2020

Self-prediction of seizures in drug resistance epilepsy using digital phenotyping: a concept study. Moontaha, Sidratul; Steckhan, Nico; Kappattanavar, Arpita; Surges, Rainer; Arnrich, Bert (2020). (Vol. 14)
[ Abstract ] [ BibTeX ] [ URL ] [ Details ]
 
Drug-resistance is a prevalent condition in children and adult patients with epilepsy. The quality of life of these patients is profoundly affected by the unpredictability of seizure occurrence. Some of these patients are capable of reporting self-prediction of their seizures by observing their affectivity. Some patients report no signs of feeling premonitory symptoms, prodromes, or aura. In this paper, we propose a concept study that will provide objective information to self-predict seizures for both the patient groups. We will develop a model using digital phenotyping which takes both ecological momentary assessment and data from sensor technology into consideration. This method will be able to provide a feedback of their premonitory symptoms so that a pre-emptive therapy can be associated to reduce seizure frequency or eliminate seizure occurrence.
@inproceedings{moontaha2020selfprediction, abstract = {Drug-resistance is a prevalent condition in children and adult patients with epilepsy. The quality of life of these patients is profoundly affected by the unpredictability of seizure occurrence. Some of these patients are capable of reporting self-prediction of their seizures by observing their affectivity. Some patients report no signs of feeling premonitory symptoms, prodromes, or aura. In this paper, we propose a concept study that will provide objective information to self-predict seizures for both the patient groups. We will develop a model using digital phenotyping which takes both ecological momentary assessment and data from sensor technology into consideration. This method will be able to provide a feedback of their premonitory symptoms so that a pre-emptive therapy can be associated to reduce seizure frequency or eliminate seizure occurrence.}, author = {Moontaha, Sidratul and Steckhan, Nico and Kappattanavar, Arpita and Surges, Rainer and Arnrich, Bert}, booktitle = {14th EAI International Conference on Pervasive Computing Technologies for Healthcare - Demos and Posters}, journal = {Pervasive Health}, keywords = {arpitakappattanavar bertarnrich nicosteckhan sidratulmoontaha}, month = {oct}, title = {Self-prediction of seizures in drug resistance epilepsy using digital phenotyping: a concept study}, volume = 14, year = 2020 }
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AbstractDrug-resistance is a prevalent condition in children and adult patients with epilepsy. The quality of life of these patients is profoundly affected by the unpredictability of seizure occurrence. Some of these patients are capable of reporting self-prediction of their seizures by observing their affectivity. Some patients report no signs of feeling premonitory symptoms, prodromes, or aura. In this paper, we propose a concept study that will provide objective information to self-predict seizures for both the patient groups. We will develop a model using digital phenotyping which takes both ecological momentary assessment and data from sensor technology into consideration. This method will be able to provide a feedback of their premonitory symptoms so that a pre-emptive therapy can be associated to reduce seizure frequency or eliminate seizure occurrence.

Constrained expectation maximisation algorithm for estimating ARMA models in state space representation. Galka, Andreas; Moontaha, Sidratul; SIniatchkin, Siniatchkin in EURASIP Journal on Advances in Signal Processing 2020.1 (2020). 1–37.
[ BibTeX ] [ URL ] [ Details ]
 
@article{galka2020constrained, author = {Galka, Andreas and Moontaha, Sidratul and SIniatchkin, Siniatchkin}, journal = {EURASIP Journal on Advances in Signal Processing 2020.1}, keywords = {sidratulmoontaha}, pages = {1-37}, title = {Constrained expectation maximisation algorithm for estimating ARMA models in state space representation.}, year = 2020 }
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2019

Bewertung von Therapieeffekten bei Epilepsie: Eine vergleichende Analyse zwischen Cox-Stuart-Berechnung und Zustandsraum-Modellierung Scharlach, Sascha; Moontaha, Sirdatul; von Spiczak, Sarah; Stephani, Ulrich; Siniatchkin, Michael; May, Theodor; Galka, Andreas; Meurer, Thomas (2019).
[ BibTeX ] [ URL ] [ Details ]
 
@presentation{scharlach2019bewertung, author = {Scharlach, Sascha and Moontaha, Sirdatul and von Spiczak, Sarah and Stephani, Ulrich and Siniatchkin, Michael and May, Theodor and Galka, Andreas and Meurer, Thomas}, journal = {11. Gemeinsame Jahrestagung der Deutschen und Österreichischen Gesellschaft für Epileptologie sowie der Schweizerischen Epilepsie-Liga}, keywords = {prior-hpi sidratulmoontaha}, title = {Bewertung von Therapieeffekten bei Epilepsie: Eine vergleichende Analyse zwischen Cox-Stuart-Berechnung und Zustandsraum-Modellierung}, year = 2019 }
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SVD Square-root Iterated Extended Kalman Filter for Modeling of Epileptic Seizure Count Time Series with External Inputs. Moontaha, Sidratul; Galka, Andreas; Siniatchkin, Michael; Scharlach, Sascha; von Spiczak, Sarah; Stephani, Ulrich; May, Theodor; Meurer, Thomas (2019). (Vol. 41) 616–619.
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Details ]
 
