基于自动视频处理的婴幼儿监控

Q4 Computer Science
L. Cattani
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Seizures in newborns have to be promptly and accurately recognized in order to establish timely treatments that could avoid an increase of the underlying brain damage. Respiratory diseases related to the occurrence of apnoea episodes may be caused by cerebrovascular events. Among the wide range of causes of apnoea, besides seizures, a relevant one is Congenital Central Hypoventilation Syndrome (CCHS). With a reported prevalence of 1 in 200,000 live births, CCHS, formerly known as Ondine’s curse, is a rare life-threatening disorder characterized by a failure of the automatic control of breathing, caused by mutations in a gene classified as PHOX2B. The reported mortality rates range from 8% to 38% of newborn with genetically confirmed CCHS. Nowadays, CCHS is considered a disorder of autonomic regulation, with related risk of sudden infant death syndrome (SIDS). Currently, the standard method of diagnosis, for both diseases, is based on polysomnography, a set of sensors such as ElectroEncephaloGram (EEG) sensors, ElectroMyoGraphy (EMG) sensors, ElectroCardio-Graphy (ECG) sensors, elastic belt sensors, pulse-oximeter and nasal flow-meters. This monitoring system is very expensive, time-consuming, moderately invasive and requires particularly skilled medical personnel, not always available in a Neonatal Intensive Care Unit (NICU). Therefore, automatic, real-time and noninvasive monitoring equipments able to reliably recognize these diseases would be of significant value in the NICU. A very appealing monitoring tool to automatically detect neonatal seizures or breathing disorders may be based on acquiring, through a network of sensors, e.g., a set of video cameras, the movements of the newborn’s body (e.g., limbs, chest) and properly processing the relevant signals. An automatic multi-sensor system could be used to permanently monitor every patient in the NICU or specific patients at home. Furthermore, a wire-free technique may be more user-friendly and highly desirable when used with infants, in particular with newborns. We have focused on a reliable method to estimate the periodicity in pathological movements based on the use of the Maximum Likelihood (ML) criterion. In particular, average differential luminance signals from multiple sensors are extracted and the presence or absence of a significant periodic component is analysed in order to detect possible pathological conditions. Analysis of the data obtained from multiple sensors placed around a patient, makes it possible to increase the reliability of the detection system. This approach is very versatile and allowed us to investigate various scenarios, including: a single RGB camera, an RGB-Depth sensor and a network of a few RGB cameras. Data fusion principles are considered to aggregate the signals from multiple sensors. In the case of respiratory diseases, since chest movements are subtle, the video can be pre-processed by a recently proposed selective magnification algorithm, namely the eulerian video magnification (EVM), which has the purpose of emphasizing small movements. Within this context, we have also developed a second improved algorithm in order to speed up the processing time required for the detection of apnoeas, limiting the computational load. Moreover, in order to have, at any time, a subject on which to test the continuously evolving detection algorithms, we have decided to realize two low-cost programmable simulators able to replicate the symptomatic movements characteristic of the diseases under consideration. The performance of the proposed detection algorithms is assessed, in terms of sensitivity, specificity, and Receiver Operating Characteristic (ROC) curves, considering real video recordings of newborns provided by a Neonatal Intensive Care Unit (NICU). The diagnostic performance of our detection systems has been compared to that of the gold standard based on a prolonged polysomnographic EEG monitoring. It is important to stress how we have always pursued simplicity, because low complexity leads to a low processing time, and this means that these algorithms can be used on a wide range of hardware devices. In particular, we have developed a smartphone App, called “Smartphone based contactless epilepsy detector” (SmartCED), able to detect neonatal clonic seizures and warn the user about their occurrence in real-time. With this powerful inexpensive monitoring system every child, or adult, could be easily monitored at home without additional hardware costs. 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引用次数: 0

