In this study, time-amplitude data were obtained by being analyzed heart sounds in the wav file format that were received from eGeneral Medical Inc. database with software which was developed in Visual Studio C#.Net. On these data, Discrete Fourier Transform (DFT), Periodogram Power Spectrum Density (PSD), Shannon Energy and Short-time Fourier Transform (STFT) methods were used besides Hamming, Hanning and Blackman windowing functions. These methods were compared in terms of code process time and graphically selectiveness. As a result, it is seen that the process time of Shannon Energy method was much faster than DFT, STFT and Periodogram PSD methods and more efficient in classification of S1 and S2 heart sounds.

Bu çaly?mada, eGeneral Medical Inc. ait veri bankasyndan alynan wav formatyndaki kalp sesleri Visual Studio C#.Net'de hazyrlanan yazylym ile çözümlenerek zaman genlik verileri elde edildi. Bu veriler üzerinde Hamming, Hanning ve Blackman pencereleme ile beraber Ayryk Fourier Dönü?ümü (AFD), Shannon Enerji ve Periodogram Güç Spektrum Yo?unlu?u (PGSY), Kysa zamanly Fourier Dönü?ümü (KZFD) teknikleri uygulandy. Uygulanan teknikler i?letim performanslary ve grafiksel seçicilik yönünden kyyaslandy. Sonuç olarak Shannon Enerji uygulamasynyn i?lem süresinin AFD,KZFD ve PGSY uygulamalaryna göre çok daha hyzly oldu?u ve S1 ve S2 kalp seslerini tanymlamada daha etkin bir metot oldu?u görüldü.