A Spectrogram-Based Deep Feature Assisted Computer-Aided Diagnostic System for Parkinson’s Disease

Abstract

Parkinson's disease is a neural degenerative disease. It slowly progresses from mild to severe stage, resulting in the degeneration of dopamine cells of neurons. Due to the deficiency of dopamine cells in the brain, it leads to a motor (tremor, slowness, impaired posture) and non-motor (speech, olfactory) defects in the body. Early detection of Parkinson's disease is a difficult chore as the symptoms of disease appear overtime. However, different diagnostic systems have contributed towards disease detection by considering gait, tremor and speech characteristics. Recent work has shown that speech impairments can be considered as a possible predictor for Parkinson's disease classification and remains an open research area. The speech signals show major differences and variations for Parkinson patients as compared to normal human beings. Therefore, variation in speech should be modeled using acoustic features to identify these variations. In this research, we propose three methods-the first method employs a transfer learning-based approach using spectrograms of speech recordings, the second method evaluates deep features extracted from speech spectrograms using machine learning classifiers and the third method evaluates simple acoustic feature of recordings using machine learning classifiers. The proposed frameworks are evaluated on a Spanish dataset pc-Gita. The results show that the second framework shows promising results with deep features. The highest 99.7% accuracy on vowel \o\ and read text is observed using a multilayer perceptron. Whereas 99.1% accuracy observed on vowel \i\ deep features using random forest. The deep feature-based method performs better as compared to simple acoustic features and transfer learning approaches. The proposed methodology outperforms the existing techniques on the pc-Gita dataset for Parkinson's disease detection.

Parkinson’s detection based on combined CNN and LSTM using enhanced speech signals with Variational mode decomposition

“…Some important studies that focused on the detection Parkinson's from speech signals have been summarized in Table 6. [28] proposed three methods for the detection of Parkinson's disease. In the first method, an approach based on transfer learning using spectrograms of speech recordings was preferred.…”

An algorithm for Parkinson’s disease speech classification based on isolated words analysis

“…signal attenuation) generated by the intrinsic structure of the Hamming window. In our specific application we set an overlap window equal to 50% of the window length, also in accordance with [ 14 , 22 , 29 , 49 ].…”

“…More recently [ 22 , 49 ] addressed the same corpus as in [ 37 ]. More in detail, Zahid et al [ 49 ] proposed three methods based on transfer learning, deep feature extraction, and classic machine learning, respectively.…”

“…More recently [ 22 , 49 ] addressed the same corpus as in [ 37 ]. More in detail, Zahid et al [ 49 ] proposed three methods based on transfer learning, deep feature extraction, and classic machine learning, respectively. Although the results achieved for other tasks were very satisfactory, the highest accuracy reported for isolated words, employing the transfer-learning approach, was 77%.…”

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

“…Based on speech spectrogram acoustic features, authors of [156] designed and tested 3 different DL methodologies for detection of PD. The first method uses transfer learning, the most widely used DL technique on Fourier Transformed speech spectrogram to detect PD.…”