Monitoring infants by automatic video processing: A unified approach to motion analysis

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

doi:10.1016/j.compbiomed.2016.11.010

Cited by 49 publications

(52 citation statements)

References 26 publications

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“…In another study, colored pyjamas were used to facilitate movement quantification for CS detection . Compared to other algorithms targeted on periodicity, the detection delay we used (2 seconds) is shorter than generally applied (10 seconds), while maintaining a low false detection rate. This can be attributed to the application of spectral contrast (opposed to power) and the output‐smoothening effect of the 4‐second calculation window.…”

Section: Discussionmentioning

confidence: 99%

“…Prior to this, only feasibility of detection has been demonstrated, and the generalizability of detection results, particularly to real-life situations, remained unclear. 8,10,[12][13][14] Performance was often calculated on the same data that was used to select appropriate detector settings, with short video fragments recorded in a controlled clinical environment and only the subject in view.…”

Section: Discussionmentioning

confidence: 99%

“…Most remote CS detection methods reported in literature are, like ours, targeted on movement periodicity (the exception being methods targeted on seizure sounds 23,24 and muscle activity [25][26][27] ). CS have been detected in video recordings by calculating periodicity in the luminance signal [10][11][12] and with neural networks trained on optical flow motion tracking output. 8,9 In another study, colored pyjamas were used to facilitate movement quantification for CS detection.…”

Section: Discussionmentioning

confidence: 99%

“…A number of studies have been performed on detecting CS using video recordings. [8][9][10][11][12][13][14] These studies were, however, proofs of principle, showing detection feasibility in small datasets recorded in controlled clinical settings. There is currently no working system available that has been shown to have good performance in real life settings.…”

mentioning

confidence: 94%

“…Such a system would be privacy‐friendly as there is no need for video storage or for someone to monitor output. A number of studies have been performed on detecting CS using video recordings . These studies were, however, proofs of principle, showing detection feasibility in small datasets recorded in controlled clinical settings.…”

Section: Introductionmentioning

confidence: 99%

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Automated video‐based detection of nocturnal convulsive seizures in a residential care setting

et al. 2018

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People with epilepsy need assistance and are at risk of sudden death when having convulsive seizures (CS). Automated real-time seizure detection systems can help alert caregivers, but wearable sensors are not always tolerated. We determined algorithm settings and investigated detection performance of a video algorithm to detect CS in a residential care setting. The algorithm calculates power in the 2-6 Hz range relative to 0.5-12.5 Hz range in group velocity signals derived from video-sequence optical flow. A detection threshold was found using a training set consisting of video-electroencephalogaphy (EEG) recordings of 72 CS. A test set consisting of 24 full nights of 12 new subjects in residential care and additional recordings of 50 CS selected randomly was used to estimate performance. All data were analyzed retrospectively. The start and end of CS (generalized clonic and tonic-clonic seizures) and other seizures considered desirable to detect (long generalized tonic, hyperkinetic, and other major seizures) were annotated. The detection threshold was set to the value that obtained 97% sensitivity in the training set. Sensitivity, latency, and false detection rate (FDR) per night were calculated in the test set. A seizure was detected when the algorithm output exceeded the threshold continuously for 2 seconds. With the detection threshold determined in the training set, all CS were detected in the test set (100% sensitivity). Latency was ≤10 seconds in 78% of detections. Three/five hyperkinetic and 6/9 other major seizures were detected. Median FDR was 0.78 per night and no false detections occurred in 9/24 nights. Our algorithm could improve safety unobtrusively by automated real-time detection of CS in video registrations, with an acceptable latency and FDR. The algorithm can also detect some other motor seizures requiring assistance.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

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Automated video‐based detection of nocturnal convulsive seizures in a residential care setting

et al. 2018

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Designing a Deep Learning Model for Video Anomaly Detection-Based Surveillance

Kukade,

Panse

2023

ICT Analysis and Applications

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Audio- and video-based estimation of the sleep stages of newborns in Neonatal Intensive Care Unit

Cabon

Porée

Simon

et al. 2019

Biomedical Signal Processing and Control

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Premature babies have several immature functions and begin their life under high medical supervision. Since the sleep organization diers across postmenstrual age, its analysis may give a good indication of the degree of brain maturation. However, sleep analysis (polysomnography or behavioral observation) is dicult to install, time consuming and cannot systematically be used. In this context, development of new ways to automatically monitor the neonates, using contactless modalities, is necessary. Therefore, this study presents an innovative non-invasive approach to semi-automatize the classication of infant behavioral sleep states. Methods First, three descriptors were extracted from audio and video recordings: vocalizations, motion and eye state of the baby. For this purpose, an original semi-automatic algorithm for the estimation of the eye state was proposed. Secondly, the three descriptors were used in order to obtain an estimation of the behavioral sleep states. Five classiers (K-Nearest Neighbors, Linear Discriminant Analysis, Support Vector Machine, Random Forest and Multi-Layer Perceptron) were compared to an expert annotation. Results Firstly, the comparison of the semi-automatic eye state estimation to manual annotations of 10 videos led to a mean accuracy of 99.4%. Secondly, sleep stage classication was performed. Best results were obtained with Random Forest, for Quiet Alert and Active Alert stages, with 93.5% and 99.0% of accuracy respectively. Conclusion The proposed method provides a high capacity to identify alert sleep stages but the dierentiation between Quiet Sleep and Active Sleep only by behavioral observations still remains a dicult task to achieve. Signicance Results presented in this paper are new since no similar approach was proposed in the literature in the context of neonatal intensive care unit. They augur well for the automatic sleep organization assessment to improve newborn care.

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Monitoring infants by automatic video processing: A unified approach to motion analysis

Cited by 49 publications

References 26 publications

Automated video‐based detection of nocturnal convulsive seizures in a residential care setting

Automated video‐based detection of nocturnal convulsive seizures in a residential care setting

Designing a Deep Learning Model for Video Anomaly Detection-Based Surveillance

Audio- and video-based estimation of the sleep stages of newborns in Neonatal Intensive Care Unit

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