diff --git a/src/spikeinterface/core/node_pipeline.py b/src/spikeinterface/core/node_pipeline.py
index 1609f11d17..10e4885606 100644
--- a/src/spikeinterface/core/node_pipeline.py
+++ b/src/spikeinterface/core/node_pipeline.py
@@ -489,11 +489,13 @@ def check_graph(nodes):
     Check that node list is orderd in a good (parents are before children)
     """
 
-    node0 = nodes[0]
-    if not isinstance(node0, PeakSource):
-        raise ValueError(
-            "Peak pipeline graph must have as first element a PeakSource (PeakDetector or PeakRetriever or SpikeRetriever"
-        )
+    # Do not remove this, this is to remenber that in previous version the first node needed to be 
+    # a detectot but not anymore
+    # node0 = nodes[0]
+    # if not isinstance(node0, PeakSource):
+    #     raise ValueError(
+    #         "Peak pipeline graph must have as first element a PeakSource (PeakDetector or PeakRetriever or SpikeRetriever"
+    #     )
 
     for i, node in enumerate(nodes):
         assert isinstance(node, PipelineNode), f"Node {node} is not an instance of PipelineNode"
diff --git a/src/spikeinterface/preprocessing/__init__.py b/src/spikeinterface/preprocessing/__init__.py
index de25944bd2..ab1adb6942 100644
--- a/src/spikeinterface/preprocessing/__init__.py
+++ b/src/spikeinterface/preprocessing/__init__.py
@@ -20,6 +20,12 @@
     PreprocessingPipeline,
 )
 
