 patients with electrocardiography-based evidence of ictal tachycardia (IT). Seizures were divided into subgroups: those without epileptic movements and those with epileptic movements not affecting and affecting the extremities. PPG-based heart rate (HR) derived from a wrist-worn device was calculated for sections with high signal quality, which were identified using spectral entropy. Overall, IT based on PPG was identified in 37 of 62 (60%) seizures (9/19, 7/8, and 21/35 in the three groups, respectively) and could be found prior to the onset of epileptic movements affecting the extremities in 14/21 seizures. In 30/37 seizures, PPG-based IT was in good temporal agreement (<10 s) with ECG-based IT, with an average delay of 5.0 s relative to EEG onset. In summary, we observed that the identification of IT by means of a wearable PPG sensor is possible not only for non-motor seizures but also in motor seizures, which is due to the early manifestation of IT in a relevant subset of focal seizures. However, both spontaneous and epileptic movements can impair PPG-based seizure detection.
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The quality of data can be expressed in many different ways, and data quality measures may also depend on the aims of the related analysis and project.
“the quality of data can be expressed in many different ways, and data quality measures may also depend on the aims of the related analysis and project8–13”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [8]
A clean BVP signal from the Empatica E4 without noise from motion artifacts has a smooth quasi-periodic signature, which can be separated from random noise or motion artifacts by analyzing the entropy of the signal spectrum.
“A clean BVP signal from the Empatica E4 without noise from motion artifacts has a smooth quasi-periodic signature, which can be separated from random noise or, more importantly, from motion artifacts by analyzing the entropy of the signal spectrum.”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [8]
Signal quality assessments have been done under more general circumstances, but wearable data quality in the context of epilepsy has not been a widely researched topic so far.
“Signal quality assessments have been done under more general circumstances10,34–37, but wearable data quality in the context of epilepsy has not been a widely researched topic so far8,9,38.”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [8]
The Empatica E4 has been shown to facilitate seizure detection by heart rate estimation from PPG despite its susceptibility to motion artifacts.
“Moreover, the E4 has been shown to facilitate seizure detection by heart rate estimation from PPG despite its susceptibility to motion artifacts8,72.”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [8]
There is cursory evidence that the BVP signal quality measure correctly maps periods of good and poor signal quality to estimations of the heart rate.
“While there is cursory evidence that it correctly maps periods of good and poor signal quality to estimations of the heart rate8,9”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [8]
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Cites
A Real-Time QRS Detection Algorithm
Pan, J., & Tompkins, W. J. (1985) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.1985.325532 · Cited by 7,855 · see citing works
On the use of windows for harmonic analysis with the discrete Fourier transform
Harris, F. J. (1978) · Proceedings of the IEEE · doi:10.1109/proc.1978.10837 · Cited by 7,326 · see citing works
Photoplethysmography and its application in clinical physiological measurement
Allen, J. (2007) · Physiological Measurement · doi:10.1088/0967-3334/28/3/r01 · Cited by 3,967 · see citing works
<b>CircStat</b> : A <i>MATLAB</i> Toolbox for Circular Statistics
Berens, P. (2009) · Journal of Statistical Software · doi:10.18637/jss.v031.i10 · Cited by 3,099 · see citing works
On the Analysis of Fingertip Photoplethysmogram Signals
Elgendi, M. (2012) · Current Cardiology Reviews · doi:10.2174/157340312801215782 · Cited by 1,206 · see citing works
Zhang, Z., Pi, Z., & Liu, B. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2359372 · Cited by 759 · see citing works
Activity Classification Using Realistic Data From Wearable Sensors
Pärkkä, J., Ermes, M., Korpipää, P., Mäntyjärvi, J., Peltola, J., & Korhonen, I. (2006) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2005.856863 · Cited by 759 · see citing works
Investigating sources of inaccuracy in wearable optical heart rate sensors
Bent, B., Goldstein, B. A., Kibbe, W. A., & Dunn, J. P. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0226-6 · Cited by 673 · see citing works
Multiparameter Respiratory Rate Estimation From the Photoplethysmogram
