{
 "cells": [
  {
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   "source": [
    "# Εργαστηριο Αναλυσης Ηλεκτροκαρδιογραφηματος (ECG)\n",
    "\n",
    "## Βιοιατρικα Σηματα & Εφαρμογες\n",
    "\n",
    "---\n",
    "\n",
    "### Τι ειναι αυτο το εργαστηριο;\n",
    "\n",
    "Σε αυτο το εργαστηριο θα δουλεψουμε με **πραγματικα δεδομενα** \n",
    "ηλεκτροκαρδιογραφηματος (ECG) απο τη βαση δεδομενων MIT-BIH \n",
    "του PhysioNet. Αυτη η βαση περιεχει καταγραφες απο πραγματικους \n",
    "ασθενεις και εχει χρησιμοποιηθει σε χιλιαδες ερευνητικες εργασιες \n",
    "παγκοσμιως.\n",
    "\n",
    "Θα μαθουμε πως να:\n",
    "- Φορτωνουμε και να βλεπουμε ενα ECG σημα\n",
    "- Καθαριζουμε τον θορυβο με ψηφιακα φιλτρα\n",
    "- Εντοπιζουμε τους καρδιακους παλμους αυτοματα\n",
    "- Υπολογιζουμε τον καρδιακο ρυθμο (Heart Rate)\n",
    "- Μετραμε τη μεταβλητοτητα του ρυθμου (HRV)\n",
    "- Αναγνωριζουμε απλες αρρυθμιες\n",
    "\n",
    "### Πως θα δουλεψουμε;\n",
    "\n",
    "Θα χωριστουμε σε **3 ομαδες**. Καθε ομαδα θα αναλυσει \n",
    "ενα **διαφορετικο** ECG record. Στο τελος θα συγκρινουμε \n",
    "τα αποτελεσματα μεταξυ ομαδων.\n",
    "\n",
    "| Ομαδα | Record | Περιγραφη |\n",
    "|-------|--------|-----------|\n",
    "| A | 100 | Σχεδον φυσιολογικη καταγραφη |\n",
    "| B | 106 | Καταγραφη με πολλες εκτοπες κοιλιακες συστολες (PVC) |\n",
    "| C | 119 | Καταγραφη με ακανονιστο ρυθμο |\n",
    "\n",
    "**ΣΗΜΑΝΤΙΚΟ:** Καθε ομαδα αλλαζει ΜΟΝΟ τον αριθμο record \n",
    "στο αντιστοιχο κελι κωδικα. Ολος ο υπολοιπος κωδικας ειναι \n",
    "ιδιος για ολες τις ομαδες.\n",
    "\n",
    "### Διαρκεια: 3 ωρες\n",
    "\n",
    "| Ωρα | Περιεχομενο |\n",
    "|-----|------------|\n",
    "| 1η | Κατανοηση σηματος, θορυβος, φιλτραρισμα |\n",
    "| 2η | Peak detection, HR, HRV |\n",
    "| 3η | Αρρυθμιες, συγκριση ομαδων |\n",
    "\n",
    "### Τι χρειαζεστε\n",
    "\n",
    "- Python 3\n",
    "- Βιβλιοθηκες: numpy, matplotlib, scipy, wfdb\n",
    "- Βασικες γνωσεις Python (for loops, numpy arrays)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "abf298cf-96ca-49c0-bf17-08d827f94b91",
   "metadata": {},
   "outputs": [
    {
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      "   ------------------------------ --------- 10/13 [neurokit2]\n",
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      "   ------------------------------ --------- 10/13 [neurokit2]\n",
      "   --------------------------------- ------ 11/13 [aiohttp]\n",
      "   --------------------------------- ------ 11/13 [aiohttp]\n",
      "   --------------------------------- ------ 11/13 [aiohttp]\n",
      "   --------------------------------- ------ 11/13 [aiohttp]\n",
      "   --------------------------------- ------ 11/13 [aiohttp]\n",
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      "   --------------------------------- ------ 11/13 [aiohttp]\n",
      "   --------------------------------- ------ 11/13 [aiohttp]\n",
