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- W3201743587 abstract "Heart rate measurement through Ballistocardiogram (BCG) signal is an efficient method for long-term cardiac activity monitoring in real-time, especially for patients with cardiovascular and cerebrovascular disease. In this study, we propose a one-dimensional (1D) U-net++ to identify the position of J-peak in BCG signals automatically. The proposed 1D U-net++ is based on a 1D convolution neural network through dense skip connection backward transfer data features. The low-level and high-level data features of the BCG signals are combined with the last layer features of 1D U-net++ to shorten the semantic gap when the encoder and decoder feature skip connection. The BCG signals of eight healthy subjects were collected for experimental verification, and the accuracy and precision of J-peak detection reached 99.4% and 99.3%, respectively. The experimental results demonstrate that our proposed method can effectively identify J-peak in BCG signal." @default.
- W3201743587 created "2021-10-11" @default.
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- W3201743587 date "2021-10-06" @default.
- W3201743587 modified "2023-10-16" @default.
- W3201743587 title "1D U-NET++: AN EFFECTIVE METHOD FOR BALLISTOCARDIOGRAM J-PEAK DETECTION" @default.
- W3201743587 doi "https://doi.org/10.1142/s0219519421400583" @default.
- W3201743587 hasPublicationYear "2021" @default.
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