diff --git a/biosppy/plotting.py b/biosppy/plotting.py
index 2e4177a8..0447b448 100644
--- a/biosppy/plotting.py
+++ b/biosppy/plotting.py
@@ -1820,6 +1820,187 @@ def plot_pcg(ts=None,
     else:
         # close
         plt.close(fig)
+        
+def plot_egm(ts=None,
+             raw=None,
+             filtered=None,
+             rhythm=None,
+             active_regions=None,
+             ts_windowed = None,
+             windowed = None,
+             lat_index=None,
+             lat=None,
+             df = None,
+             freqs = None,
+             power = None,
+             entropy = None,
+             oi = None,
+             ri = None,
+             units=None,
+             path=None,
+             show=False):
+    """Create a summary plot from the output of signals.egm.egm.
+    
+    Parameters
+    ----------
+    ts : array
+        Signal time axis reference (seconds).
+    raw : array
+        Raw EGM signal.
+    filtered : array
+        Filtered EGM signal.
+    rhythm : str
+        Rhythm of the EGM signal. Must be 'sinus' or 'af'.
+    active_regions : array
+        Active regions of the EGM signal.
+    ts_windowed : array, optional
+        Signal time axis reference (seconds) for windowed signal.
+    windowed : array, optional
+        Windowed EGM signal.
+    act : int, optional
+        Index corresponding to the activation time of the EGM signal.
+    lat : float, optional
+        Activation time in milliseconds (ms).
+    df : float, optional
+        Dominant frequency of the EGM signal (Hz).
+    freqs : array, optional
+        Frequencies of the dominant frequency spectrum.
+    power : array, optional
+        Power of the dominant frequency spectrum.
+    entropy : float, optional
+        Entropy of the EGM signal.
+    oi : float, optional
+        Organization index of the EGM signal.
+    ri : float, optional
+        Regularity index of the EGM signal.
+    units : str, optional
+        Units of the vertical axis. If provided, the plot title will include
+        the units information. Default is None.
+    path : str, optional
+        If provided, the plot will be saved to the specified file.
+    show : bool, optional
+        If True, show the plot immediately.
+        
+    """
+    # get matplotlib parameters
+    plt.rcParams.update(_get_params())
+    
+    if rhythm == 'sinus':
+        attr = 'Sinus Rhythm'
+    elif rhythm == 'af':
+        attr = 'Atrial Fibrillation'
+
+    fig = plt.figure(figsize=(12, 5))
+    fig.suptitle('EGM Summary - ' + attr, fontsize=12, fontweight='bold')
+    gs = gridspec.GridSpec(6, 2)
+    fig.subplots_adjust(top=0.88, bottom=0.12, hspace=0.9, wspace=0.3,
+                        left=0.1, right=0.96)
+
+    # signal
+    ax1 = fig.add_subplot(gs[:4, 0])
+    ax1.set_title('Signal')
+
+    ax1.plot(ts, raw, linewidth=MED_LW, label='Raw',
+             color=color_palette('blue'))
+    ax1.plot(ts, filtered+np.mean(raw), linewidth=MINOR_LW, label='Filtered',
+                color=color_palette('dark-red'))
+
+    ax1.set_ylabel('Amplitude' if units is None else 'Amplitude (%s)' % units)
+    ax1.legend(loc='upper right')
+    ax1.tick_params(axis='x', which='both', bottom=False, top=False,
+                    labelbottom=False)
+    ax1.spines['bottom'].set_visible(False)
+
+    # filtered signal
+    ax2 = fig.add_subplot(gs[4:8, 0], sharex=ax1)
+    ax2.set_title('Active EGM regions')
+
+    ymin = np.min(filtered)
+    ymax = np.max(filtered)
+    alpha = 0.1 * (ymax - ymin)
+    ymax += alpha
+    ymin -= alpha
+
+    ax2.plot(ts, filtered, linewidth=MED_LW, color=color_palette('blue'))
+    ax2.plot(ts, active_regions*max(filtered), linewidth=MINOR_LW,
+             color=color_palette('dark-red'))
+    ax2.set_ylabel('Amplitude' if units is None else 'Amplitude (%s)' % units)
+    ax2.tick_params(axis='x', which='both', bottom=False, top=False,
+                    labelbottom=False)
+    ax2.spines['bottom'].set_visible(False)
+
+    # align y axis labels
+    fig.align_ylabels([ax1, ax2])
+
+    if rhythm == 'sinus':
+        # activation time
+        ax4 = fig.add_subplot(gs[1:5, 1])
+        ax4.set_title('Activation Time')
+        
+        if windowed is not None:
+            egm_windowed = windowed
+        else:
+            egm_windowed = filtered
+            ts_windowed = ts
+
+        ymin = np.min(egm_windowed)
+        ymax = np.max(egm_windowed)
+        alpha = 0.1 * (ymax - ymin)
+        ymax += alpha
+        ymin -= alpha
+        
+
+        
+        ax4.plot(ts_windowed, egm_windowed, linewidth=MINOR_LW, color=color_palette('blue'))
+        ax4.plot(ts_windowed[int(lat_index)], egm_windowed[int(lat_index)], label='Activation Time = ' + '%.2f' % lat + ' ms', marker='o', markersize=4, color=color_palette('dark-red'))
