Tremor Detection Using Parametric and Non-Parametric Spectral Estimation Methods: A Comparison with Clinical Assessment.
In the clinic, tremor is diagnosed during a time-limited process in which patients are observed and the characteristics of tremor are visually assessed. For some tremor disorders, a more detailed analysis of these characteristics is needed. Accelerometry and electromyography can be used to obtain a better insight into tremor. Typically, routine clinical assessment of accelerometry and electromyography data involves visual inspection by clinicians and occasionally computational analysis to obtain objective characteristics of tremor. However, for some tremor disorders these characteristics may be different during daily activity. This variability in presentation between the clinic and daily life makes a differential diagnosis more difficult. A long-term recording of tremor by accelerometry and/or electromyography in the home environment could help to give a better insight into the tremor disorder. However, an evaluation of such recordings using routine clinical standards would take too much time. We evaluated a range of techniques that automatically detect tremor segments in accelerometer data, as accelerometer data is more easily obtained in the home environment than electromyography data. Time can be saved if clinicians only have to evaluate the tremor characteristics of segments that have been automatically detected in longer daily activity recordings. We tested four non-parametric methods and five parametric methods on clinical accelerometer data from 14 patients with different tremor disorders. The consensus between two clinicians regarding the presence or absence of tremor on 3943 segments of accelerometer data was employed as reference. The nine methods were tested against this reference to identify their optimal parameters. Non-parametric methods generally performed better than parametric methods on our dataset when optimal parameters were used. However, one parametric method, employing the high frequency content of the tremor bandwidth under consideration (High Freq) performed similarly to non-parametric methods, but had the highest recall values, suggesting that this method could be employed for automatic tremor detection.
Department of Neurology, University Medical Center Groningen (UMCG), University of Groningen, Groningen, the Netherlands.
Octavio
O
Martinez Manzanera
Department of Neurology, University Medical Center Groningen (UMCG), University of Groningen, Groningen, the Netherlands.
Jan Willem
JW
Elting
Department of Neurology, University Medical Center Groningen (UMCG), University of Groningen, Groningen, the Netherlands.
Johannes H
JH
van der Hoeven
Department of Neurology, University Medical Center Groningen (UMCG), University of Groningen, Groningen, the Netherlands.
Natasha M
NM
Maurits
D061725:Accelerometry / Q000379:methods; D000368:Aged; D000369:Aged, 80 and over; D004576:Electromyography / Q000379:methods; D020329:Essential Tremor / Q000175:diagnosis; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D012815:Signal Processing, Computer-Assisted; D014202:Tremor / Q000175:diagnosis*