Enhancing dynamic ECG heartbeat classification with lightweight transformer model

2022 ◽  
pp. 102236
Author(s):  
Lingxiao Meng ◽  
Wenjun Tan ◽  
Jiangang Ma ◽  
Ruofei Wang ◽  
Xiaoxia Yin ◽  
...  
Keyword(s):  
Heartbeat Classification ◽  
Transformer Model

Robust wave-feature adaptive heartbeat classification based on self-attention mechanism using a transformer model

2021 ◽  
Author(s):  
Shuaicong Hu ◽  
Wenjie Cai ◽  
Tijie Gao ◽  
Jiajun Zhou ◽  
Mingjie Wang
Keyword(s):  
Model Performance ◽  
Attention Mechanism ◽  
Classification Method ◽  
Ectopic Beat ◽  
Supraventricular Arrhythmia ◽  
Segmentation Method ◽  
Ventricular Ectopic Beat ◽  
Heartbeat Classification ◽  
Symbolic Representations ◽  
Transformer Model

Abstract Objective: Electrocardiography is a common method for screening cardiovascular diseases. Accurate heartbeat classification assists in diagnosis and has attracted great attention. In this paper, we proposed an automatic heartbeat classification method based on a transformer neural network using a self-attention mechanism. Approach: An adaptive heartbeat segmentation method was designed to selectively focus on the time-dependent representation of heartbeats. A one-dimensional convolution layer was used to embed wave characteristics into symbolic representations, and then, a transformer block using multi-head attention was applied to deal with the dependence of wave-embedding. The model was trained and evaluated using the MIT-BIH arrhythmia database (MIT-DB). To improve the model performance, the model pre-trained on MIT-BIH supraventricular arrhythmia database (MIT-SVDB) was used and fine-tuned on MIT-DB. Main results: The proposed method was verified using the MIT-DB for two groups. In the first group, our method attained F1 scores of 0.86 and 0.96 for the supraventricular ectopic beat (SVEB) class and ventricular ectopic beat (VEB) class, respectively. In the second group, our method achieved an average F1 value of 99.83% and better results than other state-of-the-art methods. Significance: We proposed a novel heartbeat classification method based on a transformer model. This method provides a new solution for real-time electrocardiogram heartbeat classification, which can be applied to wearable devices.

Power Losses in RF Inductor of Ferrite-Free Closed-Loop Inductively-Coupled Low Pressure Mercury Lamps

2020 ◽  
pp. 89-94 ◽  
Author(s):  
Ekaterina V. Lovlya ◽  
Oleg A. Popov
Keyword(s):  
Closed Loop ◽  
Low Pressure ◽  
Mercury Vapour ◽  
Plasma Power ◽  
Power Losses ◽  
Inductive Discharge ◽  
Inductively Coupled ◽  
Radial Inhomogeneity ◽  
Transformer Model ◽  
Rf Inductor

RF inductor power losses of ferrite-free electrode-less low pressure mercury inductively-coupled discharges excited in closed-loop dielectric tube were studied. The modelling was made within the framework of low pressure inductive discharge transformer model for discharge lamps with tubes of 16, 25 and 38 mm inner diam. filled with the mixture of mercury vapour (7.5×10–3 mm Hg) and argon (0.1, 0.3 and 1.0 mm Hg) at RF frequencies of 1, 7; 3.4 and 5.1 MHz and plasma power of (25–500) W. Discharges were excited with the help of the induction coil of 3, 4 and 6 turns placed along the inner perimeter of the closed-loop tube. It was found that the dependence of coil power losses, Pcoil, on the discharge plasma power, Ppl, had the minimum while Pcoil decreased with RF frequency, tube diameter and coil number of turns. The modelling results were found in good qualitative agreement with the experimental data; quantitative discrepancies are believed to be due skin-effect and RF electric field radial inhomogeneity that were not included in discharge modelling.

scholarly journals A Spam Transformer Model for SMS Spam Detection

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Xiaoxu Liu ◽  
Haoye Lu ◽  
Amiya Nayak
Keyword(s):  
Spam Detection ◽  
Transformer Model

Precise and efficient heartbeat classification using a novel lightweight-modified method

2021 ◽  
Vol 68 ◽  
pp. 102771
Author(s):  
Yunqing Liu ◽  
Yanrui Jin ◽  
Jinlei Liu ◽  
Chengjin Qin ◽  
Ke Lin ◽  
...  
Keyword(s):  
Heartbeat Classification ◽  
Modified Method

scholarly journals Input Parallel Output Series Structure of Planar Medium Frequency Transformers for 200 kW Power Converter: Model and Parameters Evaluation

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1450
Author(s):  
Alessandro La Ganga ◽  
Roberto Re ◽  
Paolo Guglielmi
Keyword(s):  
Electric Vehicles ◽  
High Performance ◽  
Power Converter ◽  
Electrical Model ◽  
Solid State Transformer ◽  
Medium Frequency ◽  
Analytical Treatment ◽  
Transformer Model ◽  
Planar Medium ◽  
Parallel Output

Nowadays, the demand for high power converters for DC applications, such as renewable sources or ultra-fast chargers for electric vehicles, is constantly growing. Galvanic isolation is mandatory in most of these applications. In this context, the Solid State Transformer (SST) converter plays a fundamental role. The adoption of the Medium Frequency Transformers (MFT) guarantees galvanic isolation in addition to high performance in reduced size. In the present paper, a multi MFT structure is proposed as a solution to improve the power density and the modularity of the system. Starting from 20kW planar transformer model, experimentally validated, a multi-transformer structure is analyzed. After an analytical treatment of the Input Parallel Output Series (IPOS) structure, an equivalent electrical model of a 200kW IPOS (made by 10 MFTs) is introduced. The model is validated by experimental measurements and tests.

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