+        ax4.set_ylabel('Amplitude' if units is None else 'Amplitude (%s)' % units)
+        ax4.legend(loc='upper right')
+        ax4.set_xlabel('Time (s)')
+        
+    elif rhythm == 'af':
+        
+        ax3 = fig.add_subplot(gs[0:4, 1])        
+        egm_windowed = filtered
+        ymin = np.min(egm_windowed)
+        ymax = np.max(egm_windowed)
+        alpha = 0.1 * (ymax - ymin)
+        ymax += alpha
+        ymin -= alpha
+
+        ax3.plot(freqs,power, linewidth=MINOR_LW, color=color_palette('blue'))
+        ax3.axvline(df,linewidth=MINOR_LW, color=color_palette('dark-red'))
+        ax3.text(df+0.5, max(power)/2,'Dominant Frequency = ' + '%.2f' % df + ' Hz')
+        ax3.set_ylabel('Power')
+        ax3.set_xlim(0, 20)
+        ax3.set_xlabel('Frequency (Hz)')
+        
+        # text below ax4 left side
+        ax5 = fig.add_subplot(gs[4:5, 1])
+        ax5.text(0, 0, 'Entropy = %.2f' % entropy)
+        ax5.text(0, -1, 'Organization Index = %.2f' % oi)
+        ax5.text(0, -2, 'Regularity Index = %.2f' % ri)
+        ax5.axis('off')
+        
+
+    # add logo
+    add_logo(fig)
+
+    # save to file
+    if path is not None:
+        path = utils.normpath(path)
+        root, ext = os.path.splitext(path)
+        ext = ext.lower()
+        if ext not in ['png', 'jpg']:
+            path = root + '.png'
+
+        fig.savefig(path, dpi=200, bbox_inches='tight')
+
+    # show
+    if show:
+        plt.show()
+    else:
+        # close
+        plt.close(fig)
 
 
 def _plot_rates(thresholds, rates, variables,
diff --git a/biosppy/signals/egm.py b/biosppy/signals/egm.py
new file mode 100644
index 00000000..dfa351e5
--- /dev/null
+++ b/biosppy/signals/egm.py
@@ -0,0 +1,925 @@
+# -*- coding: utf-8 -*-
+"""
+biosppy.signals.egm
+-------------------
+
+This module provides methods to process intracardiac EGM (Electrogram) signals.
+
+:copyright: (c) 2015-2016 by Instituto de Telecomunicacoes
+:license: BSD 3-clause, see LICENSE for more details.
+"""
+# 3rd party
+import numpy as np
+import scipy
+import matplotlib.pyplot as plt
+import inspect
+import warnings
+
+# local
+from .. import plotting, utils
+
+
+def egm(signal=None, sampling_rate=1000., type = 'bipolar', rhythm = None, woi=None, reference=None, method = 'nleo', threshold=None, show=True):
+    """ Process intracardiac EGM (Electrogram) signals.
+    The outputs of this function depend on the rhythm of the EGM signal.
+    
+    Parameters
+    ----------
+    signal : array
+        An array with the EGM signal.
+    sampling_rate : int, float, optional
+        Sampling frequency (Hz) of the EGM signal (default is 1000 Hz).
+    type : str, optional
+        Type of EGM signal. Must be 'bipolar' or 'unipolar' (default is 'bipolar').
+    rhythm : str
+        Rhythm of the EGM signal. Must be 'sinus' or 'af'.
+    woi : array, optional
+        Window of interest as [from, to] in samples. If provided, the EGM signal will be cropped to this window.
+    reference : int, optional
+        Reference sample index for the window of interest. If provided, the window will be centered around this reference.
+    method : str, optional
+        Method to calculate activation time. Must be 'nleo', 'dvdt', 'max', or 'min' (default is 'nleo').
+    threshold : float, optional
+        Threshold for the NLEO signal. If provided, the NLEO signal will be thresholded.
+    show : bool, optional
+       If True, show a summary plot.
+    
+    Returns
+    ----------
+    ts : array
+        Signal time axis reference (seconds). 
+    filtered : array
+        Filtered EGM signal.
+    active_regions : array
+        Active regions of the EGM signal.
+    ts_windowed : array
+        Time axis reference for the windowed EGM signal (seconds).
+    windowed : array
+        Windowed EGM signal.
+    lat_index : int
+        Index corresponding to the activation time of the EGM signal.
+    lat : float
+        Activation time in milliseconds.
+    df : float
+        Dominant frequency (Hz).
+    freqs : array
+        Frequencies of dominant frequency spectrum (Hz).
+    power : array
+        Power of dominant frequency spectrum.
+    entropy : float
+        Shannon entropy of the EGM signal.
+    oi : float
+        Organization index of the EGM signal.
+    ri : array
+        Regularity index of the EGM signal.
+    """
+    
+    warnings.filterwarnings("ignore")
+    
+    # check inputs
+    if signal is None:
+        raise TypeError("Please specify an input signal.")
+    
+    if type is None or type not in ['bipolar', 'unipolar']:
+        raise ValueError("Invalid type. Must be 'bipolar' or 'unipolar'.")
+    
+    if rhythm is None or rhythm not in ['sinus', 'af']:
+        raise ValueError("Invalid rhythm. Must be 'sinus' or 'af'.")
