Open Datasets for Bio-signal
電気通信大学・孫光鎬研究室において計測された、生体信号データセットを公開します。
生体計測研究者がアルゴリズム開発にご活用いただければ幸いです。
本データセットに関する問い合わせは、Guanghao.Sun[at]uec.ac.jp
Dataset-1: 非接触レーダから計測された呼吸・心拍信号
作成者： Keisuke Edanami, Guanghao Sun.
説 明： Medical radar signal dataset for non-contact respiration and heart rate measurement. Data in Brief, 2021.
https://doi.org/10.1016/j.dib.2021.107724
Medical radars remotely measure the periodic movements of the chest wall induced by breathing and heartbeat and have been widely recognized in healthcare. To the best of our knowledge, no well-characterized medical radar datasets are shared publicly. Therefore, we provide non-contact respiratory and cardiac signal datasets measured using a medical radar and simultaneously measured reference signals using electrocardiogram (ECG) and respiratory belt transducer. The datasets were collected from nine healthy subjects using 24.25 GHz and 10.525 GHz Doppler radars at a physiological laboratory in Japan. Furthermore, we generated MATLAB code to pre-process the signals and calculate the respiratory and heart rates. The datasets generated could be reused by biomedical researchers to investigate the signal-processing algorithm for non-contact vital sign measurement.
Medical radars remotely measure the periodic movements of the chest wall induced by breathing and heartbeat and have been widely recognized in healthcare. To the best of our knowledge, no well-characterized medical radar datasets are shared publicly. Therefore, we provide non-contact respiratory and cardiac signal datasets measured using a medical radar and simultaneously measured reference signals using electrocardiogram (ECG) and respiratory belt transducer. The datasets were collected from nine healthy subjects using 24.25 GHz and 10.525 GHz Doppler radars at a physiological laboratory in Japan. Furthermore, we generated MATLAB code to pre-process the signals and calculate the respiratory and heart rates. The datasets generated could be reused by biomedical researchers to investigate the signal-processing algorithm for non-contact vital sign measurement.
Dataset-2: 非接触レーダから計測された呼吸・心拍信号---イソフルラン麻酔時のラットの計測データ
作成者： Guanghao Sun, Masaki Kurosawa, Yoshiki Ninomiya, Kohei Baba, Yutaka Kano
説 明： Updating ”Medical Radar Signal Dataset for Non-Contact Respiration and Heart Rate Measurement” with Expanded Data on Laboratory Rats under Isoflurane Anesthesia. Data in Brief, 2024.
The dataset presented in this article is an update of the dataset provided by K. Edanami and G. Sun entitled “Medical Radar Signal Dataset for Non-Contact Respiration and Heart Rate Measurement”. The new dataset includes radar signals and reference laser measurements from experiments conducted on anesthetized rats. The rats were placed in a prone position, and isoflurane was administered in varying concentrations to maintain anesthesia. A 24 GHz radar and laser sensor were positioned above the rats to capture the necessary data. The dataset contains time-stamped radar I and Q channel signals as well as laser measurements. Additionally, MATLAB code for signal visualization and FFT (fast Fourier transform)-based respiration rate estimation is provided. This comprehensive dataset and accompanying MATLAB code facilitate the advancement of non-invasive respiration measurement techniques in small animals, with potential applications in biomedical research.
The dataset presented in this article is an update of the dataset provided by K. Edanami and G. Sun entitled “Medical Radar Signal Dataset for Non-Contact Respiration and Heart Rate Measurement”. The new dataset includes radar signals and reference laser measurements from experiments conducted on anesthetized rats. The rats were placed in a prone position, and isoflurane was administered in varying concentrations to maintain anesthesia. A 24 GHz radar and laser sensor were positioned above the rats to capture the necessary data. The dataset contains time-stamped radar I and Q channel signals as well as laser measurements. Additionally, MATLAB code for signal visualization and FFT (fast Fourier transform)-based respiration rate estimation is provided. This comprehensive dataset and accompanying MATLAB code facilitate the advancement of non-invasive respiration measurement techniques in small animals, with potential applications in biomedical research.
