pcg_model_for_ecg_Myocardial_Infarction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "pcg model for ecg_Myocardial Infarction.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/M-torki/ECG-Classification/blob/main/pcg_model_for_ecg_Myocardial_Infarction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4cbgwZWWfWpp"
      },
      "source": [
        "# New Section"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2AxXHsKjdttT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "07cfb57b-aa9c-4965-9710-4c25213a1f87"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "99bnoATSeEQM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d0c79d43-1162-4702-82e3-f0bea485c2eb"
      },
      "source": [
        "cd drive/MyDrive/"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "igt0MZSDeEXU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1596a8c3-1c47-41bd-edf7-e1a0e2806d33"
      },
      "source": [
        "cd physionet.org/files/ptbdb/1.0.0/"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/physionet.org/files/ptbdb/1.0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tK7irkIkeEkQ"
      },
      "source": [
        "# import biosppy\n",
        "# import neurokit2 as nk\n",
        "# import wfdb\n",
        "# # from hrvanalysis import remove_outliers, remove_ectopic_beats, interpolate_nan_values\n",
        "# from wfdb import processing\n",
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import pickle\n",
        "import matplotlib.pyplot as plt\n",
        "from glob import glob\n",
        "%matplotlib inline\n",
        "import scipy\n",
        "import shutil\n",
        "from IPython.display import display \n",
        "import keras\n",
        "from keras import optimizers, losses, activations, models\n",
        "from keras.layers import Dense, BatchNormalization, Input, Activation, Dropout, Convolution1D, Conv1D, MaxPool1D , MaxPooling1D, GlobalMaxPool1D, GlobalAveragePooling1D, concatenate, MaxPool2D\n",
        "from keras.preprocessing import image\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D , Lambda, GaussianNoise\n",
        "from keras.utils.np_utils import to_categorical\n",
        "from keras.optimizers import Adam , Adadelta , Adagrad , SGD\n",
        "from keras import regularizers\n",
        "import keras.backend as K\n",
        "\n",
        "\n",
        "\n",
        "# from biosppy.signals import ecg"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gKgbEoL5eIKN",
        "outputId": "95e71106-76b5-45d2-b1b5-fec6c4b872cb"
      },
      "source": [
        "train_hel = sorted(glob('./New_data/train/hel/*.csv'))\n",
        "train_inf = sorted(glob('./New_data/train/inf/*.csv'))\n",
        "test_hel = sorted(glob('./New_data/test/hel/*.csv'))\n",
        "test_inf = sorted(glob('./New_data/test/inf/*.csv'))\n",
        "\n",
        "print(len(train_hel) , len(train_inf) , len(test_hel) , len(test_inf))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "928 3004 240 655\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8kMbu_noe406"
      },
      "source": [
        "y_tr_h = np.zeros(shape=len(train_hel))\n",
        "y_tr_i = np.ones(shape=len(train_inf))\n",
        "y_te_h = np.zeros(shape=len(test_hel))\n",
        "y_te_i = np.ones(shape=len(test_inf))"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sYqcGyCagS-3",
        "outputId": "6f2cce3d-e626-4db4-d593-036ad89fd510"
      },
      "source": [
        "y_tr_h[:10]"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "gzyJgrvJictP",
        "outputId": "d6875c3a-4c2b-4fa7-c457-7f2f3c2ae80a"
      },
      "source": [
        "df = pd.read_csv(train_hel[0] , index_col=0)\n",
        "df[:5]"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>i</th>\n",
              "      <th>ii</th>\n",
              "      <th>iii</th>\n",
              "      <th>avr</th>\n",
              "      <th>avl</th>\n",
              "      <th>avf</th>\n",
              "      <th>v1</th>\n",
              "      <th>v2</th>\n",
              "      <th>v3</th>\n",
              "      <th>v4</th>\n",
              "      <th>v5</th>\n",
