Remote RRI Estimation: A Signal Reconstruction Approach with Multi-channel Input and Channel-Wise Attention Mechanism

• Item type: SIG Technical Reports(1)
• 公開日: 2024-05-08
• タイトル: Remote RRI Estimation: A Signal Reconstruction Approach with Multi-channel Input and Channel-Wise Attention Mechanism
• タイトル / 言語: en
• タイトル: Remote RRI Estimation: A Signal Reconstruction Approach with Multi-channel Input and Channel-Wise Attention Mechanism
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [SeMI] 1
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: Graduate School of Science and Technology, Keio University
• 著者所属: Department of Information and Computer Science, Faculty of Science and Technology, Keio University
• 著者所属: Department of Information and Computer Science, Faculty of Science and Technology, Keio University
• 著者所属(英): en
• 著者所属(英): Graduate School of Science and Technology, Keio University
• 著者所属(英): en
• 著者所属(英): Department of Information and Computer Science, Faculty of Science and Technology, Keio University
• 著者所属(英): en
• 著者所属(英): Department of Information and Computer Science, Faculty of Science and Technology, Keio University
• 著者名: Shengze, Wang; Mondher, Bouazizi; Tomoaki, Ohtsuki
• 著者名(英): Shengze, Wang; Mondher, Bouazizi; Tomoaki, Ohtsuki
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: This paper presents a radar-based heart rate monitoring and Inter-beat interval (IBI) estimation. Traditional IBI estimation relies on peak detection combined with signal processing techniques. Our approach uses signal reconstruction with a neural network, which significantly improves the accuracy and robustness of IBI estimation compared to conventional methods. By utilizing multi-channel input data, our approach effectively mitigates various sources of noise and interference, resulting in highly accurate and reliable IBI predictions. Instead of relying solely on peak detection, we use the U-net architecture and cross-channel attention mechanism to reconstruct the triangular waveform generated from the ground truth ECG signals. The incorporation of multi-channel input and channel-wise attention mechanism improves our model’s ability to discriminate and emphasize critical features from different input channels, further improving IBI estimation accuracy. To evaluate the accuracy and robustness of our proposed method, we trained our model on the open-source dataset [1], and then performed leave-one-subjectout validation, which ensures our approach to be evaluated only on unseen independent subjects. We have achieved a Root Mean Square Error (RMSE) of 26.7 ms for the IBI of each heart-beat. Our results demonstrate the transformative potential of adopting signal reconstruction methods supported by state-of-the-art deep learning techniques. This shift in perspective promises more accurate and robust heart rate and IBI estimation, opening new avenues for improving the accuracy and reliability of cardiac monitoring systems.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: This paper presents a radar-based heart rate monitoring and Inter-beat interval (IBI) estimation. Traditional IBI estimation relies on peak detection combined with signal processing techniques. Our approach uses signal reconstruction with a neural network, which significantly improves the accuracy and robustness of IBI estimation compared to conventional methods. By utilizing multi-channel input data, our approach effectively mitigates various sources of noise and interference, resulting in highly accurate and reliable IBI predictions. Instead of relying solely on peak detection, we use the U-net architecture and cross-channel attention mechanism to reconstruct the triangular waveform generated from the ground truth ECG signals. The incorporation of multi-channel input and channel-wise attention mechanism improves our model’s ability to discriminate and emphasize critical features from different input channels, further improving IBI estimation accuracy. To evaluate the accuracy and robustness of our proposed method, we trained our model on the open-source dataset [1], and then performed leave-one-subjectout validation, which ensures our approach to be evaluated only on unseen independent subjects. We have achieved a Root Mean Square Error (RMSE) of 26.7 ms for the IBI of each heart-beat. Our results demonstrate the transformative potential of adopting signal reconstruction methods supported by state-of-the-art deep learning techniques. This shift in perspective promises more accurate and robust heart rate and IBI estimation, opening new avenues for improving the accuracy and reliability of cardiac monitoring systems.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA11851388
• 書誌情報: 研究報告モバイルコンピューティングと新社会システム（MBL）; 巻 2024-MBL-111, 号 19, p. 1-6, 発行日 2024-05-08
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2188-8817
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会