+    
+    # ensure numpy
+    signal = np.array(signal)
+
+    sampling_rate = float(sampling_rate)
+    
+    # set outputs
+    
+    windowed = None
+    ts_windowed = None
+    lat_index = None
+    lat = None
+    df = None
+    freqs = None
+    spectrum = None
+    entropy = None
+    organization = None
+    regularity = None
+    
+    # filter signal
+    
+    if type == 'bipolar':
+        # bandpass filter for bipolar signals
+        lowcut = 30.0
+        highcut = 300.0
+        order = 4
+        b, a = scipy.signal.butter(order, [lowcut, highcut], btype='band', fs=sampling_rate)
+        filtered_signal = scipy.signal.filtfilt(b, a, signal)
+        
+    else:
+        # bandpass filter for unipolar signals
+        lowcut = 0.05
+        highcut = 600.0
+        order = 4
+        b, a = scipy.signal.butter(order, [lowcut, highcut], btype='band', fs=sampling_rate)
+        filtered_signal = scipy.signal.filtfilt(b, a, signal)
+        
+    # get active regions
+    _, _, nleo_threshold, _, _ = nleo(filtered_signal, sampling_rate=sampling_rate, plot=False)
+    
+    if rhythm == 'sinus':
+        
+        # calculate activation time using specified method
+        methods = {
+            'nleo': nleo_lat,
+            'dvdt': dvdt_lat,
+            'max': max_lat,
+            'min': min_lat
+        }
+        
+        if method not in methods:
+            raise ValueError("Invalid method. Must be 'nleo', 'dvdt','max', or 'min'.")
+        
+        lat_index, lat = call_lat_method(lat_method=methods[method], filtered_signal=filtered_signal, sampling_rate=sampling_rate, woi=woi, reference=reference, plot=False)
+        
+    else:
+        # for atrial fibrillation, calculate dominant frequency, entropy, organization index, and regularity index
+        
+        df, freqs, spectrum = dominant_frequency(filtered_signal, sampling_rate=sampling_rate, plot=False)
+        
+        entropy, = shannon_entropy(filtered_signal, sampling_rate=sampling_rate, plot=False)
+        
+        organization, = organization_index(filtered_signal, sampling_rate=sampling_rate)
+        
+        regularity, = regularity_index(filtered_signal, sampling_rate=sampling_rate)
+        
+        
+    if woi is not None:
+        windowed, = get_woi(signal, int(woi[0]), int(woi[1]), int(reference))
+        ts_windowed = np.linspace(0, (len(windowed)-1)/sampling_rate, len(windowed), endpoint=True)
+        
+    # get time vectors
+    length = len(signal)
+    T = (length - 1) / sampling_rate
+    ts = np.linspace(0, T, length, endpoint=True)
+    
+    args = [ts, filtered_signal, nleo_threshold, ts_windowed, windowed, lat_index, lat, df, freqs, spectrum, entropy, organization, regularity]
+    names = ['ts', 'filtered', 'active_regions', 'ts_windowed', 'windowed', 'lat_index', 'lat', 'df', 'freqs', 'power', 'entropy', 'oi', 'ri']
+        
+    warnings.resetwarnings()
+    
+    if show:
+        plotting.plot_egm(ts=ts,
+             raw=signal,
+             filtered=filtered_signal,
+             rhythm=rhythm,
+             active_regions=nleo_threshold,
+             ts_windowed=ts_windowed,
+             windowed = windowed,
+             lat_index=lat_index,
+             lat=lat,
+             df = df,
+             freqs = freqs,
+             power = spectrum,
+             entropy = entropy,
+             oi = organization,
+             ri = regularity,
+             units=None,
+             path=None,
+             show=show)
+        
+    return utils.ReturnTuple(args, names)
+        
+def call_lat_method(lat_method, filtered_signal, sampling_rate, verbose=False, **kwargs):
+    
+    """Checks **kwargs against valid optional parameters for the selected method
+    and calls it with the valid arguments.
+
+    Parameters
+    ----------
+    lat_method : function
+        The method to calculate activation time (e.g., 'nleo_activation_time').
+    filtered_signal : array
+        The preprocessed EGM signal to analyze.
+    sampling_rate : float
+        The sampling frequency of the EGM signal.
+    woi : list, optional
+        Window of interest as [from, to] in samples. If provided, the EGM signal will be cropped to this window.
+    reference : int, optional
+        Reference sample index for the window of interest. If provided, the window will be centered around this reference.
+    plot : bool, optional
+        If True, plots the EGM signal with activation time marked.
+    **kwargs : dict
+        Additional keyword arguments to pass to the activation time method.
+
+    Returns
+    -------
+    lat_index : int
+        Index corresponding to the activation time of the EGM signal.
+    lat : float
+        Activation time in milliseconds. If woi is provided, time is centered in the window.
+    """
+    
+    sig = inspect.signature(lat_method)
+    allowed_args = sig.parameters.keys()
+    valid_args = {kwarg: val for kwarg, val in kwargs.items() if kwarg in allowed_args}
+    if verbose:
+        print(f"Passed valid parameters for activation method: {lat_method}: {valid_args}")
+    lat_index, lat = lat_method(
+        signal=filtered_signal, sampling_rate=sampling_rate, **valid_args
+    )
+    return (lat_index, lat)
+    
+    
+
+
+
+def get_woi(signal, woi_from, woi_to, reference=None):
+    """
+    Extracts a window of interest (WOI) from the EGM signal.
+    
+    Parameters:
+    ----------
+    signal : array
+        An array with the EGM signal.
+    woi_from : int
+        Starting index of the window of interest.
+    woi_to : int
+        Ending index of the window of interest.
+    reference : int, optional
+        Reference sample index for the window of interest. If provided, the window will be centered around this reference.