              "      <th>v6</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0310</td>\n",
              "      <td>-1.1485</td>\n",
              "      <td>-1.1795</td>\n",
              "      <td>0.5585</td>\n",
              "      <td>0.6055</td>\n",
              "      <td>-1.1640</td>\n",
              "      <td>0.1660</td>\n",
              "      <td>0.2580</td>\n",
              "      <td>0.5730</td>\n",
              "      <td>0.1290</td>\n",
              "      <td>-0.3825</td>\n",
              "      <td>0.0120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0290</td>\n",
              "      <td>-1.1485</td>\n",
              "      <td>-1.1775</td>\n",
              "      <td>0.5595</td>\n",
              "      <td>0.6035</td>\n",
              "      <td>-1.1630</td>\n",
              "      <td>0.1675</td>\n",
              "      <td>0.2585</td>\n",
              "      <td>0.5735</td>\n",
              "      <td>0.1280</td>\n",
              "      <td>-0.3835</td>\n",
              "      <td>0.0140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0300</td>\n",
              "      <td>-1.1450</td>\n",
              "      <td>-1.1750</td>\n",
              "      <td>0.5570</td>\n",
              "      <td>0.6030</td>\n",
              "      <td>-1.1595</td>\n",
              "      <td>0.1655</td>\n",
              "      <td>0.2585</td>\n",
              "      <td>0.5760</td>\n",
              "      <td>0.1290</td>\n",
              "      <td>-0.3810</td>\n",
              "      <td>0.0150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0275</td>\n",
              "      <td>-1.1475</td>\n",
              "      <td>-1.1750</td>\n",
              "      <td>0.5595</td>\n",
              "      <td>0.6015</td>\n",
              "      <td>-1.1610</td>\n",
              "      <td>0.1620</td>\n",
              "      <td>0.2590</td>\n",
              "      <td>0.5760</td>\n",
              "      <td>0.1280</td>\n",
              "      <td>-0.3820</td>\n",
              "      <td>0.0160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0295</td>\n",
              "      <td>-1.1490</td>\n",
              "      <td>-1.1785</td>\n",
              "      <td>0.5595</td>\n",
              "      <td>0.6045</td>\n",
              "      <td>-1.1635</td>\n",
              "      <td>0.1620</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.5770</td>\n",
              "      <td>0.1275</td>\n",
              "      <td>-0.3820</td>\n",
              "      <td>0.0155</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        i      ii     iii     avr  ...      v3      v4      v5      v6\n",
              "0  0.0310 -1.1485 -1.1795  0.5585  ...  0.5730  0.1290 -0.3825  0.0120\n",
              "1  0.0290 -1.1485 -1.1775  0.5595  ...  0.5735  0.1280 -0.3835  0.0140\n",
              "2  0.0300 -1.1450 -1.1750  0.5570  ...  0.5760  0.1290 -0.3810  0.0150\n",
              "3  0.0275 -1.1475 -1.1750  0.5595  ...  0.5760  0.1280 -0.3820  0.0160\n",
              "4  0.0295 -1.1490 -1.1785  0.5595  ...  0.5770  0.1275 -0.3820  0.0155\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "usoZpjNDOLty"
      },
      "source": [
        "df = []"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lp9hi7LBhwF1",
        "outputId": "a3201c6e-9045-44f9-8ceb-e760313813c6"
      },
      "source": [
        "x_train = []\n",
        "\n",
        "for i in range(len(train_hel)):\n",
        "    s = pd.read_csv(train_hel[i] , index_col=0)\n",
        "    s = (s-np.mean(s))/(np.std(s)+1e-30)\n",
        "    x_train.append(s)\n",
        "\n",
        "for i in range(len(train_inf)):\n",
        "    s = pd.read_csv(train_inf[i] , index_col=0)\n",
        "    s = (s-np.mean(s))/(np.std(s)+1e-30)\n",
        "    x_train.append(s)\n",
        "\n",
        "x_train = np.array(x_train)\n",
        "x_train.shape"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3932, 8000, 12)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WZeBwAvugVxQ",
        "outputId": "f5a7ba68-4360-40bd-dcab-01ab0fe0bb79"
      },
      "source": [