      "   --------------------------------- ------ 11/13 [aiohttp]\n",
      "   --------------------------------- ------ 11/13 [aiohttp]\n",
      "   ------------------------------------ --- 12/13 [wfdb]\n",
      "   ------------------------------------ --- 12/13 [wfdb]\n",
      "   ------------------------------------ --- 12/13 [wfdb]\n",
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      "   ------------------------------------ --- 12/13 [wfdb]\n",
      "   ------------------------------------ --- 12/13 [wfdb]\n",
      "   ---------------------------------------- 13/13 [wfdb]\n",
      "\n",
      "Successfully installed aiohappyeyeballs-2.6.1 aiohttp-3.13.4 aiosignal-1.4.0 frozenlist-1.8.0 fsspec-2026.3.0 multidict-6.7.1 neurokit2-0.2.13 pandas-2.3.3 propcache-0.4.1 pytz-2026.1.post1 soundfile-0.13.1 wfdb-4.3.1 yarl-1.23.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  WARNING: Failed to remove contents in a temporary directory 'C:\\Users\\george\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\~andas'.\n",
      "  You can safely remove it manually.\n"
     ]
    }
   ],
   "source": [
    "# ============================================\n",
    "# ΕΓΚΑΤΑΣΤΑΣΗ (μόνο την πρώτη φορά)\n",
    "# ============================================\n",
    "!py -m pip install wfdb neurokit2\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1e07682f-af65-476a-a7b5-1e14426828b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ολες οι βιβλιοθηκες φορτωθηκαν επιτυχως!\n"
     ]
    }
   ],
   "source": [
    "import wfdb\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import signal\n",
    "from scipy.fft import fft, fftfreq\n",
    "from collections import Counter\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "print(\"Ολες οι βιβλιοθηκες φορτωθηκαν επιτυχως!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b7b50eb-6503-4de6-800f-a98d00295f7c",
   "metadata": {},
   "source": [
    "# ΘΕΩΡΙΑ 1 — Τι ειναι το ECG;\n",
    "\n",
    "---\n",
    "\n",
    "## Η καρδια ως ηλεκτρικο οργανο\n",
    "\n",
    "Οταν σκεφτομαστε την καρδια, συνηθως τη φανταζομαστε ως μια \n",
    "αντλια που σπρωχνει αιμα. Αυτο ειναι σωστο, αλλα η καρδια \n",
    "ειναι κατι πολυ περισσοτερο: ειναι ενα ηλεκτρικο οργανο.\n",
    "\n",
    "Καθε καρδιακος παλμος ξεκιναει απο ενα ηλεκτρικο σημα \n",
    "που δημιουργειται αυτοματα μεσα στην καρδια, χωρις εντολη \n",
    "απο τον εγκεφαλο. Αν αποκοψουμε ολα τα νευρα που πηγαινουν \n",
    "στην καρδια, αυτη θα συνεχισει να χτυπαει μονη της. \n",
    "Αυτο συμβαινει γιατι η καρδια εχει δικο της ηλεκτρικο \n",
    "συστημα αγωγης.\n",
    "\n",
    "## Το ηλεκτρικο συστημα αγωγης βημα-βημα\n",
    "\n",
    "Φανταστειτε την καρδια σαν ενα σπιτι με 4 δωματια \n",
    "(2 κολποι πανω, 2 κοιλιες κατω) και ενα ηλεκτρικο κουμπι \n",
    "που αναβει τα φωτα με συγκεκριμενη σειρα:\n",
    "\n",
    "### Βημα 1 — SA node (Φλεβοκομβος)\n",
