Time-frequency analysis of accelerometry data for detection of myoclonic seizures

T.M.E. Nijsen, R.M. Aarts, P.J.M. Cluitmans, P.A.M. Griep

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

57 Citaties (Scopus)
121 Downloads (Pure)

Uittreksel

Four time-frequency and time-scale methods are studied for their ability of detecting myoclonic seizures from accelerometric data. Methods that are used are: the short-time Fourier transform (STFT), the Wigner distribution (WD), the continuous wavelet transform (CWT) using a Daubechies wavelet, and a newly introduced model-based matched wavelet transform (MOD). Real patient data are analyzed using these four time-frequency and time-scale methods. To obtain quantitative results, all four methods are evaluated in a linear classification setup. Data from 15 patients are used for training and data from 21 patients for testing. Using features based on the CWT and MOD, the success rate of the classifier was 80%. Using STFT or WD-based features, the classification success is reduced. Analysis of the false positives revealed that they were either clonic seizures, the onset of tonic seizures, or sharp peaks in "normal" movements indicating that the patient was making a jerky movement. All these movements are considered clinically important to detect. Thus, the results show that both CWT and MOD are useful for the detection of myoclonic seizures. On top of that, MOD has the advantage that it consists of parameters that are related to seizure duration and intensity that are physiologically meaningful. Furthermore, in future work, the model can also be useful for the detection of other motor seizure types.
Originele taal-2Engels
Pagina's (van-tot)1197-1203
Aantal pagina's7
TijdschriftIEEE Transactions on Information Technology in Biomedicine
Volume14
Nummer van het tijdschrift5
DOI's
StatusGepubliceerd - 2010

Vingerafdruk

Accelerometry
Wavelet transforms
Wavelet Analysis
Seizures
Fourier transforms
Fourier Analysis
Classifiers
Testing

Citeer dit

@article{87ab71a57cf548f7be6c99924942f48d,
title = "Time-frequency analysis of accelerometry data for detection of myoclonic seizures",
abstract = "Four time-frequency and time-scale methods are studied for their ability of detecting myoclonic seizures from accelerometric data. Methods that are used are: the short-time Fourier transform (STFT), the Wigner distribution (WD), the continuous wavelet transform (CWT) using a Daubechies wavelet, and a newly introduced model-based matched wavelet transform (MOD). Real patient data are analyzed using these four time-frequency and time-scale methods. To obtain quantitative results, all four methods are evaluated in a linear classification setup. Data from 15 patients are used for training and data from 21 patients for testing. Using features based on the CWT and MOD, the success rate of the classifier was 80{\%}. Using STFT or WD-based features, the classification success is reduced. Analysis of the false positives revealed that they were either clonic seizures, the onset of tonic seizures, or sharp peaks in {"}normal{"} movements indicating that the patient was making a jerky movement. All these movements are considered clinically important to detect. Thus, the results show that both CWT and MOD are useful for the detection of myoclonic seizures. On top of that, MOD has the advantage that it consists of parameters that are related to seizure duration and intensity that are physiologically meaningful. Furthermore, in future work, the model can also be useful for the detection of other motor seizure types.",
author = "T.M.E. Nijsen and R.M. Aarts and P.J.M. Cluitmans and P.A.M. Griep",
year = "2010",
doi = "10.1109/TITB.2010.2058123",
language = "English",
volume = "14",
pages = "1197--1203",
journal = "IEEE Transactions on Information Technology in Biomedicine",
issn = "1089-7771",
publisher = "Institute of Electrical and Electronics Engineers",
number = "5",

}

Time-frequency analysis of accelerometry data for detection of myoclonic seizures. / Nijsen, T.M.E.; Aarts, R.M.; Cluitmans, P.J.M.; Griep, P.A.M.

In: IEEE Transactions on Information Technology in Biomedicine, Vol. 14, Nr. 5, 2010, blz. 1197-1203.

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

TY - JOUR

T1 - Time-frequency analysis of accelerometry data for detection of myoclonic seizures

AU - Nijsen, T.M.E.

AU - Aarts, R.M.

AU - Cluitmans, P.J.M.

AU - Griep, P.A.M.

PY - 2010

Y1 - 2010

N2 - Four time-frequency and time-scale methods are studied for their ability of detecting myoclonic seizures from accelerometric data. Methods that are used are: the short-time Fourier transform (STFT), the Wigner distribution (WD), the continuous wavelet transform (CWT) using a Daubechies wavelet, and a newly introduced model-based matched wavelet transform (MOD). Real patient data are analyzed using these four time-frequency and time-scale methods. To obtain quantitative results, all four methods are evaluated in a linear classification setup. Data from 15 patients are used for training and data from 21 patients for testing. Using features based on the CWT and MOD, the success rate of the classifier was 80%. Using STFT or WD-based features, the classification success is reduced. Analysis of the false positives revealed that they were either clonic seizures, the onset of tonic seizures, or sharp peaks in "normal" movements indicating that the patient was making a jerky movement. All these movements are considered clinically important to detect. Thus, the results show that both CWT and MOD are useful for the detection of myoclonic seizures. On top of that, MOD has the advantage that it consists of parameters that are related to seizure duration and intensity that are physiologically meaningful. Furthermore, in future work, the model can also be useful for the detection of other motor seizure types.

AB - Four time-frequency and time-scale methods are studied for their ability of detecting myoclonic seizures from accelerometric data. Methods that are used are: the short-time Fourier transform (STFT), the Wigner distribution (WD), the continuous wavelet transform (CWT) using a Daubechies wavelet, and a newly introduced model-based matched wavelet transform (MOD). Real patient data are analyzed using these four time-frequency and time-scale methods. To obtain quantitative results, all four methods are evaluated in a linear classification setup. Data from 15 patients are used for training and data from 21 patients for testing. Using features based on the CWT and MOD, the success rate of the classifier was 80%. Using STFT or WD-based features, the classification success is reduced. Analysis of the false positives revealed that they were either clonic seizures, the onset of tonic seizures, or sharp peaks in "normal" movements indicating that the patient was making a jerky movement. All these movements are considered clinically important to detect. Thus, the results show that both CWT and MOD are useful for the detection of myoclonic seizures. On top of that, MOD has the advantage that it consists of parameters that are related to seizure duration and intensity that are physiologically meaningful. Furthermore, in future work, the model can also be useful for the detection of other motor seizure types.

U2 - 10.1109/TITB.2010.2058123

DO - 10.1109/TITB.2010.2058123

M3 - Article

C2 - 20667813

VL - 14

SP - 1197

EP - 1203

JO - IEEE Transactions on Information Technology in Biomedicine

JF - IEEE Transactions on Information Technology in Biomedicine

SN - 1089-7771

IS - 5

ER -