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    A novel multichannel sparse convolutional autoencoder for electrocardiogram signal compression
    (Churchill Livingstone Inc Medical Publishers, 2025) Bekiryazici, Tahir; Damkaci, Mehmet; Aydemir, Gurkan; Gurkan, Hakan
    Electrocardiogram (ECG) signal compression is paramount in continuously monitoring cardiac patients, as it reduces data storage and transmission costs. Deep neural networks, particularly autoencoders, offer significant potential for compressing ECG signals by mapping them to lower-dimensional spaces. This paper presents a novel multichannel convolutional autoencoder model designed to compress ECG signals efficiently. The proposed approach encodes the ECG signal into a four-channel lower-dimensional space using a convolutional encoder, which is subsequently reconstructed by a deconvolutional decoder. Unlike traditional autoencoderbased methods, the first channel in the model remains unconstrained, while increasing levels of sparsity constraints are imposed on the remaining channels. Different quantization levels are applied to each channel to optimize compression further, reflecting the varying numerical ranges caused by the sparsity constraints. The quantized channels are then encoded using Huffman coding, resulting in a higher compression ratio. The model's effectiveness is evaluated on a popular benchmark dataset, using normalized percent root mean square difference (PRDN) error and compression ratio as performance metrics. The proposed method achieves an average compression ratio of 20.23:1, with an average PRDN error of 9.86%, demonstrating its capability to compress ECG signals efficiently while maintaining reconstruction accuracy.
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    Coverage Area Estimation Based on Convolutional Neural Networks
    (Ieee, 2024) Erba, Ugur; Bekiryazici, Tahir; Aydemir, Gurkan; Tabakcioglu, Mehmet Baris
    In wireless communication systems, one of the most crucial problems is to solve the problem where to deploy the transmitter. It is essential to determine the coverage area before installing the transmitter. In simulation programs, coverage areas are calculated using ray tracing techniques and numerical integral-based wave propagation models, and positioning is done accordingly. However, when dealing with a large area and obstacles between the transmitter and receiver, the computational load significantly increases. Considering the inadequacy of traditional methods in complex urban environments and rapidly changing conditions, the integration of deep learning techniques is aimed at providing a more accurate and flexible solution. In this context, deep learning models Convolutional Neural Networks (CNNs) based, particularly, are highlighted as a potential solution for base station positioning. Among the advantages of CNNs is their ability to adapt more quickly to complex environmental variables. The developed CNN-based model has shown promising results in coverage area estimation and has the potential to enhance the performance of wireless communication networks. This study aims to contribute to the future reliability, speed, and effectiveness of wireless communication networks.
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    Electrocardiogram Signal Compression Using Deep Convolutional Autoencoder with Constant Error and Flexible Compression Rate
    (Elsevier Science Inc, 2024) Bekiryazici, Tahir; Aydemir, Gurkan; Gurkan, Hakan
    Objectives Electrocardiogram (ECG) signals are beneficial for diagnosing cardiac diseases. The cardiac patients' life quality likely increases with continuous or long-period recording and monitoring of ECG signals, leading to better and early diagnosis of disease and heart attacks. However, continuous ECG recording necessitates high data rates and storage, which means high costs. Therefore, ECG compression is a handy concept that facilitates continuous monitoring of ECG signals. Deep neural networks open up new horizons for compression and also for ECG compression by providing high compression rates and quality. Although they bring constant compression ratios with better average quality, the compression quality of individual samples is not guaranteed, which may lead to misdiagnoses. This study aims to investigate the effect of compression quality on the diagnoses and to develop a deep neural network-based compression strategy that guarantees a quality-bound in return for varying compression ratios. Materials and methods The effect of the compression quality on the arrhythmia diagnoses is tested by comparing the performance of the deep learning-based ECG classifier on the original ECG recordings and the distorted recordings using a lossy compression algorithm with different compression error levels. Then, a compression error upper limit is calculated in terms of normalized percent root mean square difference (PRDN) error, which also coincides with the findings of the previous studies in the literature. Lastly, to enable deep learning in ECG compression, a single encoder-multi-decoder convolutional autoencoder architecture, and multiple quantization levels are proposed to guarantee a desired upper limit on the error rate. Results The efficiency of the proposed method is demonstrated on a popular benchmark data set for ECG compression methods using a transfer learning approach. The PRDN error is fixed to various values, and the average compression rates are reported. An average of 13.019 : 1 compression is achieved for a 10% PRDN error rate, assessed as a fair quality threshold for reconstruction error. It has also been shown that the compression model has a runtime that can be run in real-time on wearable devices such as commercial smartwatches. Conclusion This study proposes a deep learning-based ECG compression algorithm that guarantees a desired upper limit on the compression error. This model may facilitate an eHealth solution for continuous monitoring of ECG signals of individuals, especially cardiac patients. (c) 2024 AGBM. Published by Elsevier Masson SAS. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
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    Stacked causal convolutional autoencoder based speech compression method
    (Ieee, 2024) Bekiryazici, Tahir; Aydemir, Gurkan; Gurkan, Hakan
    This study proposes a speech compression method based on one-dimensional convolutional autoencoder and residual vector quantization. The proposed method offers different compression ratios at low bit rates. Speech quality evaluation metric (PESQ) was used to test the performance of the proposed method. Experimental results show that the proposed method achieves a PESQ value of 1.903 for 2.5 kbps and 2.24 for 5 kbps.