+    
+    Returns:
+    ----------
+    egm_woi : array
+        The EGM signal cropped to the window of interest.
+    """
+    
+    if reference is not None:
+        idx_from = reference + woi_from
+        idx_to = reference + woi_to
+    else:
+        idx_from = woi_from
+        idx_to = woi_to
+                
+    return utils.ReturnTuple((signal[idx_from:idx_to],),
+                             ('egm_woi',))
+
+def nleo_lat(signal=None, sampling_rate=1000., woi = None, reference = None, plot=False):
+    
+    """
+    Calculate the NLEO (Non-Linear Energy Operator) and activation times from EGM signals.
+    
+    Parameters:
+    ----------
+    egm : np.ndarray
+        An array with the EGM signal.
+    sampling_rate : int, float, optional
+        Sampling frequency (Hz).
+    woi : list, optional
+        Window of interest as [from, to] in samples. If provided, the EGM signal will be cropped to this window.
+    reference : int, optional
+        Reference sample index for the window of interest. If provided, the window will be centered around this reference.
+    plot : bool, optional
+        If True, plots the EGM signal with activation time marked.
+    
+    Returns:
+    ----------
+    nleo : np.ndarray
+        The NLEO of the EGM signal.
+    nleo_filt : np.ndarray
+        The low-pass filtered NLEO.
+    lat_index : np.ndarray
+        Activation time corresponding to the EGM signal.
+    """
+    
+    warnings.warn("Activation time can only be calculated for sinus rhythm signals.")
+    
+    _, _, _,lat_index, lat = nleo(signal, sampling_rate, woi=woi, reference=reference, plot=plot)
+    
+    return utils.ReturnTuple((lat_index, lat),
+                             ('lat_index', 'lat'))
+    
+
+def nleo(signal=None, sampling_rate=1000., woi = None, reference = None, threshold = None, plot=False):
+    """
+    Calculate activation time based on the NLEO (Non-Linear Energy Operator) method.
+    
+    Parameters:
+    ----------
+    signal : array
+        An array with the EGM signal.
+    sampling_rate : int, float, optional
+        Sampling frequency (Hz) of the EGM signal (default is 1000 Hz).
+    woi : list, optional
+        Window of interest as [from, to] in samples. If provided, the EGM signal will be cropped to this window.
+    reference : int, optional
+        Reference sample index for the window of interest. If provided, the window will be centered around this reference.
+    plot : bool, optional
+        If True, plots the EGM signal with activation time marked.
+    
+    Returns:
+    ----------
+    nleo : array
+        The NLEO of the EGM signal.
+    nleo_filt : array
+        The low-pass filtered NLEO.
+    nleo_threshold : array
+        The thresholded NLEO signal.
+    lat_index : int
+        Index corresponding to the activation time of the EGM signal.
+    lat : float
+        Activation time in milliseconds. If woi is provided, time is centered in the window.
+    """
+    
+    if woi is not None:
+        signal, = get_woi(signal, int(woi[0]), int(woi[1]), int(reference))
+            
+    egm_squared = signal**2
+    egm_rs = signal[1:]
+    egm_ls = signal[:-1]
+    
+    # top and tail signals
+    egm_squared = egm_squared[1:-1]
+    egm_rs = egm_rs[1:]
+    egm_ls = egm_ls[:-1]
+    
+    # calculate nleo
+    nleo = egm_squared - (egm_rs * egm_ls)
+    
+    # add zeros to beginning and end
+    nleo = np.concatenate(([0], nleo, [0]))
+
+    # low-pass filter (zero phase)
+    cutoff = 24 # Hz
+    order = 8
+    b, a = scipy.signal.butter(order, cutoff, btype='low', fs = sampling_rate)
+    nleo_filt = scipy.signal.filtfilt(b, a, nleo)
+    
+    if threshold is None:
+        P1 = np.percentile(abs(nleo_filt), 5)
+        P2 = np.percentile(abs(nleo_filt), 95)
+        threshold = P1 + 0.2 * (P2)
+    nleo_threshold = np.zeros_like(nleo_filt)
+    nleo_threshold[abs(nleo_filt) >= threshold] = 1
+    
+    # remove non-active parts shorter than 30 samples
+    min_inactive_length = 30
+    inactive_regions = np.where(nleo_threshold == 0)[0]
+    diff_inactive = np.diff(inactive_regions)
+    split_indices = np.where(diff_inactive > 1)[0]
+    start_idx = 0
+    for split_idx in split_indices:
+        region = inactive_regions[start_idx:split_idx + 1]
+        if len(region) < min_inactive_length:
+            nleo_threshold[region] = 1
+        start_idx = split_idx + 1
+    # check last region
+    region = inactive_regions[start_idx:]
+    if len(region) < min_inactive_length:
+        nleo_threshold[region] = 1
+    
+    # activation time 
+    x = np.linspace(1, len(nleo_filt), len(nleo_filt))
+    auc = scipy.integrate.cumulative_trapezoid(nleo_filt, x, initial=0)
+    lat_index = np.interp(auc[-1] / 2, auc, x)
+    
+    # convert to time
+    lat = lat_index / sampling_rate * 1000  # convert to milliseconds
+    # center in window
+    # if woi is not None:
+    #     lat = lat - woi[0]  # center in window
+
+    if plot:
+        
+        time = np.arange(len(signal)) / sampling_rate
+        plt.plot(time, signal, label='Signal')
+        plt.plot(time[int(lat_index)], signal[int(lat_index)], label='Activation Time', marker='o', markersize=8, color='red')
+        plt.tight_layout()
+        plt.show()
+        
+ 
+    return utils.ReturnTuple((nleo, nleo_filt, nleo_threshold, lat_index, lat),
+                            ('nleo', 'nleo_filt', 'nleo_threshold', 'lat_index', 'lat'))
+
+    
+
+def dvdt_lat(signal=None, sampling_rate=1000., woi = None, reference = None, plot=False):
+    
+    """
+    Calculate activation time based on maximum negative dv/dt from EGM signal.