摘要

这项工作的目标是发展非侵入性和低成本的系统,通过分析适当的视频信号来监测和自动诊断特定的新生儿疾病。我们专注于监测婴儿潜在的疾病风险,其特征是存在或不存在一个或多个身体部位的有节奏的运动。癫痫和呼吸系统疾病是特别考虑的,但方法是一般的。癫痫发作被定义为突然的神经和行为改变。这是一种与年龄有关的现象,也是中枢神经系统功能障碍的最常见迹象。研究表明,新生儿癫痫发作的发生率在活产儿中为2‰,早产儿为11‰,出生时体重小于2500克的婴儿为13‰。必须及时准确地识别新生儿癫痫发作,以便建立及时的治疗方法,避免潜在脑损伤的增加。呼吸系统疾病与呼吸暂停发作的发生有关,可能由脑血管事件引起。在引起呼吸暂停的众多原因中,除癫痫发作外,先天性中枢性低通气综合征(CCHS)也是一个相关的原因。据报道,CCHS的患病率为20万分之一,以前被称为Ondine的诅咒,是一种罕见的危及生命的疾病,其特征是呼吸自动控制失败,由PHOX2B基因突变引起。据报告,遗传证实的CCHS新生儿死亡率为8%至38%。目前,CCHS被认为是一种自主调节障碍,具有婴儿猝死综合征(SIDS)的相关风险。目前,诊断这两种疾病的标准方法是基于多导睡眠图,这是一组传感器,如脑电图(EEG)传感器、肌电图(EMG)传感器、心电图(ECG)传感器、弹性带传感器、脉搏血氧仪和鼻流量仪。这种监测系统非常昂贵,耗时,具有中度侵入性,需要特别熟练的医务人员,而新生儿重症监护病房(NICU)并不总是可以使用。因此,能够可靠识别这些疾病的自动、实时、无创监测设备在新生儿重症监护病房具有重要价值。自动检测新生儿癫痫发作或呼吸障碍的一种非常吸引人的监测工具可能基于通过传感器网络(例如一组摄像机)获取新生儿身体(例如四肢、胸部)的运动并适当处理相关信号。自动多传感器系统可用于永久监测新生儿重症监护病房的每位患者或家中的特定患者。此外,对于婴儿,特别是新生儿,使用无线技术可能更加用户友好和非常可取。我们重点研究了一种可靠的方法来估计基于使用最大似然(ML)标准的病理运动的周期性。特别是,从多个传感器提取平均差分亮度信号,并分析存在或不存在一个重要的周期成分,以检测可能的病理条件。对放置在患者周围的多个传感器获得的数据进行分析,可以提高检测系统的可靠性。这种方法非常通用,允许我们研究各种场景,包括:单个RGB相机,一个RGB深度传感器和几个RGB相机组成的网络。采用数据融合原理对来自多个传感器的信号进行聚合。在呼吸系统疾病的情况下,由于胸部运动是微妙的,视频可以通过最近提出的选择性放大算法进行预处理,即欧拉视频放大(EVM),其目的是强调微小的运动。在此背景下,我们还开发了第二种改进算法,以加快检测呼吸暂停所需的处理时间,限制计算负载。此外,为了在任何时候都有一个对象来测试不断发展的检测算法,我们决定实现两个低成本的可编程模拟器,能够复制所考虑的疾病的症状运动特征。考虑到新生儿重症监护病房(NICU)提供的新生儿真实视频记录,从灵敏度、特异性和受试者工作特征(ROC)曲线方面评估了所提出的检测算法的性能。我们的检测系统的诊断性能已与基于长时间多导睡眠图脑电图监测的金标准进行了比较。重要的是要强调我们如何始终追求简单性,因为低复杂性导致低处理时间,这意味着这些算法可以在各种硬件设备上使用。 特别是,我们开发了一款智能手机App,名为“基于智能手机的非接触式癫痫检测器”(SmartCED),能够检测新生儿阵发性癫痫发作并实时警告用户。有了这种功能强大、价格低廉的监控系统,每个孩子或成年人都可以在家中轻松监控,而无需额外的硬件成本。虽然集成了复杂的软件,但SmartCED的设计是为了方便直观地使用。事实上,这款应用程序提供了一个用户友好的界面,以便将其扩展到即使是不熟练的员工。用户必须启动应用程序,框架病人,并开始监测病人与一个简单的触摸。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Monitoring Infants by Automatic Video Processing
This work has, as its objective, the development of non-invasive and low-cost systems for monitoring and automatic diagnosing specific neonatal diseases by means of the analysis of suitable video signals. We focus on monitoring infants potentially at risk of diseases characterized by the presence or absence of rhythmic movements of one or more body parts. Seizures and respiratory diseases are specifically considered, but the approach is general. Seizures are defined as sudden neurological and behavioural alterations. They are age-dependent phenomena and the most common sign of central nervous system dysfunction. Studies indicate an incidence rate of neonatal seizures of 2‰ for live births, 11‰ for preterm neonates, and 13‰ for infants weighing less than 2500 g at birth. Seizures in newborns have to be promptly and accurately recognized in order to establish timely treatments that could avoid an increase of the underlying brain damage. Respiratory diseases related to the occurrence of apnoea episodes may be caused by cerebrovascular events. Among the wide range of causes of apnoea, besides seizures, a relevant one is Congenital Central Hypoventilation Syndrome (CCHS). With a reported prevalence of 1 in 200,000 live births, CCHS, formerly known as Ondine’s curse, is a rare life-threatening disorder characterized by a failure of the automatic control of breathing, caused by mutations in a gene classified as PHOX2B. The reported mortality rates range from 8% to 38% of newborn with genetically confirmed CCHS. Nowadays, CCHS is considered a disorder of autonomic regulation, with related risk of sudden infant death syndrome (SIDS). Currently, the standard method of diagnosis, for both diseases, is based on polysomnography, a set of sensors such as ElectroEncephaloGram (EEG) sensors, ElectroMyoGraphy (EMG) sensors, ElectroCardio-Graphy (ECG) sensors, elastic belt sensors, pulse-oximeter and nasal flow-meters. This monitoring system is very expensive, time-consuming, moderately invasive and requires particularly skilled medical personnel, not always available in a Neonatal Intensive Care Unit (NICU). Therefore, automatic, real-time and noninvasive monitoring equipments able to reliably recognize these diseases would be of significant value in the NICU. A very appealing monitoring tool to automatically detect neonatal seizures or breathing disorders may be based on acquiring, through a network of sensors, e.g., a set of video cameras, the movements of the newborn’s body (e.g., limbs, chest) and properly processing the relevant signals. An automatic multi-sensor system could be used to permanently monitor every patient in the NICU or specific patients at home. Furthermore, a wire-free technique may be more user-friendly and highly desirable when used with infants, in particular with newborns. We have focused on a reliable method to estimate the periodicity in pathological movements based on the use of the Maximum Likelihood (ML) criterion. In particular, average differential luminance signals from multiple sensors are extracted and the presence or absence of a significant periodic component is analysed in order to detect possible pathological conditions. Analysis of the data obtained from multiple sensors placed around a patient, makes it possible to increase the reliability of the detection system. This approach is very versatile and allowed us to investigate various scenarios, including: a single RGB camera, an RGB-Depth sensor and a network of a few RGB cameras. Data fusion principles are considered to aggregate the signals from multiple sensors. In the case of respiratory diseases, since chest movements are subtle, the video can be pre-processed by a recently proposed selective magnification algorithm, namely the eulerian video magnification (EVM), which has the purpose of emphasizing small movements. Within this context, we have also developed a second improved algorithm in order to speed up the processing time required for the detection of apnoeas, limiting the computational load. Moreover, in order to have, at any time, a subject on which to test the continuously evolving detection algorithms, we have decided to realize two low-cost programmable simulators able to replicate the symptomatic movements characteristic of the diseases under consideration. The performance of the proposed detection algorithms is assessed, in terms of sensitivity, specificity, and Receiver Operating Characteristic (ROC) curves, considering real video recordings of newborns provided by a Neonatal Intensive Care Unit (NICU). The diagnostic performance of our detection systems has been compared to that of the gold standard based on a prolonged polysomnographic EEG monitoring. It is important to stress how we have always pursued simplicity, because low complexity leads to a low processing time, and this means that these algorithms can be used on a wide range of hardware devices. In particular, we have developed a smartphone App, called “Smartphone based contactless epilepsy detector” (SmartCED), able to detect neonatal clonic seizures and warn the user about their occurrence in real-time. With this powerful inexpensive monitoring system every child, or adult, could be easily monitored at home without additional hardware costs. SmartCED is designed for an easy and intuitive utilization, although it integrates complex software. The App presents, indeed, a user-friendly interface in order to extend its use to even unskilled staff. The user has to start the App, frame the patient and start monitoring the patient with a simple touch.
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来源期刊
Electronic Letters on Computer Vision and Image Analysis
Electronic Letters on Computer Vision and Image Analysis Computer Science-Computer Vision and Pattern Recognition
CiteScore
2.50
自引率
0.00%
发文量
19
审稿时长
12 weeks
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