Design of a stimulation protocol to predict temperature distribution in subcutaneous tissue using the finite element model

Moxibustion is a traditional Oriental medicine therapy that treats the symptoms of a disease with thermal stimulation. However, it is difficult to control the strength of the thermal or chemical stimulus generated by the various types and amounts of moxa and to prevent energy loss through the skin. To overcome these problems, we previously developed a method to efficiently provide RF thermal stimulation to subcutaneous tissue. In this paper, we propose a finite element model (FEM) to predict temperature distributions in subcutaneous tissue after radio-frequency thermal stimulation. To evaluate the performance of the developed FEM, temperature distributions were obtained from the FEM, and in vivo experiments were conducted using the RF stimulation system at subcutaneous tissue depths of 5 and 10 mm in the femoral region of a rabbit model. High correlation coefficients between simulated and actual temperature distributions—0.98 at 5 mm and 0.99 at 10 mm—were obtained, despite some slight errors in the temperature distribution at each depth. These results demonstrate that the FEM described here can be used to determine thermal stimulation profiles produced by RF stimulation of subcutaneous tissue.

Amplitude based beat detection for atrial fibrillation in pacemaker.

Bradycardia is defined as a sinus rhythm of less than 60 beats per minute and atrial tachyarrhythmia including atrial fibrillation (AF) is frequently associated with bradycardia. Pacemaker is the only effective treatment for symptomatic bradycardia and automatic mode switching (AMS) function is built in pacemaker to switch mode in the presence of atrial tachyarrhythmia. AMS algorithms consider appropriate mode switching in case of undersensing or oversensing and this consideration makes their onset time and resynchronization time late. Current pacemakers have onset time from 2.5 seconds to 26 seconds and resynchronization time from 3.4 seconds to 143 seconds according to manufacturers. In this work, we proposed beat detection algorithm based on amplitude difference between peak and trough for accurate extraction of atrial rate achieving faster mode switching. Evaluation of beat detection algorithm was conducted with six canine AF electrogram (EGM) data. Result showed 96.64% sensitivity, 95.5% positive predictive value in average. With this, transition from AF to normal sinus rhythm could be detected faster than existing AMS algorithms. In conclusion, proposed algorithm can efficiently detect beats in EGM during AF and from this, we can implement faster AMS algorithm.