    "Ο φυσικος βηματοδοτης της καρδιας. Βρισκεται στον δεξιο \n",
    "κολπο και παραγει αυτοματα ηλεκτρικους παλμους με ρυθμο \n",
    "60-100 ανα λεπτο. Δεν χρειαζεται εντολη απο τον εγκεφαλο.\n",
    "\n",
    "### Βημα 2 — Εξαπλωση στους κολπους\n",
    "Το ηλεκτρικο σημα εξαπλωνεται σε ολους τους κολπους. \n",
    "Οι κολποι αποπολωνονται και συσπωνται, σπρωχνοντας αιμα \n",
    "προς τις κοιλιες. Αυτο δημιουργει το **P wave** στο ECG.\n",
    "\n",
    "### Βημα 3 — AV node (Κολποκοιλιακος κομβος)\n",
    "Το σημα φτανει στον AV node, που λειτουργει σαν φυλακας: \n",
    "κραταει το σημα για 120-200 ms πριν το περασει στις κοιλιες. \n",
    "Αυτη η καθυστερηση ειναι σκοπιμη: δινει χρονο στο��ς κολπους \n",
    "να αδειασουν το αιμα τους. Αυτο φαινεται στο ECG \n",
    "ως **PR interval**.\n",
    "\n",
    "### Βημα 4 — His-Purkinje\n",
    "Μετα τον AV node, το σημα ταξιδευει πολυ γρηγορα μεσω \n",
    "ειδικων ινων (δεσμη του His, ινες Purkinje) σε ολες \n",
    "τις κοιλιες. Αυτη η ταυτοχρονη αποπολωση δημιουργει \n",
    "το **QRS complex** — το πιο εντονο μερος του ECG.\n",
    "\n",
    "### Βημα 5 — Επαναπολωση\n",
    "Μετα τη συσπαση, οι κοιλιες επιστρεφουν στην ηρεμη \n",
    "κατασταση τους. Αυτο δημιουργει το **T wave**.\n",
    "\n",
    "## Τι μεταραει ακριβως το ECG;\n",
    "\n",
    "| Μεταραει | ΔΕΝ μεταραει |\n",
    "|----------|-------------|\n",
    "| Ηλεκτρικη δραστηριοτητα καρδιας | Ροη αιματος |\n",
    "| Χρονους αγωγης | Αρτηριακη πιεση |\n",
    "| Ρυθμο και κανονικοτητα | Μηχανικη δυναμη |\n",
    "\n",
    "Αυτο σημαινει οτι μπορει να εχεις τελειο ECG αλλα η καρδια \n",
    "να μην αντλει σωστα, ή αντιστροφα."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "265df39a-a59a-4eca-b54b-65e72d688a2d",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# ΘΕΩΡΙΑ 2 — Τα Κυματα του ECG αναλυτικα\n",
    "\n",
    "---\n",
    "\n",
    "## P wave — Αποπολωση κολπων\n",
    "\n",
    "Η πρωτη μικρη καμπουρα πριν το μεγαλο spike. \n",
    "Σημαινει οτι οι κολποι ενεργοποιηθηκαν.\n",
    "\n",
    "- Διαρκεια: περιπου 80 ms\n",
    "- Πλατος: μικροτερο απο 0.25 mV\n",
    "- Αν λειπει: πιθανη κολπικη μαρμαρυγη\n",
    "\n",
    "## PR interval — Χρονος αγωγης κολπων προς κοιλιες\n",
    "\n",
    "Η αποσταση απο αρχη P μεχρι αρχη QRS.\n",
    "\n",
    "- Φυσιολογικο: 120-200 ms\n",
    "- Παρατεταμενο (πανω απο 200 ms): AV block\n",
    "\n",
    "## QRS complex — Αποπολωση κοιλιων\n",
    "\n",
    "Το πιο εντυπωσιακο μερος: τρια κυματα μαζι \n",
    "(Q κατω, R πανω, S κατω).\n",
    "\n",
    "- Φυσιολογικο: κατω απο 120 ms\n",
    "- Αν ειναι πλατυ: πιθανη διαταραχη αγωγης ή PVC\n",
    "- Το R peak ειναι αυτο που εντοπιζουμε αλγοριθμικα\n",
    "\n",
    "## ST segment — Φαση πλατο\n",
    "\n",
    "Η γραμμη μεταξυ QRS και T wave. Πρεπει να ειναι ισιωτικη.\n",
    "\n",
    "- Ανυψωση ST: πιθανο οξυ εμφραγμα (STEMI) — ΕΠΕΙΓΟΝ\n",
    "- Καταπτωση ST: πιθανη ισχαιμια\n",
