Detection of cardiac activity using a 5.8 GHz radio frequency sensor

Abstract

A 5.8-GHz ISM-Band radio-frequency sensor has been developed for non-contact measurement of respiration and heart rate from stationary and semi-stationary subjects at a distance of 0.5 to 1.5 meters. We report on the accuracy of the heart rate measurements obtained using two algorithmic approaches, as compared to a reference heart rate obtained using a pulse oximeter. Simultaneous Photoplethysmograph (PPG) and non-contact sensor recordings were recorded over fifteen minute periods for ten healthy subjects (8M/2F, ages 29.6 + or - 5.6 yrs) One algorithm is based on automated detection of individual peaks associated with each cardiac cycle; a second algorithm extracts a heart rate over a 60-second period using spectral analysis. Peaks were also extracted manually for comparison with the automated method. The peak-detection methods were less accurate than the spectral methods, but suggest the possibility of acquiring beat by beat data; the spectral algorithms measured heart rate to within + or -10% for the ten subjects chosen. Non-contact measurement of heart rate will be useful in chronic disease monitoring for conditions such as heart failure and cardiovascular disease.

Doppler Radar-Based Non-Contact Health Monitoring for Obstructive Sleep Apnea Diagnosis: A Comprehensive Review

“…Studies have reported some commendable achievements, such as error of less than 0.5 breath per minute for respiratory rate and one beat per minute for heart rate [26,27,61]. In addition, the achieved accuracy of Doppler radar in measuring physiological parameters, such as respiratory rate is 92% [39], heart rate is 88% [39,59,81] to 91% [40], and with the addition of harmonics interference, the average error can be reduced further to 3.2% [84]. However, conventional FFT may not always be able to reliably separate the rich sinusoidal components due to smearing and leakage problems, particularly from the limited data samples [74].…”

“…Advances in hardware circuitry design, antenna exploratory, signal processing techniques, and classification algorithms have also been explored. However, the main challenges in using Doppler radar systems for physiological measurement is still the analysis and processing of the received signal data [59].…”

“…Low-pass filters, high-pass filters, notch filters, and complex digital signal processing algorithms have also been reported for noise filtering and DC offsets elimination [26,27,33,[61][62][63]. The reported filters are types of Sallen-Key [61,[64][65][66], Elliptic [62,67], Butterworth, and RC passive [33,55,59,68] filters. Phase and Self-Injection-Locked (PSIL) oscillator with dual-tuning voltage-controlled oscillator has also been reported to achieve high signal-to-noise ratio [56].…”

“…The ranges of the reported filter bandwidth frequencies for respiratory rate are from 0.1-0.5 Hz which is equivalent to 6-30 breaths per minute. The heart rate frequencies are from 0.8-2.0 Hz which is equivalent to 48-120 beats per minute [13,26,32,39,40,59,61,63,65,69].…”

Development of A Respiration Rate Meter - A Low-Cost Design Approach

“…A system for wearable respiration monitoring system based on digital respiratory inductive plethysmography has been described by We et al [4]. In their design of respiration rate meter, Vasu et al and Choi et al used radio frequency sensors [15,16], whereas Miwa and Sakai [17] proposed the use of body sound for the development of a heart rate and respiration measurement system. Implementation of optical methods, ultrasonic proximity sensor, impedance plethysmography, ECG signal (via wavelet), facial tracking method and real-time vision based system are also studied by Scalise et al [18], Min et al [19], Ansari et al [20], Santo and Carbajal [21], Khalidi et al [6] and Tan et al [1] respectively regarding the measurement of respiration rate.…”