| 1. |
- Fang, Bo, et al.
(author)
-
Dual-Channel Neural Network for Atrial Fibrillation Detection From a Single Lead ECG Wave
- 2023
-
In: IEEE journal of biomedical and health informatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 2168-2194 .- 2168-2208. ; 27:5, s. 2296-2305
-
Journal article (peer-reviewed)abstract
- With the dramatic progress of wearable devices, continuous collection of single lead ECG wave is able to be implemented in a comfortable fashion. Data mining on single lead ECG wave is therefore attracting increasing attention, where atrial fibrillation (AF) detection is a hot topic. In this paper, we propose a dual-channel neural network for AF detection from a single lead ECG wave. Two primary phases are included, the data preprocessing part followed by a dual-channel neural network. A two-stage denoising procedure is developed for data preprocessing, so as to tackle the high noise and disturbance which generally resides in the ECG wave collected by wearable devices. Then the time-frequency spectrum and Poincare plot of the denoised ECG signal are imported into the developed dual-channel neural network for feature extraction and AF detection. On the 2017 PhysioNet/CinC Challenge database, the F1 values were 0.83, 0.90, and 0.75 for AF rhythm and normal rhythm, and other rhythm, respectively. The results well validate the effectiveness of the proposed method for AF detection from a single lead ECG wave, and also indicate its performance advantages over some state-of-the-art counterparts.
|
|
| 2. |
- He, Tongyue, et al.
(author)
-
Toward Wearable Sensors : Advances, Trends, and Challenges
- 2023
-
In: ACM Computing Surveys. - : Association for Computing Machinery (ACM). - 0360-0300 .- 1557-7341. ; 55:14S
-
Research review (peer-reviewed)abstract
- Sensors suitable for wearable devices have many special characteristics compared to other sensors, such as stability, sensitivity, sensor volume, biocompatibility, and so on. With the development of wearable technology, amazing wearable sensors have attracted a lot of attention, and some researchers have done a large number of technology explorations and reviews. However, previous surveys generally were concerned with a specified application and comprehensively reviewed the computing techniques for the signals required by this application, as well as how computing can promote data processing. There is a gap in the opposite direction, i.e., the fundamental data source actively stimulates application rather than from the application to the data, and computing promotes the acquisition of data rather than data processing. To fill this gap, starting with different parts of the body as the source of signal, the fundamental data sources that can be obtained and detected are explored by combining the three sensing principles, as well as discussing and analyzing the existing and potential applications of machine learning in simplifying sensor designs and the fabrication of sensors.
|
|
| 3. |
- Wang, Yazhou, et al.
(author)
-
n-Type Organic Electrochemical Transistors with High Transconductance and Stability
- 2023
-
In: Chemistry of Materials. - : American Chemical Society (ACS). - 0897-4756 .- 1520-5002. ; 35:2, s. 405-415
-
Journal article (peer-reviewed)abstract
- An n-type conjugated polymer based on diazaisoindigo (AIID) and fluorinated thiophene units is introduced. Combining the strong electron-accepting properties of AIID with backbone fluorination produced gAIID-2FT, leading to organic electrochemical transistors (OECTs) with normalized values of 4.09 F cm-1 V-1 s-1 and a normalized transconductance (gm,norm) of 0.94 S cm-1. The resulting OECTs exhibit exceptional operational stability and long shelf-life in ambient conditions, preserving 100% of the original maximum drain current after over 3 h of continuous operation and 28 days of storage in the air. Our work highlights the advantages of integrating strong electron acceptors with donor fluorination to boost the performance and stability of n-type OECTs.
|
|