Karlen, W., Raman, S., Ansermino, J. M., & Dumont, G. A. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2246160 · Cited by 486 · see citing works
An Efficient Algorithm for Automatic Peak Detection in Noisy Periodic and Quasi-Periodic Signals
Scholkmann, F., Boss, J. M., & Wolf, M. (2012) · Algorithms · doi:10.3390/a5040588 · Cited by 456 · see citing works
Zhang, Z. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2406332 · Cited by 388 · see citing works
Orphanidou, C., Bonnici, T., Charlton, P., Clifton, D., Vallance, D., & Tarassenko, L. (2014) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2338351 · Cited by 368 · see citing works
Optimal Signal Quality Index for Photoplethysmogram Signals
Elgendi, M. (2016) · Bioengineering · doi:10.3390/bioengineering3040021 · Cited by 338 · see citing works
Effects of Seizures on Autonomic and Cardiovascular Function
Devinsky, O. (2004) · Epiliepsy currents/Epilepsy currents · doi:10.1111/j.1535-7597.2004.42001.x · Cited by 328 · see citing works
Zijlmans, M., Flanagan, D., & Gotman, J. (2002) · Epilepsia · doi:10.1046/j.1528-1157.2002.37801.x · Cited by 316 · see citing works
Electrocardiographic Changes at the Onset of Epileptic Seizures
Leutmezer, F., Schernthaner, C., Lurger, S., Pötzelberger, K., & Baumgartner, C. (2003) · Epilepsia · doi:10.1046/j.1528-1157.2003.34702.x · Cited by 314 · see citing works
Autonomic alterations and cardiac changes in epilepsy
Sevcencu, C., & Struijk, J. J. (2010) · Epilepsia · doi:10.1111/j.1528-1167.2009.02479.x · Cited by 299 · see citing works
Automatic identification of artifacts in electrodermal activity data
Taylor, S., Jaques, N., Chen, W., Fedor, S., Sano, A., & Picard, R. (2015) · 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7318762 · Cited by 279 · see citing works
Two-Stage Approach for Detection and Reduction of Motion Artifacts in Photoplethysmographic Data
Krishnan, R., Natarajan, B., & Warren, S. (2010) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2009.2039568 · Cited by 263 · see citing works
Ictal tachycardia: The head–heart connection
Eggleston, K. S., Olin, B., & Fisher, R. S. (2014) · Seizure · doi:10.1016/j.seizure.2014.02.012 · Cited by 242 · see citing works
Autonomic changes with seizures correlate with postictal EEG suppression
Poh, M., Loddenkemper, T., Reinsberger, C., Swenson, N. C., Goyal, S., Madsen, J. R., & Picard, R. W. (2012) · Neurology · doi:10.1212/wnl.0b013e318258f7f1 · Cited by 242 · see citing works
Regalia, G., Onorati, F., Lai, M., Caborni, C., & Picard, R. W. (2019) · Epilepsy Research · doi:10.1016/j.eplepsyres.2019.02.007 · Cited by 240 · see citing works
Heart rate and EKG changes in 102 seizures: analysis of influencing factors
Opherk, C., Coromilas, J., & Hirsch, L. J. (2002) · Epilepsy Research · doi:10.1016/s0920-1211(02)00215-2 · Cited by 237 · see citing works
Automated seizure detection systems and their effectiveness for each type of seizure
Ulate-Campos, A., Coughlin, F., Gaínza-Lein, M., Fernández, I. S., Pearl, P. L., & Loddenkemper, T. (2016) · Seizure · doi:10.1016/j.seizure.2016.06.008 · Cited by 233 · see citing works
A Motion-Tolerant Adaptive Algorithm for Wearable Photoplethysmographic Biosensors
Yousefi, R., Nourani, M., Ostadabbas, S., & Panahi, I. M. S. (2013) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2013.2264358 · Cited by 229 · see citing works
Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors
Onorati, F., Regalia, G., Caborni, C., Migliorini, M., Bender, D., Poh, M., Frazier, C., Kovitch Thropp, E., Mynatt, E. D., Bidwell, J., et al. (2017) · Epilepsia · doi:10.1111/epi.13899 · Cited by 212 · see citing works
A Wearable Body Condition Sensor System with Wireless Feedback Alarm Functions
Xu, K., Fujita, Y., Lu, Y., Honda, S., Shiomi, M., Arie, T., Akita, S., & Takei, K. (2021) · Advanced Materials · doi:10.1002/adma.202008701 · Cited by 208 · see citing works
Wearable technology in epilepsy: The views of patients, caregivers, and healthcare professionals
Bruno, E., Simblett, S., Lang, A., Biondi, A., Odoi, C., Schulze-Bonhage, A., Wykes, T., & Richardson, M. P. (2018) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2018.05.044 · Cited by 191 · see citing works
Self-citationGuggisberg, A. G., Hess, C. W., & Mathis, J. (2007) · SLEEP · doi:10.1093/sleep/30.6.755 · Cited by 186 · see citing works
Ultrasensitive skin-like wearable optical sensors based on glass micro/nanofibers
Zhang, L., Pan, J., Zhang, Z., Wu, H., Yao, N., Cai, D., Xu, Y., Zhang, J., Sun, G., Wang, L., et al. (2020) · Opto-Electronic Advances · doi:10.29026/oea.2020.190022 · Cited by 186 · cited 2× · see citing works
“Other promising opto-electronic methods have been investigated in recent advances in the field of wearable sensors [34,35]; however, these are not yet available for clinical use.”