+    
+    Parameters:
+    ----------
+    signal : array
+        An array with the EGM signal.
+    sampling_rate : int, float, optional
+        Sampling frequency (Hz) of the EGM signal (default is 1000 Hz).
+    woi : list, optional
+        Window of interest as [from, to] in samples. If provided, the EGM signal will be cropped to this window.
+    reference : int, optional
+        Reference sample index for the window of interest. If provided, the window will be centered around this reference.
+    plot : bool, optional
+        If True, plots the EGM signal with activation time marked.
+        
+    Returns:
+    ----------
+    lat_index : int
+        Index corresponding to the activation time of the EGM signal.
+    lat : float
+        Activation time in milliseconds. If woi is provided, time is centered in the window.
+    """
+    
+    warnings.warn("Activation time can only be calculated for sinus rhythm signals.")
+
+    if woi is not None:
+        signal, = get_woi(signal, int(woi[0]), int(woi[1]), int(reference))
+        
+    # compute dv/dt
+    dvdt = np.gradient(signal)
+    
+    # get activation time as index of maximum -dv/dt
+    lat_index = np.argmax(-dvdt)
+    
+    # convert to time
+    lat = lat_index / sampling_rate * 1000  # convert to milliseconds
+    # center in window
+    # if woi is not None:
+    #     lat = lat - woi[0]  # center in window
+    
+    if plot:
+        time = np.arange(len(signal)) / sampling_rate
+        plt.plot(time, signal, label='Signal')
+        plt.plot(time[lat_index], signal[lat_index], label='Activation Time', marker='o', markersize=8, color='red')
+        plt.tight_layout()
+        plt.show()
+
+    return utils.ReturnTuple((lat_index, lat),
+                             ('lat_index', 'lat'))
+
+
+
+def max_lat(signal=None, sampling_rate=1000., woi = None, reference = None, plot=False):
+    """
+    Calculate activation time based on maximum amplitude from EGM signal.
+    
+    Parameters:
+    ----------
+    signal : array
+        An array with the EGM signal.
+    sampling_rate : int, float, optional
+        Sampling frequency (Hz) of the EGM signal (default is 1000 Hz).
+    woi : list, optional
+        Window of interest as [from, to] in samples. If provided, the EGM signal will be cropped to this window.
+    reference : int, optional
+        Reference sample index for the window of interest. If provided, the window will be centered around this reference.
+    plot : bool, optional
+        If True, plots the EGM signal with activation time marked.
+        
+    Returns:
+    ----------
+    lat_index : int
+        Index corresponding to the activation time of the EGM signal.
+    lat : float
+        Activation time in milliseconds. If woi is provided, time is centered in the window.
+    """
+    
+    warnings.warn("Activation time can only be calculated for sinus rhythm signals.")
+
+    if woi is not None:
+        signal, = get_woi(signal, int(woi[0]), int(woi[1]), int(reference))
+        
+    # get activation time as index of maximum amplitude
+    lat_index = np.argmax(signal)
+    
+    # convert to time
+    lat = lat_index / sampling_rate * 1000  # convert to milliseconds
+    # center in window
+    # if woi is not None:
+    #     lat = lat - woi[0]  # center in window
+    
+    if plot:
+        time = np.arange(len(signal)) / sampling_rate
+        plt.plot(time, signal, label='EGM Signal')
+        plt.plot(time[lat_index], signal[lat_index], label='Activation Time', marker='o', markersize=8, color='red')
+        plt.tight_layout()
+        plt.show()
+
+    return utils.ReturnTuple((lat_index, lat),
+                             ('lat_index', 'lat'))
+
+def min_lat(signal=None, sampling_rate=1000., woi = None, reference = None, plot=False):
+    """
+    Calculate activation time based on minimum amplitude from EGM signal.
+    
+    Parameters:
+    ----------
+    signal : array
+        An array with the EGM signal.
+    sampling_rate : int, float, optional
+        Sampling frequency (Hz) of the EGM signal (default is 1000 Hz).
+    woi : list, optional
+        Window of interest as [from, to] in samples. If provided, the EGM signal will be cropped to this window.
+    reference : int, optional
+        Reference sample index for the window of interest. If provided, the window will be centered around this reference.
+    plot : bool, optional
+        If True, plots the EGM signal with activation time marked.
+        
+    Returns:
+    ----------
+    lat_index : int
+        Index corresponding to the activation time of the EGM signal.
+    lat : float
+        Activation time in milliseconds. If woi is provided, time is centered in the window.
+    """
+    
+    warnings.warn("Activation time can only be calculated for sinus rhythm signals.")