+from .detect_artifacts import (
+    detect_artifact_periods,
+    detect_artifact_periods_by_envelope,
+    detect_saturation_periods
+)
+
 # for snippets
 from .align_snippets import AlignSnippets
 from warnings import warn
diff --git a/src/spikeinterface/preprocessing/detect_artifacts.py b/src/spikeinterface/preprocessing/detect_artifacts.py
new file mode 100644
index 0000000000..3e42facdc5
--- /dev/null
+++ b/src/spikeinterface/preprocessing/detect_artifacts.py
@@ -0,0 +1,342 @@
+from __future__ import annotations
+
+import numpy as np
+
+from spikeinterface.core.base import base_period_dtype
+# from spikeinterface.core.core_tools import define_function_handling_dict_from_class
+# from spikeinterface.preprocessing.silence_periods import SilencedPeriodsRecording
+from spikeinterface.preprocessing.rectify import RectifyRecording
+from spikeinterface.preprocessing.common_reference import CommonReferenceRecording
+from spikeinterface.preprocessing.filter_gaussian import GaussianFilterRecording
+from spikeinterface.core.job_tools import fix_job_kwargs
+from spikeinterface.core.recording_tools import get_noise_levels
+from spikeinterface.core.node_pipeline import PeakDetector, base_peak_dtype, run_node_pipeline, PipelineNode
+import numpy as np
+
+
+
+artifact_dtype = base_period_dtype
+
+
+# this will be extend with channel boundaries if needed
+# extended_artifact_dtype = artifact_dtype + [
+#     # TODO
+# ]
+
+
+
+def detect_artifact_periods(
+    recording,
+    method="envelope",
+    method_kwargs=None,
+    job_kwargs=None,
+):
+    """
+    Detect artifacts with several possible methods:
+      * 'saturation' using detect_artifact_periods_by_envelope()
+      * 'envelope' using detect_saturation_periods()
+    
+    See sub methods for more information on parameters.
+    """
+
+    if method_kwargs is None:
+        method_kwargs = dict()
+
+    if method == "envelope":
+        artifact_periods, envelope = detect_artifact_periods_by_envelope(recording, **method_kwargs, job_kwargs=job_kwargs)
+    elif method == "saturation":
+        artifact_periods = detect_saturation_periods(recording, **method_kwargs, job_kwargs=job_kwargs)
+    else:
+        raise ValueError(f"detect_artifact_periods() method='{method}' is not valid")
+    
+    return artifact_periods
+
+
+
+## detect_period_artifacts_saturation Zone
+
+def _collapse_events(events):
+    """
+    If events are detected at a chunk edge, they will be split in two.
+    This detects such cases and collapses them in a single record instead.
+    """
+    order = np.lexsort((events["start_sample_index"], events["segment_index"]))
+    events = events[order]
+    to_drop = np.zeros(events.size, dtype=bool)
+
+    # compute if duplicate
+    for i in np.arange(events.size - 1):
+        same = events["end_sample_index"][i] == events["start_sample_index"][i + 1]
+        if same:
+            to_drop[i] = True
+            events["start_sample_index"][i + 1] = events["start_sample_index"][i]
+
+    return events[~to_drop].copy()
+
+
+class _DetectSaturation(PipelineNode):
+
+    name = "detect_saturation"
+    preferred_mp_context = None
+    _compute_has_extended_signature = True
+
+    def __init__(
+        self,
+        recording,
+        saturation_threshold_uV,
+        voltage_per_sec_threshold,
+        proportion,
+    ):
+        PipelineNode.__init__(self, recording, return_output=True)
+
+        gains = recording.get_channel_gains()
+        offsets = recording.get_channel_offsets()
+        num_chans = recording.get_num_channels()
+
+        self.voltage_per_sec_threshold = voltage_per_sec_threshold
+        thresh = np.full((num_chans, ), saturation_threshold_uV)
+        # 0.98 is empirically determined as the true saturating point is
+        # slightly lower than the documented saturation point of the probe        
+        self.saturation_threshold_unscaled = (thresh - offsets) / gains * 0.98
+        
+        self.sampling_frequency = recording.get_sampling_frequency()
+        self.proportion = proportion
+        self._dtype = np.dtype(artifact_dtype)
+        self.gain  = recording.get_channel_gains()
+        self.offset = recording.get_channel_offsets()
+
+    def get_trace_margin(self):
+        return 0
+
+    def get_dtype(self):
+        return self._dtype
+
+    def compute(self, traces, start_frame, end_frame, segment_index, max_margin):
+
+        saturation = np.mean(np.abs(traces) > self.saturation_threshold_unscaled, axis=1)
+
+        if self.voltage_per_sec_threshold is not None:
+            fs = self.sampling_frequency
+            # then compute the derivative of the voltage saturation
+            n_diff_saturated = np.mean(np.abs(np.diff(traces, axis=0)) / fs >= self.voltage_per_sec_threshold, axis=1)
+            # Note this means the velocity is not checked for the last sample in the
+            # check because we are taking the forward derivative
+            n_diff_saturated = np.r_[n_diff_saturated, 0]
+
+            # if either of those reaches more than the proportion of channels labels the sample as saturated
+            saturation = np.logical_or(saturation > self.proportion, n_diff_saturated > self.proportion)
+        else:
+            saturation = saturation > self.proportion
+
+        intervals = np.flatnonzero(np.diff(saturation, prepend=False, append=False))
+        n_events = len(intervals) // 2  # Number of saturation periods
+        events = np.zeros(n_events, dtype=artifact_dtype)
+
+        for i, (start, stop) in enumerate(zip(intervals[::2], intervals[1::2])):
+            events[i]["start_sample_index"] = start + start_frame
+            events[i]["end_sample_index"] = stop + start_frame
+            events[i]["segment_index"] = segment_index
+
+        return (events, )
+
+
+def detect_saturation_periods(
+    recording,
+    saturation_threshold_uV,  # 1200 uV
+    voltage_per_sec_threshold=None,  # 1e-8 V.s-1
+    proportion=0.5,
+    job_kwargs=None,
+):
+    """
+    Detect amplifier saturation events (either single sample or multi-sample periods) in the data.
+    Saturation detection with this function should be applied to the raw data, before preprocessing.
+    However, saturation periods detected should be zeroed out after preprocessing has been performed.
+
+    Saturation is detected by a voltage threshold, and optionally a derivative threshold that
+    flags periods of high velocity changes in the voltage. See _DetectSaturation.compute()
+    for details on the algorithm.
+
+    Parameters
+    ----------
+    recording : BaseRecording
+        The recording on which to detect the saturation events.
+    saturation_threshold_uV : float
+        The voltage saturation threshold in volts. This will depend on the recording
+        probe and amplifier gain settings. For NP1 the value of 1200 uV is recommended (IBL).
+        Note that NP2 probes are more difficult to saturate than NP1.
+    voltage_per_sec_threshold : None | float
+        The first-derivative threshold in volts per second. Periods of the data over which the change
