Development of a Novel Intraoperative Neuromonitoring System Using an Accelerometer Sensor in Thyroid Surgery: A Porcine Model Study

OBJECTIVES: The sensitivity and positive predictive value of widely used intraoperative neuromonitoring (IONM) using electromyography (EMG) of the vocalis muscle in thyroid surgery are controversial. Thus, we developed a novel IONM system with an accelerometer sensor that uses the piezoelectric effect instead of EMG to detect laryngeal twitching. The objective of this study was to evaluate the feasibility and safety of this novel IONM system during thyroid surgery in a porcine model. METHODS: We developed an accelerometer sensor that uses the piezoelectric effect to measure laryngeal twitching in three dimensions. This novel accelerometer sensor was placed in the anterior neck skin (transcutaneous) or postcricoid area. Stimulus thresholds, amplitude, and latency of laryngeal twitching measured using the accelerometer sensor were compared to those measured through EMG of the vocalis muscle. RESULTS: The amplitudes of the accelerometer sensor at the anterior neck and postcricoid area were significantly lower than those of EMG because of differences in the measurement method used to evaluate laryngeal movement. However, no significant differences in stimulus thresholds between the EMG endotracheal tube and transcutaneous or postcricoid accelerometer sensors were observed. CONCLUSION: Accelerometer sensors located at the anterior neck or postcricoid area were able to identify laryngeal twitching. The stimulus intensity measured with these sensors was equivalent to that from conventional vocalis EMG. Our novel IONM system with an accelerometer sensor that checks changes in surface acceleration can be an alternative to EMG of the vocalis muscle for IONM in the future.

Quantitative assessment of stroke patients based on dynamic time warping algorithm / 生物医学工程学杂志

In order to accurately evaluate the similarity of motions during daily rehabilitation training for stroke patients, this paper proposed a novel quantitative assessment method based on dynamic time warping (DTW) algorithm. Firstly, the raw accelerometer signals were preprocessed to eliminate the noise. Secondly, the similarity between the accelerometer signals and four standard task templates was calculated respectively, and then the motion was recognized based on the similarity measurements. Finally, the corresponding quantitative assessment model was used to compute the result. The clinical experimental results showed that there were significant differences in the shortest path distance ( value) of DTW between different tasks, and the classification accuracy could be up to 91% when the value was selected as the classification feature. Additionally, with the process of rehabilitation, the value decreased gradually, which means that the value can be taken as the assessment index to evaluate the quality of designated tasks for stroke patients. It also indicated that the value could be applied into the scene of automatic prescription generation and interactive gaming to determine whether it is needed to change the rehabilitation plan or adjust the game difficulty level, so as to implement the individualized rehabilitation services.