1
|
Brown BM, Boyne AMH, Hassan AM, Allam AK, Cotton RJ, Haneef Z. Computer vision for automated seizure detection and classification: A systematic review. Epilepsia 2024; 65:1176-1202. [PMID: 38426252 DOI: 10.1111/epi.17926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 02/08/2024] [Accepted: 02/09/2024] [Indexed: 03/02/2024]
Abstract
Computer vision (CV) shows increasing promise as an efficient, low-cost tool for video seizure detection and classification. Here, we provide an overview of the fundamental concepts needed to understand CV and summarize the structure and performance of various model architectures used in video seizure analysis. We conduct a systematic literature review of the PubMed, Embase, and Web of Science databases from January 1, 2000 to September 15, 2023, to identify the strengths and limitations of CV seizure analysis methods and discuss the utility of these models when applied to different clinical seizure phenotypes. Reviews, nonhuman studies, and those with insufficient or poor quality data are excluded from the review. Of the 1942 records identified, 45 meet inclusion criteria and are analyzed. We conclude that the field has shown tremendous growth over the past 2 decades, leading to several model architectures with impressive accuracy and efficiency. The rapid and scalable detection offered by CV models holds the potential to reduce sudden unexpected death in epilepsy and help alleviate resource limitations in epilepsy monitoring units. However, a lack of standardized, thorough validation measures and concerns about patient privacy remain important obstacles for widespread acceptance and adoption. Investigation into the performance of models across varied datasets from clinical and nonclinical environments is an essential area for further research.
Collapse
Affiliation(s)
- Brandon M Brown
- Department of Neurology, Baylor College of Medicine, Houston, Texas, USA
| | - Aidan M H Boyne
- Department of Neurology, Baylor College of Medicine, Houston, Texas, USA
| | - Adel M Hassan
- Department of Neurology, Baylor College of Medicine, Houston, Texas, USA
| | - Anthony K Allam
- Department of Neurology, Baylor College of Medicine, Houston, Texas, USA
| | - R James Cotton
- Shirley Ryan Ability Lab, Chicago, Illinois, USA
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, Illinois, USA
| | - Zulfi Haneef
- Department of Neurology, Baylor College of Medicine, Houston, Texas, USA
- Neurology Care Line, Michael E. DeBakey VA Medical Center, Houston, Texas, USA
| |
Collapse
|
2
|
Rai P, Knight A, Hiillos M, Kertész C, Morales E, Terney D, Larsen SA, Østerkjerhuus T, Peltola J, Beniczky S. Automated analysis and detection of epileptic seizures in video recordings using artificial intelligence. Front Neuroinform 2024; 18:1324981. [PMID: 38558825 PMCID: PMC10978750 DOI: 10.3389/fninf.2024.1324981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 02/27/2024] [Indexed: 04/04/2024] Open
Abstract
Introduction Automated seizure detection promises to aid in the prevention of SUDEP and improve the quality of care by assisting in epilepsy diagnosis and treatment adjustment. Methods In this phase 2 exploratory study, the performance of a contactless, marker-free, video-based motor seizure detection system is assessed, considering video recordings of patients (age 0-80 years), in terms of sensitivity, specificity, and Receiver Operating Characteristic (ROC) curves, with respect to video-electroencephalographic monitoring (VEM) as the medical gold standard. Detection performances of five categories of motor epileptic seizures (tonic-clonic, hyperkinetic, tonic, unclassified motor, automatisms) and psychogenic non-epileptic seizures (PNES) with a motor behavioral component lasting for >10 s were assessed independently at different detection thresholds (rather than as a categorical classification problem). A total of 230 patients were recruited in the study, of which 334 in-scope (>10 s) motor seizures (out of 1,114 total seizures) were identified by VEM reported from 81 patients. We analyzed both daytime and nocturnal recordings. The control threshold was evaluated at a range of values to compare the sensitivity (n = 81 subjects with seizures) and false detection rate (FDR) (n = all 230 subjects). Results At optimal thresholds, the performance of seizure groups in terms of sensitivity (CI) and FDR/h (CI): tonic-clonic- 95.2% (82.4, 100%); 0.09 (0.077, 0.103), hyperkinetic- 92.9% (68.5, 98.7%); 0.64 (0.59, 0.69), tonic- 78.3% (64.4, 87.7%); 5.87 (5.51, 6.23), automatism- 86.7% (73.5, 97.7%); 3.34 (3.12, 3.58), unclassified motor seizures- 78% (65.4, 90.4%); 4.81 (4.50, 5.14), and PNES- 97.7% (97.7, 100%); 1.73 (1.61, 1.86). A generic threshold recommended for all motor seizures under study asserted 88% sensitivity and 6.48 FDR/h. Discussion These results indicate an achievable performance for major motor seizure detection that is clinically applicable for use as a seizure screening solution in diagnostic workflows.
Collapse
Affiliation(s)
| | - Andrew Knight
- Neuro Event Labs, Tampere, Finland
- Department of Medicine and Health Technology, Tampere University, Tampere, Finland
| | | | | | | | - Daniella Terney
- Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund, Denmark
| | - Sidsel Armand Larsen
- Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund, Denmark
| | - Tim Østerkjerhuus
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
| | - Jukka Peltola
- Department of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Neurology, Tampere University Hospital, Tampere, Finland
| | - Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund, Denmark
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| |
Collapse
|
3
|
Boivin V, Shahriari M, Faure G, Mellul S, Tiassou ED, Jouvet P, Noumeir R. Multimodality Video Acquisition System for the Assessment of Vital Distress in Children. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115293. [PMID: 37300019 DOI: 10.3390/s23115293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 05/23/2023] [Accepted: 05/29/2023] [Indexed: 06/12/2023]
Abstract
In children, vital distress events, particularly respiratory, go unrecognized. To develop a standard model for automated assessment of vital distress in children, we aimed to construct a prospective high-quality video database for critically ill children in a pediatric intensive care unit (PICU) setting. The videos were acquired automatically through a secure web application with an application programming interface (API). The purpose of this article is to describe the data acquisition process from each PICU room to the research electronic database. Using an Azure Kinect DK and a Flir Lepton 3.5 LWIR attached to a Jetson Xavier NX board and the network architecture of our PICU, we have implemented an ongoing high-fidelity prospectively collected video database for research, monitoring, and diagnostic purposes. This infrastructure offers the opportunity to develop algorithms (including computational models) to quantify vital distress in order to evaluate vital distress events. More than 290 RGB, thermographic, and point cloud videos of each 30 s have been recorded in the database. Each recording is linked to the patient's numerical phenotype, i.e., the electronic medical health record and high-resolution medical database of our research center. The ultimate goal is to develop and validate algorithms to detect vital distress in real time, both for inpatient care and outpatient management.