        "y_train = np.concatenate((y_tr_h , y_tr_i) , axis=0)\n",
        "y_train.shape"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3932,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oaEpze3Yl9lY",
        "outputId": "052687f8-dfe8-403f-e9bf-b5e0f441ea79"
      },
      "source": [
        "x_test = []\n",
        "\n",
        "for i in range(len(test_hel)):\n",
        "    s = pd.read_csv(test_hel[i] , index_col=0)\n",
        "    s = (s-np.mean(s))/(np.std(s)+1e-30)\n",
        "    x_test.append(s)\n",
        "\n",
        "for i in range(len(test_inf)):\n",
        "    s = pd.read_csv(test_inf[i] , index_col=0)\n",
        "    s = (s-np.mean(s))/(np.std(s)+1e-30)\n",
        "    x_test.append(s)\n",
        "\n",
        "x_test = np.array(x_test)\n",
        "x_test.shape"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(895, 8000, 12)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lqX7MHNbo3aD",
        "outputId": "ffa0f28e-ff06-4ed1-a22b-7befefb6adac"
      },
      "source": [
        "y_test = np.concatenate((y_te_h , y_te_i) , axis=0)\n",
        "y_test.shape"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(895,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "upGcGAvAvKvB"
      },
      "source": [
        "x_train = np.expand_dims(x_train , -1)\n",
        "x_test = np.expand_dims(x_test , -1)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kZFeMYZfoUJX"
      },
      "source": [
        "# x_train = x_train.reshape(3932, 8000 , 12 , 1)\n",
        "# x_test = x_test.reshape(895, 8000 , 12 , 1)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PrGtPs0Kplwu",
        "outputId": "c3b8b7c9-1e1a-4a01-a74d-f459c31ae6d5"
      },
      "source": [
        "x_train = np.concatenate((x_train , x_train[:928] , x_train[:928] , x_train[:928] , x_train[928:928+(4*928-3004)]) , axis=0 )\n",
        "x_train.shape"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7424, 8000, 12, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s4nE6fz3xEuW",
        "outputId": "63cc7986-6892-4ce7-c6c3-aa75791ad3b4"
      },
      "source": [
        "y_train = np.concatenate((y_train , y_train[:928] , y_train[:928] , y_train[:928] , y_train[928:928+(4*928-3004)]) , axis=0 )\n",
        "y_train.shape"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7424,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vIbgS4m9xoYr",
        "outputId": "5374db5d-8074-4a94-a62c-88e43adcd305"
      },
      "source": [
        "y_train = to_categorical(y_train)\n",
        "y_test = to_categorical(y_test)\n",
        "print(y_train[0])"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1. 0.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Ktvb4JqFF-g"
      },
      "source": [
        "import random \n",
        "\n",
        "temp = list(zip(x_test, y_test))\n",
        "random.shuffle(temp)\n",
        "x_test, y_test = zip(*temp)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v2Uo_Y83FR7F"
      },
      "source": [
        "temp = list(zip(x_train, y_train))\n",
        "random.shuffle(temp)\n",
        "x_train, y_train = zip(*temp)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8MFzFpDOHA7t",
        "outputId": "2c3dfc4b-e189-45e0-dc9f-2ccc3066cda3"
      },
      "source": [
        "x_train = np.array(x_train)\n",
        "x_train.shape"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7424, 8000, 12, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mRSZyr_zHwO8",
        "outputId": "d92f4758-ed2c-4649-914c-37dd69b763ef"
      },
      "source": [
        "y_train = np.array(y_train)\n",
        "y_train.shape"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7424, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bW2S3SjnIROT",
        "outputId": "a33b8091-a1ac-4803-9863-5248e62ae614"
      },
      "source": [
        "x_test = np.array(x_test)\n",
        "x_test.shape\n",
        "\n",
        "y_test = np.array(y_test)\n",
        "y_test.shape"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(895, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1KtOq6trGzl3",
        "outputId": "f8d74fe3-75ed-4bae-8b4d-88b912216ef7"
