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    Compression of ECG Signals Using Long Short-Term Memory based Sequence-to-Sequence Autoencoder
    (Institute of Electrical and Electronics Engineers Inc., 2020) Aydemir, Gürkan; Bekiryazıcı, Tahir; Gürkan, Hakan
    This study proposes a novel long short-term memory based sequence-to-sequence autoencoder model to compress ECG signals. The efficiency of this new method is illustrated on MIT-BIH Arrhythmia dataset. In the conducted experiments, the proposed architecture achieves %21.14 mean-independent percentage mean square difference (MPRD) with a constant compression ratio value of 33 : 1. © 2020 IEEE.
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    Driver Drowsiness Detection using MobileNets and Long Short-term Memory
    (Institute of Electrical and Electronics Engineers Inc., 2021) Aydemir, Gürkan; Kurnaz, Oguzhan; Bekiryazıcı, Tahir; Avcı, Adem; Kocakulak, Mustafa
    Deep learning has been studied extensively for driver drowsiness detection using video data. However, since the proposed deep learning methods are computationally cumbersome, the commercial driver drowsiness detection methods are still using hand-crafted features such as lane deviation and percentage of eye closure. This study investigates a deep learning model that provides a fair drowsiness detection performance with a lightweight architecture. In the proposed method, Dlib library was used to detect the driver's face in individual frames of video data. The detected faces are fed into a pre-defined convolutional neural network architecture. Then, a long short-term memory network was used to capture the temporal information between the frame sequences to assess the state of drowsiness. The proposed model achieves a detection accuracy of 80% in a popular benchmark dataset. It was also verified that the model could be implemented on a commercial and inexpensive development board with a frame rate of 5 frames per second.
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    ECG Signal Compression Based on Wavelet Packet Transform and Residual Vector Quantization
    (Institute of Electrical and Electronics Engineers Inc., 2025) Beki?Ryazici, Tahir; Aydemir, Gürkan; Gürkan, Hakan
    Efficient compression of electrocardiogram (ECG) signals is of great importance, particularly in facilitating data transmission and reducing storage costs in remote monitoring systems. For this purpose, a two-stage compression method based on Wavelet Packet Transform (WPT) and Residual Vector Quantization (RVQ) has been developed. The performance of the proposed method was tested on the widely used MIT-BIH Arrhythmia dataset. Performance evaluation was conducted using statistical metrics such as compression ratio (CR), percentage root-mean-square difference normalized (PRDN), and quality score (QS). Experimental results show that the method achieved a compression ratio of 8.8 : 1 with 6.54% PRDN and 17.6 : 1 with 14.38% PRDN. © 2025 IEEE.