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Abstract Heart disease is a significant global health issue. Traditional methods for heart rate monitoring typically require close physical contact, which limits the continuity and convenience of monitoring. To achieve real-time, non-contact heartbeat monitoring, researchers have introduced millimeter-wave radar technology. The technology’s penetration and privacy offer a potential solution for heart condition monitoring. Therefore, this study utilized frequency-modulated continuous wave (FMCW) radar for heart rate monitoring. Firstly, the collected millimeter-wave radar signals were preprocessed to accurately locate the area of cardiac activity in the human body. Secondly, an adaptive variational mode decomposition (A-VMD) algorithm was designed to extract the heartbeat signal, considering signal variations caused by random body movements and respiration and their harmonics, to obtain an accurate heartbeat signal. Finally, the accurate heart rate is obtained by weighted estimation based on the harmonic relationship of the heartbeat signal. The study invited ten subjects to participate in the experiment to verify the effectiveness of this method. The results show that this method can reduce the influence of and random body movements and respiration and harmonics on heart rate monitoring, the average absolute error of heart rate estimation is less than four bpm.

Nowadays, heart disease is the leading cause of death. The high mortality rate and escalating occurrence of heart diseases worldwide warrant the requirement for a fast and efficient diagnosis of such ailments. The purpose is to design an automated system for the classification of abnormal heartbeat audio signals to assist cardiologists. To the best of our knowledge, this is the first study that uses a single neural network model for the classification of eight different types of heartbeat audio signals. The proposed recurrent neural network (RNN) model using Long short-term memory (LSTM) is developed on two publically available databases such as the PASCAL challenge and the 2017 PhysioNet challenge. Mel frequency cepstrum coefficient (MFCC) is applied to extract the dominant features, and a bandpass filter is used to remove the noise from both of the datasets. Afterward, the downsampling technique is used to fix the size of the sampling rate of each sound signal to 20KHz and 300 Hz for the Pascal and PhysioNet database, respectively. The proposed model is compared with multi-layer perceptron (MLP) in terms of different performance evaluation matrices. Furthermore, the outcomes of five machine learning (ML) models are also analyzed. The proposed model has achieved the highest classification accuracy of 0.9971 on the Pascal database, and 0.9870 accuracy on the PhysioNet challenge dataset, which is consistently superior to its competitor approaches. The proposed model provides significant assistance to the cardiac consultant in detecting heart valve diseases.

The respiratory and heartbeat signals can accurately reflect the health status of the tester, which is of great clinical significance. Compared with the traditional contact detection method, the non-contact radar detection method does not require the tester to wear any sensor equipment and will not cause any discomfort to the tester. The frequency modulated continuous wave (FMCW) radar has the characteristics of simple structure, high resolution, strong stability, and low transmission power and is used for the detection of respiratory and heartbeat signals. This paper designs a low-power, low-cost respiratory, and heartbeat signal detection system based on FMCW radar. In addition, the variational modal decomposition (VMD) method is used to separate respiration and heartbeat signals to obtain accurate respiration and heartbeat rates. The results show that the radar system for detecting respiratory and heartbeat signals has high detection accuracy. [ABSTRACT FROM AUTHOR]

The feasibility of monitoring driving psychological fatigue through unconstrained heartbeat extraction by using pressure sensors array was investigated. The pressure signals were obtained by close contact between subject's back and backrest of car seat. Correlation coefficients of electrocardiogram signals and pressure signals were used to select the optimal monitoring point due to the unavoidable adjustment of sitting posture during long times of driving. Lifting wavelet transform was applied for de-noising. J-J interval signals, which represent heartbeat signals, were extracted. The correlation dimension of heartbeat signals, reflecting the complicated degree and the chaotic level of a nonlinear dynamic system, was used as the indicator to determine the psychological fatigue. Its validity was verified by the consistency of correlation dimension with R-R interval (represents cardiac cycle). Finally, questionnaires during driving were administered. The effectiveness of this method to judge driver psychological fatigue was confirmed. This finding indicates that driving psychological fatigue can be monitored through unconstrained heartbeat signal extraction by using the pressure sensor array.

This work presents a new Ultra Wide Band (UWB) beamforming fifth-order derivative Gaussian pulse transmitter with dual small Vivaldi antennas for remote acquisition of vital signals in impulse radar applications. The system consists of a programmable delay circuit (PDC or τ), a UWB pulse generator (PG) circuit and an array of two Vivaldi planar antennas. The circuits is designed using the 0.18μm CMOS IBM process. Spice simulations show the pulse generation with 90mVpp amplitude and 300ps width. The average power consumption is 120μW per pulse, using a 2V power supply at a pulse repetition rate (PRR) of 100MHz. Full tridimensional electromagnetic simulations, using CST MWS, show the main lobe radiation with a gain of 5.5dB, and a beam steering between 20° and -17.5° for azimuthal (θ) angles at the center frequency of 7.5GHz. [ABSTRACT FROM AUTHOR]

Abstract: We present a multifractal analysis of heartbeat and heart rate time series in human races. In order to improve the training of athletes, we compare heart rate multifractal behavior in free and constant speed 10,000 m runnings. We analyze also marathon races, free pace 42.195 km running, we compare the first and the second half heartbeat signals to measure the effect of fatigue. We find that freedom for choosing the own pace variation could be the racing condition for keeping good health conditions in an exhausting exercise. [Copyright &y& Elsevier]

This work presents a new Ultra Wide Band (UWB) beamforming fifth-order derivative Gaussian pulse transmitter with dual small Vivaldi antennas for remote acquisition of vital signals in impulse radar applications. The system consists of a programmable delay circuit (PDC or τ), a UWB pulse generator (PG) circuit and an array of two Vivaldi planar antennas. The circuits is designed using the 0.18µm CMOS IBM process. Spice simulations show the pulse generation with 90mVpp amplitude and 300ps width. The average power consumption is 120µW per pulse, using a 2V power supply at a pulse repetition rate (PRR) of 100MHz. Full tridimensional electromagnetic simulations, using CST MWS, show the main lobe radiation with a gain of 5.5dB, and a beam steering between 20º and -17.5º for azimuthal (θ) angles at the center frequency of 7.5GHz.