Researcher:
Gürsoy, Beren Semiz

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Beren Semiz

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Gürsoy

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Gürsoy, Beren Semiz

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2022 - 20235

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    Quantifying respiration effects on cardiac vibrations using teager energy operator and gradient boosted trees
    (Institute of Electrical and Electronics Engineers Inc., 2022) Department of Electrical and Electronics Engineering; Department of Electrical and Electronics Engineering; Gürsoy, Beren Semiz; İmirzalıoğlu, Mine; Faculty Member; Undergraduate Student; Department of Electrical and Electronics Engineering; College of Engineering; College of Engineering; 332403; N/A
    This work proposes a novel beat scoring system for quantifying the effects of exhalation and inhalation on the seismocardiogram (SCG) signals in rest and physiologically modulated conditions. Data from 19 subjects during rest, listening to classical music and recovery states were used. First, the SCG and electrocardiogram (ECG) signals were segmented into exhalation and inhalation phases using the respiration signal; and a representative SCG beat for each exhale and inhale phase was constructed using the ECG R-peak locations. Second, the significant differences across the exhalation- and inhalation-induced SCG beats were detected and extracted using the Teager- Kaiser energy operator. Finally, a gradient-based beat scoring system was developed using extreme gradient boosted trees and monotonic mapping. For the rest, classical music and recovery sessions, the area under the receiver operating characteristic curve was found to be 0.978, 0.874, 0.985, respectively. On the other hand, the kernel density estimation distributions of the inhalation and exhalation scores had an overlap of 14.2%, 41.2%, 10.6%, respectively. Overall, our results show that different physiological modulations directly change the effect of respiration on the SCG morphology, thus standardization across the beats should be studied for achieving more reliable and accurate investigation of cardiovascular parameters. Clinical relevance - Such a system can potentially allow for more informed and clinically useful SCG analysis by providing valuable insights regarding the intra-recording variability caused by the respiratory system.
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    Physical activity recognition using deep transfer learning with convolutional neural networks
    (Institute of Electrical and Electronics Engineers Inc., 2022) Department of Electrical and Electronics Engineering; Department of Computer Engineering; N/A; N/A; Gürsoy, Beren Semiz; Gürsoy, Mehmet Emre; Ataseven, Berke; Madani, Alireza; Faculty Member; Faculty Member; Master Student; Master Student; Department of Electrical and Electronics Engineering; Department of Computer Engineering; College of Engineering; College of Engineering; N/A; Graduate School of Sciences and Engineering; 332403; 330368; N/A; N/A
    Current wearable devices are capable of monitoring various health indicators as well as fitness and/or physical activity types. However, even on the latest models of many wearable devices, users need to manually enter the type of work-out or physical activity they are performing. In order to automate real-time physical activity recognition, in this study, we develop a deep transfer learning-based physical activity recognition framework using acceleration data acquired through inertial measurement units (IMUs). Towards this goal, we modify a pre-trained version of the GoogLeNet convolutional neural network and fine-tune it with data from IMUs. To make IMU data compatible with GoogLeNet, we propose three novel data transform approaches based on continuous wavelet transform: Horizontal Concatenation (HC), Acceleration-Magnitude (AM), and Pixelwise Axes-Averaging (PA). We evaluate the performance of our approaches using the real-world PAMAP2 dataset. The three approaches result in 0.93, 0.95 and 0.98 validation accuracy and 0.75, 0.85 and 0.91 test accuracy, respectively. The PA approach yields the highest weighted F1 score (0.91) and activity-specific true positive ratios. Overall, our methods and results show that accurate real-time physical activity recognition can be achieved using transfer learning and convolutional neural networks.
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    Unveiling the relationships between seismocardiogram signals, physical activity types and metabolic equivalent of task scores
    (Institute of Electrical and Electronics Engineers (IEEE), 2023) Department of Electrical and Electronics Engineering; Department of Computer Engineering; Gürsoy, Beren Semiz; Tokmak, Fadime; Faculty Member; Undergraduate Student; Department of Electrical and Electronics Engineering; Department of Computer Engineering; College of Engineering; College of Engineering; 332403; N/A