      },
      "source": [
        "y_train[:10]"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 1.],\n",
              "       [0., 1.],\n",
              "       [1., 0.],\n",
              "       [1., 0.],\n",
              "       [1., 0.],\n",
              "       [0., 1.],\n",
              "       [0., 1.],\n",
              "       [1., 0.],\n",
              "       [1., 0.],\n",
              "       [0., 1.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rZMM4T24tWzR"
      },
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(8, (3,3),strides = (1,1),kernel_initializer='glorot_uniform'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(keras.layers.ELU())\n",
        "\n",
        "model.add(Conv2D(8, (3,3),strides = (2,1),kernel_initializer='glorot_uniform'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(keras.layers.ELU())\n",
        "\n",
        "model.add(MaxPool2D(pool_size=(2, 1), strides= (2,1)))\n",
        "\n",
        "model.add(Conv2D(16, (3,3),strides = (2,1),kernel_initializer='glorot_uniform'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(keras.layers.ELU())\n",
        "\n",
        "\n",
        "model.add(Conv2D(32, (3,3),strides = (2,1),kernel_initializer='glorot_uniform'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(keras.layers.ELU())\n",
        "\n",
        "model.add(MaxPool2D(pool_size=(2, 1), strides= (2,1)))\n",
        "\n",
        "model.add(Conv2D(32, (3,3),strides = (2,1),kernel_initializer='glorot_uniform'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(keras.layers.ELU())\n",
        "\n",
        "\n",
        "# model.add(Conv2D(256, (3,3),strides = (1,1),kernel_initializer='glorot_uniform'))\n",
        "# model.add(BatchNormalization())\n",
        "# model.add(keras.layers.ELU())\n",
        "\n",
        "model.add(MaxPool2D(pool_size=(2, 2), strides= (2,2)))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(32))\n",
        "model.add(keras.layers.ELU())\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(2, activation='sigmoid'))\n",
        "\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vLCkD_Ee2Og4"
      },
      "source": [
        "m = Sequential()\n",
        "m.add(GaussianNoise(0.1))\n",
        "m.add(Conv1D(16,\n",
        "                input_shape=[8000, 12],\n",
        "                kernel_size=80,\n",
        "                strides=4,\n",
        "                padding='same',\n",
        "                kernel_initializer='glorot_uniform',\n",
        "                kernel_regularizer=regularizers.l2(l=0.0001)))\n",
        "m.add(BatchNormalization())\n",
        "m.add(Activation('relu'))\n",
        "m.add(MaxPooling1D(pool_size=4, strides=None))\n",
        "\n",
        "m.add(Conv1D(24,\n",
        "                kernel_size=3,\n",
        "                strides=1,\n",
        "                padding='same',\n",
        "                kernel_initializer='glorot_uniform',\n",
        "                kernel_regularizer=regularizers.l2(l=0.0001)))\n",
        "m.add(BatchNormalization())\n",
        "m.add(Activation('relu'))\n",
        "m.add(MaxPooling1D(pool_size=4, strides=None))\n",
        "\n",
        "m.add(Conv1D(48,\n",
        "                kernel_size=3,\n",
        "                strides=1,\n",
        "                padding='same',\n",
        "                kernel_initializer='glorot_uniform',\n",
        "                kernel_regularizer=regularizers.l2(l=0.0001)))\n",
        "m.add(BatchNormalization())\n",
        "m.add(Activation('relu'))\n",
        "m.add(MaxPooling1D(pool_size=4, strides=None))\n",
        "\n",
        "m.add(Conv1D(96,\n",
        "                kernel_size=3,\n",
        "                strides=1,\n",
        "                padding='same',\n",
        "                kernel_initializer='glorot_uniform',\n",
        "                kernel_regularizer=regularizers.l2(l=0.0001)))\n",
        "m.add(BatchNormalization())\n",
        "m.add(Activation('relu'))\n",
        "m.add(MaxPooling1D(pool_size=4, strides=None))\n",
        "\n",
