- Email: [email protected]
wake behaviour videos
S30
Abstracts/Sleep Medicine 16 (2015) S2–S199
misdiagnosed. Our goal is to develop a smartphone-based EMGsystem that can provide clinicians with an objective measure in clinical practice during the SCIT. Materials and methods: A student-team of the Capstone Design Project, an interdisciplinary program offered by the UBC Departments of Electrical and Computer Engineering [http://www.ece.ubc.ca/courses/capstones] assigned for developing the hard- and software solutions in collaboration with the Sleep/Wake-Behaviour Research Lab [Department of Pediatrics, UBC]. The task criterion was that the completed EMG system should be small, non-invasive, user-friendly, fast, accurate, and low-cost (defined as under $500) for use in clinical practice. Several possible hardware and software platforms were evaluated based on these principles along with safety, sustainability and ethics considerations. Results: (1) The combination of a Bitalino-based hardware and an Android-based software was chosen as the most suitable solution. (a) Bitalino [http://www.bitalino.com/] is a hardware platform designed for acquiring physiological signals and was selected based on an evaluation of overall system design, application development support, feasibility, sustainability, and value for its cost with respect to other hardware options. (b) Android [https:// source.android.com/] was chosen to be the software platform of choice due to the wide range open-source resources, community support, and ease of future developments. (2) A functional prototype was implemented, which can acquire EMG data, transfer it via a wireless Bluetooth interface to a mobile phone, and graph the EMG signal on the mobile phone’s screen. The prototype is a 25 g (without battery), 4 × 2 × 1.5 cm, single-channel hardware prototype and a software application usable on any mobile phone running Android 4.0 or higher. With the addition of more sensors, the system is scalable to acquire data from up to five different channels. The system also features a 3.7 V, 700 mAh rechargeable battery that can be interchanged with higher capacity alternatives. Conclusion: The described prototype shows excellent potential to enable the acquisition and analysis of EMG signals during the Suggested Clinical Immobilization Test, to provide clinicians with an objective measure of activity and tone, and to support clinicians’ observations. Acknowledgements: Treatable Intellectual Disability Endeavour – British Columbia, Children’s Sleep Network, and BC Children’s Foundation. http://dx.doi.org/10.1016/j.sleep.2015.02.073
Expert video analysis (EVA)-video-viewer-prototype for annotating sleep/wake behaviour videos H. Garn 1, G. Kloesch 2, D. Wong 3,4, G. Mcallister 3, A. Barbosa 5, E. Vatikiotis-Bateson 6, S. Stockler 7, O. Ipsiroglu 3 1 Technical University of Vienna and Austrian Institute of Technology L University of Vienna Y of British Columbia, Austria 2 Sleep Lab Department of Neurology, Medical University of Vienna, Vienna, Austria 3 Sleep/Wake Behaviour Clinic and Research Lab, BC Children’s Hospital, University of British Columbia, Canada 4 WP IV Person Centered Medicine, TIDE, BC, Canada 5 Federal University of Minas Gerais, Belo Horizonte, Brazil 6 Cognitive Systems Program, University of British Columbia, Canada 7 Treatable Intellectual Disability Endeavour – British Columbia (TIDE-BC), Division of Biochemical Diseases, Department of Pediatrics, Faculty of Medicine, University of British Columbia, Canada
Introduction: In sleep medicine, polysomnography applied in a hospital/hotel-based lab setting has proven its usefulness in the diagnosis of sleep disorders, but has limitations. We claim that motor events can also be monitored through a video camera, and analyzed using customized software. Materials and methods: Currently, automatic detection and analysis of video streams to quantify features characterizing motions indicative of disorders (e.g. REM-Sleep-Behaviour) is not available. Some efforts have been described, but the development of an expert video analysis (EVA) software that performs automatic classifications of patient’s motion poses some challenges. Before starting the development of image processing algorithms for detection and classification of motion events, the events derived from the recorded videos need to be described in terms of mathematical features. Therefore a representative number of video sequences have to be annotated by clinicians, and in a consensus meeting, subspecialists have to agree on the classification of events. This clinical event database will provide the necessary information for the development of the EVA-software and support further facilitation of medical diagnoses. Results: We present the recently developed ‘EVA-Video-ViewerPrototype’ software with a user