    "\n",
    "## T wave — Επαναπολωση κοιλιων\n",
    "\n",
    "Στρογγυλο κυμα μετα το QRS. Δειχνει οτι οι κοιλιες \n",
    "\"επαναφορτιζονται\" για τον επομενο παλμο.\n",
    "\n",
    "- Αν ειναι αντεστραμμενο: πιθανη ισχαιμια\n",
    "\n",
    "## QT interval — Συνολικος κοιλιακος χρονος\n",
    "\n",
    "Απο αρχη QRS μεχρι τελος T.\n",
    "\n",
    "- Φυσιολογικο: κατω απο 440 ms (ανδρες)\n",
    "- Παρατεταμενο QT: κινδυνος αρρυθμιων"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d80c95c2-d621-4be8-9974-442bb692996c",
   "metadata": {},
   "source": [
    "# ΘΕΩΡΙΑ 3 — Καρδιακος Ρυθμος (Heart Rate)\n",
    "\n",
    "---\n",
    "\n",
    "## Τι ειναι RR interval;\n",
    "\n",
    "Η χρονικη αποσταση μεταξυ δυο διαδοχικων R peaks \n",
    "ονομαζεται RR interval και μεταρειται σε δευτερολεπτα.\n",
    "\n",
    "Τυπος: RR = (R2 - R1) / sampling_rate\n",
    "\n",
    "## Τυπος Heart Rate\n",
    "\n",
    "HR = 60 / μεσος ορος RR (σε bpm)\n",
    "\n",
    "## Παραδειγματα\n",
    "\n",
    "- RR = 0.75 sec --> HR = 60/0.75 = 80 bpm (φυσιολογικο)\n",
    "- RR = 0.50 sec --> HR = 60/0.50 = 120 bpm (ταχυκαρδια)\n",
    "- RR = 1.20 sec --> HR = 60/1.20 = 50 bpm (βραδυκαρδια)\n",
    "\n",
    "## Φυσιολογικες τιμες\n",
    "\n",
    "| Κατασταση | HR (bpm) | RR (sec) |\n",
    "|-----------|----------|----------|\n",
    "| Βραδυκαρδια | κατω απο 60 | πανω απο 1.0 |\n",
    "| Φυσιολογικο | 60-100 | 0.6-1.0 |\n",
    "| Ταχυκαρδια | πανω απο 100 | κατω απο 0.6 |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2d6afd62-153e-41ab-b364-5aab09299d85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================================\n",
      "RECORD: 100\n",
      "Sampling Rate: 360 Hz\n",
      "Diarkeia: 1805.6 sec (30.1 min)\n",
      "Synolo deigmaton: 650000\n",
      "Synolo annotations: 2274\n",
      "==================================================\n"
     ]
    }
   ],
   "source": [
    "# =============================================\n",
    "# ΚΑΘΕ ΟΜΑΔΑ ΑΛΛΑΖΕΙ ΜΟΝΟ ΑΥΤΗ ΤΗ ΓΡΑΜΜΗ\n",
    "# =============================================\n",
    "\n",
    "record_id = \"100\"    # Omada A: \"100\"\n",
    "                     # Omada B: \"106\"\n",
    "                     # Omada C: \"119\"\n",
    "\n",
    "# ΦΟΡΤΩΣΗ ΔΕΔΟΜΕΝΩΝ\n",
    "record = wfdb.rdrecord(record_id, pn_dir='mitdb')\n",
    "annotation = wfdb.rdann(record_id, 'atr', pn_dir='mitdb')\n",
    "\n",
    "ecg = record.p_signal[:, 0]\n",
    "fs = record.fs\n",
    "\n",
    "print(\"=\" * 50)\n",
    "print(\"RECORD: \" + record_id)\n",
    "print(\"Sampling Rate: \" + str(fs) + \" Hz\")\n",
    "print(\"Diarkeia: \" + str(round(len(ecg)/fs, 1)) + \" sec (\" + str(round(len(ecg)/fs/60, 1)) + \" min)\")\n",
    "print(\"Synolo deigmaton: \" + str(len(ecg)))\n",
    "print(\"Synolo annotations: \" + str(len(annotation.symbol)))\n",
