diff --git a/docs/algorithmfunctioning.rst b/docs/algorithmfunctioning.rst
index a7a9942..74d5858 100644
--- a/docs/algorithmfunctioning.rst
+++ b/docs/algorithmfunctioning.rst
@@ -47,7 +47,7 @@ A Butterworth filter implementation is available to remove high frequency noise.
     
     import heartpy as hp
     
-    filtered = hp.butter_lowpass_filter(data, cutoff=5, sample_rate=100.0, order=3)
+    filtered = hp.filter_signal(data, cutoff=5, sample_rate=100.0, order=3, filtertype='lowpass')
     
 .. image:: images/butterworth.jpeg
 
diff --git a/docs/quickstart.rst b/docs/quickstart.rst
index d0eaed9..ddd4861 100644
--- a/docs/quickstart.rst
+++ b/docs/quickstart.rst
@@ -60,7 +60,6 @@ Several optional arguments are available:
 
 * **windowsize:** _optional_ `windowsize` is the window size used for the calculation of the moving average. The windowsize is defined as `windowsize * samplerate`. Default windowsize=0.75.
 * **report_time:** _optional_ whether to report total processing time of process() loop.
-* **calc_fft:** _optional_ whether to calculate frequency domain measures. Default = false Note: can cause slowdowns in some cases.
 * **calc_freq:** _optional_ whether to calculate frequency domain measures. Default = false Note: can cause slowdowns in some cases.
 * **freq_method:** _optional_ method used to extract the frequency spectrum. Available: 'fft' (Fourier Analysis), 'periodogram', and 'welch' (Welch's method), Default = 'welch'
 * **interp_clipping:** if True, clipping parts of the signal are identified and the implied peak shape is interpolated. Default=False