Medical Radar Signal Dataset for Non-Contact Respiration and Heart Rate Measurement

Edanami, Keisuke; Sun, Guanghao

doi:10.1016/j.dib.2021.107724

Cited by 19 publications

(21 citation statements)

References 3 publications

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“…A physiological laboratory in Japan has publicly shared a well-characterized medical radar dataset that can be reused by biomedical researchers to study signal processing algorithms for non-contact vital signs measurement [18]. They provided noncontact respiratory and cardiac signal datasets obtained through the use of a medical radar, they also simultaneously collected reference signals using an electrocardiogram (ECG) and respiratory belt transducer The experiment was carried out on nine healthy volunteers with an average age of 24, 5 males, and 4 females.…”

Section: Model Validation a Experimental Configuration And Data Acqui...mentioning

confidence: 99%

Fast QRS Complex Detection Algorithm Based on RMS Shifting Concept for Heart Rate Estimation Using an Electrocardiogram

MBOYI¹,

Ryu²,

Mbindzoukou³

2023

Preprint

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<p>Accurate detection of QRS complex in electrocardiogram (ECG) signals is essential for reliable estimation of the heart rate. However, traditional QRS detection algorithms often have low performance in the presence of various types of noise and signal abnormalities and require additional memory resources to track undetected peaks. In this work, we propose a novel QRS complex detection algorithm based on the root mean square (RMS) shifting concept. The concept of RMS shifting consists to remove an amount proportional to the RMS of the pre-processed signal for moving all the P and T waves of the electrocardiogram toward the negative part of the y-axis and keep only the R peaks in the positive part. Then, all the roots can be softly detected using the corollary of the intermediate value theorem known as Bolzano's theorem. The detection of R peaks is ensured by Rolle's theorem. Our proposed model has been implemented and evaluated on a diverse set of ECG datasets and its performances are comparable to that of spectral analysis based on the FFT algorithm widely used nowadays. For the construction of this model, we used a sample of an electrocardiogram signal from the MIT/BIH Arrhythmia database stored and provided by Simulink. The peaks detected by our algorithm have been verified and confirmed by the well-known Pan-Tompkins Algorithm used by MATLAB. Then, Our model has been applied to a publicly shared electrocardiogram database provided by a Japanese physiological laboratory. The comparison of the heart rate estimated by the proposed method and the spectral analysis method shows a low absolute error average (0.89 bpm), a low relative error average (1.37%), a low root mean square error (1.05 bpm), and a correlation coefficient very close to 1 (0.9938). We also measured the CPU time to assess the performance of our proposed method and we found that our algorithm is twice as fast as the conventional method. Therefore, we inferred that our model is reliable for estimating heart rate for electrocardiography applications.</p>

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Section: Model Validation a Experimental Configuration And Data Acqui...mentioning

confidence: 99%

Fast QRS Complex Detection Algorithm Based on RMS Shifting Concept for Heart Rate Estimation Using an Electrocardiogram

MBOYI¹,

Ryu²,

Mbindzoukou³

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Experiments and analysis: To validate the algorithm, open datasets that consist of data from nine healthy subjects were used [7]. The datasets included 10-GHz and 24-GHz Doppler radar data, as well as electrocardiogram data for reference.…”

mentioning

confidence: 99%

“…The proposed algorithm was compared with the ML estimation by , the AMDF by and the FFT-based method commonly used for radar heart rate estimation. The MATLAB program for the FFT-based method was provided by the dataset authors [7]. The first experiment was conducted directly on the original data with a sampling rate of 1000Hz.…”

mentioning

confidence: 99%

Periodic average magnitude difference function for remote heart rate monitoring

Zhang

Wei²,

Dong

et al. 2023

Preprint

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With the increasing attention on remote monitoring of human heart rate by radar, there is a need to develop a method that can estimate heart rate quickly and reliably. In this study, a new estimation method using a periodic average magnitude difference function (PAMDF) is proposed to estimate the heart rate from the radar signal. PAMDF advances the classical average magnitude difference function (AMDF) with the help of maximum likelihood (ML) theory. It operates in the time domain and estimates the heart rate by calculating the signal magnitude difference between all heartbeat periods. The proposed technique is more accurate than AMDF and allows rounding interpolation to improve resolution, while maintaining the low complexity advantage of AMDF. The algorithm was validated using radar data from a publicly available dataset.

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“…Fig. 1 Accuracy histogram for nine subjects from [7]. 24 Ghz radar data with sampling rate of 300 Hz.…”

mentioning

confidence: 99%

“…Experiments and analysis: To validate the algorithm, two open datasets that consist of radar data and reference electrocardiogram data from [7] and [8] were used. The former used 10 and 24 GHz Doppler radar to collect data from nine subjects.…”

mentioning

confidence: 99%

Periodic average magnitude difference function for remote heart rate monitoring

Zhang

Wei

Dong

et al. 2023

Electronics Letters

View full text Add to dashboard Cite

With the increasing attention on remote monitoring of human heart rate by radar, there is a need to develop a method that can estimate heart rate quickly and reliably. In this study, a new estimation method using a periodic average magnitude difference function (PAMDF) is proposed to estimate the heart rate from the radar signal. PAMDF advances the classical average magnitude difference function (AMDF) with the help of maximum likelihood (ML) theory. It operates in the time domain and estimates the heart rate by calculating the signal magnitude difference between all heartbeat periods. The proposed technique is more accurate than AMDF and has a similar performance to the ML estimation, while maintaining the low complexity advantage of AMDF. The algorithm was validated using radar data from two publicly available datasets.

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Medical Radar Signal Dataset for Non-Contact Respiration and Heart Rate Measurement

Cited by 19 publications

References 3 publications

Fast QRS Complex Detection Algorithm Based on RMS Shifting Concept for Heart Rate Estimation Using an Electrocardiogram

Fast QRS Complex Detection Algorithm Based on RMS Shifting Concept for Heart Rate Estimation Using an Electrocardiogram

Periodic average magnitude difference function for remote heart rate monitoring

Periodic average magnitude difference function for remote heart rate monitoring

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