+
+    if woi is not None:
+        signal, = get_woi(signal, int(woi[0]), int(woi[1]), int(reference))
+        
+    # get activation time as index of minimum amplitude
+    lat_index = np.argmin(signal)
+    
+    # convert to time
+    lat = lat_index / sampling_rate * 1000  # convert to milliseconds
+    # center in window
+    # if woi is not None:
+    #     lat = lat - woi[0]  # center in window
+    
+    if plot:
+        time = np.arange(len(signal)) / sampling_rate
+        plt.plot(time, signal, label='EGM Signal')
+        plt.plot(time[lat_index], signal[lat_index], label='Activation Time', marker='o', markersize=8, color='red')
+        plt.tight_layout()
+        plt.show()
+
+    return utils.ReturnTuple((lat_index, lat),
+                             ('lat_index', 'lat'))
+
+def get_activation_times(signals=None, sampling_rate=1000., woi=None, reference=None, method='nleo'):
+    """
+    Calculate activation times for multiple EGM signals.
+    
+    Parameters:
+    ----------
+    signals : array
+        A 2D array where each row is an EGM signal.
+    sampling_rate : int, float, optional
+        Sampling frequency (Hz) of the EGM signals (default is 1000 Hz).
+    woi : list, optional
+        Window of interest as [from, to] in samples. If provided, each EGM signal will be cropped to this window.
+    reference : int, optional
+        Reference sample index for the window of interest. If provided, the window will be centered around this reference.
+    
+    Returns:
+    lat_indexes : int
+        Indexes corresponding to the activation times of each EGM signal.
+    lats : float
+        Activation times in milliseconds. If woi is provided, time is centered in the window.
+    """
+    
+    warnings.warn("Activation time can only be calculated for sinus rhythm signals.")
+
+    n_signals = signals.shape[0]
+    lat_idx = np.zeros(n_signals)
+    lat_times = np.zeros(n_signals)
+    
+    for i in range(n_signals):
+        if method == 'nleo':
+            _, _, lat_index, lat = nleo_lat(signals[i, :], sampling_rate, woi=woi, reference=reference)
+        elif method == 'dvdt':
+            lat_index, lat = dvdt_lat(signals[i, :], sampling_rate, woi=woi, reference=reference)
+        elif method == 'max':
+            lat_index, lat = max_lat(signals[i, :], sampling_rate, woi=woi, reference=reference)
+        elif method == 'min':
+            lat_index, lat = min_lat(signals[i, :], sampling_rate, woi=woi, reference=reference)
+        else:
+            raise ValueError("Invalid method. Must be 'nleo', 'dvdt','max', or 'min'.")
+        lat_idx[i] = lat_index
+        lat_times[i] = lat
+    return utils.ReturnTuple((lat_idx, lat_times),
+                             ('lat_indexes', 'lats'))
+
+def compare_activation_times(signal=None, sampling_rate=1000., woi=None, reference=None, plot=False):
+    """
+    Compare activation times calculated by different methods for a single EGM signal.
+    
+    Parameters:
+    ----------
+    signal : array
+        An array with the EGM signal.
+    sampling_rate : int, float, optional
+        Sampling frequency (Hz) of the EGM signal (default is 1000 Hz).
+    woi : list, optional
+        Window of interest as [from, to] in samples. If provided, the EGM signal will be cropped to this window.
+    reference : int, optional
+        Reference sample index for the window of interest. If provided, the window will be centered around this reference.
+    plot : bool, optional
+        If True, plots the EGM signal with activation times marked.
+    
+    Returns:
+    ----------
+    lat_index_nleo : int
+        Index corresponding to the activation time from NLEO method.
+    lat_index_dvdt : int
+        Index corresponding to the activation time from -dv/dt method.
+    lat_index_max : int
+        Index corresponding to the activation time from maximum amplitude method.
+    lat_index_min : int
+        Index corresponding to the activation time from minimum amplitude method.
+    lat_nleo : float
+        Activation time in milliseconds from NLEO method.
+    lat_dvdt : float
+        Activation time in milliseconds from -dv/dt method.
+    lat_max : float
+        Activation time in milliseconds from maximum amplitude method.
+    lat_min : float
+        Activation time in milliseconds from minimum amplitude method.
+    """
+    
+    warnings.warn("Activation time can only be calculated for sinus rhythm signals.")
+    
+   # run all methods
+    lat_index_nleo, lat_nleo = nleo_lat(signal, sampling_rate, woi=woi, reference=reference)
+    lat_index_dvdt, lat_dvdt = dvdt_lat(signal, sampling_rate, woi=woi, reference=reference)
+    lat_index_max, lat_max = max_lat(signal, sampling_rate, woi=woi, reference=reference)
+    lat_index_min, lat_min = min_lat(signal, sampling_rate, woi=woi, reference=reference)
+    
+    if plot:
+        if woi is not None:
+            signal, = get_woi(signal, int(woi[0]), int(woi[1]), int(reference))
+        time = np.arange(len(signal)) / sampling_rate
+        plt.figure()
+        plt.plot(time, signal, label='Signal')
+        plt.plot(time[int(lat_index_nleo)], signal[int(lat_index_nleo)], label='NLEO Activation Time', marker='o', markersize=8)
+        plt.plot(time[int(lat_index_dvdt)], signal[int(lat_index_dvdt)], label='-dv/dt Activation Time', marker='o', markersize=8)
+        plt.plot(time[int(lat_index_max)], signal[int(lat_index_max)], label='Max Amplitude Activation Time', marker='o', markersize=8)
+        plt.plot(time[int(lat_index_min)], signal[int(lat_index_min)], label='Min Amplitude Activation Time', marker='o', markersize=8)
+        plt.legend()
+        plt.tight_layout()
+        plt.show()
+    
+    return utils.ReturnTuple((lat_index_nleo, lat_index_dvdt, lat_index_max, lat_index_min, lat_nleo, lat_dvdt, lat_max, lat_min),
+                             ('lat_index_nleo', 'lat_index_dvdt', 'lat_index_max', 'lat_index_min', 'lat_nleo', 'lat_dvdt', 'lat_max', 'lat_min'))
+
+def get_voltage(signal=None, woi=None, reference=None):
+    """
+    Get the maximum voltage of the EGM signal.