BackgroundAutomated Epileptic Seizure Detection Based on Wearable ECG and PPG in a Hospital Environment
Vandecasteele, K., De Cooman, T., Gu, Y., Cleeren, E., Claes, K., Paesschen, W., Huffel, S., & Hunyadi, B. (2017) · Sensors · doi:10.3390/s17102338 · Cited by 174 · see citing works
Multimodal nocturnal seizure detection in a residential care setting
Arends, J. B. A. M., Thijs, R. D., Gutter, T., Ungureanu, C., Cluitmans, P. J. M., Dijk, J. V., van Andel, J. M., Tan, F. I. Y., de Weerd, A., Vledder, B., et al. (2018) · Neurology · doi:10.1212/wnl.0000000000006545 · Cited by 124 · see citing works
Statistical approach for the detection of motion/noise artifacts in Photoplethysmogram
Selvaraj, N., Mendelson, Y., Shelley, K. H., Silverman, D. G., & Chon, K. H. (2011) · 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/iembs.2011.6091232 · Cited by 119 · see citing works
Non-EEG seizure detection systems and potential SUDEP prevention: State of the art
de Vel, A. V., Cuppens, K., Bonroy, B., Milošević, M., Jansen, K., Huffel, S. V., Vanrumste, B., Cras, P. P., Lagae, L., & Ceulemans, B. (2016) · Seizure · doi:10.1016/j.seizure.2016.07.012 · Cited by 117 · see citing works
Novel techniques for automated seizure registration: Patients' wants and needs
Hoppe, C., Feldmann, M., Blachut, B., Surges, R., Elger, C. E., & Helmstaedter, C. (2015) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2015.08.006 · Cited by 116 · see citing works
Detection of generalized tonic–clonic seizures using surface electromyographic monitoring
Halford, J. J., Sperling, M. R., Nair, D. R., Dlugos, D., Tatum, W. O., Harvey, J. H., French, J. A., Pollard, J. R., Faught, E., Noe, K. H., et al. (2017) · Epilepsia · doi:10.1111/epi.13897 · Cited by 108 · see citing works
Automated Detection of Convulsive Seizures Using a Wearable Accelerometer Device
Kusmakar, S., Karmakar, C. K., Yan, B., O'Brien, T. J., Muthuganapathy, R., & Palaniswami, M. S. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2845865 · Cited by 101 · see citing works
Beniczky, S., Wiebe, S., Jeppesen, J., Tatum, W. O., Brázdil, M., Wang, Y., Herman, S. T., & Ryvlin, P. (2021) · Epilepsia · doi:10.1111/epi.16818 · Cited by 99 · see citing works
Ye, Y., Cheng, Y., He, W., Hou, M., & Zhang, Z. (2016) · IEEE Sensors Journal · doi:10.1109/jsen.2016.2597265 · Cited by 94 · see citing works
Signal quality and patient experience with wearable devices for epilepsy management
Nasseri, M., Nurse, E., Glasstetter, M., Böttcher, S., Gregg, N. M., Laks Nandakumar, A., Joseph, B., Pal Attia, T., Viana, P. F., Bruno, E., et al. (2020) · Epilepsia · doi:10.1111/epi.16527 · Cited by 90 · see citing works
Self-citationIctal heart rate increase precedes EEG discharge in drug-resistant mesial temporal lobe seizures
Gennaro, G. D., Quarato, P. P., Sebastiano, F., Esposito, V., Onorati, P., Grammaldo, L. G., Meldolesi, G., Mascia, A., Falco, C. D., Scoppetta, C., et al. (2004) · Clinical Neurophysiology · doi:10.1016/j.clinph.2003.12.016 · Cited by 80 · see citing works
Pre-ictal heart rate changes: A systematic review and meta-analysis
Bruno, E., Biondi, A., & Richardson, M. P. (2018) · Seizure · doi:10.1016/j.seizure.2018.01.003 · Cited by 66 · see citing works
Self-citationPeri-ictal ECG changes in childhood epilepsy: Implications for detection systems
Jansen, K., Varon, C., Huffel, S. V., & Lagae, L. G. (2013) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2013.06.030 · Cited by 62 · see citing works
Matlab Implementation of Pan Tompkins ECG QRS detector.