interface, compatible with standard PC hardware, software and databases. This software allows (a) marking and (b) annotation of medically relevant epochs, as it allows replaying sleep videos together with EMG, audio or EEG signals in (a) still images or (b) real time, (c) continuously at various speeds (most important in fast-forward modus) or (d) jumps from motion event to motion event. There is room for editing comments (e.g. annotation, classification of motor events, which can be structured hierarchically according classification guidelines). A menu and navigation buttons make the EVA-Video-Viewer-Prototype userfriendly. An attached structured database enables a user-friendly collection and management of large data files (e.g. 800 MB recordings). Conclusion: The new tool enables the establishment of a simple user-friendly clinical database of video recordings. The database will form the core for further research and will be the scientific basis for the future development of automatic analysis software for sleep/wake behaviour analyses from stand-alone video recordings. Acknowledgements: Sources of Funding (Support): Victoria Foundation FASDActionFund; Treatable Intellectual DisabilityEndeavour – British Columbia. http://dx.doi.org/10.1016/j.sleep.2015.02.074
An ultrasonic contactless sensor for breathing monitoring P. Arlotto 1, M. Grimaldi 1, R. Naeck 2, J. Ginoux 3 1 Protee Laboratory, EA3819, Toulon University, France 2 Clinical Research Unit, Hôpital Ste Musse, Toulon, France 3 ISITV, Toulon University, LSIS, UMR CNRS 7296, France
Introduction: When obstructive sleep apnea is suspected, poly(somno)graphy is performed. The presence of numerous sensors and nasal cannula can disrupt sleep and cause many arousals which induces a bias in the measurement, i.e., an underestimation of apneas. Materials and methods: This work investigates an analysis of the viability of an ultrasonic device to quantify the breathing activity, without contact and without any perception by the subject. Based on a low power ultrasonic active source and transducer, the device measures the frequency shift produced by the velocity difference between the exhaled air flow and the ambient environment, i.e., the Doppler effect. After acquisition and digitization, a specific signal processing is applied to separate the effects of breath from those due to subject movements from the Doppler signal. The distance between the source and the sensor, about 50 cm,
Abstracts/Sleep Medicine 16 (2015) S2–S199
misdiagnosed. Our goal is to develop a smartphone-based EMGsystem that can provide clinicians with an objective measure in clinical practice during the SCIT. Materials and methods: A student-team of the Capstone Design Project, an interdisciplinary program offered by the UBC Departments of Electrical and Computer Engineering [http://www.ece.ubc.ca/courses/capstones] assigned for developing the hard- and software solutions in collaboration with the Sleep/Wake-Behaviour Research Lab [Department of Pediatrics, UBC]. The task criterion was that the completed EMG system should be small, non-invasive, user-friendly, fast, accurate, and low-cost (defined as under $500) for use in clinical practice. Several possible hardware and software platforms were evaluated based on these principles along with safety, sustainability and ethics considerations. Results: (1) The combination of a Bitalino-based hardware and an Android-based software was chosen as the most suitable solution. (a) Bitalino [http://www.bitalino.com/] is a hardware platform designed for acquiring physiological signals and was selected based on an evaluation of overall system design, application development support, feasibility, sustainability, and value for its cost with respect to other hardware options. (b) Android [https:// source.android.com/] was chosen to be the software platform of choice due to the wide range open-source resources, community support, and ease of future developments. (2) A functional prototype was implemented, which can acquire EMG data, transfer it via a wireless Bluetooth interface to a mobile phone, and graph the EMG signal on the mobile phone’s screen. The prototype is a 25 g (without battery), 4 × 2 × 1.5 cm, single-channel hardware prototype and a software application usable on any mobile phone running Android 4.0 or higher. With the addition of more sensors, the system is scalable to acquire data from up to five different channels. The system also features a 3.7 V, 700 mAh rechargeable battery that can be interchanged with higher capacity alternatives. Conclusion: The described prototype shows excellent potential to enable the acquisition and analysis of EMG signals during the Suggested Clinical Immobilization Test, to provide clinicians with an objective measure of activity and tone, and to support clinicians’ observations. Acknowledgements: Treatable Intellectual Disability Endeavour – British Columbia, Children’s Sleep Network, and BC Children’s Foundation. http://dx.doi.org/10.1016/j.sleep.2015.02.073
