Short time Fourier and wavelet transform for accelerometric detection of myoclonic seizures

The Short Time Fourier Transform (STFT) and the Continuous Wavelet Transform (CWT) of accelerometer signals measured in patients with epilepsy are analyzed. Characteristics of the spectrogram and scalogram are studied in order to get more insight in how these transforms might be useful to derive suitable features for myoclonic seizure detection. First some artificial elementary patterns that represent some main charac- teristics of the patterns observed during myoclonic seizures and other movements, are studied. Second the analysis is performed on real patient data. Information from the analysis is used to motivate the use of certain features for detection of myoclonic seizures. Third, for both the STFT and the CWT four feature sets are evaluated in a linear classification setup. Incorporating knowledge from our analysis leads to better detection results than using the spectral power / wavelet coefficients as feature without incorporating this knowledge. This preliminary study shows that both the STFT and the CWT can be valuable for feature extraction for detecting myoclonic seizures. The STFT is more susceptible for false detections. I. INTRODUCTION Detection of seizures in institutionalized people with re- fractory epilepsy contributes to the management of daily care. Detections can be used to trigger an alarm during seizures that might be harmful to the patient, but also the detection of smaller, subtle seizures can be important for diagnostic purposes, for example to get more insight in the patients behavior or to titre anti-epileptic drugs. Previously we reported the potential value of accelerometry for detecting seizures that have movement as the most important clinical manifestation, so called motor seizures (1). We found that 74% of the motor seizures detected, consisted of characteristic myoclonic waveforms in 3-D accelerometer (ACM) signals. Myoclonic seizures typically show a short, shock-like waveform in the ACM-signal. Because the clinical manifestation of myoclonic seizures is very subtle, they are often missed by the current available detection systems. There is a clinical importance of detecting these subtle seizures. Often a patient has many myoclonic seizures during the night and thus the sleep rhythm can be disturbed. Severe motor seizures are often preceded by myoclonic seizures and thus the detection of myoclonic seizures could be used for early warning. In our detection setup, we employ a supervised learning approach and thus ap- propriate features and classifier need to be chosen. Experience from more explored research areas, such as speech and audio, shows that the success of classification critically depends on the choice of features and much less on the complexity of the type of classifier (2). Therefore we focus on suitable features rather than on classification methods. In ACM-literature the choice for features depends on the type of activities the authors aim to detect. For distinguishing between normal daily activities, such as sitting, standing, lying, and movement in general, statistical properties of the amplitude of the signal such as mean an standard deviation seem to be effective (3). When distinguishing between various complex movement patterns, features derived from time-frequency measures such as the short-time Fourier transform (4), or a wavelet transform, (5) are also applied. In this work time-frequency properties of myoclonic seizure patterns in accelerometer signals are studied. A comparison is made between the Short Time Fourier Transform (STFT) and the Continuous Wavelet Transform (CWT) to derive salient features for seizure detection. First, the most important characteristics of the myoclonic seizures are represented in an artificial signal. Second, real patient data are analyzed, after we come to understand how these important properties are reflected in the time-frequency measures. The insight gained in this way is used to derive features for myoclonic seizure detection. Third, this approach is evaluated by testing four feature sets for both STFT and CWT in a discriminant analysis setup on a subset of the clinical data.