Sedghamiz, H. (2014) · doi:10.13140/rg.2.2.14202.59841 · Cited by 62 · see citing works
Buonaura, G. C., Toschi, N., Provini, F., Corazza, I., Bisulli, F., Barletta, G., Vandi, S., Montagna, P., Guerrisi, M., Tinuper, P., et al. (2012) · Sleep Medicine · doi:10.1016/j.sleep.2011.11.007 · Cited by 55 · see citing works
Seizure detection using heart rate variability: A prospective validation study
Jeppesen, J., Fuglsang‐Frederiksen, A., Johansen, P., Christensen, J., Wüstenhagen, S., Tankişi, H., Qerama, E., & Beniczky, S. (2020) · Epilepsia · doi:10.1111/epi.16511 · Cited by 54 · see citing works
Ictal autonomic changes as a tool for seizure detection: a systematic review
van Westrhenen, A., Cooman, T. D., Lazeron, R. H. C., Huffel, S. V., & Thijs, R. D. (2018) · Clinical Autonomic Research · doi:10.1007/s10286-018-0568-1 · Cited by 53 · see citing works
Hirsch, M., Altenmüller, D., & Schulze‐Bonhage, A. (2015) · Epilepsia · doi:10.1111/epi.13117 · Cited by 49 · see citing works
Self-citationWojcikowski, M., & Pankiewicz, B. (2020) · Sensors · doi:10.3390/s20061783 · Cited by 47 · see citing works
Vieluf, S., Reinsberger, C., Atrache, R. E., Jackson, M. C., Schubach, S., Ufongene, C., Loddenkemper, T., & Meisel, C. (2020) · Scientific Reports · doi:10.1038/s41598-020-68434-z · Cited by 42 · see citing works
Evaluation of Wearable Electronics for Epilepsy: A Systematic Review
Rukasha, T., Woolley, S. I., Kyriacou, T., & Collins, T. (2020) · Electronics · doi:10.3390/electronics9060968 · Cited by 40 · see citing works
Biomarkers of seizure severity derived from wearable devices
Beniczky, S., Arbune, A.-A., Jeppesen, J., & Ryvlin, P. (2020) · Epilepsia · doi:10.1111/epi.16492 · Cited by 39 · see citing works
Böttcher, S., Bruno, E., Manyakov, N. V., Epitashvili, N., Claes, K., Glasstetter, M., Thorpe, S., Lees, S., Dümpelmann, M., Van Laerhoven, K., et al. (2021) · JMIR mHealth and uHealth · doi:10.2196/27674 · Cited by 39 · see citing works
Self-citationBruno, E., Böttcher, S., Viana, P. F., Amengual‐Gual, M., Joseph, B., Epitashvili, N., Dümpelmann, M., Glasstetter, M., Biondi, A., Van Laerhoven, K., et al. (2021) · Epilepsia · doi:10.1111/epi.17044 · Cited by 37 · see citing works
Self-citationBruno, E., Biondi, A., Böttcher, S., Lees, S., Schulze‐Bonhage, A., & Richardson, M. P. (2020) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2020.107478 · Cited by 35 · see citing works
Self-citationPeri‐ictal heart rate variability parameters as surrogate markers of seizure severity
Arbune, A.-A., Jeppesen, J., Conradsen, I., Ryvlin, P., & Beniczky, S. (2020) · Epilepsia · doi:10.1111/epi.16491 · Cited by 31 · see citing works
Photoplethysmographic evaluation of generalized tonic‐clonic seizures
Mohammadpour, F., Tamilia, E., Coughlin, F. R., Hammond, S., Atrache, R. E., Jackson, M. C., Bendsen‐Jensen, M., Kim, B., Connolly, J., Manganaro, S., et al. (2020) · Epilepsia · doi:10.1111/epi.16590 · Cited by 22 · see citing works
Post‐ictal accelerometer silence as a marker of post‐ictal immobility
Bruno, E., Böttcher, S., Biondi, A., Epitashvili, N., Manyakov, N. V., Lees, S., Schulze‐Bonhage, A., & Richardson, M. P. (2020) · Epilepsia · doi:10.1111/epi.16552 · Cited by 14 · see citing works