In this paper a nonlinear filtering algorithm for count time series is developed that takes the non-negativity of the data into account and preserves positive definiteness of the covariance matrices of the model. For this purpose, a recently proposed variant of Kalman Filtering based on Singular Value Decomposition is incorporated into Iterative Extended Kalman Filtering, in order to estimate the states of a nonlinear state space model. The resulting algorithm is applied to the evaluation and design of therapies for patients suffering from Myoclonic Astatic Epilepsy, employing time series of daily seizure rate. The analysis provides a decision whether for a specific patient a particular anti-epileptic drug is increasing or reducing the seizure rate. Through a simulation study the proposed algorithm is validated. Additionally, for clinical data results obtained by the proposed algorithm are compared with the results from a Cox-Stuart trend test as well as with the visual assessment of experienced pediatric epileptologists.
@inproceedings{moontaha2019squareroot, abstract = {In this paper a nonlinear filtering algorithm for count time series is developed that takes the non-negativity of the data into account and preserves positive definiteness of the covariance matrices of the model. For this purpose, a recently proposed variant of Kalman Filtering based on Singular Value Decomposition is incorporated into Iterative Extended Kalman Filtering, in order to estimate the states of a nonlinear state space model. The resulting algorithm is applied to the evaluation and design of therapies for patients suffering from Myoclonic Astatic Epilepsy, employing time series of daily seizure rate. The analysis provides a decision whether for a specific patient a particular anti-epileptic drug is increasing or reducing the seizure rate. Through a simulation study the proposed algorithm is validated. Additionally, for clinical data results obtained by the proposed algorithm are compared with the results from a Cox-Stuart trend test as well as with the visual assessment of experienced pediatric epileptologists.}, author = {Moontaha, Sidratul and Galka, Andreas and Siniatchkin, Michael and Scharlach, Sascha and von Spiczak, Sarah and Stephani, Ulrich and May, Theodor and Meurer, Thomas}, booktitle = {41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society}, keywords = {prior-hpi sidratulmoontaha}, pages = {616-619}, publisher = {IEEE}, title = {SVD Square-root Iterated Extended Kalman Filter for Modeling of Epileptic Seizure Count Time Series with External Inputs}, volume = 41, year = 2019 }
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URNhttp://dx.doi.org/10.1109/EMBC.2019.8857159
AbstractIn this paper a nonlinear filtering algorithm for count time series is developed that takes the non-negativity of the data into account and preserves positive definiteness of the covariance matrices of the model. For this purpose, a recently proposed variant of Kalman Filtering based on Singular Value Decomposition is incorporated into Iterative Extended Kalman Filtering, in order to estimate the states of a nonlinear state space model. The resulting algorithm is applied to the evaluation and design of therapies for patients suffering from Myoclonic Astatic Epilepsy, employing time series of daily seizure rate. The analysis provides a decision whether for a specific patient a particular anti-epileptic drug is increasing or reducing the seizure rate. Through a simulation study the proposed algorithm is validated. Additionally, for clinical data results obtained by the proposed algorithm are compared with the results from a Cox-Stuart trend test as well as with the visual assessment of experienced pediatric epileptologists.

2018

Analysis of the effects of medication for the treatment of epilepsy by ensemble Iterative Extended Kalman Filtering. Moontaha, Sidratul; Galka, Andreas; Meurer, Thomas; Siniatchkin, Michael (2018). (Vol. 40) 187–190.
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Details ]
 
This paper proposes an objective methodology for the analysis of epileptic seizure count time series by developing a non-linear state space model. An iterative extended Kalman filter (IEKF) is employed for the estimation of the states of the non-linear state space model. In order to improve convergence of the IEKF, the recently proposed Levenberg-Marquardt variant of the IEKF is explored. As external inputs time-dependent dosages of several simultaneously administered anticonvulsants are included. The aim of the analysis is to decide whether each anticonvulsant decreases or increases the number of seizures per day. The performance of the analysis is analyzed for simulated data, as well as for real data from a patient suffering from myoclonic-astatic epilepsy.
@inproceedings{moontaha2018analysis, abstract = {This paper proposes an objective methodology for the analysis of epileptic seizure count time series by developing a non-linear state space model. An iterative extended Kalman filter (IEKF) is employed for the estimation of the states of the non-linear state space model. In order to improve convergence of the IEKF, the recently proposed Levenberg-Marquardt variant of the IEKF is explored. As external inputs time-dependent dosages of several simultaneously administered anticonvulsants are included. The aim of the analysis is to decide whether each anticonvulsant decreases or increases the number of seizures per day. The performance of the analysis is analyzed for simulated data, as well as for real data from a patient suffering from myoclonic-astatic epilepsy.}, author = {Moontaha, Sidratul and Galka, Andreas and Meurer, Thomas and Siniatchkin, Michael}, booktitle = {40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society}, keywords = {prior-hpi sidratulmoontaha}, pages = {187-190}, publisher = {IEEE}, title = {Analysis of the effects of medication for the treatment of epilepsy by ensemble Iterative Extended Kalman Filtering}, volume = 40, year = 2018 }
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URNhttp://dx.doi.org/10.1109/EMBC.2018.8512179
AbstractThis paper proposes an objective methodology for the analysis of epileptic seizure count time series by developing a non-linear state space model. An iterative extended Kalman filter (IEKF) is employed for the estimation of the states of the non-linear state space model. In order to improve convergence of the IEKF, the recently proposed Levenberg-Marquardt variant of the IEKF is explored. As external inputs time-dependent dosages of several simultaneously administered anticonvulsants are included. The aim of the analysis is to decide whether each anticonvulsant decreases or increases the number of seizures per day. The performance of the analysis is analyzed for simulated data, as well as for real data from a patient suffering from myoclonic-astatic epilepsy.