Analysis of Voice Quality After Thyroid Surgery.

OBJECTIVES: Voice changes are a common complication after a thyroidectomy, which is a surgical procedure involving partial or total removal of the thyroid gland. The main objective of this work is to examine the possible voice disorders after thyroid surgery. More precisely, it is an investigation of partial and total thyroidectomy, as well as the effects that cancerous and noncancerous thyroid glands can have regarding postsurgical vocal and their association with age and gender. METHODS: Patients were evaluated using acoustic voice parameters, including harmonics-to-noise ratio (HNR), fundamental frequency (F0), jitter, speaker phonation frequency (SPF) range, cepstral peak prominence (CPP), maximum phonational frequency range (MPFR), and shimmer at the preoperative stage and postoperatively at the 1 day, and first-month stages. RESULTS: Results demonstrated a significant change in F0 parameters, SPF range, and CPP feature 1 month after surgery, depending on the type of thyroidectomy and thyroid pathology. No significant changes were observed in the HNR, shimmer, and jitter features. Age was associated with the CPP parameter in the entire sample. In contrast, the MPFR parameter was also related to the type of thyroidectomy in the entire sample. However, maximum F0 was significantly associated with the type of thyroidectomy, specifically in the female sample. CONCLUSIONS: Results indicated that a thyroidectomy can have a negative impact on voice quality. The age and type of thyroidectomy performed are not responsible for this change. Potentially this change can be due to factors such as nerve damage or the subjects' experience, such as job, anxiety, and their physical condition, as well as treatments they may have undergone before thyroidectomy. Further efforts are needed to fully understand the background of voice changes after thyroidectomy.

Real-Time System for Driver Fatigue Detection Based on a Recurrent Neuronal Network.

In recent years, the rise of car accident fatalities has grown significantly around the world. Hence, road security has become a global concern and a challenging problem that needs to be solved. The deaths caused by road accidents are still increasing and currently viewed as a significant general medical issue. The most recent developments have made in advancing knowledge and scientific capacities of vehicles, enabling them to see and examine street situations to counteract mishaps and secure travelers. Therefore, the analysis of driver's behaviors on the road has become one of the leading research subjects in recent years, particularly drowsiness, as it grants the most elevated factor of mishaps and is the primary source of death on roads. This paper presents a way to analyze and anticipate driver drowsiness by applying a Recurrent Neural Network over a sequence frame driver's face. We used a dataset to shape and approve our model and implemented repetitive neural network architecture multi-layer model-based 3D Convolutional Networks to detect driver drowsiness. After a training session, we obtained a promising accuracy that approaches a 92% acceptance rate, which made it possible to develop a real-time driver monitoring system to reduce road accidents.