        "m.add(Lambda(lambda x: K.mean(x, axis=1)))  # Same as GAP for 1D Conv Layer\n",
        "# m.add(Flatten())\n",
        "# m.add(Dropout(0.5))\n",
        "# m.add(Dense(16, activation='relu'))\n",
        "\n",
        "m.add(Dropout(0.5))\n",
        "m.add(Dense(2, activation='sigmoid'))"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HtArStAYEFVV"
      },
      "source": [
        "m.build(input_shape=(None, 8000, 12))"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BLmXEl2VzHfv",
        "outputId": "df1cee40-e8fe-4da8-efde-fac6721e8c7e"
      },
      "source": [
        "m.summary()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "gaussian_noise (GaussianNois (None, 8000, 12)          0         \n",
            "_________________________________________________________________\n",
            "conv1d (Conv1D)              (None, 2000, 16)          15376     \n",
            "_________________________________________________________________\n",
            "batch_normalization (BatchNo (None, 2000, 16)          64        \n",
            "_________________________________________________________________\n",
            "activation (Activation)      (None, 2000, 16)          0         \n",
            "_________________________________________________________________\n",
            "max_pooling1d (MaxPooling1D) (None, 500, 16)           0         \n",
            "_________________________________________________________________\n",
            "conv1d_1 (Conv1D)            (None, 500, 24)           1176      \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 500, 24)           96        \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 500, 24)           0         \n",
            "_________________________________________________________________\n",
            "max_pooling1d_1 (MaxPooling1 (None, 125, 24)           0         \n",
            "_________________________________________________________________\n",
            "conv1d_2 (Conv1D)            (None, 125, 48)           3504      \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 125, 48)           192       \n",
            "_________________________________________________________________\n",
            "activation_2 (Activation)    (None, 125, 48)           0         \n",
            "_________________________________________________________________\n",
            "max_pooling1d_2 (MaxPooling1 (None, 31, 48)            0         \n",
            "_________________________________________________________________\n",
            "conv1d_3 (Conv1D)            (None, 31, 96)            13920     \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 31, 96)            384       \n",
            "_________________________________________________________________\n",
            "activation_3 (Activation)    (None, 31, 96)            0         \n",
            "_________________________________________________________________\n",
            "max_pooling1d_3 (MaxPooling1 (None, 7, 96)             0         \n",
            "_________________________________________________________________\n",
            "lambda (Lambda)              (None, 96)                0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 96)                0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 2)                 194       \n",
            "=================================================================\n",
            "Total params: 34,906\n",
            "Trainable params: 34,538\n",
            "Non-trainable params: 368\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pafL7Li0jyXW"
      },
      "source": [
        "m.compile(optimizer=Adadelta(learning_rate=0.0001),\n",
        "                loss='binary_crossentropy',\n",
        "                metrics=['accuracy'])"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1NkEOlTOwTWS",
        "outputId": "c0de2433-d22c-4716-dcd1-f626b2cc7390"
      },
      "source": [
        "# reduce_lr = ReduceLROnPlateau(monitor='val_acc', factor=0.5, patience=10, min_lr=0.0001, verbose=1)\n",
        "batch_size = 32\n",
        "\n",
        "m.fit(x=x_train,\n",