+        in velocity is greater than this threshold will be detected as saturation events. Use `None` to
+        skip this method and only use `saturation_threshold_uV` for detection. Otherwise, the value should be
+        empirically determined (IBL use 1e-8 V.s-1) for NP1 probes.
+
+    proportion :
+    mute_window_samples :
+    job_kwargs :
+
+    most useful for NP1
+    can use ratio as a intuition for the value but dont do it in code
+
+    Returns
+    -------
+
+    """
+    if job_kwargs:
+        job_kwargs = {}
+
+    # if saturation_threshold_uV < 0.1:
+    #    raise ValueError(f"The `saturation_threshold_uV` should be in microvolts. "
+    #                     f"Your value: {saturation_threshold_uV} is almost certainly in volts.")
+
+    job_kwargs = fix_job_kwargs(job_kwargs)
+
+    node0 = _DetectSaturation(
+        recording,
+        saturation_threshold_uV=saturation_threshold_uV,
+        voltage_per_sec_threshold=voltage_per_sec_threshold,
+        proportion=proportion,
+    )
+
+    saturation_periods = run_node_pipeline(recording, [node0], job_kwargs=job_kwargs, job_name="detect saturation artifacts")
+
+    return _collapse_events(saturation_periods)
+
+
+
+## detect_artifact_periods_by_envelope Zone
+
+class _DetectThresholdCrossing(PeakDetector):
+
+    name = "threshold_crossings"
+    preferred_mp_context = None
+
+    def __init__(
+        self,
+        recording,
+        detect_threshold=5,
+        noise_levels=None,
+        seed=None,
+        noise_levels_kwargs=dict(),
+    ):
+        PeakDetector.__init__(self, recording, return_output=True)
+        if noise_levels is None:
+            random_slices_kwargs = noise_levels_kwargs.pop("random_slices_kwargs", {}).copy()
+            random_slices_kwargs["seed"] = seed
+            noise_levels = get_noise_levels(recording, return_in_uV=False, random_slices_kwargs=random_slices_kwargs)
+        self.abs_thresholds = noise_levels * detect_threshold
+        # internal dtype
+        self._dtype = np.dtype([
+                ("sample_index", "int64"),
+                ("segment_index", "int64"),
+                ("front", "bool")
+            ]
+        )
+
+    def get_trace_margin(self):
+        return 0
+
+    def get_dtype(self):
+        return self._dtype
+
+    def compute(self, traces, start_frame, end_frame, segment_index, max_margin):
+        z = np.median(traces / self.abs_thresholds, 1)
+        threshold_mask = np.diff((z > 1) != 0, axis=0)
+        indices = np.flatnonzero(threshold_mask)
+        threshold_crossings = np.zeros(indices.size, dtype=self._dtype)
+        threshold_crossings["sample_index"] = indices
+        threshold_crossings["segment_index"] = segment_index
+        threshold_crossings["front"][::2] = True
+        threshold_crossings["front"][1::2] = False
+        return (threshold_crossings,)
+
+
+def detect_artifact_periods_by_envelope(
+    recording,
+    detect_threshold=5,
+    # min_duration_ms=50,
+    freq_max=20.0,
+    seed=None,
+    job_kwargs=None,
+    random_slices_kwargs=None,
+):
+    """
+    Function to detect putative artifact periods as threshold crossings of
+    a global envelope of the channels.
+
+    Parameters
+    ----------
+    recording : RecordingExtractor
+        The recording extractor to detect putative artifacts
+    detect_threshold : float, default: 5
+        The threshold to detect artifacts. The threshold is computed as `detect_threshold * noise_level`
+    freq_max : float, default: 20
+        The maximum frequency for the low pass filter used
+    seed : int | None, default: None
+        Random seed for `get_noise_levels`.
+        If none, `get_noise_levels` uses `seed=0`.
+    **noise_levels_kwargs : Keyword arguments for `spikeinterface.core.get_noise_levels()` function
+
+    """
+
+    envelope = RectifyRecording(recording)
+    envelope = GaussianFilterRecording(envelope, freq_min=None, freq_max=freq_max)
+    envelope = CommonReferenceRecording(envelope)
+
+    job_kwargs = fix_job_kwargs(job_kwargs)
+    if random_slices_kwargs is None:
+        random_slices_kwargs = {}
+    else:
+        random_slices_kwargs = random_slices_kwargs.copy()
+    random_slices_kwargs["seed"] = seed
+    noise_levels = get_noise_levels(envelope, return_in_uV=False, random_slices_kwargs=random_slices_kwargs)
+
+    node0 = _DetectThresholdCrossing(
+        envelope, detect_threshold=detect_threshold, noise_levels=noise_levels, seed=seed,
+    )
+
+    threshold_crossings = run_node_pipeline(
+        envelope,
+        [node0],
+        job_kwargs,
+        job_name="detect artifact on  envelope",
+    )
+
+    order = np.lexsort((threshold_crossings["sample_index"], threshold_crossings["segment_index"]))
+    threshold_crossings = threshold_crossings[order]
+
+    artifacts = _transform_internal_dtype_to_artifact_dtype(threshold_crossings, recording)
+
+    return artifacts, envelope
+
+
+def _transform_internal_dtype_to_artifact_dtype(artifacts, recording):
+
+    num_seg = recording.get_num_segments()
+
+    final_artifacts = []
+    for seg_index in range(num_seg):
+        mask = artifacts["segment_index"] == seg_index
+        sub_thr = artifacts[mask]
+        if len(sub_thr) > 0:
+            if not sub_thr["front"][0]:
+                local_thr = np.zeros(1, dtype=np.dtype(base_peak_dtype + [("front", "bool")]))
+                local_thr["sample_index"] = 0
+                local_thr["front"] = True
+                sub_thr = np.hstack((local_thr, sub_thr))
+            if sub_thr["front"][-1]:
+                local_thr = np.zeros(1, dtype=np.dtype(base_peak_dtype + [("front", "bool")]))
+                local_thr["sample_index"] = recording.get_num_samples(seg_index)
+                local_thr["front"] = False
+                sub_thr = np.hstack((sub_thr, local_thr))
+            
+            local_artifact = np.zeros(sub_thr.size/2, dtype=artifact_dtype)
+            local_artifact["start_index"] = sub_thr["sample_index"][::2]
+            local_artifact["stop_index"] = sub_thr["sample_index"][1::2]
+            local_artifact["segment_index"] = seg_index
+            final_artifacts.append(local_artifact)
+    
+    if len(final_artifacts) > 0:
+        final_artifacts = np.concatenate(final_artifacts)
+    else:
+        final_artifacts = np.zeros(0, dtype=artifact_dtype)
+    return final_artifacts
\ No newline at end of file
diff --git a/src/spikeinterface/preprocessing/preprocessing_classes.py b/src/spikeinterface/preprocessing/preprocessing_classes.py
index fe9d95c506..47839db7a0 100644
--- a/src/spikeinterface/preprocessing/preprocessing_classes.py
+++ b/src/spikeinterface/preprocessing/preprocessing_classes.py
@@ -50,7 +50,7 @@
 from .depth_order import DepthOrderRecording, depth_order
 from .astype import AstypeRecording, astype
 from .unsigned_to_signed import UnsignedToSignedRecording, unsigned_to_signed
-from .silence_artifacts import SilencedArtifactsRecording, silence_artifacts
+# from .silence_artifacts import SilencedArtifactsRecording, silence_artifacts
 
