Patolojik Dinlenme Tremorlerinin Kompleks Düzlemde Adaptif Tahmini

Abstract

Bu çalışmada, Parkinson hastalarında sıklıkla görülen patolojik dinlenme tremorlerinin kompleks düzlemde adaptif tahmini gerçekleştirilmiştir. Bu kapsamda, ilk olarak anlık olarak ölçülen patolojik “sağ el ve sol el” veya “sağ bacak ve sol bacak” tremorleri kompleks düzlemde ifade edilmiş ve ardından, bu kompleks-değerli patolojik tremorler, bir-adım-ileri kesin linear (strcitly linear, SL) ve geniş linear (Widely linear, WL) tabanlı tahmin ediciler vasıtasıyla adaptif olarak tahmin edilmiştir. Burada, SL tabanlı tahmin edici, kompleks-değerli en küçük ortalama kare (Complex-valued least mean square, CLMS) algoritması ile eğitilirken, WL tabanlı tahmin edici ise artırılmış CLMS (Augmented CLMS) algoritması ile eğitilmiştir. Tahmin edicilerin başarımları, gerçek dünya verisi olan patolojik dinlenme tremorleri üzerinde mutlak hata ve tahmin kazancı açısından incelenmiştir. Yapılan benzetim sonuçları; kompleks-değerli patolojik dinlenme tremorlerinin dairesel olmayan davranış sergilediğini ve bu yüzden de WL tabanlı tahmin edicinin, SL versiyonuna kıyasla daha üstün bir başarım sergilediğini ortaya koymuştur.

Keywords

• Parkinson hastalığı
• Dinlenme Tremorü
• Kompleks Düzlem
• Adaptif Tahmin
• Parkinson hastalığı, Dinlenme Tremorü, Kompleks Düzlem, Adaptif Tahmin

Adaptive Prediction of Pathological Resting Tremors in the Complex Domain

Abstract

In this study, adaptive estimation of pathological resting tremors, which is frequently encountered in Parkinson’s patients, is performed in the complex domain. In this context, pathological “right hand and left hand” or “right leg and left leg” tremors, which were measured instantaneously, are first expressed in the complex domain. Then, these complex-valued pathological tremors are predicted adaptively using one-step-ahead strictly linear (SL) and widely linear (WL) based predictors. Here, the SL based predictor is trained by the Complex-valued least mean square (CLMS) algorithm, while the WL based predictor is trained by the augmented CLMS (ACLMS) algorithm. The performances of these predictors were examined in terms of absolute error and prediction gain on pathological resting tremors as real-world data. Simulation results reveal that complex-valued pathological resting tremors exhibit non-circular behavior and thus the WL based predictor outperforms the SL version.

Keywords

• Parkinson’s disease
• Resting Tremor
• Complex Domain
• Adaptive Prediction
• Parkinson’s disease, Resting Tremor, Complex Domain, Adaptive Prediction

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