Collapse
Affiliation(s)
- Vincent Boivin
- CHU Sainte-Justine Research Centre, Montréal, QC H3T 1C5, Canada
- Department of Electrical Engineering, Ecole de Technologie Supérieure (ETS), Montréal, QC H3C 1K3, Canada
| | - Mana Shahriari
- CHU Sainte-Justine Research Centre, Montréal, QC H3T 1C5, Canada
- Department of Pediatrics, Université de Montréal (UdeM), Montréal, QC H3T 1C5, Canada
| | - Gaspar Faure
- CHU Sainte-Justine Research Centre, Montréal, QC H3T 1C5, Canada
| | - Simon Mellul
- CHU Sainte-Justine Research Centre, Montréal, QC H3T 1C5, Canada
| | | | - Philippe Jouvet
- CHU Sainte-Justine Research Centre, Montréal, QC H3T 1C5, Canada
- Department of Pediatrics, Université de Montréal (UdeM), Montréal, QC H3T 1C5, Canada
| | - Rita Noumeir
- CHU Sainte-Justine Research Centre, Montréal, QC H3T 1C5, Canada
- Department of Electrical Engineering, Ecole de Technologie Supérieure (ETS), Montréal, QC H3C 1K3, Canada
| |
Collapse
|
4
|
Boccignone G, D’Amelio A, Ghezzi O, Grossi G, Lanzarotti R. An Evaluation of Non-Contact Photoplethysmography-Based Methods for Remote Respiratory Rate Estimation. SENSORS (BASEL, SWITZERLAND) 2023; 23:3387. [PMID: 37050444 PMCID: PMC10098914 DOI: 10.3390/s23073387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Revised: 03/16/2023] [Accepted: 03/21/2023] [Indexed: 06/19/2023]
Abstract
The respiration rate (RR) is one of the physiological signals deserving monitoring for assessing human health and emotional states. However, traditional devices, such as the respiration belt to be worn around the chest, are not always a feasible solution (e.g., telemedicine, device discomfort). Recently, novel approaches have been proposed aiming at estimating RR in a less invasive yet reliable way, requiring the acquisition and processing of contact or remote Photoplethysmography (contact reference and remote-PPG, respectively). The aim of this paper is to address the lack of systematic evaluation of proposed methods on publicly available datasets, which currently impedes a fair comparison among them. In particular, we evaluate two prominent families of PPG processing methods estimating Respiratory Induced Variations (RIVs): the first encompasses methods based on the direct extraction of morphological features concerning the RR; and the second group includes methods modeling respiratory artifacts adopting, in the most promising cases, single-channel blind source separation. Extensive experiments have been carried out on the public BP4D+ dataset, showing that the morphological estimation of RIVs is more reliable than those produced by a single-channel blind source separation method (both in contact and remote testing phases), as well as in comparison with a representative state-of-the-art Deep Learning-based approach for remote respiratory information estimation.
Collapse
|
5
|
Nokelainen P, Perez-Macias JM, Himanen SL, Hakala A, Tenhunen M. Methods for Detecting Abnormal Ventilation in Children - the Case Study of 13-Years old Pitt-Hopkins Girl. Child Neurol Open 2023; 10:2329048X231151361. [PMID: 36844470 PMCID: PMC9944179 DOI: 10.1177/2329048x231151361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 12/27/2022] [Indexed: 02/23/2023] Open
Abstract
We present contactless technology measuring abnormal ventilation and compare it with polysomnography (PSG). A 13-years old girl with Pitt-Hopkins syndrome presented hyperpnoea periods with apneic spells. The PSG was conducted simultaneously with Emfit movement sensor (Emfit, Finland) and video camera with depth sensor (NEL, Finland). The respiratory efforts from PSG, Emfit sensor, and NEL were compared. In addition, we measured daytime breathing with tracheal microphone (PneaVox,France). The aim was to deepen the knowledge of daytime hyperpnoea periods and ensure that no upper airway obstruction was present during sleep. The signs of upper airway obstruction were not detected despite of minor sleep time. Monitoring respiratory effort with PSG is demanding in all patient groups. The used unobtrusive methods were capable to reveal breathing frequency and hyperpnoea periods. Every day diagnostics need technology like this for monitoring vital signs at hospital wards and at home from subjects with disabilities and co-operation difficulties.