 _all_preprocesser_dict = {
     # filter stuff
@@ -90,7 +90,7 @@
     DirectionalDerivativeRecording: directional_derivative,
     AstypeRecording: astype,
     UnsignedToSignedRecording: unsigned_to_signed,
-    SilencedArtifactsRecording: silence_artifacts,
+    # SilencedArtifactsRecording: silence_artifacts,
 }
 # we control import in the preprocessing init by setting an __all__
 
diff --git a/src/spikeinterface/preprocessing/silence_artifacts.py b/src/spikeinterface/preprocessing/silence_artifacts.py
deleted file mode 100644
index f8323b2e78..0000000000
--- a/src/spikeinterface/preprocessing/silence_artifacts.py
+++ /dev/null
@@ -1,224 +0,0 @@
-from __future__ import annotations
-
-import numpy as np
-
-from spikeinterface.core.base import base_peak_dtype
-from spikeinterface.core.core_tools import define_function_handling_dict_from_class
-from spikeinterface.core.job_tools import split_job_kwargs, fix_job_kwargs
-from spikeinterface.core.recording_tools import get_noise_levels
-from spikeinterface.core.node_pipeline import PeakDetector
-from spikeinterface.preprocessing.silence_periods import SilencedPeriodsRecording
-from spikeinterface.preprocessing.rectify import RectifyRecording
-from spikeinterface.preprocessing.common_reference import CommonReferenceRecording
-from spikeinterface.preprocessing.filter_gaussian import GaussianFilterRecording
-
-
-class DetectThresholdCrossing(PeakDetector):
-
-    name = "threshold_crossings"
-    preferred_mp_context = None
-
-    def __init__(
-        self,
-        recording,
-        detect_threshold=5,
-        noise_levels=None,
-        seed=None,
-        noise_levels_kwargs=dict(),
-    ):
-        PeakDetector.__init__(self, recording, return_output=True)
-        if noise_levels is None:
-            random_slices_kwargs = noise_levels_kwargs.pop("random_slices_kwargs", {}).copy()
-            random_slices_kwargs["seed"] = seed
-            noise_levels = get_noise_levels(recording, return_in_uV=False, random_slices_kwargs=random_slices_kwargs)
-        self.abs_thresholds = noise_levels * detect_threshold
-        self._dtype = np.dtype(base_peak_dtype + [("front", "bool")])
-
-    def get_trace_margin(self):
-        return 0
-
-    def get_dtype(self):
-        return self._dtype
-
-    def compute(self, traces, start_frame, end_frame, segment_index, max_margin):
-        z = np.median(traces / self.abs_thresholds, 1)
-        threshold_mask = np.diff((z > 1) != 0, axis=0)
-        indices = np.flatnonzero(threshold_mask)
-        threshold_crossings = np.zeros(indices.size, dtype=self._dtype)
-        threshold_crossings["sample_index"] = indices
-        threshold_crossings["front"][::2] = True
-        threshold_crossings["front"][1::2] = False
-        return (threshold_crossings,)
-
-
-def detect_period_artifacts_by_envelope(
-    recording,
-    detect_threshold=5,
-    min_duration_ms=50,
-    freq_max=20.0,
-    seed=None,
-    noise_levels=None,
-    **noise_levels_kwargs,
-):
-    """
-    Docstring for detect_period_artifacts. Function to detect putative artifact periods as threshold crossings of
-    a global envelope of the channels.
-
-    Parameters
-    ----------
-    recording : RecordingExtractor
-        The recording extractor to detect putative artifacts
-    detect_threshold : float, default: 5
-        The threshold to detect artifacts. The threshold is computed as `detect_threshold * noise_level`
-    freq_max : float, default: 20
-        The maximum frequency for the low pass filter used
-    min_duration_ms : float, default: 50
-        The minimum duration for a threshold crossing to be considered as an artefact.
-    noise_levels : array
-        Noise levels if already computed
-    seed : int | None, default: None
-        Random seed for `get_noise_levels`.
-        If none, `get_noise_levels` uses `seed=0`.
-    **noise_levels_kwargs : Keyword arguments for `spikeinterface.core.get_noise_levels()` function
-
-    """
-
-    envelope = RectifyRecording(recording)
-    envelope = GaussianFilterRecording(envelope, freq_min=None, freq_max=freq_max)
-    envelope = CommonReferenceRecording(envelope)
-
-    from spikeinterface.core.node_pipeline import (
-        run_node_pipeline,
-    )
-
-    _, job_kwargs = split_job_kwargs(noise_levels_kwargs)
-    job_kwargs = fix_job_kwargs(job_kwargs)
-
-    node0 = DetectThresholdCrossing(
-        recording, detect_threshold=detect_threshold, noise_levels=noise_levels, seed=seed, **noise_levels_kwargs
-    )
-
-    threshold_crossings = run_node_pipeline(
-        recording,
-        [node0],
-        job_kwargs,
-        job_name="detect threshold crossings",
-    )
-
-    order = np.lexsort((threshold_crossings["sample_index"], threshold_crossings["segment_index"]))
-    threshold_crossings = threshold_crossings[order]
-
-    periods = []
-    fs = recording.sampling_frequency
-    max_duration_samples = int(min_duration_ms * fs / 1000)
-    num_seg = recording.get_num_segments()
-
-    for seg_index in range(num_seg):
-        sub_periods = []
-        mask = threshold_crossings["segment_index"] == seg_index
-        sub_thr = threshold_crossings[mask]
-        if len(sub_thr) > 0:
-            local_thr = np.zeros(1, dtype=np.dtype(base_peak_dtype + [("front", "bool")]))
-            if not sub_thr["front"][0]:
-                local_thr["sample_index"] = 0
-                local_thr["front"] = True
-                sub_thr = np.hstack((local_thr, sub_thr))
-            if sub_thr["front"][-1]:
-                local_thr["sample_index"] = recording.get_num_samples(seg_index)
-                local_thr["front"] = False
-                sub_thr = np.hstack((sub_thr, local_thr))
-
-            indices = np.flatnonzero(np.diff(sub_thr["front"]))
-            for i, j in zip(indices[:-1], indices[1:]):
-                if sub_thr["front"][i]:
-                    start = sub_thr["sample_index"][i]
-                    end = sub_thr["sample_index"][j]
-                    if end - start > max_duration_samples:
-                        sub_periods.append((start, end))
-
-        periods.append(sub_periods)
-
-    return periods, envelope
-
-
-class SilencedArtifactsRecording(SilencedPeriodsRecording):
-    """
-    Silence user-defined periods from recording extractor traces. The code will construct
-    an enveloppe of the recording (as a low pass filtered version of the traces) and detect
-    threshold crossings to identify the periods to silence. The periods are then silenced either
-    on a per channel basis or across all channels by replacing the values by zeros or by
-    adding gaussian noise with the same variance as the one in the recordings
-
-    Parameters
-    ----------
-    recording : RecordingExtractor
-        The recording extractor to silence putative artifacts
-    detect_threshold : float, default: 5
-        The threshold to detect artifacts. The threshold is computed as `detect_threshold * noise_level`
-    freq_max : float, default: 20
-        The maximum frequency for the low pass filter used
-    min_duration_ms : float, default: 50
-        The minimum duration for a threshold crossing to be considered as an artefact.
-    noise_levels : array
-        Noise levels if already computed
-    seed : int | None, default: None
-        Random seed for `get_noise_levels` and `NoiseGeneratorRecording`.
-        If none, `get_noise_levels` uses `seed=0` and `NoiseGeneratorRecording` generates a random seed using `numpy.random.default_rng`.
-    mode : "zeros" | "noise", default: "zeros"
-        Determines what periods are replaced by. Can be one of the following:
-
-        - "zeros": Artifacts are replaced by zeros.
-
-        - "noise": The periods are filled with a gaussion noise that has the
-                   same variance that the one in the recordings, on a per channel
-                   basis
-    **noise_levels_kwargs : Keyword arguments for `spikeinterface.core.get_noise_levels()` function
-
-    Returns
-    -------
-    silenced_recording : SilencedArtifactsRecording
-        The recording extractor after silencing detected artifacts
-    """
-
-    _precomputable_kwarg_names = ["list_periods"]
-
-    def __init__(
-        self,
-        recording,
-        detect_threshold=5,
-        verbose=False,
-        freq_max=20.0,
-        min_duration_ms=50,
-        mode="zeros",
-        noise_levels=None,
-        seed=None,
-        list_periods=None,
-        **noise_levels_kwargs,
-    ):
-
-        if list_periods is None:
-            list_periods, _ = detect_period_artifacts_by_envelope(
-                recording,
-                detect_threshold=detect_threshold,
-                min_duration_ms=min_duration_ms,
-                freq_max=freq_max,
-                seed=seed,
-                noise_levels=noise_levels,
-                **noise_levels_kwargs,
-            )
-
-            if verbose:
-                for i, periods in enumerate(list_periods):
-                    total_time = np.sum([end - start for start, end in periods])
-                    percentage = 100 * total_time / recording.get_num_samples(i)
-                    print(f"{percentage}% of segment {i} has been flagged as artifactual")
-
-        SilencedPeriodsRecording.__init__(
-            self, recording, list_periods, mode=mode, noise_levels=noise_levels, seed=seed, **noise_levels_kwargs
-        )
-
-
-# function for API
-silence_artifacts = define_function_handling_dict_from_class(
-    source_class=SilencedArtifactsRecording, name="silence_artifacts"
-)
diff --git a/src/spikeinterface/preprocessing/silence_periods.py b/src/spikeinterface/preprocessing/silence_periods.py
index c9b6e2abe4..040e1275be 100644
--- a/src/spikeinterface/preprocessing/silence_periods.py
+++ b/src/spikeinterface/preprocessing/silence_periods.py
@@ -8,6 +8,8 @@
 from spikeinterface.core import get_noise_levels
 from spikeinterface.core.generate import NoiseGeneratorRecording
 from spikeinterface.core.job_tools import split_job_kwargs
+from spikeinterface.core.base import base_period_dtype
+
 
