Human heart rate can provide useful information about the activities that occur in the body. One of information which may be obtained from recording the heart rate or electrocardiogram is commonly called a person's level of deep sleep (sleep stages). From a person's electrocardiogram signal, the level of deep sleep recognizable by extracting features that represent the electrocardiogram signal as a whole. Extraction is done so that the dimension of the data can be reduced so that the classification process can be more easily done. This study aims to extract fractal features and morphology of the electrocardiogram signal obtained from PhysioNet. Prior to the extraction of morphological features of the electrocardiogram signal, first performed â€œWavelet Denoisingâ€ to remove the noise contained in the signal.