        "        y=y_train,\n",
        "        batch_size=batch_size,\n",
        "        epochs=30,\n",
        "        verbose=1,\n",
        "        validation_split = 0.2,\n",
        ")\n",
        "        # callbacks=[reduce_lr])"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "186/186 [==============================] - 5s 18ms/step - loss: 0.2575 - accuracy: 0.9156 - val_loss: 0.1670 - val_accuracy: 0.9502\n",
            "Epoch 2/30\n",
            "186/186 [==============================] - 3s 14ms/step - loss: 0.2532 - accuracy: 0.9201 - val_loss: 0.1680 - val_accuracy: 0.9522\n",
            "Epoch 3/30\n",
            "186/186 [==============================] - 3s 14ms/step - loss: 0.2553 - accuracy: 0.9185 - val_loss: 0.1674 - val_accuracy: 0.9515\n",
            "Epoch 4/30\n",
            "186/186 [==============================] - 3s 16ms/step - loss: 0.2569 - accuracy: 0.9198 - val_loss: 0.1670 - val_accuracy: 0.9502\n",
            "Epoch 5/30\n",
            "186/186 [==============================] - 3s 15ms/step - loss: 0.2521 - accuracy: 0.9231 - val_loss: 0.1669 - val_accuracy: 0.9515\n",
            "Epoch 6/30\n",
            "186/186 [==============================] - 3s 15ms/step - loss: 0.2516 - accuracy: 0.9260 - val_loss: 0.1677 - val_accuracy: 0.9522\n",
            "Epoch 7/30\n",
            "186/186 [==============================] - 3s 15ms/step - loss: 0.2597 - accuracy: 0.9172 - val_loss: 0.1667 - val_accuracy: 0.9515\n",
            "Epoch 8/30\n",
            "186/186 [==============================] - 3s 15ms/step - loss: 0.2559 - accuracy: 0.9226 - val_loss: 0.1665 - val_accuracy: 0.9515\n",
            "Epoch 9/30\n",
            "186/186 [==============================] - 3s 15ms/step - loss: 0.2492 - accuracy: 0.9234 - val_loss: 0.1664 - val_accuracy: 0.9515\n",
            "Epoch 10/30\n",
            "186/186 [==============================] - 3s 15ms/step - loss: 0.2426 - accuracy: 0.9320 - val_loss: 0.1657 - val_accuracy: 0.9515\n",
            "Epoch 11/30\n",
            "186/186 [==============================] - 3s 15ms/step - loss: 0.2557 - accuracy: 0.9194 - val_loss: 0.1662 - val_accuracy: 0.9522\n",
            "Epoch 12/30\n",
            "186/186 [==============================] - 3s 15ms/step - loss: 0.2576 - accuracy: 0.9184 - val_loss: 0.1657 - val_accuracy: 0.9515\n",
            "Epoch 13/30\n",
            "186/186 [==============================] - 3s 15ms/step - loss: 0.2556 - accuracy: 0.9137 - val_loss: 0.1659 - val_accuracy: 0.9522\n",
            "Epoch 14/30\n",
            "186/186 [==============================] - 3s 14ms/step - loss: 0.2459 - accuracy: 0.9221 - val_loss: 0.1658 - val_accuracy: 0.9522\n",
            "Epoch 15/30\n",
            "186/186 [==============================] - 3s 15ms/step - loss: 0.2593 - accuracy: 0.9173 - val_loss: 0.1651 - val_accuracy: 0.9502\n",
            "Epoch 16/30\n",
            "186/186 [==============================] - 3s 15ms/step - loss: 0.2472 - accuracy: 0.9261 - val_loss: 0.1657 - val_accuracy: 0.9522\n",
            "Epoch 17/30\n",
            "186/186 [==============================] - 3s 15ms/step - loss: 0.2476 - accuracy: 0.9253 - val_loss: 0.1647 - val_accuracy: 0.9515\n",
            "Epoch 18/30\n",
            "186/186 [==============================] - 3s 15ms/step - loss: 0.2517 - accuracy: 0.9165 - val_loss: 0.1648 - val_accuracy: 0.9515\n",
            "Epoch 19/30\n",
            "186/186 [==============================] - 3s 15ms/step - loss: 0.2546 - accuracy: 0.9205 - val_loss: 0.1643 - val_accuracy: 0.9515\n",
            "Epoch 20/30\n",
            "186/186 [==============================] - 3s 15ms/step - loss: 0.2467 - accuracy: 0.9253 - val_loss: 0.1647 - val_accuracy: 0.9522\n",
            "Epoch 21/30\n",
            "186/186 [==============================] - 3s 15ms/step - loss: 0.2417 - accuracy: 0.9275 - val_loss: 0.1652 - val_accuracy: 0.9529\n",
            "Epoch 22/30\n",
            "186/186 [==============================] - 3s 14ms/step - loss: 0.2631 - accuracy: 0.9150 - val_loss: 0.1648 - val_accuracy: 0.9522\n",