 
 class SilencedPeriodsRecording(BasePreprocessor):
@@ -48,7 +50,9 @@ class SilencedPeriodsRecording(BasePreprocessor):
     def __init__(
         self,
         recording,
-        list_periods,
+        periods=None,
+        # this is keep for backward compatibility
+        list_periods=None,
         mode="zeros",
         noise_levels=None,
         seed=None,
@@ -56,25 +60,27 @@ def __init__(
     ):
         available_modes = ("zeros", "noise")
         num_seg = recording.get_num_segments()
-        if num_seg == 1:
-            if isinstance(list_periods, (list, np.ndarray)) and np.array(list_periods).ndim == 2:
-                # when unique segment accept list instead of list of list/arrays
-                list_periods = [list_periods]
-        # some checks
-        assert mode in available_modes, f"mode {mode} is not an available mode: {available_modes}"
 
-        assert isinstance(list_periods, list), "'list_periods' must be a list (one per segment)"
-        assert len(list_periods) == num_seg, "'list_periods' must have the same length as the number of segments"
-        assert all(
-            isinstance(list_periods[i], (list, np.ndarray)) for i in range(num_seg)
-        ), "Each element of 'list_periods' must be array-like"
+        # handle backward compatibility with previous version
+        if list_periods is not None:
+            assert periods is None
+            periods = _all_period_list_to_periods_vec(list_periods, num_seg)
+        else:
+            assert list_periods is None
+            if not isinstance(periods, np.ndarray):
+                raise ValueError(f"periods must be a np.array with dtype {base_period_dtype}")
+
+            if periods.dtype.fields is None:
+                # this is the old format : list[list[int]]
+                periods = _all_period_list_to_periods_vec(periods, num_seg)
 