Collapse
Affiliation(s)
- Pekka Nokelainen
- Department of Clinical Neurophysiology, Medical Imaging Centre,
Pirkanmaa Hospital District, Tampere, Finland,Outpatient Clinic for Patients with Intellectual Disability,
Pirkanmaa Hospital District, Tampere, Finland
| | | | - Sari-Leena Himanen
- Department of Clinical Neurophysiology, Medical Imaging Centre,
Pirkanmaa Hospital District, Tampere, Finland,Faculty of Medicine and Health Technology,
Tampere
University, Tampere, Finland
| | | | - Mirja Tenhunen
- Department of Clinical Neurophysiology, Medical Imaging Centre,
Pirkanmaa Hospital District, Tampere, Finland,Faculty of Medicine and Health Technology,
Tampere
University, Tampere, Finland,Department of Medical Physics, Tampere University
Hospital, Medical Imaging Centre, Pirkanmaa
Hospital District, Tampere, Finland,Mirja Tenhunen, Department of Clinical
Neurophysiology, Tampere University Hospital, Medical Imaging Centre and
Hospital Pharmacy, Pirkanmaa Hospital District, Tampere, Finland.
| |
Collapse
|
6
|
Heart Rate Variability Analysis for Seizure Detection in Neonatal Intensive Care Units. Bioengineering (Basel) 2022; 9:bioengineering9040165. [PMID: 35447725 PMCID: PMC9031489 DOI: 10.3390/bioengineering9040165] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 03/28/2022] [Accepted: 04/05/2022] [Indexed: 12/03/2022] Open
Abstract
In Neonatal Intensive Care Units (NICUs), the early detection of neonatal seizures is of utmost importance for a timely clinical intervention. Over the years, several neonatal seizure detection systems were proposed to detect neonatal seizures automatically and speed up seizure diagnosis, most based on the EEG signal analysis. Recently, research has focused on other possible seizure markers, such as electrocardiography (ECG). This work proposes an ECG-based NSD system to investigate the usefulness of heart rate variability (HRV) analysis to detect neonatal seizures in the NICUs. HRV analysis is performed considering time-domain, frequency-domain, entropy and multiscale entropy features. The performance is evaluated on a dataset of ECG signals from 51 full-term babies, 29 seizure-free. The proposed system gives results comparable to those reported in the literature: Area Under the Receiver Operating Characteristic Curve = 62%, Sensitivity = 47%, Specificity = 67%. Moreover, the system’s performance is evaluated in a real clinical environment, inevitably affected by several artefacts. To the best of our knowledge, our study proposes for the first time a multi-feature ECG-based NSD system that also offers a comparative analysis between babies suffering from seizures and seizure-free ones.
Collapse
|
7
|
Leo M, Bernava GM, Carcagnì P, Distante C. Video-Based Automatic Baby Motion Analysis for Early Neurological Disorder Diagnosis: State of the Art and Future Directions. SENSORS (BASEL, SWITZERLAND) 2022; 22:866. [PMID: 35161612 PMCID: PMC8839211 DOI: 10.3390/s22030866] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 01/18/2022] [Accepted: 01/20/2022] [Indexed: 06/14/2023]
Abstract
Neurodevelopmental disorders (NDD) are impairments of the growth and development of the brain and/or central nervous system. In the light of clinical findings on early diagnosis of NDD and prompted by recent advances in hardware and software technologies, several researchers tried to introduce automatic systems to analyse the baby's movement, even in cribs. Traditional technologies for automatic baby motion analysis leverage contact sensors. Alternatively, remotely acquired video data (e.g., RGB or depth) can be used, with or without active/passive markers positioned on the body. Markerless approaches are easier to set up and maintain (without any human intervention) and they work well on non-collaborative users, making them the most suitable technologies for clinical applications involving children. On the other hand, they require complex computational strategies for extracting knowledge from data, and then, they strongly depend on advances in computer vision and machine learning, which are among the most expanding areas of research. As a consequence, also markerless video-based analysis of movements in children for NDD has been rapidly expanding but, to the best of our knowledge, there is not yet a survey paper providing a broad overview of how recent scientific developments impacted it. This paper tries to fill this gap and it lists specifically designed data acquisition tools and publicly available datasets as well. Besides, it gives a glimpse of the most promising techniques in computer vision, machine learning and pattern recognition which could be profitably exploited for children motion analysis in videos.
Collapse
Affiliation(s)
- Marco Leo
- Institute of Applied Sciences and Intelligent Systems (ISASI), National Research Council of Italy, Via Monteroni Snc, 73100 Lecce, Italy; (P.C.); (C.D.)
| | - Giuseppe Massimo Bernava
- Institute for Chemical-Physical Processes (IPCF), National Research Council of Italy, Viale Ferdinando Stagno d’Alcontres 37, 98158 Messina, Italy;
| | - Pierluigi Carcagnì
- Institute of Applied Sciences and Intelligent Systems (ISASI), National Research Council of Italy, Via Monteroni Snc, 73100 Lecce, Italy; (P.C.); (C.D.)
| | - Cosimo Distante
- Institute of Applied Sciences and Intelligent Systems (ISASI), National Research Council of Italy, Via Monteroni Snc, 73100 Lecce, Italy; (P.C.); (C.D.)
| |
Collapse
|
8
|
Bionic for Training: Smart Framework Design for Multisensor Mechatronic Platform Validation. SENSORS 2021; 22:s22010249. [PMID: 35009792 PMCID: PMC8749724 DOI: 10.3390/s22010249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 12/20/2021] [Accepted: 12/27/2021] [Indexed: 12/04/2022]
Abstract
Home monitoring supports the continuous improvement of the therapy by sharing data with healthcare professionals. It is required when life-threatening events can still occur after hospital discharge such as neonatal apnea. However, multiple sources of external noise could affect data quality and/or increase the misdetection rate. In this study, we developed a mechatronic platform for sensor characterizations and a framework to manage data in the context of neonatal apnea. The platform can simulate the movement of the abdomen in different plausible newborn positions by merging data acquired simultaneously from three-axis accelerometers and infrared sensors. We simulated nine apnea conditions combining three different linear displacements and body postures in the presence of self-generated external noise, showing how it is possible to reduce errors near to zero in phenomena detection. Finally, the development of a smart 8Ws-based software and a customizable mobile application were proposed to facilitate data management and interpretation, classifying the alerts to guarantee the correct information sharing without specialized skills.
