Detection of Parkinson's Disease using Speech Signals with L1-Norm SVM and Chi-Square Based Feature Selection Algorithms

Abstract

Parkinson's disease is one of the common neurodegenerative disorders. Speech / voice impairment is considered as one of the early symptoms of Parkinson's disease. Sound signal processing methods can potentially assess and measure Parkinson's disease-related sound impairment. In this study, an effective machine learning technique has been proposed to diagnose Parkinson's disease from speech signals. In the proposed method, a data set containing features extracted from speech signals of healthy people and Parkinson's patients was used. Highly distinctive features in the data set were selected using L1-Norm Support Vector Machine and Chi-Square Based feature selection algorithms. The feature sets obtained from the two methods were combined and used in the classification stage. In the classification stage, the achievement of proposed method was increased with majority voting method, which used the prediction results of Support Vector Machine, K-Nearest Neighbor and Random Subspace K-Nearest Neighbor Ensembles classifiers. The proposed method with 95.11% accuracy outperformed previous studies using the same dataset. Since Parkinson's disease will be diagnosed automatically with the proposed method, this application can be used as a helpful tool for physicians in their decision-making process.

Keywords

• Classification
• feature selection
• Parkinson’s disease
• speech signal

L1-Norm DVM ve Ki-Kare Tabanlı Öznitelik Seçme Algoritmaları ile Parkinson Hastalığının Konuşma Sinyalleri Üzerinden Saptanması

Abstract

Parkinson hastalığı, genel nöro-dejeneratif bozukluklardan biridir. Konuşma / ses bozukluğu Parkinson hastalığının erken dönemdeki semptomlarından biri olarak kabul edilir. Ses sinyallerini işleme yöntemleri, Parkinson hastalığı ile ilgili ses bozukluğunu potansiyel olarak değerlendirebilir ve ölçebilir. Bu çalışmada, Parkinson hastalığını konuşma sinyallerinden teşhis etmek için etkili bir makine öğrenmesi tekniği önerilmiştir. Önerilen yöntemde, sağlıklı kişilerin ve Parkinson hastalarının konuşma sinyallerinden çıkarılan öznitelikleri içeren bir veri seti kullanılmıştır. L1-Norm Destek Vektör Makinesi ve Ki-Kare Tabanlı öznitelik seçme algoritmaları kullanılarak veri setinde bulunan ayırt ediciliği yüksek öznitelikler seçilmiştir. İki yöntemden elde edilen öznitelik setleri birleştirilerek sınıflandırma aşamasında kullanılmıştır. Sınıflandırma aşamasında Destek Vektör Makinesi, K-En Yakın Komşu ve Rasgele Alt Uzay K-En Yakın Komşu Toplulukları sınıflandırıcılarının tahmin sonuçlarının kullanıldığı Çoğunluk Oylaması yöntemi ile önerilen yöntemin başarımı artırılmıştır. Önerilen yöntem %95.11 doğruluk ile aynı veri setini kullanan geçmiş çalışmalara göre daha iyi bir performans sağlamıştır. Önerilen yöntem ile Parkinson hastalığı otomatik olarak teşhis edileceğinden bu uygulama hekimlere karar verme süresinde yardımcı bir araç olarak kullanılabilir.

Keywords

• Konuşma sinyalleri
• öznitelik seçme
• Parkinson hastalığı
• sınıflandırma

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