Random Fourier Features-Based Deep Learning Improvement with Class Activation Interpretability for Nerve Structure Segmentation.

Peripheral nerve blocking (PNB) is a standard procedure to support regional anesthesia. Still, correct localization of the nerve's structure is needed to avoid adverse effects; thereby, ultrasound images are used as an aid approach. In addition, image-based automatic nerve segmentation from deep learning methods has been proposed to mitigate attenuation and speckle noise ultrasonography issues. Notwithstanding, complex architectures highlight the region of interest lacking suitable data interpretability concerning the learned features from raw instances. Here, a kernel-based deep learning enhancement is introduced for nerve structure segmentation. In a nutshell, a random Fourier features-based approach was utilized to complement three well-known semantic segmentation architectures, e.g., fully convolutional network, U-net, and ResUnet. Moreover, two ultrasound image datasets for PNB were tested. Obtained results show that our kernel-based approach provides a better generalization capability from image segmentation-based assessments on different nerve structures. Further, for data interpretability, a semantic segmentation extension of the GradCam++ for class-activation mapping was used to reveal relevant learned features separating between nerve and background. Thus, our proposal favors both straightforward (shallow) and complex architectures (deeper neural networks).

Pattern recognition of surface EMG biological signals by means of Hilbert spectrum and fuzzy clustering.

A novel method for hand movement pattern recognition from electromyography (EMG) biological signals is proposed. These signals are recorded by a three-channel data acquisition system using surface electrodes placed over the forearm, and then processed to recognize five hand movements: opening, closing, supination, flexion, and extension. Such method combines the Hilbert-Huang analysis with a fuzzy clustering classifier. A set of metrics, calculated from the time contour of the Hilbert Spectrum, is used to compute a discriminating three-dimensional feature space. The classification task in this feature-space is accomplished by a two-stage procedure where training cases are initially clustered with a fuzzy algorithm, and test cases are then classified applying a nearest-prototype rule. Empirical analysis of the proposed method reveals an average accuracy rate of 96% in the recognition of surface EMG signals.

Towards high accuracy classification of MER signals for target localization in Parkinson's disease.

In recent years Microelectrode recording (MER) analysis has proved to be a powerful localization tool of basal ganglia for Parkinson disease's treatment, especially the Subthalamic Nucleus (STN). In this paper, a signal-dependent method is presented for identification of the STN and other brain zones in Parkinsonian patients. The proposed method, refereed as optimal wavelet feature extraction method (OWFE), is constructed by lifting schemes (LS), which are a flexible and fast implementation of the wavelet transform (WT). The operators in the LS are optimized by means of Genetic Algorithms and Lagrange multipliers considering information contained in MER signals. Then a basic Bayesian classifier (LDC) is used to identify STN and other types of basal ganglia nuclei. The proposed method introduced several advantages from similar works reported in literature. First, the method is signal-dependent and non a priori information is required to decompose the MER signal. Second, the classification accuracy is mostly depended on the feature selection stage because it is not enhanced by elaborated classifiers such as support vector machines or hidden Markov models. Finally, the generalization property of the OWFE has been validated with two databases and different types of classifiers such as k-NN classifier and quadratic Bayesian classifier (QDC). Results have shown that proposed method is able to identify the STN with average accuracy superior than 97%.

Automatic identification of various nuclei in the basal ganglia for Parkinson's disease neurosurgery.

Stereotactic neurosurgery for Parkinson's disease (PD) is one of the most used treatments for relief symptoms of this degenerative disorder. Current methods include ablation and deep brain stimulation (DBS) that can be applied to the various nuclei in the basal ganglia (BG), for instance to the Subthalamic nucleus (STN) or the Ventral medial nucleus (Vim). Identification of thus regions must be rigorous and within a minimum position error. Usually, skilled specialist identifies the brain area by comparing and listening to the rhythm created by the temporal and spatial aggregation of action potentials presented in microelectrode recordings (MER). We present a novel system for automatic identification of the various nuclei in the BG which addresses the limitations of the subjectivity and the non-stationary nature of MER signals. This system incorporates the time-frequency analysis using the Hilbert-Huang Transform (HHT), which is a recent tool for processing nonlinear and non-stationary data, with a dynamic classifier based on Hidden Markov Models (HMM). Classification accuracy in two different databases is compared to validate the performance of the proposed method. Results show that system can recognize selected nuclei with a mean accuracy of 90%.