Collapse
|
9
|
Souley Dosso Y, Greenwood K, Harrold J, Green JR. RGB-D scene analysis in the NICU. Comput Biol Med 2021; 138:104873. [PMID: 34600329 DOI: 10.1016/j.compbiomed.2021.104873] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 09/09/2021] [Accepted: 09/13/2021] [Indexed: 11/20/2022]
Abstract
Continuity of care is achieved in the neonatal intensive care unit (NICU) through careful documentation of all events of clinical significance, including clinical interventions and routine care events (e.g., feeding, diaper change, weighing, etc.). As a step towards automating this documentation process, we propose a scene recognition algorithm that can automatically identify key features in a single image of the patient environment, paired with a rule-based sentence generator to caption the scene. Color and depth video were obtained from 29 newborn patients from the Children's Hospital of Eastern Ontario (CHEO) using an Intel RealSense SR300 RGB-D camera and manual bedside event annotation. Image processing techniques are implemented to classify two lighting conditions: brightness level and phototherapy. A deep neural network is developed for three image classification tasks: on-going intervention, bed occupancy, and patient coverage. Transfer learning is leveraged in the feature extraction layers, such that weights learned from a generic data-rich task are applied to the clinical domain where data collection is complex and costly. Different depth fusion techniques are implemented and compared among classification tasks, where the depth and color data are fused as an RGB-D image (image fusion) or separately at various layers in the network (network fusion). Promising results were obtained with >84% sensitivity and >73% F1 measure across all context variables despite the large class imbalance. RGBD-based models are shown to outperform RGB models on most tasks. In general, a 4-channel image fusion and network fusion at the 11th layer of the VGG-16 architecture were preferred. Ultimately, achieving complete scene understanding through multimodal computer vision could form the basis for a semi-automated charting system to assist clinical staff.
Collapse
Affiliation(s)
- Yasmina Souley Dosso
- Department of Systems and Computer Engineering, Carleton University, 1125 Colonel By Drive, Ottawa, ON, K1S 5B6, Canada
| | - Kim Greenwood
- Department of Mechanical Engineering, Faculty of Engineering, University of Ottawa, 75 Laurier Ave. E, Ottawa, ON, K1N 6N5, Canada; Clinical Engineering, Children's Hospital of Eastern Ontario, 401 Smyth Rd, Ottawa, ON, K1H 8L1, Canada
| | - JoAnn Harrold
- Neonatology, Children's Hospital of Eastern Ontario, 401 Smyth Rd, Ottawa, ON, K1H 8L1, Canada
| | - James R Green
- Department of Systems and Computer Engineering, Carleton University, 1125 Colonel By Drive, Ottawa, ON, K1S 5B6, Canada.
| |
Collapse
|
10
|
Zuzarte I, Sternad D, Paydarfar D. Predicting apneic events in preterm infants using cardio-respiratory and movement features. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 209:106321. [PMID: 34380078 PMCID: PMC8898595 DOI: 10.1016/j.cmpb.2021.106321] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 07/25/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Preterm neonates are prone to episodes of apnea, bradycardia and hypoxia (ABH) that can lead to neurological morbidities or even death. There is broad interest in developing methods for real-time prediction of ABH events to inform interventions that prevent or reduce their incidence and severity. Using advances in machine learning methods, this study develops an algorithm to predict ABH events. METHODS Following previous studies showing that respiratory instabilities are closely associated with bouts of movement, we present a modeling framework that can predict ABH events using both movement and cardio-respiratory features derived from routine clinical recordings. In 10 preterm infants, movement onsets and durations were estimated with a wavelet-based algorithm that quantified artifactual distortions of the photoplethysmogram signal. For prediction, cardio-respiratory features were created from time-delayed correlations of inter-beat and inter-breath intervals with past values; movement features were derived from time-delayed correlations with inter-breath intervals. Gaussian Mixture Models and Logistic Regression were used to develop predictive models of apneic events. Performance of the models was evaluated with ROC curves. RESULTS Performance of the prediction framework (mean AUC) was 0.77 ± 0.04 for 66 ABH events on training data from 7 infants. When grouped by the severity of the associated bradycardia during the ABH event, the framework was able to predict 83% and 75% of the most severe episodes in the 7-infant training set and 3-infant test set, respectively. Notably, inclusion of movement features significantly improved the predictions compared with modeling with only cardio-respiratory signals. CONCLUSIONS Our findings suggest that recordings of movement provide important information for predicting ABH events in preterm infants, and can inform preemptive interventions designed to reduce the incidence and severity of ABH events.
Collapse
Affiliation(s)
- Ian Zuzarte
- Department of Bioengineering, Northeastern University, Boston, MA 02115, United States
| | - Dagmar Sternad
- Departments of Biology, Electrical and Computer Engineering & Physics, Northeastern University, Boston, MA 02115, United States
| | - David Paydarfar
- Department of Neurology, Dell Medical School, Austin, TX 78712, United States; Oden Institute for Computational Sciences and Engineering, The University of Texas at Austin, Austin, TX 78712, United States.
| |
Collapse
|
11
|
Kodama Y, Okamoto J, Imai K, Asano H, Uchiyama A, Masamune K, Wada M, Muragaki Y. Video-based neonatal state assessment method for timing of procedures. Pediatr Int 2021; 63:685-692. [PMID: 33034092 DOI: 10.1111/ped.14501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 09/09/2020] [Accepted: 09/28/2020] [Indexed: 11/28/2022]
Abstract
BACKGROUND Procedures should be performed when an infant is most receptive to disruptions in order to reduce the stress on the infant. However, frequent direct observations place a heavy burden on medical staff. There is therefore a need for a method for quantitatively and automatically evaluating the neonatal state. METHODS Ten infants in our hospital were enrolled in this study. The states of the infants were assessed by medical staff using the Brazelton Neonatal Behavioral Assessment Scale and were recorded on video at the same time. The recorded states were reclassified as activity levels, a new state classification method that includes middle activity, which is the appropriate time for a procedure. Using image analysis, motions of the infant were quantified as two indices: activity and pause time. Activity and pause time were compared for each activity level. The cutoff values of the indices were calculated, and the sensitivity and specificity of the middle activity were calculated. RESULTS There was a significant difference between all groups of activity level (P < 0.01). The maximum sensitivity and specificity of middle activity were 71.7% and 51.2%, respectively. CONCLUSIONS The neonatal state of infants can be quantitatively and automatically evaluated using video cameras, and the activity level can be used to determine an appropriate time for procedures in infants. This will reduce the burden on medical staff and lead to less stressful procedures for infants.