    Objective: The diagnosis of metabolic syndrome and cardiovascular disorders can highly benefit from physical activity and energy expenditure assessment. In this study, we investigated the relationship between metabolic equivalent of task (MET) scores and seismocardiogram (SCG)-derived parameters. Methods: We worked with the PAMAP2 dataset and focused on the 3-axial chest acceleration data. We first segmented the 3-axial SCG signals into respiration (0-1 Hz), cardiac vibrations (1-20 Hz) and heart sounds (20-40 Hz) components. Additionally, we investigated their combinations: 0-20 Hz, 1-40 Hz and 0-40 Hz. We then windowed each signal, and extracted time and frequency domain features from each window. Using the MET scores and activity types, we trained linear regression and random forest classification models first using 80-20% split, then with leave-one-subject-out cross-validation (LOSO-CV). Additionally, we investigated the significance of each feature and axis. Results: For the 80-20% task, the best performing frequency bands were 0-1 Hz, 0-20 Hz, and 0-40 Hz, which yielded a (MET mean-squared-error, classification accuracy) pair of (0.354, 0.952), (0.367, 0.904), and (0.377, 0.914), respectively. When LOSO-CV was applied, we obtained (1.059, 0.865), (0.681, 0.868), and (0.804, 0.875) for each band, respectively. Additionally, our results revealed that the lateral axis provides the most critical information about cardiorespiratory effect of performed activities. Conclusion: Different SCG components can provide unique and substantial contributions to activity and energy expenditure assessment. Significance: This framework can be leveraged in the design of wearable systems for monitoring the activity and energy expenditure levels, and understanding their relationship with underlying cardiorespiratory parameters.
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    Automatic subject identification using scale-based ballistocardiogram signals
    (Springer Science and Business Media Deutschland GmbH, 2022) Shandhi, Md Mobashir Hasan; Orlandic, Lara; Mooney, Vincent J.; Inan, Omer T.; Department of Computer Engineering; Department of Electrical and Electronics Engineering; Gürsoy, Mehmet Emre; Gürsoy, Beren Semiz; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Electrical and Electronics Engineering; College of Engineering; College of Engineering; 330368; 332403
    Many electronic devices such as weighing scales, fitness equipment and medical devices are nowadays shared by multiple users. In such devices, automatic identification of device users becomes an important step towards improved user convenience and personalized service. In this paper, we propose a novel approach for subject identification using ballistocardiogram (BCG) signals collected unobtrusively from a modified weighing scale. Our approach first segments BCG signals into heartbeats using signal filtering and beat detection techniques, and averages beats to obtain smoother ensemble averaged BCG frames that are more robust to noise. Second, it extracts features related to subjects’ cardiovascular performance and musculoskeletal system from their BCG frames. Finally, it trains a machine learning model for predicting the owner of an unlabeled BCG recording based on its features. We evaluated our approach through a pilot experimental study with subjects’ BCG signals recorded at rest and following different physiological modulation. Our approach achieves up to 97% identification accuracy at rest conditions and incurs a 15–20% accuracy drop on average under physiological modulation. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
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    PySio: a new python toolbox for physiological signal visualization and feature analysis
    (Verasonics, 2022) Department of Electrical and Electronics Engineering; Department of Computer Engineering; Gürsoy, Beren Semiz; Nacitarhan, Özgün Ozan; Faculty Member; Undergraduate Student; Department of Electrical and Electronics Engineering; Department of Computer Engineering; College of Engineering; College of Engineering; 332403; N/A
    In physiological signal analysis, identifying meaningful relationships and inherent patterns in signals can provide valuable information regarding subjects' physiological state and changes. Although MATLAB has been widely used in signal processing and feature analysis, Python has recently dethroned MATLAB with the rise of data science, machine learning and artificial intelligence. Hence, there is a compelling need for a Python package for physiological feature analysis and extraction to achieve compatibility with downstream models often trained in Python. Thus, we present a novel visualization and feature analysis Python toolbox, PySio, to enable rapid, efficient and user-friendly analysis of physiological signals. First, the user should import the signal-of-interest with the corresponding sampling rate. After importing, the user can either analyze the signal as it is, or can choose a specific region for more detailed analysis. PySio enables the user to (i) visualize and analyze the physiological signals (or user-selected segments of the signals) in time domain, (ii) study the signals (or user-selected segments of the signals) in frequency domain through discrete Fourier transform and spectrogram representations, and (iii) investigate and extract the most common time (energy, entropy, zero crossing rate and peaks) and frequency (spectral entropy, rolloff, centroid, spread, peaks and bandpower) domain features, all with one click. Clinical relevance - As the physiological signals originate directly from the underlying physiological events, proper analysis of the signal patterns can provide valuable information in personalized treatment and wearable technology applications.