{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Artifact Removal"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Mean Subtraction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from vitalDSP.filtering.artifact_removal import ArtifactRemoval\n",
        "import plotly.io as pio\n",
        "pio.renderers.default = \"sphinx_gallery\"\n",
        "# pio.renderers.default = \"plotly_mimetype\"  # or \"plotly_mimetype\"\n",
        "# from IPython.display import display, HTML\n",
        "# display(HTML('<script src=\"https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.6/require.min.js\"></script>'))\n",
        "import numpy as np\n",
        "from plotly import graph_objects as go\n",
        "import os\n",
        "from vitalDSP.notebooks import load_sample_ecg_small, plot_trace\n",
        "\n",
        "\n",
        "signal_col, date_col = load_sample_ecg_small()\n",
        "signal_col = np.array(signal_col)\n",
        "\n",
        "ar = ArtifactRemoval(signal_col)\n",
        "cleaned_signal = ar.mean_subtraction()\n",
        "\n",
        "plot_trace(signal_col,cleaned_signal)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Baseline Correction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from vitalDSP.filtering.artifact_removal import ArtifactRemoval\n",
        "import numpy as np\n",
        "from plotly import graph_objects as go\n",
        "import os\n",
        "from vitalDSP.notebooks import load_sample_ecg_small, plot_trace\n",
        "\n",
        "fs = 128\n",
        "signal_col, date_col = load_sample_ecg_small()\n",
        "signal_col = np.array(signal_col)\n",
        "\n",
        "ar = ArtifactRemoval(signal_col)\n",
        "cleaned_signal = ar.baseline_correction(cutoff=0.5, fs=fs)\n",
        "\n",
        "plot_trace(signal_col,cleaned_signal)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Wavelet Denoising"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from vitalDSP.filtering.artifact_removal import ArtifactRemoval\n",
        "import numpy as np\n",
        "from plotly import graph_objects as go\n",
        "import os\n",
        "from vitalDSP.notebooks import load_sample_ecg_small, plot_trace\n",
        "\n",
        "fs = 128\n",
        "signal_col, date_col = load_sample_ecg_small()\n",
        "signal_col = np.array(signal_col)\n",
        "\n",
        "ar = ArtifactRemoval(signal_col)\n",
        "\n",
        "cleaned_signal = ar.wavelet_denoising(\n",
        "    wavelet_type='db', \n",
        "    level=2, \n",
        "    order=4, \n",
        "    smoothing='gaussian', \n",
        "    sigma=1.0  # This will now be passed correctly through smoothing_params\n",
        ")\n",
        "plot_trace(signal_col,np.real(cleaned_signal),title=\"Debauchy & Gaussian\")\n",
        "\n",
        "clean_signal = ar.wavelet_denoising(wavelet_type='haar', level=3, \n",
        "                                    smoothing='median', kernel_size=5)\n",
        "plot_trace(signal_col,np.real(cleaned_signal),title=\"Haar & Median\")\n",
        "\n",
        "clean_signal = ar.wavelet_denoising(wavelet_type='sym', level=3, \n",
        "                                    smoothing='moving_average', window_size=7)\n",
        "plot_trace(signal_col,np.real(cleaned_signal),title=\"Symmetric & MAE\")\n",
        "\n",
        "custom_wavelet = np.array([0.1,0.2,0.4,0.2,0.1,1,-0.5,0.1,0.2,0.5,0.2,0.2])\n",
        "clean_signal = ar.wavelet_denoising(wavelet_type='custom', \n",
        "                                    custom_wavelet=custom_wavelet, \n",
        "                                    smoothing='lowpass', cutoff=0.3, fs=fs)\n",
        "plot_trace(signal_col,np.real(cleaned_signal),title=\"Custom & Low-pass\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Adaptive Filtering"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from vitalDSP.filtering.artifact_removal import ArtifactRemoval\n",
        "import numpy as np\n",
        "from plotly import graph_objects as go\n",
        "import os\n",
        "from vitalDSP.utils.data_processing.synthesize_data import generate_ecg_signal\n",
        "from vitalDSP.notebooks import load_sample_ecg_small, plot_trace\n",
        "\n",
        "fs = 128\n",
        "signal_col, date_col = load_sample_ecg_small()\n",
        "signal_col = np.array(signal_col)\n",
        "\n",
        "offset = 1000\n",
        "target = generate_ecg_signal(sfecg=128, duration=100, Anoise=0.01, hrmean=70, sfint=512)\n",
        "reference_signal = target[offset:min(len(target),len(signal_col))]\n",
        "\n",
        "signal_col = signal_col[offset:min(len(target),len(signal_col))]\n",
        "ar = ArtifactRemoval(signal_col)\n",
        "\n",
        "cleaned_signal = ar.adaptive_filtering(reference_signal, learning_rate=0.01, num_iterations=50)\n",
        "\n",
        "plot_trace(signal_col,cleaned_signal)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Notch Filter"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from vitalDSP.filtering.artifact_removal import ArtifactRemoval\n",
        "import numpy as np\n",
        "from plotly import graph_objects as go\n",
        "import os\n",
        "from vitalDSP.notebooks import load_sample_ecg_small, plot_trace\n",
        "\n",
        "fs = 128\n",
        "signal_col, date_col = load_sample_ecg_small()\n",
        "signal_col = np.array(signal_col)\n",
        "\n",
        "ar = ArtifactRemoval(signal_col)\n",
        "cleaned_signal = ar.notch_filter(freq=5, fs=fs, Q=2)\n",
        "\n",
        "plot_trace(signal_col,cleaned_signal)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## PCA Artifact Removal"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from vitalDSP.filtering.artifact_removal import ArtifactRemoval\n",
        "import numpy as np\n",
        "from plotly import graph_objects as go\n",
        "import os\n",
        "from vitalDSP.notebooks import load_sample_ecg_small, plot_trace\n",
        "\n",
        "fs = 128\n",
        "signal_col, date_col = load_sample_ecg_small()\n",
        "signal_col = np.array(signal_col)\n",
        "\n",
        "ar = ArtifactRemoval(signal_col)\n",
        "cleaned_signal = ar.pca_artifact_removal(num_components=1,window_size=5, overlap=1) #QRS T Components\n",
        "\n",
        "plot_trace(signal_col,cleaned_signal)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## ICA Artifact Removal"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from vitalDSP.filtering.artifact_removal import ArtifactRemoval\n",
        "import numpy as np\n",
        "from plotly import graph_objects as go\n",
        "import os\n",
        "from vitalDSP.notebooks import load_sample_ecg_small, plot_trace\n",
        "\n",
        "fs = 128\n",
        "signal_col, date_col = load_sample_ecg_small()\n",
        "signal_col = np.array(signal_col)\n",
        "\n",
        "ar = ArtifactRemoval(signal_col)\n",
        "cleaned_signal = ar.ica_artifact_removal(num_components=1, window_size=3, \n",
        "                                        step_size=1, batch_size=200)\n",
        "\n",
        "plot_trace(signal_col,cleaned_signal)"
      ]
    }
  ],
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      "display_name": "wearables",
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