Collapse
Affiliation(s)
- Yu Kodama
- Institute of Advanced Biomedical Engineering & Science, Tokyo Women's Medical University, Tokyo, Japan.,Human Resources and General Affairs Department, Atom Medical Corporation, Tokyo, Japan
| | - Jun Okamoto
- Institute of Advanced Biomedical Engineering & Science, Tokyo Women's Medical University, Tokyo, Japan
| | - Ken Imai
- Department of Neonatology, Tokyo Women's Medical University, Tokyo, Japan
| | - Hidetsugu Asano
- Institute of Advanced Biomedical Engineering & Science, Tokyo Women's Medical University, Tokyo, Japan.,Technical Department, Atom Medical Corporation, Tokyo, Japan
| | - Atsushi Uchiyama
- Department of Neonatology, Tokyo Women's Medical University, Tokyo, Japan.,Department of Pediatrics, Tokai University School of Medicine, Kanagawa, Japan
| | - Ken Masamune
- Institute of Advanced Biomedical Engineering & Science, Tokyo Women's Medical University, Tokyo, Japan
| | - Masaki Wada
- Department of Neonatology, Tokyo Women's Medical University, Tokyo, Japan
| | - Yoshihiro Muragaki
- Institute of Advanced Biomedical Engineering & Science, Tokyo Women's Medical University, Tokyo, Japan.,Department of Neurosurgery, Neurological Institute, Tokyo Women's Medical University, Tokyo, Japan
| |
Collapse
|
12
|
Wiegandt FC, Biegger D, Fast JF, Matusiak G, Mazela J, Ortmaier T, Doll T, Dietzel A, Bohnhorst B, Pohlmann G. Detection of Breathing Movements of Preterm Neonates by Recording Their Abdominal Movements with a Time-of-Flight Camera. Pharmaceutics 2021; 13:pharmaceutics13050721. [PMID: 34068978 PMCID: PMC8156597 DOI: 10.3390/pharmaceutics13050721] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 05/10/2021] [Accepted: 05/11/2021] [Indexed: 12/20/2022] Open
Abstract
In order to deliver an aerosolized drug in a breath-triggered manner, the initiation of the patient’s inspiration needs to be detected. The best-known systems monitoring breathing patterns are based on flow sensors. However, due to their large dead space volume, flow sensors are not advisable for monitoring the breathing of (preterm) neonates. Newly-developed respiratory sensors, especially when contact-based (invasive), can be tested on (preterm) neonates only with great effort due to clinical and ethical hurdles. Therefore, a physiological model is highly desirable to validate these sensors. For developing such a system, abdominal movement data of (preterm) neonates are required. We recorded time sequences of five preterm neonates’ abdominal movements with a time-of-flight camera and successfully extracted various breathing patterns and respiratory parameters. Several characteristic breathing patterns, such as forced breathing, sighing, apnea and crying, were identified from the movement data. Respiratory parameters, such as duration of inspiration and expiration, as well as respiratory rate and breathing movement over time, were also extracted. This work demonstrated that respiratory parameters of preterm neonates can be determined without contact. Therefore, such a system can be used for breathing detection to provide a trigger signal for breath-triggered drug release systems. Furthermore, based on the recorded data, a physiological abdominal movement model of preterm neonates can now be developed.
Collapse
Affiliation(s)
- Felix C. Wiegandt
- Division of Translational Biomedical Engineering, Fraunhofer Institute for Toxicology and Experimental Medicine ITEM, 30625 Hannover, Germany; (D.B.); (T.D.)
- Correspondence: (F.C.W.); (G.P.); Tel.: +49-511-5350-287 (F.C.W.); +49-511-5350-116 (G.P.)
| | - David Biegger
- Division of Translational Biomedical Engineering, Fraunhofer Institute for Toxicology and Experimental Medicine ITEM, 30625 Hannover, Germany; (D.B.); (T.D.)
| | - Jacob F. Fast
- Institute of Mechatronic Systems, Leibniz Universität Hannover, 30823 Garbsen, Germany; (J.F.F.); (T.O.)
- Department of Phoniatrics and Pediatric Audiology, Hannover Medical School, 30625 Hannover, Germany
| | - Grzegorz Matusiak
- Division of Infectious Diseases, Department of Neonatology, Poznan University of Medical Sciences, 61-701 Poznan, Poland; (G.M.); (J.M.)
| | - Jan Mazela
- Division of Infectious Diseases, Department of Neonatology, Poznan University of Medical Sciences, 61-701 Poznan, Poland; (G.M.); (J.M.)
| | - Tobias Ortmaier
- Institute of Mechatronic Systems, Leibniz Universität Hannover, 30823 Garbsen, Germany; (J.F.F.); (T.O.)
| | - Theodor Doll
- Division of Translational Biomedical Engineering, Fraunhofer Institute for Toxicology and Experimental Medicine ITEM, 30625 Hannover, Germany; (D.B.); (T.D.)
- Department of Otorhinolaryngology, Hannover Medical School, 30625 Hannover, Germany
| | - Andreas Dietzel
- Institute of Microtechnology, Technische Universität Braunschweig, 38124 Braunschweig, Germany;
| | - Bettina Bohnhorst
- Department of Pediatric Pulmonology, Allergology and Neonatology, Hannover Medical School, 30625 Hannover, Germany;
| | - Gerhard Pohlmann
- Division of Translational Biomedical Engineering, Fraunhofer Institute for Toxicology and Experimental Medicine ITEM, 30625 Hannover, Germany; (D.B.); (T.D.)
