3 research outputs found
Convolutional Autoencoder With Sequential and Channel Attention for Robust ECG Signal Denoising With Edge Device Implementation
No full textElectrocardiograms (ECG) are vital for diagnosing various cardiac conditions but are often corrupted by noise from multiple sources, which can hinder accurate interpretation. Denoising ECG signals is particularly challenging because noise usually overlaps with the frequency range of the signal of interest. We proposed a convolutional autoencoder with sequential and channel attention (CAE-SCA) to address this issue. Sequential attention (SA) is based on long short-term memory (LSTM), which captures causal-temporal relationships. Meanwhile, channel attention (CA) is used to emphasize important features within channels. SA is applied to the skip connection of each encoder block, and CA is applied after each decoder block. We validated the CAE-SCA using the MIT-BIH and SHDB-AF databases as clean ECG signals, with the MIT-BIH Noise Stress Test Database as the noise source. Experimental results give an average SNR value of 16.187 dB, RMSE of 0.059, and PRD value of 18.529 in the MIT-BIH database. While in the SHDB-AF dataset, the model obtained 15.308 dB of SNR, 0.049 of RMSE, and 19.220 of PRD. These results demonstrate our CAE-SCA outperforms all the state-of-the-art methods across all tested metrics. For efficiency, CAE-SCA achieved competitive results in the metrics of floating-point operations (FLOPs), inference time, and total parameters. This allowed CAE-SCA to be implemented in edge devices as we tested using our custom ECG acquisition circuit. A significance test further confirms a statistically significant improvement in SNR values achieved by the CAE-SCA compared to baseline models, suggesting the CAE-SCA’s potential for advancing ECG processing in healthcare applications
Mixed attention mechanism on ResNet-DeepLabV3+ for paddy field segmentation
Full text linkRice cultivation monitoring is crucial for Indonesia, where paddy field areas de clined by 2.45% according to the Central Bureau of Statistics due to land func tion changes and shifting crop preferences. Regular monitoring of paddy field distribution is essential for understanding agricultural land utilization by farmers and landowners. Satellite imagery has become increasingly common for agricul tural land observation, but traditional neural networks alone provide insufficient segmentation accuracy. This study proposes an enhanced deep learning architec ture combining residual network (ResNet)-DeepLabV3+ with coordinate atten tion (CA) and spatial group-wise enhancement (SGE) modules. The attention mechanisms establish direct connections between context vectors and inputs, enabling the model to prioritize relevant spatial and spectral features for precise paddy field identification. The CA module enhances spectral feature discrim ination, whereas the SGE improves spatial characteristic representation. The experimental results demonstrate superior performance over the baseline meth ods, achieving intersection over union (IoU) of 0.85, dice coefficient of 0.89, and accuracy of 0.95. The proposed mixed attention mechanism significantly improves the accuracy and efficiency of automatic crop area identification from satellite imagery
Characterising acute and chronic care needs: insights from the Global Burden of Disease Study 2019
No full textChronic care manages long-term, progressive conditions, while acute care addresses short-term conditions. Chronic conditions increasingly strain health systems, which are often unprepared for these demands. This study examines the burden of conditions requiring acute versus chronic care, including sequelae. Conditions and sequelae from the Global Burden of Diseases Study 2019 were classified into acute or chronic care categories. Data were analysed by age, sex, and socio-demographic index, presenting total numbers and contributions to burden metrics such as Disability-Adjusted Life Years (DALYs), Years Lived with Disability (YLD), and Years of Life Lost (YLL). Approximately 68% of DALYs were attributed to chronic care, while 27% were due to acute care. Chronic care needs increased with age, representing 86% of YLDs and 71% of YLLs, and accounting for 93% of YLDs from sequelae. These findings highlight that chronic care needs far exceed acute care needs globally, necessitating health systems to adapt accordingly.
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