A computer-aided diagnosis system of parkinson's disease based on hilbert spectrum features of speech.

Early detection and intervention are very important for the diagnosis and treatment of Parkinson’s disease. Firstly, according to the characteristics of dysarthria and abnormal sensitivity to the pronunciation of words such as ‘[pɑ˥’, ‘pjɑʊ˥’, ‘ pən˥’, and ‘pjɛn˥’ in patients with early Parkinson’s disease, a Chinese tongue twister “[pɑ˥ paɪ⇃ pjɑʊ˥ pɪŋ˥ pən˥ peɪ⇃ pʰo˥, pʰɑʊ˥ ˩pɪŋ˥ pɪŋ˥ pʰaɪ↿ peɪ⇃ pjɛn˥ pʰɑʊ⇃” was designed. The original recordings of Parkinson’s suspected patients were collected in a hospital geriatrics clinic to construct a tongue twister speech database. Secondly, Hilbert transform is introduced to process audio data to obtain the instantaneous amplitude signal of speech, the peak value, peak position, peak width and peak area of each syllable corresponding to the instantaneous amplitude and the total speech length, are extracted to construct the machine learning speech feature data set. Finally, SVM is introduced to train and evaluate the speech features of Parkinson’s disease suspected patients, its performance is compared with those of BP and LSTM models. The results show that the recognition accuracy of the three machine learning models is 92.10%, 88.94% and 90.18%, respectively, which indicates that the speech features extracted in this paper are effective. Combined with instantaneous amplitude features of speech and SVM model, a computer-aided diagnosis system for Parkinson’s disease is built. The clinical trial results show that the recognition accuracy of the system for Parkinson’s disease patients can reach 91.43%, and the system has fast response speed and strong anti-interference ability, which can fully assist doctors in making real-time and remote selections of patients with Parkinson’s disease.