- Correspondence: (F.C.W.); (G.P.); Tel.: +49-511-5350-287 (F.C.W.); +49-511-5350-116 (G.P.)
| |
Collapse
|
13
|
Cabon S, Porée F, Cuffel G, Rosec O, Geslin F, Pladys P, Simon A, Carrault G. Voxyvi: A system for long-term audio and video acquisitions in neonatal intensive care units. Early Hum Dev 2021; 153:105303. [PMID: 33453631 DOI: 10.1016/j.earlhumdev.2020.105303] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 11/04/2020] [Accepted: 12/21/2020] [Indexed: 11/18/2022]
Abstract
BACKGROUND In the European Union, 300,000 newborn babies are born prematurely every year. Their care is ensured in Neonatal Intensive Care Units (NICU) where vital signs are constantly monitored. In addition, other descriptors such as motion, facial and vocal activities have been shown to be essential to assess neurobehavioral development. AIM In the scope of the European project Digi-NewB, we aimed to develop and evaluate a new audio-video device designed to non-invasively acquire multi-modal data (audio, video and thermal images), while fitting the wide variety of bedding environment in NICU. METHODS Firstly, a multimodal system and associated software and guidelines to collect data in neonatal intensive care unit were proposed. Secondly, methods for post-evaluation of the acquisition phase were developed, including the study of clinician feedback and a qualitative analysis of the data. RESULTS The deployment of 19 acquisition devices in six French hospitals allowed to record more than 500 newborns of different gestational and postmenstrual ages. After the acquisition phase, clinical feedback was mostly positive. In addition, quality of more than 300 recordings was inspected and showed that 77% of the data is exploitable. In depth, the percentage of sole presence of the newborn was estimated at 62% within recordings. CONCLUSIONS This study demonstrates that audio-video acquisitions are feasible on a large scale in real life in NICU. The experience also allowed us to make a clear observation of the requirements and challenges that will have to be overcome in order to set up audio-video monitoring methods.
Collapse
Affiliation(s)
- S Cabon
- Univ Rennes, Inserm, LTSI - UMR 1099, F-35000, France.
| | - F Porée
- Univ Rennes, Inserm, LTSI - UMR 1099, F-35000, France
| | - G Cuffel
- Voxygen, Pleumeur-Bodou F-22560, France
| | - O Rosec
- Voxygen, Pleumeur-Bodou F-22560, France
| | - F Geslin
- CHU Rennes, Rennes F-35000, France
| | - P Pladys
- CHU Rennes, Rennes F-35000, France
| | - A Simon
- Univ Rennes, Inserm, LTSI - UMR 1099, F-35000, France
| | - G Carrault
- Univ Rennes, Inserm, LTSI - UMR 1099, F-35000, France
| |
Collapse
|
14
|
Singh H, Kusuda S, McAdams RM, Gupta S, Kalra J, Kaur R, Das R, Anand S, Pandey AK, Cho SJ, Saluja S, Boutilier JJ, Saria S, Palma J, Kaur A, Yadav G, Sun Y. Machine Learning-Based Automatic Classification of Video Recorded Neonatal Manipulations and Associated Physiological Parameters: A Feasibility Study. CHILDREN-BASEL 2020; 8:children8010001. [PMID: 33375101 PMCID: PMC7822162 DOI: 10.3390/children8010001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 12/15/2020] [Accepted: 12/18/2020] [Indexed: 11/16/2022]
Abstract
Our objective in this study was to determine if machine learning (ML) can automatically recognize neonatal manipulations, along with associated changes in physiological parameters. A retrospective observational study was carried out in two Neonatal Intensive Care Units (NICUs) between December 2019 to April 2020. Both the video and physiological data (heart rate (HR) and oxygen saturation (SpO2)) were captured during NICU hospitalization. The proposed classification of neonatal manipulations was achieved by a deep learning system consisting of an Inception-v3 convolutional neural network (CNN), followed by transfer learning layers of Long Short-Term Memory (LSTM). Physiological signals prior to manipulations (baseline) were compared to during and after manipulations. The validation of the system was done using the leave-one-out strategy with input of 8 s of video exhibiting manipulation activity. Ten neonates were video recorded during an average length of stay of 24.5 days. Each neonate had an average of 528 manipulations during their NICU hospitalization, with the average duration of performing these manipulations varying from 28.9 s for patting, 45.5 s for a diaper change, and 108.9 s for tube feeding. The accuracy of the system was 95% for training and 85% for the validation dataset. In neonates <32 weeks’ gestation, diaper changes were associated with significant changes in HR and SpO2, and, for neonates ≥32 weeks’ gestation, patting and tube feeding were associated with significant changes in HR. The presented system can classify and document the manipulations with high accuracy. Moreover, the study suggests that manipulations impact physiological parameters.
Collapse
Affiliation(s)
- Harpreet Singh
- Child Health Imprints (CHIL) Pte. Ltd., Singapore 048545, Singapore; (S.G.); (J.K.); (R.K.); (R.D.)
- Correspondence: ; Tel.: +65-91-9910861112
| | - Satoshi Kusuda
- Department of Pediatrics, Kyorin University, Tokyo 181-8612, Japan;
| | - Ryan M. McAdams
- Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI 53726, USA;
| | - Shubham Gupta
- Child Health Imprints (CHIL) Pte. Ltd., Singapore 048545, Singapore; (S.G.); (J.K.); (R.K.); (R.D.)
| | - Jayant Kalra
- Child Health Imprints (CHIL) Pte. Ltd., Singapore 048545, Singapore; (S.G.); (J.K.); (R.K.); (R.D.)
| | - Ravneet Kaur
- Child Health Imprints (CHIL) Pte. Ltd., Singapore 048545, Singapore; (S.G.); (J.K.); (R.K.); (R.D.)
| | - Ritu Das
- Child Health Imprints (CHIL) Pte. Ltd., Singapore 048545, Singapore; (S.G.); (J.K.); (R.K.); (R.D.)