            "Epoch 23/30\n",
            "186/186 [==============================] - 3s 15ms/step - loss: 0.2537 - accuracy: 0.9210 - val_loss: 0.1639 - val_accuracy: 0.9515\n",
            "Epoch 24/30\n",
            "186/186 [==============================] - 3s 15ms/step - loss: 0.2521 - accuracy: 0.9231 - val_loss: 0.1642 - val_accuracy: 0.9515\n",
            "Epoch 25/30\n",
            "186/186 [==============================] - 3s 15ms/step - loss: 0.2540 - accuracy: 0.9196 - val_loss: 0.1640 - val_accuracy: 0.9522\n",
            "Epoch 26/30\n",
            "186/186 [==============================] - 3s 15ms/step - loss: 0.2489 - accuracy: 0.9177 - val_loss: 0.1638 - val_accuracy: 0.9515\n",
            "Epoch 27/30\n",
            "186/186 [==============================] - 3s 14ms/step - loss: 0.2525 - accuracy: 0.9187 - val_loss: 0.1640 - val_accuracy: 0.9529\n",
            "Epoch 28/30\n",
            "186/186 [==============================] - 3s 14ms/step - loss: 0.2542 - accuracy: 0.9163 - val_loss: 0.1639 - val_accuracy: 0.9529\n",
            "Epoch 29/30\n",
            "186/186 [==============================] - 3s 14ms/step - loss: 0.2542 - accuracy: 0.9204 - val_loss: 0.1632 - val_accuracy: 0.9515\n",
            "Epoch 30/30\n",
            "186/186 [==============================] - 3s 14ms/step - loss: 0.2453 - accuracy: 0.9240 - val_loss: 0.1635 - val_accuracy: 0.9529\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f32482b7a90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w7NNq3DI-a1o",
        "outputId": "1c0b4b28-3894-4ddf-94d8-cdda841ed9d5"
      },
      "source": [
        "m.evaluate(x_test , y_test)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "28/28 [==============================] - 0s 7ms/step - loss: 0.1453 - accuracy: 0.9732\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.14527316391468048, 0.97318434715271]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "b5u8Y4idw8Nd",
        "outputId": "58c1ea5a-fd02-4976-f339-d4b384775285"
      },
      "source": [
        "pred = m.predict(x_test)\n",
        "pr = [np.argmax(pred[i]) for i in range(len(y_test))]\n",
        "Y = [np.argmax(y_test[i]) for i in range(len(y_test))]\n",
        "\n",
        "# for i in range(len(y_test)):\n",
        "#     pred.append(np.argmax(m.predict(x_test[i])))\n",
        "#     Y.append(np.argmax(y_test[i]))\n",
        "\n",
        "conf_plotter(Y , pr , ['Healthy' , 'Myocardial \\n Infarction'] , normalize=1, cmap = plt.cm.Greens ,mode='test', save=1 , name='Myocardial Infarction_try1.png')"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3245f7f450>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 504x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mGiwFwmEHpH0",
        "outputId": "4cd2ff22-cb41-406f-f09d-2950d7382621"
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "target_names = ['Healthy' , 'Myocardial Infarction']\n",
        "print(classification_report(Y, pr, target_names=target_names))"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                       precision    recall  f1-score   support\n",
            "\n",
            "              Healthy       0.94      0.96      0.95       240\n",
            "Myocardial Infarction       0.98      0.98      0.98       655\n",
            "\n",
            "             accuracy                           0.97       895\n",
            "            macro avg       0.96      0.97      0.97       895\n",
            "         weighted avg       0.97      0.97      0.97       895\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "h922i1BPIAKe",
        "outputId": "865c4b8b-490d-4181-ecdb-50d2fde0bb3b"
      },
      "source": [
        "pred = m.predict(x_train)\n",
        "pr = [np.argmax(pred[i]) for i in range(len(y_train))]\n",
        "Y = [np.argmax(y_train[i]) for i in range(len(y_train))]\n",
        "\n",
        "# for i in range(len(y_test)):\n",
        "#     pred.append(np.argmax(m.predict(x_test[i])))\n",
        "#     Y.append(np.argmax(y_test[i]))\n",
        "\n",
