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- MakaleAçık Erişim2023Scopus SJRQ2Automated Hypertension Detection Using ConvMixer and Spectrogram Techniques with Ballistocardiograph Signals
Ozcelik, Salih T. A.; Uyanık, Hakan; Deniz, Erkan; Sengur, Abdulkadir
Blood pressure is the pressure exerted by the blood in the veins against the walls of the veins. If this value is above normal levels, it is known as high blood pressure (HBP) or hypertension (HPT). This health problem which often referred to as the “silent killer” reduces the quality of life and causes severe damage to many body parts in various ways. Besides, its mortality rate is very high. Hence, rapid and effective diagnosis of this health problem is crucial. In this study, an automatic diagnosis of HPT has been proposed using ballistocardiography (BCG) signals. The BCG signals were transformed to the time-frequency domain using the spectrogram method. While creating the spectrogram images, parameters such as window type, window length, overlapping rate, and fast Fourier transform size were adjusted. Then, these images were classified using ConvMixer architecture, similar to vision transformers (ViT) and multi-layer perceptron (MLP)-mixer structures, which have attracted a lot of attention. Its performance was compared with classical architectures such as ResNet18 and ResNet50. The results obtained showed that the ConvMixer structure gave very successful results and a very short operation time. Our proposed model has obtained an accuracy of 98.14%, 98.79%, and 97.69% for the ResNet18, ResNet50, and ConvMixer architectures, respectively. In addition, it has been observed that the processing time of the ConvMixer architecture is relatively short compared to these two architectures.
AtıfScopus21WoS16OpenAlex21
İncele - BildiriYalnız künye2020Performance Evaluation of Different Window Functions for Audio Fingerprint Based Audio Search Algorithm
Uyanik, Hakan; Koseoglu, Murat
Window functions are structures that are frequently used to increase the quality of the transformation when processing the signal in the time-frequency domain, especially when creating the spectrogram of the signal. In calculations for frequency information, Fourier transforms of short-time samples of the signal are transformed into spectrogram data with the help of window functions, and then operations are performed by using these data. In this study, the performance of three different window functions, which are used in obtaining spectrogram data in the algorithm developed for audio fingerprint-based audio recognition, have been tested under different artificial noise conditions. The results of these three different window functions were compared graphically, and a truth table was obtained based on the analyzed database. As a result of the simulations carried out in MATLAB, it has been observed that the hanning window function gives better spectral results for different noise levels, and accordingly it produces better matching results compared to the other two window functions as reported in former studies on different applications. In addition, some improvements about window length have been added into algorithm to obtain better results.
AtıfScopus12OpenAlex10İncele