This study proposes the use of flexible capacitive electrodes for reducing motion artifacts in a wearable electrocardiogram (ECG) device. The capacitive electrodes have conductive foam on their surface, a shield, an optimal input bias resistor, and guarding feedback. The electrodes are integrated in a chest belt, and the acquired signals are transmitted wirelessly for ambulatory heart rate monitoring. We experimentally validated the electrode performance with subjects standing and walking on a treadmill at speeds of up to 7 km/h. The results confirmed the highly accurate heart rate detection capacity of the developed system and its feasibility for daily-life ECG monitoring.

The results indicate that this study provides a promising approach to make the BCI speller more reliable and efficient.

No abstract

Electroencephalography (EEG) is electrical brain activity that can be measured on the scalp with Ag/AgCl electrodes and conductive gel. However, time‐consuming preparation procedures, dehydration of the gel, and skin irritation are crucial drawbacks of using such electrodes. Alternative approaches involving the use of spiky dry electrodes have their own drawbacks such as limited skin–electrode contact area, high skin–electrode impedance, and pain. Reverse‐curve‐arch‐shaped dry EEG electrodes for use in increasing the skin–electrode contact area on hairy scalps are presented. The proposed electrode was fabricated from sterling silver using a three‐dimensional printer. To increase the contact area between the skin and an electrode, an electrode was designed to have reverse‐curve arches which were arranged in a row on the electrode base. The curvature of the arches was designed to match the curvature of the scalp to maximise the contact area and disperse the pressing force. To validate the proposed electrode design, comparison experiments for EEG and skin–electrode contact impedance were conducted, and the proposed electrode was found to perform better than a commercially available finger‐type dry electrode.

A rtificial intelligence using deep learning (DL) technology has recently exhibited promising performance in various fields of medicine, particularly medical image analyses (1-6). These initial successes have elevated expectations for implementing high-performance artificial intelligence in daily clinical practice to innovate and improve its quality and effectiveness (7,8).However, the introduction of DL in daily practice remains in its infancy. One of the most important challenges in bringing DL into daily practice is validating the performance of this technology in actual clinical practice (9,10). The vast majority of previous investigations evaluated the performance of DL systems using retrospectively and conveniently collected test data sets in an experimental manner (11). High performance in these investigations may be biased and cannot guarantee reproduction in realworld practice (9). Therefore, a DL system needs to be evaluated while being used in the actual clinical practice to understand its realistic and unbiased performance.Lung nodule identification in patients with cancer is a relevant task because it may indicate metastasis (9,12). Although chest CT is the standard examination for pulmonary metastasis evaluation, chest radiography may help in the surveillance for newly occurring metastasis at follow-up evaluations, especially in facilities with limited access to CT. However, radiologists' performance in detection of lung nodules on chest radiographs has been unsatisfactory (13-16). Therefore, the computer-aided Background: A computer-aided detection (CAD) system may help surveillance for pulmonary metastasis at chest radiography in situations where there is limited access to CT.Purpose: To evaluate whether a deep learning (DL)-based CAD system can improve diagnostic yield for newly visible lung metastasis on chest radiographs in patients with cancer. Materials and Methods:A regulatory-approved CAD system for lung nodules was implemented to interpret chest radiographs from patients referred by the medical oncology department in clinical practice. In this retrospective diagnostic cohort study, chest radiographs interpreted with assistance from a CAD system after the implementation (January to April 2019, CAD-assisted interpretation group) and those interpreted before the implementation (September to December 2018, conventional interpretation group) of the CAD system were consecutively included. The diagnostic yield (frequency of true-positive detections) and false-referral rate (frequency of false-positive detections) of formal reports of chest radiographs for newly visible lung metastasis were compared between the two groups using generalized estimating equations. Propensity score matching was performed between the two groups for age, sex, and primary cancer.Results: A total of 2916 chest radiographs from 1521 patients (1546 men, 1370 women; mean age, 62 years) and 5681 chest radiographs from 3456 patients (2941 men, 2740 women; mean age, 62 years) were analyzed in the CAD-assisted interpretation and...