Feature Mapping and Deep Long Short Term Memory Network-Based Efficient Approach for Parkinson's Disease Diagnosis

Demir F., ŞENGÜR A., ARI A., Siddique K., Alswaitti M.

IEEE ACCESS, cilt.9, ss.149456-149464, 2021 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 9
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1109/access.2021.3124765
  • Dergi Adı: IEEE ACCESS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Sayfa Sayıları: ss.149456-149464
  • Anahtar Kelimeler: Feature extraction, Support vector machines, Long short term memory, Convolution, Radio frequency, Data models, Wavelet transforms, Convolutional structure, deep LSTM network, feature mapping, PD diagnosis, speech disorders, SIGNAL-PROCESSING ALGORITHMS, SPEECH, CLASSIFICATION, MODEL
  • İnönü Üniversitesi Adresli: Evet

Özet

In this paper, a novel approach was developed for Parkinson's disease (PD) diagnosis based on speech disorders. When the literature about the speech disorders-based PD diagnosis was reviewed, it was seen that the most of approaches were concentrated on the feature selection as the datasets contained a huge number of features. In contrast, in the proposed approach, instead of eliminating some of the features by using any feature selection method, all features were initially used for forming a mapping procedure where the input feature vectors were converted to the input images. Then, a deep Long Short Term Memory (LSTM) network was employed for PD detection where the obtained images were used. The deep LSTM network carried out both feature extraction and classification processes and its training was carried out in an end-to-end fashion. The activations in the convolutional layer were converted to sequence data through the sequence-folding and sequence-unfolding layers. The activations in the LSTM output with learning parameters were conveyed to the Softmax layer for the classification process. A publically available PD dataset was used in the experimental works and classification accuracy, sensitivity, specificity, precision, and F-score metrics were used for performance evaluation. The obtained accuracy, sensitivity, specificity, precision and F-score values were 94.27%, 0.960, 0.960, 0.910 and 0.930, respectively. The obtained results were also compared with some of the published results and it had seen that most of the achievements of the proposed method are better than the compared methods.