Noncontact Neonatal Vital Sign Monitoring and Quantified Assessment of Movement Using IR-UWB Radar Sensor

Abstract

본 논문에서는 IR-UWB 레이더 센서를 사용하여 신생아 활력징후를 모니터링하고 움직임의 정량화된 평가를 위한 비접촉 방법을 제안한다. IR-UWB 레이더 센서는 거리분해능이 좋고 작은 움직임에도 민감해 신생아 활력징후 검출과 신생아·청소년 움직임의 정량화·평가에 적합하다. 먼저 IR-UWB 레이더를 이용해 신생아 호흡수와 심박수를 모니터링하는 방법이 소개된다. 이 연구의 목적은 IR-UWB 레이더를 사용하여 신생아 중환자실의 심박수와 호흡수를 평가하고 기존의 심전도 및 IPG와 비교하여 정확성과 신뢰성을 평가하는 것이다. 둘째, 주의력결핍/과잉행동장애(ADHD)가 있는 청소년들의 과잉행동량을 정량화하는 방법이 소개된다. 이 연구의 목적은 ADHD중에서도 과잉행동군(hyperactivity)과 건강한 대조군(HC)을 모니터링하기 위한 IR-UWB 레이더의 차별적 가치를 검사하는 것이었다. 셋째, 신생아 수면/기상 모니터링 방법이 소개된다. 이 연구의 목적은 IR-UWB 레이더를 사용하여 신생아에서 수면과 깨어있는 상태를 정량적으로 구별하고 측정할 수 있는 가능성을 조사하고 비디오 기록을 이용한 행동 관찰 기반 수면과 깨어있는 상태 분석과 정확도를 비교하는 것이었다. 비디오 캠코더, aEEG 및 액티그래피(Actigraphy)의 데이터가 참조용으로 동시에 기록되었다. 수면/기상 알고리즘에서 레이더 신호는 호흡 파형 신호의 형태 및 움직임 신호의 특성을 고려하여 처리되었다.|In this dissertation, we propose noncontact method of monitoring neonatal vital signs and quantified assessment of movement using IR-UWB radar sensors. IR-UWB radar sensors have good distance resolution and are sensitive to small movements, making them suitable for detecting neonatal vital signs and quantifying and evaluating movements of neonates and youth.

Firstly, the method of monitoring neonatal respiration rate and heart rate using radar is introduced. The purpose of this study was to assess heart rates (HRs) and respiratory rates (RRs) in the neonatal intensive care unit (NICU) using IR-UWB radar and to evaluate its accuracy and reliability compared to conventional electrocardiography (ECG)/impedance pneumography (IPG). The HR and RR were recorded in 34 neonates between 3 and 72 days of age during minimal movement (51 measurements in total) using IR-UWB radar (HR_Rd, RR_Rd) and ECG/IPG (HR_ECG, RR_IPG) simultaneously. The radar signals were processed in real time using algorithms for neonates. Radar and ECG/IPG measurements were compared using concordance correlation coefficients (CCCs) and Bland-Altman plots. From the 34 neonates, 12,530 HR samples and 3,504 RR samples were measured. Both the HR and RR measured using the two methods were highly concordant when the neonates had minimal movements (CCC = 0.95 between the RR_Rd and RR_IPG, CCC = 0.97 between the HR_Rd and HR_ECG). In the Bland-Altman plot, the mean biases were 0.17 breaths/min (95% limit of agreement [LOA] -7.0–7.3) between the RR_Rd and RR_IPG and -0.23 bpm (95% LOA -5.3–4.8) between the HR_Rd and HR_ECG. Moreover, the agreement for the HR and RR measurements between the two modalities was consistently high regardless of neonate weight.

Secondly, the method of quantification of hyperactivity in youth with attention-deficit/hyperactivity disorder(ADHD) is introduced. The purpose of this study was to test the discriminative value of IR-UWB radar for monitoring hyperactive individuals with ADHD and healthy controls (HCs). A total of 10 ADHD patients and 15 HCs underwent hyperactivity assessment using IR-UWB radar during a 22-min continuous performance test. We applied functional ANOVA to compare the mean functions of activity level between the 2 groups. We found that the mean function of activity over time was significantly different and that the activity level of the ADHD group slightly increased over time with high dispersion after approximately 7 min, which means that the difference in activity level between the two groups became evident at this period.

Thirdly, the method of monitoring neonatal sleep/wake is introduced. The purpose of this study was to investigate the possibility of quantitatively distinguishing and measuring sleep/wake states in neonates using IR-UWB radar and to compare its accuracy with behavioral observation-based sleep/wake analyses using video recordings. One preterm and three term neonates in the NICU were enrolled, and voluntary movements and vital signs were measured by radar at ages ranging from 2 to 27 days. Data from a video camcorder, amplitude-integrated electroencephalography(aEEG), and actigraphy were simultaneously recorded for reference. Radar signals were processed using a sleep/wake decision algorithm integrated with breathing signals and movement features. The average recording time for the analysis was 13.0 (7.0–20.5) hours across neonates. Compared with video analyses, the sleep/wake decision algorithm for neonates correctly classified 72.2% of sleep epochs and 80.6% of wake epochs and achieved a final Cohen’s kappa coefficient of 0.49 (0.41–0.59) and an overall accuracy of 75.2%.