Özbağlanım ve Cepstral analiz kullanarak nöromüsküler hastalıkların sınıflandırılması

Bu çalışma, yapay sinir ağlarından yararlanılarak myopathy ve neuropathy nöromüsküler hastalıkların sınıflandırlmasını amaçlamaktadır. Çalışmada neuropathy, myopathy ve normal olmak üzere toplam 59 kişiden kaydedilen Elektromyogram (EMG) işaretleri kullanılmıştır. EMG işaretlerine, öz bağlanımlı (Autoregressive: AR) ve Cepstral metotları uygulanarak katsayılar elde edilmiştir. Bu katsayılar yapay sinir ağlarına'(YSA) girdi olarak kullanılmıştır. AR ve Cepstral'a ait katsayılar YSA da eğitime tabii tutulduktan sonra sınıflama ve test performansları incelenmiştir.

Neuromuscular disease classification using autoregressive and Cesptral analysis

The aim of this study is to classify myopathy and neuropathy neuromuscular disease using artificial neural network (ANN). Electromyogram (EMG) signals are recorded from myopathy, neuropathy and normal of neuromuscular disease of totally 59 subjects. By applying Cepstral and Autoregressive (AR) methods to the EMG signals, Cepstral and AR coefficients are obtained. These coefficients are then used as inputs to the ANN. After they are trained by ANN, classification and test performance are investigated.

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