Self-powered implantable devices have the potential to extend device operation time inside the body and reduce the necessity for high-risk repeated surgery. Without the technological innovation of in vivo energy harvesters driven by biomechanical energy, energy harvesters are insufficient and inconvenient to power titanium-packaged implantable medical devices. Here, we report on a commercial coin battery-sized high-performance inertia-driven triboelectric nanogenerator (I-TENG) based on body motion and gravity. We demonstrate that the enclosed five-stacked I-TENG converts mechanical energy into electricity at 4.9 μW/cm3 (root-mean-square output). In a preclinical test, we show that the device successfully harvests energy using real-time output voltage data monitored via Bluetooth and demonstrate the ability to charge a lithium-ion battery. Furthermore, we successfully integrate a cardiac pacemaker with the I-TENG, and confirm the ventricle pacing and sensing operation mode of the self-rechargeable cardiac pacemaker system. This proof-of-concept device may lead to the development of new self-rechargeable implantable medical devices.

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.

In this study, a new method for acupuncture point detection using the impedance measurement system based on the PSM (Phase Space Method) is presented. The developed device has been developed as detectors for acupuncture points which are used for diagnosis and treatment in acupuncture. In this system, multi-frequency current injection and voltage measurements are both performed by the surface electrodes, which are controlled by a microcontroller. Also, the microcontroller process continuous time demodulation of the modulated signal by multi frequency components using the adaptive notch filter. After that, PSM is applied about each frequency using an acupuncture equivalent model which is proposed in the pre-study.

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.

No abstract