| | - Saket Anand
- Department of Computer Science and Engineering, Indraprastha Institute of Information Technology, New Delhi 110020, India;
| | - Ashish Kumar Pandey
- Department of Mathematics, Indraprastha Institute of Information Technology, New Delhi 110020, India;
| | - Su Jin Cho
- College of Medicine, Ewha Womans University Seoul, Seoul 03760, Korea;
| | - Satish Saluja
- Department of Neonatology, Sir Ganga Ram Hospital, New Delhi 110060, India;
| | - Justin J. Boutilier
- Department of Industrial and Systems Engineering, College of Engineering, University of Wisconsin, Madison, WI 53706, USA;
| | - Suchi Saria
- Machine Learning and Healthcare Lab, Johns Hopkins University, 3400 N. Charles St, Baltimore, MD 21218, USA;
| | - Jonathan Palma
- Department of Pediatrics, Stanford University, Stanford, CA 94305, USA;
| | - Avneet Kaur
- Department of Neonatology, Apollo Cradle Hospitals, New Delhi 110015, India;
| | - Gautam Yadav
- Department of Pediatrics, Kalawati Hospital, Rewari 123401, India;
| | - Yao Sun
- Division of Neonatology, University of California, San Francisco, CA 92521, USA;
| |
Collapse
|
15
|
Sun Y, de With PHN, Kommers D, Wang W, Joshi R, Shan C, Tan T, Aarts RM, van Pul C, Andriessen P. Automatic and Continuous Discomfort Detection for Premature Infants in a NICU Using Video-Based Motion Analysis. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:5995-5999. [PMID: 31947213 DOI: 10.1109/embc.2019.8857597] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Frequent pain and discomfort in premature infants can lead to long-term adverse neurodevelopmental outcomes. Video-based monitoring is considered to be a promising contactless method for identification of discomfort moments. In this study, we propose a video-based method for automated detection of infant discomfort. The method is based on analyzing facial and body motion. Therefore, motion trajectories are estimated from frame to frame using optical flow. For each video segment, we further calculate the motion acceleration rate and extract 18 time- and frequency-domain features characterizing motion patterns. A support vector machine (SVM) classifier is then applied to video sequences to recognize infant status of comfort or discomfort. The method is evaluated using 183 video segments for 11 infants from 17 heel prick events. Experimental results show an AUC of 0.94 for discomfort detection and the average accuracy of 0.86 when combining all proposed features, which is promising for clinical use.
Collapse
|
16
|
van Westrhenen A, Petkov G, Kalitzin SN, Lazeron RHC, Thijs RD. Automated video-based detection of nocturnal motor seizures in children. Epilepsia 2020; 61 Suppl 1:S36-S40. [PMID: 32378204 PMCID: PMC7754425 DOI: 10.1111/epi.16504] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 03/24/2020] [Accepted: 03/24/2020] [Indexed: 01/14/2023]
Abstract
Seizure detection devices can improve epilepsy care, but wearables are not always tolerated. We previously demonstrated good performance of a real‐time video‐based algorithm for detection of nocturnal convulsive seizures in adults with learning disabilities. The algorithm calculates the relative frequency content based on the group velocity reconstruction from video‐sequence optical flow. We aim to validate the video algorithm on nocturnal motor seizures in a pediatric population. We retrospectively analyzed the algorithm performance on a database including 1661 full recorded nights of 22 children (age = 3‐17 years) with refractory epilepsy at home or in a residential care setting. The algorithm detected 118 of 125 convulsions (median sensitivity per participant = 100%, overall sensitivity = 94%, 95% confidence interval = 61%‐100%) and identified all 135 hyperkinetic seizures. Most children had no false alarms; 81 false alarms occurred in six children (median false alarm rate [FAR] per participant per night = 0 [range = 0‐0.47], overall FAR = 0.05 per night). Most false alarms (62%) were behavior‐related (eg, awake and playing in bed). Our noncontact detection algorithm reliably detects nocturnal epileptic events with only a limited number of false alarms and is suitable for real‐time use.
Collapse
Affiliation(s)
- Anouk van Westrhenen
- Stichting Epilepsie Instellingen Nederland, Heemstede, the Netherlands.,Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
| | - George Petkov
- Stichting Epilepsie Instellingen Nederland, Heemstede, the Netherlands
| | - Stiliyan N Kalitzin
- Stichting Epilepsie Instellingen Nederland, Heemstede, the Netherlands.,Images Sciences Institute, University of Utrecht, Utrecht, the Netherlands
| | - Richard H C Lazeron
- Academic Center of Epileptology Kempenhaeghe, Heeze, the Netherlands.,Faculty of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Roland D Thijs
- Stichting Epilepsie Instellingen Nederland, Heemstede, the Netherlands.,Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
| |
Collapse
|
17
|
Geertsema EE, Visser GH, Sander JW, Kalitzin SN. Automated non-contact detection of central apneas using video. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101658] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
|
18
|
van der Lende M, Arends JB, Lamberts RJ, Tan HL, de Lange FJ, Sander JW, Aerts AJ, Swart HP, Thijs RD. The yield of long-term electrocardiographic recordings in refractory focal epilepsy. Epilepsia 2019; 60:2215-2223. [PMID: 31637707 PMCID: PMC6899995 DOI: 10.1111/epi.16373] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2019] [Revised: 09/29/2019] [Accepted: 09/29/2019] [Indexed: 12/11/2022]
Abstract
Objective To determine the incidence of clinically relevant arrhythmias in refractory focal epilepsy and to assess the potential of postictal arrhythmias as risk markers for sudden unexpected death in epilepsy (SUDEP). Methods We recruited people with refractory focal epilepsy without signs of ictal asystole and who had at least one focal seizure per month and implanted a loop recorder with 2‐year follow‐up. The devices automatically record arrhythmias. Subjects and caregivers were instructed to make additional peri‐ictal recordings. Clinically relevant arrhythmias were defined as asystole ≥ 6 seconds; atrial fibrillation < 55 beats per minute (bpm), or > 200 bpm and duration > 30 seconds; persistent sinus bradycardia < 40 bpm while awake; and second‐ or third‐degree atrioventricular block and ventricular tachycardia/fibrillation. We performed 12‐lead electrocardiography (ECG) and tilt table testing to identify non–seizure‐related causes of asystole. Results We included 49 people and accumulated 1060 months of monitoring. A total of 16 474 seizures were reported, of which 4679 were captured on ECG. No clinically relevant arrhythmias were identified. Three people had a total of 18 short‐lasting (<6 seconds) periods of asystole, resulting in an incidence of 2.91 events per 1000 patient‐months. None of these coincided with a reported seizure; one was explained by micturition syncope. Other non–clinically relevant arrhythmias included paroxysmal atrial fibrillation (n = 2), supraventricular tachycardia (n = 1), and sinus tachycardia with a right bundle branch block configuration (n = 1). Significance We found no clinically relevant arrhythmias in people with refractory focal epilepsy during long‐term follow‐up. The absence of postictal arrhythmias does not support the use of loop recorders in people at high SUDEP risk.