        "conf_plotter(Y , pr , ['Healthy' , 'Myocardial \\n Infarction'] , normalize=1, cmap = plt.cm.Reds ,mode='train', save=0 , name='RawData_Deep_physionet.png')"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3245ffa550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 504x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-7hs5mUxZvLp",
        "outputId": "f8e9e8a8-8ab6-4e7f-a181-ee92e8c4be25"
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "target_names = ['Healthy' , 'Myocardial Infarction']\n",
        "print(classification_report(Y, pr, target_names=target_names))"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                       precision    recall  f1-score   support\n",
            "\n",
            "              Healthy       0.93      0.97      0.95      3712\n",
            "Myocardial Infarction       0.97      0.93      0.95      3712\n",
            "\n",
            "             accuracy                           0.95      7424\n",
            "            macro avg       0.95      0.95      0.95      7424\n",
            "         weighted avg       0.95      0.95      0.95      7424\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UFJxxlJj61dM"
      },
      "source": [
        "    def conf_plotter(y_true, y_pred, classes,\n",
        "                      normalize=0,\n",
        "                      title=None,\n",
        "                      cmap=plt.cm.pink_r,\n",
        "                      mode = 'train',\n",
        "                      save = 0,\n",
        "                      name = 'conf.jpg'):\n",
        "        # if not title:\n",
        "        #     if mode == 'train':\n",
        "        #         title = 'Normalized confusion matrix on train dataset'\n",
        "        #     if mode == 'test':\n",
        "        #         title = 'Normalized confusion matrix on test dataset'\n",
        "        #     if mode == 'prob':\n",
        "        #         title = 'Normalized confusion matrix on Non-Overlapping test dataset'\n",
        "                \n",
        "\n",
        "        from sklearn.metrics import confusion_matrix\n",
        "        \n",
        "        cm = confusion_matrix(y_true, y_pred)\n",
        "\n",
        "        if normalize:\n",
        "            cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "\n",
        "        fig, ax = plt.subplots(figsize=(7,7))\n",
        "        im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "        ax.figure.colorbar(im, ax=ax ,fraction=0.046, pad=0.04)\n",
        "        ax.set(xticks=np.arange(cm.shape[0]),\n",
        "              yticks=np.arange(cm.shape[1]),\n",
        "              xticklabels=classes, yticklabels=classes,\n",
        "              title=title,\n",
        "              ylabel= 'True label',\n",
        "              xlabel='Predicted label')\n",
        "        \n",
        "        for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] ):\n",
        "                        \n",
        "            item.set_fontsize(12)\n",
        "        \n",
        "        for item in (ax.get_xticklabels() + ax.get_yticklabels()):\n",
        "            \n",
        "            item.set_fontsize(10)\n",
        "\n",
        "            \n",
        "                    \n",
        "        plt.gca().invert_yaxis()\n",
        "        plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
        "                rotation_mode=\"anchor\")\n",
        "\n",
        "        fmt = '.2f' if normalize else 'd'\n",
        "        thresh = cm.max() / 2.\n",
        "        for i in range(cm.shape[0]):\n",
        "            for j in range(cm.shape[1]):\n",
        "                ax.text(j, i, format(cm[i, j], fmt),\n",
        "                        ha=\"center\", va=\"center\",\n",
        "                        color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "        fig.tight_layout()\n",
        "        if save == 1:\n",
        "            plt.savefig(name)\n",
        "        return ax"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RIkW0JL_66rp"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}