Tran Vinh Phuc, Ali Al-Jumaily Adel
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:4202-5. doi: 10.1109/EMBC.2015.7319321.
Long term continuous patient monitoring is required in many health systems for monitoring and analytical diagnosing purposes. Most of monitoring systems had shortcomings related to their functionality or patient comfortably. Non-contact continuous monitoring systems have been developed to address some of these shortcomings. One of such systems is non-contact physiological vital signs assessments for chronic heart failure (CHF) patients. This paper presents a novel automated estimation algorithm for the non-contact physiological vital signs assessments for CHF patients based on a patented novel non-contact biomotion sensor. A database consists of twenty CHF patients with New York Heart Association (NYHA) heart failure Classification Class II & III, whose underwent full Polysomnography (PSG) analysis for the diagnosis of sleep apnea, disordered sleep, or both, were selected for the study. The patients mean age is 68.89 years, with mean body weight of 86.87 kg, mean BMI of 28.83 (obesity) and mean recorded sleep duration of 7.78 hours. The propose algorithm analyze the non-contact biomotion signals and estimate the patients' respiratory and heart rates. The outputs of the algorithm are compared with gold-standard PSG recordings. Across all twenty patients' recordings, the respiratory rate estimation median accuracy achieved 92.4689% with median error of ± 1.2398 breaths per minute. The heart rate estimation median accuracy achieved 88.0654% with median error of ± 7.9338 beats per minute. Due to the good performance of the propose novel automated estimation algorithm, the patented novel non-contact biomotion sensor can be an excellent tool for long term continuous sleep monitoring for CHF patients in the home environment in an ultra-convenient fashion.
在许多医疗系统中，为了监测和分析诊断目的，需要对患者进行长期连续监测。大多数监测系统在功能或患者舒适度方面存在缺陷。为了解决其中一些缺陷，已经开发了非接触式连续监测系统。其中一个系统是用于慢性心力衰竭（CHF）患者的非接触式生理生命体征评估。本文提出了一种基于专利新型非接触生物运动传感器的用于CHF患者非接触式生理生命体征评估的新型自动估计算法。选择了一个由20名纽约心脏协会（NYHA）心力衰竭分级为II级和III级的CHF患者组成的数据库，这些患者接受了全面的多导睡眠图（PSG）分析以诊断睡眠呼吸暂停、睡眠障碍或两者兼有，用于该研究。患者的平均年龄为68.89岁，平均体重为86.87千克，平均体重指数为28.83（肥胖），平均记录睡眠时间为7.78小时。所提出的算法分析非接触生物运动信号并估计患者的呼吸和心率。将算法的输出与金标准PSG记录进行比较。在所有20名患者的记录中，呼吸频率估计的中位数准确率达到92.4689%，中位数误差为每分钟±1.2398次呼吸。心率估计的中位数准确率达到88.0654%，中位数误差为每分钟±7.9338次心跳。由于所提出的新型自动估计算法性能良好，该专利新型非接触生物运动传感器可以成为一种以超便捷方式在家中环境对CHF患者进行长期连续睡眠监测的出色工具。
