Automated Parkinson’s Disease Detection System Using FBSE-FAWT-Based Time-Frequency Representation of Speech Signals

Abstract

Parkinson’s disease (PD) detection from speech signals attracts significant attention from researchers due to the effectiveness of speech impairment symptoms in PD diagnosis in its early phase. The short-time Fourier transform (STFT)-based analysis suffers from fixed resolution. The discrete wavelet transform (DWT) provides multiresolution analysis. However, an analytic wavelet transform with a flexible time-frequency covering (AWTFTFC) is a generalized form of DWT with flexible selection of redundancy, dilation factor, and Q-factor. The Fourier-Bessel series expansion (FBSE)-based flexible analytic wavelet transform (FAWT) (FBSE-FAWT) is an improved version of AWTFTFC with the replacement of the Fourier-based spectrum by the FBSE-based spectrum to have better frequency resolution. In this article, FBSE-FAWT-based time-frequency representation (TFR) is used to detect PD from speech signals. In the FBSE-FAWT-based TFR method, the FBSE-FAWT technique has been used to obtain subband signals and Hilbert spectral analysis is applied to compute TFR of speech signals. Seven different pretrained networks, namely, AlexNet, GoogleNet, SqueezeNet, DenseNet-101, InceptionNet-v3, VGG-16, and ResNet-50 are trained using the obtained TFRs to detect PD. The proposed framework with ResNet-50 demonstrates the highest performance with an accuracy of 98.05%, an F1-score of 98.27%, a specificity of 98.61%, a precision of 98.93%, and a recall of 97.62%. The robustness of the proposed framework is verified in cross-task situations where the framework has been trained on spontaneous dialogue task and tested on read text task. Further, the proposed framework achieves better performance as compared with its variant, where the FBSE-FAWT-based TFR is replaced with a spectrogram, as well as compared with other existing methods in the literature.