-        for periods in list_periods:
-            if len(periods) > 0:
-                assert np.all(np.diff(np.array(periods), axis=1) > 0), "t_stops should be larger than t_starts"
-                assert np.all(
-                    periods[i][1] < periods[i + 1][0] for i in np.arange(len(periods) - 1)
-                ), "Intervals should not overlap"
+        # force order
+        order = np.lexsort((periods["start_sample_index"], periods["segment_index"]))
+        periods = periods[order]
+        _check_periods(periods, num_seg)
+
+        # some checks
+        assert mode in available_modes, f"mode {mode} is not an available mode: {available_modes}"
 
         if mode in ["noise"]:
             if noise_levels is None:
@@ -98,18 +104,57 @@ def __init__(
             noise_generator = None
 
         BasePreprocessor.__init__(self, recording)
+        
+        seg_limits = np.searchsorted(periods["segment_index"], np.arange(num_seg + 1))
         for seg_index, parent_segment in enumerate(recording._recording_segments):
-            periods = list_periods[seg_index]
-            periods = np.asarray(periods, dtype="int64")
-            periods = np.sort(periods, axis=0)
-            rec_segment = SilencedPeriodsRecordingSegment(parent_segment, periods, mode, noise_generator, seg_index)
+            i0 = seg_limits[seg_index]
+            i1 = seg_limits[seg_index+1]
+            periods_in_seg = periods[i0:i1]
+            rec_segment = SilencedPeriodsRecordingSegment(parent_segment, periods_in_seg, mode, noise_generator, seg_index)
             self.add_recording_segment(rec_segment)
 
         self._kwargs = dict(
-            recording=recording, list_periods=list_periods, mode=mode, seed=seed, noise_levels=noise_levels
+            recording=recording, periods=periods, mode=mode, seed=seed, noise_levels=noise_levels
         )
 
 
+def _all_period_list_to_periods_vec(list_periods, num_seg):
+    if num_seg == 1:
+        if isinstance(list_periods, (list, np.ndarray)) and np.array(list_periods).ndim == 2:
+            # when unique segment accept list instead of list of list/arrays
+            list_periods = [list_periods]
+    size = sum(len(p) for p in list_periods)
+    periods = np.zeros(size, dtype=base_period_dtype)
+    start = 0
+    for i in range(num_seg):
+        periods_in_seg = list_periods[i]
+        stop = start + periods_in_seg.shape[0]
+        periods[start:stop]["segment_index"] = i
+        periods[start:stop]["start_sample_index"] = periods_in_seg[:, 0]
+        periods[start:stop]["end_sample_index"] = periods_in_seg[:, 1]
+        start = stop
+    return periods
+
+def _check_periods(periods, num_seg):
+    # check dtype
+    if any(col not in np.dtype(base_period_dtype).fields for col in periods.dtype.fields):
+        raise ValueError(f"periods must be a np.array with dtype {base_period_dtype}")
+
+    # check non overlap and non negative
+    seg_limits = np.searchsorted(periods["segment_index"], np.arange(num_seg + 1))
+    for i in range(num_seg):
+        i0 = seg_limits[i]
+        i1 = seg_limits[i+1]
+        periods_in_seg = periods[i0:i1]
+        if periods_in_seg.size == 0:
+            continue
+        if len(periods) > 0:
+            if np.any(periods_in_seg["start_sample_index"] > periods_in_seg["end_sample_index"]):
+                raise ValueError("end_sample_index should be larger than start_sample_index")
+            if np.any(periods_in_seg["start_sample_index"][1:] <  periods_in_seg["end_sample_index"][:-1]):
+                raise ValueError("Intervals should not overlap")
+
+
 class SilencedPeriodsRecordingSegment(BasePreprocessorSegment):
     def __init__(self, parent_recording_segment, periods, mode, noise_generator, seg_index):
         BasePreprocessorSegment.__init__(self, parent_recording_segment)
@@ -120,18 +165,20 @@ def __init__(self, parent_recording_segment, periods, mode, noise_generator, seg
 
     def get_traces(self, start_frame, end_frame, channel_indices):
         traces = self.parent_recording_segment.get_traces(start_frame, end_frame, channel_indices)
-        traces = traces.copy()
+        
         if self.periods.size > 0:
             new_interval = np.array([start_frame, end_frame])
-            lower_index = np.searchsorted(self.periods[:, 1], new_interval[0])
-            upper_index = np.searchsorted(self.periods[:, 0], new_interval[1])
+            
+            lower_index = np.searchsorted(self.periods["end_sample_index"], new_interval[0])
+            upper_index = np.searchsorted(self.periods["start_sample_index"], new_interval[1])
 
             if upper_index > lower_index:
-                periods_in_interval = self.periods[lower_index:upper_index]
+                traces = traces.copy()
 