    "print(\"=\" * 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "03d0f2c6-1b20-4bbe-b250-bf83c950f6e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PRWTH MATIA — 10 kai 30 deuterolepta\n",
    "\n",
    "seconds = 10\n",
    "samples = int(seconds * fs)\n",
    "time = np.arange(samples) / fs\n",
    "\n",
    "fig, axes = plt.subplots(2, 1, figsize=(14, 6))\n",
    "\n",
    "axes[0].plot(time, ecg[:samples], color='blue')\n",
    "axes[0].set_title(\"Record \" + record_id + \" - Prwta 10 sec\")\n",
    "axes[0].set_xlabel(\"Chronos (sec)\")\n",
    "axes[0].set_ylabel(\"Amplitude (mV)\")\n",
    "axes[0].grid(True, alpha=0.3)\n",
    "\n",
    "samples30 = int(30 * fs)\n",
    "time30 = np.arange(samples30) / fs\n",
    "axes[1].plot(time30, ecg[:samples30], color='darkblue', linewidth=0.5)\n",
    "axes[1].set_title(\"Record \" + record_id + \" - Prwta 30 sec\")\n",
    "axes[1].set_xlabel(\"Chronos (sec)\")\n",
    "axes[1].set_ylabel(\"Amplitude (mV)\")\n",
    "axes[1].grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "609c4e1b-d4f6-4253-9861-96e5c4644f9b",
   "metadata": {},
   "source": [
    "# ΘΕΩΡΙΑ 4 — Θορυβος στο ECG\n",
    "\n",
    "---\n",
    "\n",
    "## Γιατι υπαρχει θορυβος;\n",
    "\n",
    "Στον ιδανικο κοσμο, το ECG θα εδειχνε μονο την καρδια. \n",
    "Στην πραξη ομως, τα ηλεκτροδια \"ακουν\" πολλα σηματα ταυτοχρονα. \n",
    "Αυτα ονομαζονται artefacts ή θορυβος.\n",
    "\n",
    "## Τυπος 1: Baseline Wander (κατω απο 0.5 Hz)\n",
    "\n",
    "Αργη, κυματιστη μετατοπιση ολης της γραμμης βασης.\n",
    "\n",
    "Αιτιες: αναπνοη, κινηση σωματος, κακη επαφη ηλεκτροδιου.\n",
    "\n",
    "Λυση: High-pass filter πανω απο 0.5 Hz.\n",
    "\n",
    "## Τυπος 2: Powerline Noise (ακριβως 50 Hz)\n",
    "\n",
    "Γρηγορη, κανονικη ταλαντωση απο ηλεκτρικο δικτυο.\n",
    "\n",
    "Αιτιες: πριζες, καλωδια, λαμπες κοντα στον ασθενη.\n",
    "\n",
    "Λυση: Notch filter στα 50 Hz ή low-pass κατω απο 50 Hz.\n",
    "\n",
    "## Τυπος 3: Muscle Noise / EMG (πανω απο 40 Hz)\n",
    "\n",
    "Ακανονιστος, γρηγορος θορυβος απο μυικη δραστηριοτητα.\n",
    "\n",
    "Αιτιες: κινηση, τρεμουλο, μασημα.\n",
    "\n",
    "Λυση: Low-pass filter κατω απο 40 Hz.\n",
    "\n",
    "## Η συνδυαστικη λυση\n",
    "\n",
    "Bandpass filter 0.5-40 Hz:\n",
    "- Κατω απο 0.5 Hz: αφαιρειται (drift)\n",
    "- 0.5-40 Hz: κρατιεται (χρησιμο ECG)\n",
    "- Πανω απο 40 Hz: αφαιρειται (noise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dc40f7c1-41d7-4447-b928-416f1e35fa33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1200 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ΔΗΜΙΟΥΡΓΙΑ ΤΕΧΝΗΤΟΥ ΘΟΡΥΒΟΥ\n",
    "\n",
    "t = np.arange(len(ecg)) / fs\n",
    "\n",
    "baseline_drift = 0.3 * np.sin(2 * np.pi * 0.15 * t)\n",
    "powerline = 0.1 * np.sin(2 * np.pi * 50 * t)\n",
    "muscle_noise = np.random.normal(0, 0.05, len(ecg))\n",
    "\n",
    "fig, axes = plt.subplots(5, 1, figsize=(14, 12))\n",
    "\n",