Self-citationSemi-Supervised One-Class Transfer Learning for Heart Rate Based Epileptic Seizure Detection
Cooman, T. D., Varon, C., de Vel, A. V., et al. (2017) · Computing in cardiology · doi:10.22489/cinc.2017.257-052 · Cited by 9 · see citing works
Digital semiology and time-evolution pattern of bio-signals in focal onset motor seizures
Bruno, E., Biondi, A., & Richardson, M. P. (2021) · Seizure · doi:10.1016/j.seizure.2021.03.013 · Cited by 7 · see citing works
Self-citationBöttcher, S., Manyakov, N. V., Epitashvili, N., Folarin, A., Richardson, M. P., Dümpelmann, M., Schulze‐Bonhage, A., & Laerhoven, K. V. (2019) · International Workshop on Sensor-based Activity Recognition and Interaction (iWOAR) · doi:10.1145/3361684.3361687 · Cited by 5 · see citing works
Self-citationIctal tachycardia in patients with hypothalamic hamartoma: Table 1
Antonio‐Arce, V. S., & Schulze‐Bonhage, A. (2014) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp-2014-309681 · Cited by 3 · see citing works
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Data quality evaluation in wearable monitoring
Böttcher, S., Vieluf, S., Bruno, E., Joseph, B., Epitashvili, N., Biondi, A., Zabler, N., Glasstetter, M., Dümpelmann, M., Van Laerhoven, K., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-25949-x · Cited by 91 · see citing works
Self-citationBöttcher, S., Bruno, E., Epitashvili, N., Dümpelmann, M., Zabler, N., Glasstetter, M., Ticcinelli, V., Thorpe, S., Lees, S., Van Laerhoven, K., et al. (2022) · Sensors · doi:10.3390/s22093318 · Cited by 23 · see citing works
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- Authors
- Glasstetter, M., Böttcher, S., Zabler, N., Epitashvili, N., Dümpelmann, M., Richardson, M. P., & Schulze-Bonhage, A.
- Affiliations
- University of Freiburg (DE)
- King's College London (GB)
- Epilepsy Center, Department of Neurosurgery, Medical Center—University of Freiburg, 79106 Freiburg im Breisgau, Germany
- Corresponding author
- Glasstetter, Martin
- ORCID
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2021-09-08
- Volume
- 21
- Issue
- 18
- Pages
- 6017
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC8470979
- OpenAlex
- W3196706059
- Cited by
- 21
- References
- 62
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 6 · 2023: 2 · 2022: 5 · 2021: 1
- FWCI
- 1.06
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Research Portal (King's College London) (submittedVersion, other-oa)
- Funders
- Innovative Medicines Initiative (115902)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Psychiatry and Mental health › Epilepsy research and treatment
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Keywords
- focal seizures, photoplethysmography, wearable sensors, heart rate, seizure detection, motor seizures, motion artifacts, spectral entropy, wrist-worn devices
- MeSH terms
- Wearable Electronic Devices, Electrocardiography, Electroencephalography, Heart Rate, Humans, Seizures, Signal Processing, Computer-Assisted, Tachycardia, Photoplethysmography
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