+                periods_in_interval = self.periods[lower_index:upper_index]
                 for period in periods_in_interval:
-                    onset = max(0, period[0] - start_frame)
-                    offset = min(period[1] - start_frame, end_frame)
+                    onset = max(0, period["start_sample_index"] - start_frame)
+                    offset = min(period["end_sample_index"] - start_frame, end_frame)
 
                     if self.mode == "zeros":
                         traces[onset:offset, :] = 0
@@ -143,8 +190,52 @@ def get_traces(self, start_frame, end_frame, channel_indices):
 
         return traces
 
-
 # function for API
 silence_periods = define_function_handling_dict_from_class(
     source_class=SilencedPeriodsRecording, name="silence_periods"
 )
+
+
+
+class DetectArtifactAndSilentPeriodsRecording(SilencedPeriodsRecording):
+    """
+    Class doing artifact detection and lient at the same time.
+
+    See SilencedPeriodsRecording and detect_artifact_periods for details.
+    """
+
+    _precomputable_kwarg_names = ["artifacts"]
+
+    def __init__(
+        self,
+        recording,
+        detect_artifact_method="envelope",
+        detect_artifact_kwargs=dict(),
+        periods=None,
+        mode="zeros",
+        noise_levels=None,
+        seed=None,
+        **noise_levels_kwargs,
+    ):
+
+        if artifacts is None:
+            from spikeinterface.preprocessing import detect_artifact_periods
+            artifacts = detect_artifact_periods(
+                recording,
+                method=detect_artifact_method,
+                method_kwargs=detect_artifact_kwargs,
+                job_kwargs=None,
+            )
+
+        SilencedPeriodsRecording.__init__(
+            self, recording, periods=artifacts, mode=mode, noise_levels=noise_levels, seed=seed, **noise_levels_kwargs
+        )
+        # note self._kwargs["periods"] is done by SilencedPeriodsRecording and so the computaion is done once
+
+
+
+# function for API
+detect_artifacts_and_silent_periods = define_function_handling_dict_from_class(
+    source_class=DetectArtifactAndSilentPeriodsRecording, name="silence_artifacts"
+)
+
diff --git a/src/spikeinterface/preprocessing/tests/test_detect_artifacts.py b/src/spikeinterface/preprocessing/tests/test_detect_artifacts.py
new file mode 100644
index 0000000000..b5d9a18a9b
--- /dev/null
+++ b/src/spikeinterface/preprocessing/tests/test_detect_artifacts.py
@@ -0,0 +1,125 @@
+from spikeinterface.core import generate_recording, NumpyRecording
+from spikeinterface.preprocessing import detect_artifact_periods, detect_saturation_periods
+import numpy as np
+
+
+def test_detect_artifact_periods():
+    # one segment only
+    rec = generate_recording(durations=[10.0, 10])
+    artifacts = detect_artifact_periods(rec, method="envelope", 
+                                        method_kwargs=dict(detect_threshold=5, freq_max=5.0),
+                                        )
+
+
+
+def test_detect_saturation_periods():
+
+    import scipy.signal
+    
+    """
+    TODO: NOTE: we have one sample before the saturation starts as we take the forward derivative for the velocity
+                we have an extra sample after due to taking the diff on the final saturation mask
+                this means we always take one sample before and one sample after the diff period, which is fine.
+    """
+    # num_chans = 384
+    num_chans = 32
+    sample_frequency = 30000
+    chunk_size = 30000  # This value is critical to ensure hard-coded start / stops below
+    job_kwargs = {"chunk_size": chunk_size}
+
+    # cross a chunk boundary. Do not change without changing the below.
+    sat_value = 1200
+    rng = np.random.default_rng()
+    data = rng.uniform(low=-0.5, high=0.5, size=(150000, num_chans)) * 10 * 1e-6
+
+    # Design the Butterworth filter
+    sos = scipy.signal.butter(N=3, Wn=12000 / (sample_frequency / 2), btype="low", output="sos")
+
+    # Apply the filter to the data
+    data_seg_1 = scipy.signal.sosfiltfilt(sos, data, axis=0)
+    data_seg_2 = data_seg_1.copy()
+
+    # Add test saturation at the start, end of recording
+    # as well as across and within chunks (30k samples).
+    # Two cases which are not tested are a single event
+    # exactly on the border, as it makes testing complex
+    # This was checked manually and any future breaking change
+    # on this function would be extremely unlikely only to break this case.
+    all_starts = np.array([0,    29950,  45123, 90005,  149500])
+    all_stops =  np.array([1001, 30011,  45126, 90006,  149999])
+
+    second_seg_offset = 1
+    for start, stop in zip(all_starts, all_stops):
+        data_seg_1[start : stop, :] = sat_value
+        # differentiate the second segment for testing purposes
+        data_seg_2[start : stop + second_seg_offset, :] = sat_value
+
+    # this center the int16 around 0 and saturate on positive
+    max_ = np.max(np.r_[data_seg_1.flatten(), data_seg_2.flatten()])
+    gain =  max_ / 2**15
+    offset = 0
+
+    seg_1_int16 = np.clip(
+        np.rint((data_seg_1 - offset) / gain),
+        -32768, 32767
+    ).astype(np.int16)
+    seg_2_int16 = np.clip(
+        np.rint((data_seg_2 - offset) / gain),
+        -32768, 32767
+    ).astype(np.int16)
+
+    # import matplotlib.pyplot as plt
+    # fig, ax = plt.subplots()
+    # ax.plot(seg_1_int16[:, 0])
+    # plt.show()
+
+    recording = NumpyRecording([seg_1_int16, seg_2_int16], sample_frequency)
+    recording.set_channel_gains(gain)
+    recording.set_channel_offsets([offset] * num_chans)
+
+    periods = detect_saturation_periods(
+        recording, saturation_threshold_uV=sat_value * 0.98, voltage_per_sec_threshold=1e-8, job_kwargs=job_kwargs
+    )
+
+    seg_1_periods = periods[np.where(periods["segment_index"] == 0)]
+    seg_2_periods = periods[np.where(periods["segment_index"] == 1)]
+
+    # For the start times, all are one sample before the actual saturated
+    # period starts because the derivative threshold is exceeded at one
+    # sample before the saturation starts. Therefore this one-sample-offset
+    # on the start times is an implicit test that the derivative
+    # threshold is working properly.
+    for seg_periods in [seg_1_periods, seg_2_periods]:
+        assert seg_periods["start_sample_index"][0] == all_starts[0]
+        assert np.array_equal(seg_periods["start_sample_index"][1:], np.array(all_starts)[1:] - 1)
+
+    assert np.array_equal(seg_1_periods["end_sample_index"], np.array(all_stops))
+    assert np.array_equal(seg_2_periods["end_sample_index"], np.array(all_stops) + second_seg_offset)
+
+    # Just do a quick test that a threshold slightly over the sat value is not detected.
+    # In this case we only see the derivative threshold detection. We do not play around with this
+    # threshold because the derivative threshold is not easy to predict (the baseline sample is random).
+    periods = detect_saturation_periods(
+        recording,
+        saturation_threshold_uV=sat_value * (1 / 0.98),
+        voltage_per_sec_threshold=1e-8,
+        job_kwargs=job_kwargs,
+    )
+    assert periods["start_sample_index"][0] == 1000
+    assert periods["end_sample_index"][0] == 1001
+
+    periods = detect_artifact_periods(
+        recording,
+        method="saturation",
+        method_kwargs=dict(
+                saturation_threshold_uV=sat_value * (1 / 0.98),
+                voltage_per_sec_threshold=1e-8,
+            ),
+            job_kwargs=job_kwargs,
+        )
+
+
+
+if __name__ == "__main__":
+    test_detect_artifact_periods()
+    test_detect_saturation_periods()
diff --git a/src/spikeinterface/preprocessing/tests/test_silence_artifacts.py b/src/spikeinterface/preprocessing/tests/test_silence_artifacts.py
deleted file mode 100644
index 2baa4bf1b3..0000000000
--- a/src/spikeinterface/preprocessing/tests/test_silence_artifacts.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import pytest
-
-import numpy as np
-
-from spikeinterface.core import generate_recording
-from spikeinterface.preprocessing import silence_artifacts
-
-
-def test_silence_artifacts():
-    # one segment only
-    rec = generate_recording(durations=[10.0, 10])
-    new_rec = silence_artifacts(rec, detect_threshold=5, freq_max=5.0, min_duration_ms=50)
-
-
-if __name__ == "__main__":
-    test_silence_artifacts()
diff --git a/src/spikeinterface/preprocessing/tests/test_silence.py b/src/spikeinterface/preprocessing/tests/test_silence_periods.py
similarity index 76%
rename from src/spikeinterface/preprocessing/tests/test_silence.py
rename to src/spikeinterface/preprocessing/tests/test_silence_periods.py
index e7aee1a84d..ffba9059a0 100644
--- a/src/spikeinterface/preprocessing/tests/test_silence.py
+++ b/src/spikeinterface/preprocessing/tests/test_silence_periods.py
@@ -1,11 +1,12 @@
 import pytest
 
