A comprehensive approach for high intensity blood flow signal classification using multi-scale spectrogram fusion and tunable Q-factor wavelet analysis

The proposed manuscript presents a novel automated approach for the classification of high intensity blood flow signals by combining the features obtained from the hybrid multi-scale spectrogram image samples with the tunable Q-factor wavelet transform based statistical features. Micro embolic signals, whose presence in the blood flow are early indicators of stroke condition, have transient nature. The existence of two other similar high-intensity signals (artifacts and Doppler speckle) make the traditional micro embolic signal detection a challenging procedure. The proposed method utilizes the convolutional neural network (CNN) architecture DenseNet201 to extract features from spectrogram images generated at different scales to mimic the human ear's basilar membrane for better understanding of blood flow signals. Additionally, the tunable Q-factor wavelet transform (TQWT) was employed to extract statistical features from the decomposed sub-bands, which were obtained by using an adjustable time-frequency domain analysis. This adjustable time-frequency tuning property of the TQWT has provided a better representation of the non-stationary characteristics of the Doppler ultrasound signals. A feature-level fusion technique was also applied to combine the strengths of both CNN-based and TQWT-based features to enhance the discriminative power of the proposed automated classification approach. Experimental results demonstrate the effectiveness of the proposed approach, in which the hybrid spectrogram image based features and optimum parameter set tuned TQWT based features were fused. A notable performance was achieved in distinguishment of micro embolic signals from artifacts and speckle signals by obtaining 94.42% accuracy and 94.36% F1-Score.