Expert video analysis (EVA)-video-viewer-prototype for annotating sleep/wake behaviour videos H. Garn 1, G. Kloesch 2, D. Wong 3,4, G. Mcallister 3, A. Barbosa 5, E. Vatikiotis-Bateson 6, S. Stockler 7, O. Ipsiroglu 3 1 Technical University of Vienna and Austrian Institute of Technology L University of Vienna Y of British Columbia, Austria 2 Sleep Lab Department of Neurology, Medical University of Vienna, Vienna, Austria 3 Sleep/Wake Behaviour Clinic and Research Lab, BC Children’s Hospital, University of British Columbia, Canada 4 WP IV Person Centered Medicine, TIDE, BC, Canada 5 Federal University of Minas Gerais, Belo Horizonte, Brazil 6 Cognitive Systems Program, University of British Columbia, Canada 7 Treatable Intellectual Disability Endeavour – British Columbia (TIDE-BC), Division of Biochemical Diseases, Department of Pediatrics, Faculty of Medicine, University of British Columbia, Canada
Introduction: In sleep medicine, polysomnography applied in a hospital/hotel-based lab setting has proven its usefulness in the diagnosis of sleep disorders, but has limitations. We claim that motor events can also be monitored through a video camera, and analyzed using customized software. Materials and methods: Currently, automatic detection and analysis of video streams to quantify features characterizing motions indicative of disorders (e.g. REM-Sleep-Behaviour) is not available. Some efforts have been described, but the development of an expert video analysis (EVA) software that performs automatic classifications of patient’s motion poses some challenges. Before starting the development of image processing algorithms for detection and classification of motion events, the events derived from the recorded videos need to be described in terms of mathematical features. Therefore a representative number of video sequences have to be annotated by clinicians, and in a consensus meeting, subspecialists have to agree on the classification of events. This clinical event database will provide the necessary information for the development of the EVA-software and support further facilitation of medical diagnoses. Results: We present the recently developed ‘EVA-Video-ViewerPrototype’ software with a user interface, compatible with standard PC hardware, software and databases. This software allows (a) marking and (b) annotation of medically relevant epochs, as it allows replaying sleep videos together with EMG, audio or EEG signals in (a) still images or (b) real time, (c) continuously at various speeds (most important in fast-forward modus) or (d) jumps from motion event to motion event. There is room for editing comments (e.g. annotation, classification of motor events, which can be structured hierarchically according classification guidelines). A menu and navigation buttons make the EVA-Video-Viewer-Prototype userfriendly. An attached structured database enables a user-friendly collection and management of large data files (e.g. 800 MB recordings). Conclusion: The new tool enables the establishment of a simple user-friendly clinical database of video recordings. The database will form the core for further research and will be the scientific basis for the future development of automatic analysis software for sleep/wake behaviour analyses from stand-alone video recordings. Acknowledgements: Sources of Funding (Support): Victoria Foundation FASDActionFund; Treatable Intellectual DisabilityEndeavour – British Columbia. http://dx.doi.org/10.1016/j.sleep.2015.02.074
An ultrasonic contactless sensor for breathing monitoring P. Arlotto 1, M. Grimaldi 1, R. Naeck 2, J. Ginoux 3 1 Protee Laboratory, EA3819, Toulon University, France 2 Clinical Research Unit, Hôpital Ste Musse, Toulon, France 3 ISITV, Toulon University, LSIS, UMR CNRS 7296, France
Introduction: When obstructive sleep apnea is suspected, poly(somno)graphy is performed. The presence of numerous sensors and nasal cannula can disrupt sleep and cause many arousals which induces a bias in the measurement, i.e., an underestimation of apneas. Materials and methods: This work investigates an analysis of the viability of an ultrasonic device to quantify the breathing activity, without contact and without any perception by the subject. Based on a low power ultrasonic active source and transducer, the device measures the frequency shift produced by the velocity difference between the exhaled air flow and the ambient environment, i.e., the Doppler effect. After acquisition and digitization, a specific signal processing is applied to separate the effects of breath from those due to subject movements from the Doppler signal. The distance between the source and the sensor, about 50 cm,