+    
+    Parameters:
+    ----------
+    signal : array
+        An array with the EGM signal.
+    woi : list, optional
+        Window of interest as [from, to] in samples. If provided, the EGM signal will be cropped to this window.
+    reference : int, optional
+        Reference sample index for the window of interest. If provided, the window will be centered around this reference.
+    
+    Returns:
+    ----------
+    voltage : float
+        The maximum voltage of the EGM signal.
+    """
+    warnings.filterwarnings("ignore")
+
+    idx, _ = max_lat(signal, woi=woi, reference=reference)
+    
+    warnings.resetwarnings()
+    
+    if woi is not None:
+        signal, = get_woi(signal, int(woi[0]), int(woi[1]), int(reference))
+    voltage = signal[int(idx)]
+    
+    
+    
+    return utils.ReturnTuple((voltage,),
+                             ('voltage',))
+
+def get_voltages(signals=None, woi=None, reference=None):
+    """
+    Get the maximum voltages for multiple EGM signals.
+    
+    Parameters:
+    ----------
+    signals : array
+        A 2D array where each row is an EGM signal.
+    woi : list, optional
+        Window of interest as [from, to] in samples. If provided, each EGM signal will be cropped to this window.
+    reference : int, optional
+        Reference sample index for the window of interest. If provided, the window will be centered around this reference.
+    
+    Returns:
+    ----------
+    voltages : array
+        An array of maximum voltages for each EGM signal.
+    """
+    
+    n_signals = signals.shape[0]
+    voltages = np.zeros(n_signals)
+    
+    for i in range(n_signals):
+        voltage_temp = get_voltage(signals[i, :], woi=woi, reference=reference)
+        voltages[i] = voltage_temp
+        
+    return utils.ReturnTuple((voltages,),
+                             ('voltages',))
+
+def dominant_frequency(signal=None, sampling_rate=1000., plot=False):
+    """
+    Get the dominant frequency of the EGM signal.
+    
+    Parameters:
+    ----------
+    signal : array
+        An array with the EGM signal.
+    sampling_rate : int, float, optional
+        Sampling frequency (Hz) of the EGM signal (default is 1000 Hz).
+    plot : bool, optional
+        If True, plots the power spectral density of the EGM signal.
+    
+    Returns:
+    ----------
+    dominant_freq : float
+        The dominant frequency of the EGM signal in Hz.
+    fft_freqs : array
+        The frequencies of the power spectral density of the dominant frequency spectrum.
+    psd : array
+        The power spectral density of the dominant frequency spectrum.
+    """
+    warnings.warn("Dominant frequency can only be calculated for atrial fibrillation signals.")
+        
+    # Butterworth IIR bandpass filter between 40 and 250 Hz
+    lowcut = 40.0
+    highcut = 250.0
+    order = 8
+    b, a = scipy.signal.butter(order, [lowcut, highcut], btype='band', fs=sampling_rate)
+    signal = scipy.signal.filtfilt(b, a, signal)
+        
+    # signal rectification
+    signal = np.abs(signal)
+    
+    # Butterworth IIR low-pass filter with cutoff frequency of 20 Hz
+    cutoff = 20.0
+    b, a = scipy.signal.butter(order, cutoff, btype='low', fs=sampling_rate)
+    signal = scipy.signal.filtfilt(b, a, signal)
+    
+    # compute fft with zero padding to next power of 2
+    n = len(signal)
+    nfft = 2**np.ceil(np.log2(n)).astype(int)
+    # pad signal
+    signal = np.pad(signal, (0, nfft - n), mode='constant')
+    from scipy.signal.windows import hann
+    from scipy.fft import fft, fftfreq
+    w = hann(nfft)
+    fft_vals = fft(signal*w)
+    
+    # sample spacing
+    T = 1.0 / sampling_rate
+    fft_freqs = fftfreq(nfft, T)[:nfft // 2]
+    fft_vals = fft_vals[:nfft // 2]
+    psd = 2.0 / (nfft * T) * np.abs(fft_vals)**2
+    
+    # get dominant frequency between 3 and 10 Hz
+    idx = np.where((fft_freqs >= 3) & (fft_freqs <= 10))
+    psd_max = psd[idx]
+    fft_freqs_max = fft_freqs[idx]
+    dominant_freq = fft_freqs_max[np.argmax(psd_max)]
+    
+    if plot:
+        plt.figure()
+        plt.plot(fft_freqs, psd)
+        plt.xlabel('Frequency (Hz)')
+        plt.ylabel('Power Spectral Density')
+        plt.axvline(dominant_freq, color='r', linestyle='--', label='Dominant Frequency = ' + '%.2f' % dominant_freq + " Hz")
+        plt.legend()
+        plt.xlim(0, 20)
+        plt.show()
+        
+    
+    return utils.ReturnTuple((dominant_freq, fft_freqs, psd),
+                             ('dominant_freq', 'fft_freqs', 'psd'))
+
+def shannon_entropy(signal=None, sampling_rate=1000., plot=False):
+    """
+    Calculate Shannon entropy of the EGM signal.
