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  1. Article ; Online: Res-SE-ConvNet

    Talha Ibn Mahmud / Sheikh Asif Imran / Celia Shahnaz

    IEEE Journal of Translational Engineering in Health and Medicine, Vol 10, Pp 1-

    A Deep Neural Network for Hypoxemia Severity Prediction for Hospital In-Patients Using Photoplethysmograph Signal

    2022  Volume 9

    Abstract: Determining the severity level of hypoxemia, the scarcity of saturated oxygen (SpO2) in the human body, is very important for the patients, a matter which has become even more significant during the outbreak of Covid-19 variants. Although the widespread ... ...

    Abstract Determining the severity level of hypoxemia, the scarcity of saturated oxygen (SpO2) in the human body, is very important for the patients, a matter which has become even more significant during the outbreak of Covid-19 variants. Although the widespread usage of Pulse Oximeter has helped the doctors aware of the current level of SpO2 and thereby determine the hypoxemia severity of a particular patient, the high sensitivity of the device can lead to the desensitization of the care-givers, resulting in slower response to actual hypoxemia event. There has been research conducted for the detection of severity level using various parameters and bio-signals and feeding them in a machine learning algorithm. However, in this paper, we have proposed a new residual-squeeze-excitation-attention based convolutional network (Res-SE-ConvNet) using only Photoplethysmography (PPG) signal for the comfortability of the patient. Unlike the other methods, the proposed method has outperformed the standard state-of-art methods as the result shows 96.5% accuracy in determining 3 class severity problems with 0.79 Cohen Kappa score. This method has the potential to aid the patients in receiving the benefit of an automatic and faster clinical decision support system, thus handling the severity of hypoxemia.
    Keywords Saturated oxygen ; attention ; feature map ; excitation ; deep learning ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Medical technology ; R855-855.5
    Subject code 006
    Language English
    Publishing date 2022-01-01T00:00:00Z
    Publisher IEEE
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: DOANet

    Alif Bin Abdul Qayyum / K. M. Naimul Hassan / Adrita Anika / Md. Farhan Shadiq / Md Mushfiqur Rahman / Md. Tariqul Islam / Sheikh Asif Imran / Shahruk Hossain / Mohammad Ariful Haque

    EURASIP Journal on Audio, Speech, and Music Processing, Vol 2020, Iss 1, Pp 1-

    a deep dilated convolutional neural network approach for search and rescue with drone-embedded sound source localization

    2020  Volume 18

    Abstract: Abstract Drone-embedded sound source localization (SSL) has interesting application perspective in challenging search and rescue scenarios due to bad lighting conditions or occlusions. However, the problem gets complicated by severe drone ego-noise that ... ...

    Abstract Abstract Drone-embedded sound source localization (SSL) has interesting application perspective in challenging search and rescue scenarios due to bad lighting conditions or occlusions. However, the problem gets complicated by severe drone ego-noise that may result in negative signal-to-noise ratios in the recorded microphone signals. In this paper, we present our work on drone-embedded SSL using recordings from an 8-channel cube-shaped microphone array embedded in an unmanned aerial vehicle (UAV). We use angular spectrum-based TDOA (time difference of arrival) estimation methods such as generalized cross-correlation phase-transform (GCC-PHAT), minimum-variance-distortion-less-response (MVDR) as baseline, which are state-of-the-art techniques for SSL. Though we improve the baseline method by reducing ego-noise using speed correlated harmonics cancellation (SCHC) technique, our main focus is to utilize deep learning techniques to solve this challenging problem. Here, we propose an end-to-end deep learning model, called DOANet, for SSL. DOANet is based on a one-dimensional dilated convolutional neural network that computes the azimuth and elevation angles of the target sound source from the raw audio signal. The advantage of using DOANet is that it does not require any hand-crafted audio features or ego-noise reduction for DOA estimation. We then evaluate the SSL performance using the proposed and baseline methods and find that the DOANet shows promising results compared to both the angular spectrum methods with and without SCHC. To evaluate the different methods, we also introduce a well-known parameter—area under the curve (AUC) of cumulative histogram plots of angular deviations—as a performance indicator which, to our knowledge, has not been used as a performance indicator for this sort of problem before.
    Keywords DOA estimation ; DNN ; Sound source localization ; UAV ; DREGON ; Dilated CNN ; Acoustics. Sound ; QC221-246 ; Electronic computers. Computer science ; QA75.5-76.95
    Subject code 004
    Language English
    Publishing date 2020-11-01T00:00:00Z
    Publisher SpringerOpen
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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