 from spikeinterface.core import generate_recording
-
+from spikeinterface.core import get_noise_levels
+from spikeinterface.core.base import base_period_dtype
 from spikeinterface.preprocessing import silence_periods
 
 
-from spikeinterface.core import get_noise_levels
+
 
 import numpy as np
 
@@ -18,17 +19,20 @@ def test_silence(create_cache_folder):
 
     rec = generate_recording()
 
-    rec0 = silence_periods(rec, list_periods=[[[0, 1000], [5000, 6000]], []], mode="zeros", seed=2308)
-    rec0.save(verbose=False)
+    periods = np.array([(0, 0, 1000), (0, 5000, 6000)], dtype=base_period_dtype)
+    rec0 = silence_periods(rec, periods=periods, mode="zeros", seed=2308)
+    rec0.save(format="memory", verbose=False)
     traces_in0 = rec0.get_traces(segment_index=0, start_frame=0, end_frame=1000)
-    traces_in1 = rec0.get_traces(segment_index=0, start_frame=5000, end_frame=6000)
-    traces_out0 = rec0.get_traces(segment_index=0, start_frame=2000, end_frame=3000)
     assert np.all(traces_in0 == 0)
+    traces_half0 = rec0.get_traces(segment_index=0, start_frame=900, end_frame=1100)
+    assert np.all(traces_half0[:100] == 0)
+    traces_in1 = rec0.get_traces(segment_index=0, start_frame=5000, end_frame=6000)
     assert np.all(traces_in1 == 0)
+    traces_out0 = rec0.get_traces(segment_index=0, start_frame=2000, end_frame=3000)
     assert not np.all(traces_out0 == 0)
 
-    rec1 = silence_periods(rec, list_periods=[[[0, 1000], [5000, 6000]], []], mode="noise", seed=2308)
-    rec1 = rec1.save(folder=cache_folder / "rec_w_noise", verbose=False, overwrite=True)
+    rec1 = silence_periods(rec, periods=periods, mode="noise", seed=2308)
+    rec1 = rec1.save(format="memory", verbose=False, overwrite=True)
     noise_levels = get_noise_levels(rec, return_in_uV=False)
     traces_in0 = rec1.get_traces(segment_index=0, start_frame=0, end_frame=1000)
     traces_in1 = rec1.get_traces(segment_index=0, start_frame=5000, end_frame=6000)