+    
+    Parameters:
+    ----------
+    signal : array
+        An array with the EGM signal.
+    sampling_rate : int, float, optional
+        Sampling frequency (Hz) of the EGM signal (default is 1000 Hz).
+    plot : bool, optional
+        If True, plots the histogram of the EGM signal voltages.
+    
+    Returns:
+    ----------
+    shannon_entropy : float
+        Shannon entropy of the EGM signal.
+    """
+        
+    # histogram with binsize of 0.01 mV
+    bins = np.arange(np.min(signal), np.max(signal), 0.01)
+    counts, bins = np.histogram(signal, bins=bins)
+    
+    # calculate Shannon entropy. If counts = 0, probability = 0
+    probabilities = counts / np.sum(counts)
+    probabilities = probabilities[probabilities != 0]
+    entropy = -np.sum(probabilities * np.log2(probabilities))
+    
+    if plot:
+        plt.figure()
+        plt.stairs(counts, bins)
+        plt.xlabel('Bins')
+        plt.ylabel('Probability')
+        plt.text(np.mean(bins), np.max(counts), 'Entropy = ' + '%.2f' % entropy)
+        plt.show()
+    
+    return utils.ReturnTuple((entropy,),
+                             ('shannon_entropy',))
+
+
+def organization_index(signal=None, sampling_rate=1000.):
+    """
+    Calculate organization index of the EGM signal.
+    
+    Parameters:
+    ----------
+    signal : array
+        An array with the EGM signal.
+    sampling_rate : int, float, optional
+        Sampling frequency (Hz) of the EGM signal (default is 1000 Hz).
+    
+    Returns:
+    ----------
+    organization_index : float
+        Organization index of the EGM signal.
+    """
+    
+    df, freqs, spectrum = dominant_frequency(signal, sampling_rate=sampling_rate, plot=False)
+    
+    if df >= 10:
+        raise ValueError("Dominant frequency is greater than 10 Hz. Organization index cannot be calculated.")
+    
+    # get area under power spectrum for df plus and minus 0.75 Hz
+    idx = np.where((freqs >= df - 0.75) & (freqs <= df + 0.75))
+    spectrum1 = spectrum[idx]
+    area1 = scipy.integrate.trapezoid(spectrum1)
+    
+    # get area under power spectrum for first harmonic of df plus and minus 0.75 Hz
+    idx = np.where((freqs >= 2 * df - 0.75) & (freqs <= 2 * df + 0.75))
+    spectrum2 = spectrum[idx]
+    area2 = scipy.integrate.trapezoid(spectrum2)
+    
+    # get area under power spectrum for second harmonic of df plus and minus 0.75 Hz
+    idx = np.where((freqs >= 3 * df - 0.75) & (freqs <= 3 * df + 0.75))
+    spectrum3 = spectrum[idx]
+    area3 = scipy.integrate.trapezoid(spectrum3)
+    
+    # get area between 3 and 20 Hz
+    idx = np.where((freqs >= 3) & (freqs <= 20))
+    spectrum_total = spectrum[idx]
+    area_total = scipy.integrate.trapezoid(spectrum_total)
+    
+    # calculate organization index
+    oi = (area1 + area2 + area3) / area_total
+    
+    return utils.ReturnTuple((oi,),
+                             ('organization_index',))
+
+def regularity_index(signal=None, sampling_rate=1000.):
+    """
+    Calculate regularity index of the EGM signal.
+    
+    Parameters:
+    ----------
+    signal : array
+        An array with the EGM signal.
+    sampling_rate : int, float, optional
+        Sampling frequency (Hz) of the EGM signal (default is 1000 Hz).
+    
+    Returns:
+    ----------
+    regularity_index : float
+        Regularity index of the EGM signal.
+    """
+    
+    df, freqs, spectrum = dominant_frequency(signal, sampling_rate=sampling_rate, plot=False)
+    
+    if df >= 10:
+        raise ValueError("Dominant frequency is greater than 10 Hz. Regularity index cannot be calculated.")
+    
+    # get area under power spectrum for df plus and minus 0.75 Hz
+    idx = np.where((freqs >= df - 0.75) & (freqs <= df + 0.75))
+    spectrum1 = spectrum[idx]
+    area1 = scipy.integrate.trapezoid(spectrum1)
+    
+    # get area between 3 and 20 Hz
+    idx = np.where((freqs >= 3) & (freqs <= 20))
+    spectrum_total = spectrum[idx]
+    area_total = scipy.integrate.trapezoid(spectrum_total)
+    
+    # calculate regularity index
+    ri = area1 / area_total
+    
+    return utils.ReturnTuple((ri,),
+                             ('regularity_index',))
\ No newline at end of file
diff --git a/examples/egm_bipolar_af.txt b/examples/egm_bipolar_af.txt
new file mode 100644
index 00000000..07017142
--- /dev/null
+++ b/examples/egm_bipolar_af.txt
@@ -0,0 +1,2503 @@
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diff --git a/examples/egm_bipolar_sinus.txt b/examples/egm_bipolar_sinus.txt
new file mode 100644
index 00000000..6b6152b3
--- /dev/null
+++ b/examples/egm_bipolar_sinus.txt
@@ -0,0 +1,2505 @@
+# Simple Text Format
+# Sampling Rate (Hz):= 1000.00
+# WOI: [-137, 51]
+# Reference: 2000
+# Units:= mV
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