Collapse
Affiliation(s)
- Marije van der Lende
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, the Netherlands.,Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
| | - Johan B Arends
- Academic Center for Epileptology Kempenhaeghe, Heeze, the Netherlands.,Signal Processing Group, Electronic Engineering Faculty, Technological University Eindhoven, Eindhoven, the Netherlands
| | - Robert J Lamberts
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, the Netherlands
| | - Hanno L Tan
- Heart Center, Department of Cardiology, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Frederik J de Lange
- Heart Center, Department of Cardiology, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Josemir W Sander
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, the Netherlands.,National Institute for Health Research University College London Hospitals Biomedical Research Centre, UCL Queen Square Institute of Neurology, London, UK.,Chalfont Centre for Epilepsy, Chalfont St Peter, UK
| | - Arnaud J Aerts
- Department of Cardiology, Zuyderland Medical Center, Heerlen, the Netherlands
| | - Henk P Swart
- Department of Cardiology, Antonius Hospital Sneek, Sneek, the Netherlands
| | - Roland D Thijs
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, the Netherlands.,Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands.,National Institute for Health Research University College London Hospitals Biomedical Research Centre, UCL Queen Square Institute of Neurology, London, UK.,Chalfont Centre for Epilepsy, Chalfont St Peter, UK
| |
Collapse
|
19
|
Cabon S, Porée F, Simon A, Met-Montot B, Pladys P, Rosec O, Nardi N, Carrault G. Audio- and video-based estimation of the sleep stages of newborns in Neonatal Intensive Care Unit. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.04.011] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
|
20
|
Cabon S, Porée F, Simon A, Rosec O, Pladys P, Carrault G. Video and audio processing in paediatrics: a review. Physiol Meas 2019; 40:02TR02. [PMID: 30669130 DOI: 10.1088/1361-6579/ab0096] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Video and sound acquisition and processing technologies have seen great improvements in recent decades, with many applications in the biomedical area. The aim of this paper is to review the overall state of the art of advances within these topics in paediatrics and to evaluate their potential application for monitoring in the neonatal intensive care unit (NICU). APPROACH For this purpose, more than 150 papers dealing with video and audio processing were reviewed. For both topics, clinical applications are described according to the considered cohorts-full-term newborns, infants and toddlers or preterm newborns. Then, processing methods are presented, in terms of data acquisition, feature extraction and characterization. MAIN RESULTS The paper first focuses on the exploitation of video recordings; these began to be automatically processed in the 2000s and we show that they have mainly been used to characterize infant motion. Other applications, including respiration and heart rate estimation and facial analysis, are also presented. Audio processing is then reviewed, with a focus on the analysis of crying. The first studies in this field focused on induced-pain cries and the newest ones deal with spontaneous cries; the analyses are mainly based on frequency features. Then, some papers dealing with non-cry signals are also discussed. SIGNIFICANCE Finally, we show that even if recent improvements in digital video and signal processing allow for increased automation of processing, the context of the NICU makes a fully automated analysis of long recordings problematic. A few proposals for overcoming some of the limitations are given.
Collapse
Affiliation(s)
- S Cabon
- Univ Rennes, CHU Rennes, INSERM, LTSI - UMR 1099, F-35000 Rennes, France. Voxygen, F-22560 Pleumeur-Bodou, France
| | | | | | | | | | | |
Collapse
|
21
|
Geertsema EE, Thijs RD, Gutter T, Vledder B, Arends JB, Leijten FS, Visser GH, Kalitzin SN. Automated video-based detection of nocturnal convulsive seizures in a residential care setting. Epilepsia 2018; 59 Suppl 1:53-60. [DOI: 10.1111/epi.14050] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/24/2017] [Indexed: 11/30/2022]
Affiliation(s)
- Evelien E. Geertsema
- Stichting Epilepsie Instellingen Nederland (SEIN); Heemstede The Netherlands
- Image Sciences Institute; University Medical Center Utrecht; Utrecht The Netherlands
| | - Roland D. Thijs
- Stichting Epilepsie Instellingen Nederland (SEIN); Heemstede The Netherlands
- Department of Neurology; Leiden University Medical Center; Leiden The Netherlands
| | - Therese Gutter
- Stichting Epilepsie Instellingen Nederland (SEIN); Heemstede The Netherlands
| | - Ben Vledder
- Stichting Epilepsie Instellingen Nederland (SEIN); Heemstede The Netherlands
| | - Johan B. Arends
- Academic Center for Epileptology Kempenhaeghe; Heeze The Netherlands
- Technological University Eindhoven; Eindhoven The Netherlands
| | - Frans S. Leijten
- Brain Center Rudolf Magnus; University Medical Center Utrecht; Utrecht The Netherlands
| | - Gerhard H. Visser
- Stichting Epilepsie Instellingen Nederland (SEIN); Heemstede The Netherlands
| | - Stiliyan N. Kalitzin
- Stichting Epilepsie Instellingen Nederland (SEIN); Heemstede The Netherlands
- Image Sciences Institute; University Medical Center Utrecht; Utrecht The Netherlands
| |
Collapse
|