A wire-free, non-invasive, low-cost video processing-based approach to neonatal apnoea detection

Cattani, Luca; Alinovi, Davide; Ferrari, Gianluigi; Raheli, R.; Pavlidis, Elena; Spagnoli, Carlotta; Pisani, Francesco

doi:10.1109/bioms.2014.6951538

Cited by 15 publications

(28 citation statements)

References 11 publications

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“…Motion signal extraction is performed on every level of the filtered and amplified Laplacian pyramid. In [8], the EVM algorithm was used as a video preprocessing tool to magnify small motions: then, relying on the motion signal extraction algorithm described in [14], the capability to detect breathing interruptions, or apnoea episodes, was demonstrated. Motion signal extraction in [14] detects scene variations by the use of a well known algorithm based on a simple differential filtering method, called difference of frames (DoF): outputs are computed by the difference of two consecutive frames.…”

Section: A Extraction Of Breathing Motion Informationmentioning

confidence: 99%

“…This operation, performed in the binarization blocks of Figure 1 Figure 1, and γ th is an appropriate binarization threshold to be heuristically selected 1 . Following [14], [8], the average luminance signal for each level is computed as…”

Section: A Extraction Of Breathing Motion Informationmentioning

confidence: 99%

“…In fact, facial blood circulation can cause color variations so that information on the heart rate and breathing frequency can be obtained. In [8], a non-invasive, low-cost, wire-free video processing-based approach to neonatal apnoea detection is proposed. An apnoea event is detected by adopting a wellknown estimation method on a properly extracted breathing motion signal.…”

Section: Introductionmentioning

confidence: 99%

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Spatio-temporal video processing for respiratory rate estimation

Alinovi

Cattani

Ferrari

et al. 2015

2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings

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In this paper, we present a wire-free, low-cost video processing-based technique for respiratory rate (RR) estimation. The proposed method blends together two recently presented techniques, with the purpose of emphasizing small movements, such as respiratory movements possibly present in a video stream, in order to detect them. Initially, the system performs a spatial decomposition of the video frames in a pyramidal representation, in which each layer contains different spatial details. The levels are then pixel-wise temporally filtered with an infinite impulse response (IIR) filter, purposely designed to extract components having a periodicity compatible with the respiratory rate. Afterwards a single motion signal is extracted from each level. Finally, the extracted signals are jointly analyzed according to the maximum likelihood (ML) criterion in order to estimate the respiratory rate. The parameters extracted by our algorithm show a good agreement with those indicated by a gold-standard polysomnographic system. Therefore, our results, although preliminary, are encouraging and show that the respiratory rate can be reliably measured and monitored by a low-cost, wire-free, video processing-based system.

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Section: A Extraction Of Breathing Motion Informationmentioning

confidence: 99%

Section: A Extraction Of Breathing Motion Informationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spatio-temporal video processing for respiratory rate estimation

Alinovi

Cattani

Ferrari

et al. 2015

2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings

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“…The obtained video sequences have been analyzed using algorithms developed in previous works. In particular, the algorithm described in [10] uses a Motion Magnification technique to enhance small breathing movements for Apnea Detection and is here referred to as MMAD. The algorithm described in [11], here referred to as Spatio-Temporal video-processing for RR Estimation (STRE), includes a spatio-temporal video processing system to reinforce RR estimation and apnea detection.…”

Section: Model Validation and Simulatorsmentioning

confidence: 99%

“…In [7,8,9], contactless monitoring systems are proposed: the first system is embedded in a board with multiple cameras [7], the second one analyzes respiratory movements, but does not include automatic RR estimation [8] and the last one makes use of infrared cameras [9]. Some recent innovative video-based systems for RR measurement and apnea detection are based on advanced video-processing algorithms to enhance small breathing motion, improve apnea event detection, and refine RR estimation [10,11].…”

Section: Introductionmentioning

confidence: 99%

Markov chain modeling and simulation of breathing patterns

Alinovi

Ferrari

Pisani

et al. 2017

Biomedical Signal Processing and Control

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The lack of large video databases obtained from real patients with respiratory disorders makes the design and optimization of video-based monitoring systems quite critical. The purpose of this study is the development of suitable models and simulators of breathing behaviors and disorders, such as respiratory pauses and apneas, in order to allow efficient design and test of video-based monitoring systems. More precisely, a novel Continuous-Time Markov Chain (CTMC) statistical model of breathing patterns is presented. The Respiratory Rate (RR) pattern, estimated by measured vital signs of hospital-monitored patients, is approximated as a CTMC, whose states and parameters are selected through an appropriate statistical analysis. Then, two simulators, software-and hardware-based, are proposed. After validation of the CTMC model, the proposed simulators are tested with previously developed video-based algorithms for the estimation of the RR and the detection of apnea events. Examples of application to assess the performance of systems for video-based RR estimation and apnea detection are presented. The results, in terms of Kullback-Leibler divergence, show that realistic breathing patterns, including specific respiratory disorders, can be accurately described by the proposed model; moreover, the simulators are able to reproduce practical breathing patterns for video analysis. The presented CTMC statistical model can be strategic to describe realistic breathing patterns and devise simulators useful to develop and test novel and effective video processing-based monitoring systems.

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Detection of respiratory activity in newborn infants using a noncontact vision‐based monitor

Eastwood‐Sutherland

Gale

et al. 2023

Pediatric Pulmonology

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Objective: To examine the effectiveness of a noncontact vision-based infrared respiratory monitor (IRM) in the detection of authentic respiratory motion in newborn infants.Study Design: Observational study in a neonatal intensive care unit.Methods: Eligible infants lay supine with torso exposed under the IRM's infrared depth-map camera and torso images were recorded at 30 frames/s. Respiratory motion waveforms were subsequently derived from upper (IRM upper ) and lower (IRM lower ) torso region images and compared with contemporaneous impedance pneumography (IP) and capsule pneumography (CP). Waveforms, in 15 s investigative epochs, were scanned with an 8 s sliding window for authentic respiratory waveform (spectral purity index [SPI] ≥ 0.75, minimum five complete breaths).Maximum SPI and frequency of occurrence of authentic respiratory waveform in 15 s epochs were compared between monitoring modalities in pooled and per patient data (Friedman ANOVA).Results: Recordings comprised 532 min of images from 35 infants, yielding 2131 investigative epochs, with authentic respiratory motion detected in all infants. For CP, IP, IRM upper , and IRM lower , the proportion of epochs containing authentic respiratory motion in pooled data were 65%, 50%, 36%, and 48%, with median SPI max of 0.79, 0.75, 0.70, and 0.74, respectively. Per-patient average SPI max was 0.79, 0.75, 0.69, and 0.74 for CP, IP, IRM upper , and IRM lower with proportion of authentic respiratory motion being 64%, 50%, 29%, and 49%, respectively. Conclusion:An IRM focused on the lower torso detected authentic respiratory motion with comparable performance to IP in newborn infants in intensive care and deserves further investigation.

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A wire-free, non-invasive, low-cost video processing-based approach to neonatal apnoea detection

Cited by 15 publications

References 11 publications

Spatio-temporal video processing for respiratory rate estimation

Spatio-temporal video processing for respiratory rate estimation

Markov chain modeling and simulation of breathing patterns

Detection of respiratory activity in newborn infants using a noncontact vision‐based monitor

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