Radar-Based Heart Sound Detection

Abstract

This paper introduces heart sound detection by radar systems, which enables touch-free and continuous monitoring of heart sounds. The proposed measurement principle entails two enhancements in modern vital sign monitoring. First, common touch-based auscultation with a phonocardiograph can be simplified by using biomedical radar systems. Second, detecting heart sounds offers a further feasibility in radar-based heartbeat monitoring. To analyse the performance of the proposed measurement principle, 9930 seconds of eleven persons-under-tests’ vital signs were acquired and stored in a database using multiple, synchronised sensors: a continuous wave radar system, a phonocardiograph (PCG), an electrocardiograph (ECG), and a temperature-based respiration sensor. A hidden semi-Markov model is utilised to detect the heart sounds in the phonocardiograph and radar data and additionally, an advanced template matching (ATM) algorithm is used for state-of-the-art radar-based heartbeat detection. The feasibility of the proposed measurement principle is shown by a morphology analysis between the data acquired by radar and PCG for the dominant heart sounds S1 and S2: The correlation is 82.97 ± 11.15% for 5274 used occurrences of S1 and 80.72 ± 12.16% for 5277 used occurrences of S2. The performance of the proposed detection method is evaluated by comparing the F-scores for radar and PCG-based heart sound detection with ECG as reference: Achieving an F1 value of 92.22 ± 2.07%, the radar system approximates the score of 94.15 ± 1.61% for the PCG. The accuracy regarding the detection timing of heartbeat occurrences is analysed by means of the root-mean-square error: In comparison to the ATM algorithm (144.9 ms) and the PCG-based variant (59.4 ms), the proposed method has the lowest error value (44.2 ms). Based on these results, utilising the detected heart sounds considerably improves radar-based heartbeat monitoring, while the achieved performance is also competitive to phonocardiography.

A Novel Vital-Sign Sensing Algorithm for Multiple Subjects Based on 24-GHz FMCW Doppler Radar

“…Heart rate and respiration are two essential vital signs that indicate the basic functioning of a human body [1][2][3]. They can be used for the diagnosis and prevention of cardiopulmonary diseases such as sleep apnea and arrhythmia.…”

A dataset of radar-recorded heart sounds and vital signs including synchronised reference sensor signals

“…A more convenient technology is ballistocardiography 7,8 , which however is restricted to bed-ridden persons and standardized conditions. Further options of continuous monitoring comprise more complex devices such as infrared camera-based systems, which however requires several markers on the person 9 , and distance-measuring devices such as laser- [10][11][12] or radar-based [13][14][15][16][17][18][19][20][21][22][23][24][25][26] systems. When comparing the latter two, radar systems have the advantage that they are able to penetrate clothing and allow for a very comfortable way of continuous and contactless vital sign monitoring.…”

“…A Six-Port is utilized as a quadrature interferometer for the radar application. A detailed description of the radar system is given in 17 . The Six-Port is a completely passive structure which basically consists of three quadrature hybrid couplers and one Wilkinson divider 29 .…”

“…Before the demodulation can be applied, an ellipse fitting algorithm such as presented in 42 has to be employed. Further descriptions can be taken from 17 . An example is also given in the code samples which are available online.…”

A Robust and Real-Time Capable Envelope-Based Algorithm for Heart Sound Classification: Validation under Different Physiological Conditions

“…The hidden-Markov model (HMM) is a well-established probabilistic-based model for the classification of heart sounds [11]. In [12,13] the HMM, which Schmidt et al proposed for the heart sound classification, was improved to the hidden-semi-Markov model (HSMM) and tested with a huge amount of test persons. Renna et al used convolutional neural networks (CNN) together with an underlying HMM in order to outperform the state of the art on heart sound classification [14].…”