    "axes[0].plot(time, ecg[:samples], color='blue')\n",
    "axes[0].set_title(\"1. Original ECG\")\n",
    "\n",
    "axes[1].plot(time, baseline_drift[:samples], color='orange')\n",
    "axes[1].set_title(\"2. Baseline Drift (0.15 Hz)\")\n",
    "\n",
    "axes[2].plot(time, powerline[:samples], color='red')\n",
    "axes[2].set_title(\"3. Powerline Noise (50 Hz)\")\n",
    "\n",
    "axes[3].plot(time, muscle_noise[:samples], color='green')\n",
    "axes[3].set_title(\"4. Muscle Noise (random)\")\n",
    "\n",
    "corrupted_demo = ecg[:samples] + baseline_drift[:samples] + powerline[:samples] + muscle_noise[:samples]\n",
    "axes[4].plot(time, corrupted_demo, color='purple')\n",
    "axes[4].set_title(\"5. Corrupted ECG (all noise combined)\")\n",
    "\n",
    "for ax in axes:\n",
    "    ax.grid(True, alpha=0.2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b198a15f-6b0e-4a72-9f60-d60e854a597d",
   "metadata": {},
   "source": [
    "# ΑΣΚΗΣΗ 1 — Κατανοηση Θορυβου\n",
    "---\n",
    "\n",
    "## Στοχος\n",
    "Να κατανοησετε πως ο θορυβος επηρεαζει το ECG \n",
    "και ποιος τυπος ειναι πιο καταστροφικος.\n",
    "\n",
    "## Οδηγιες\n",
    "\n",
    "### Βημα 1\n",
    "Δημιουργηστε corrupted ECG προσθετοντας ΟΛΑ \n",
    "τα ειδη θορυβου (baseline_drift + powerline + muscle_noise).\n",
    "\n",
    "### Βημα 2\n",
    "Σχεδιαστε Original πανω, Corrupted κατω.\n",
    "\n",
    "### Βημα 3\n",
    "Αυξηστε τα πλατη θορυβου x3 και ξανασχεδιαστε.\n",
    "\n",
    "## Ερωτησεις\n",
    "1. Μπορειτε ακομα να δειτε τα R peaks;\n",
    "2. Ποιος θορυβος ειναι πιο καταστροφικος; Γιατι;\n",
    "3. Αν ειχατε μονο baseline drift, θα βρισκατε \n",
    "   τα R peaks; Γιατι;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1ff6803-c1b0-4ba1-955c-93f025e44a52",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ===============================\n",
    "# ΑΣΚΗΣΗ 1 — ΓΡΑΨΤΕ ΕΔΩ\n",
    "# ===============================\n",
    "\n",
    "# Βημα 1: Prostheste thoruvo\n",
    "corrupted = ecg + ______ + ______ + ______\n",
    "\n",
    "# Βημα 2: Sxediaste\n",
    "fig, axes = plt.subplots(2, 1, figsize=(14, 6))\n",
    "\n",
    "axes[0].plot(time, ecg[:samples])\n",
    "axes[0].set_title(\"Original\")\n",
    "\n",
    "axes[1].plot(time, corrupted[:samples])\n",
    "axes[1].set_title(\"Corrupted\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Βημα 3: Auxiste x3\n",
    "corrupted_x3 = ecg + 3 * ______ + 3 * ______ + 3 * ______\n",
    "\n",
    "plt.figure(figsize=(14, 4))\n",
    "plt.plot(time, corrupted_x3[:samples])\n",
    "plt.title(\"Corrupted x3\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8d77b06-cc46-4647-9007-56904694816a",
   "metadata": {},
   "source": [
    "# ΘΕΩΡΙΑ 5 — Ψηφιακο Φιλτραρισμα\n",
    "\n",
    "---\n",
    "\n",
    "## Τι ειναι ψηφιακο φιλτρο;\n",
    "\n",
    "Ενα μαθηματικο εργαλειο που επιτρεπει ορισμενες \n",
    "συχνοτητες να περασουν ενω μπλοκαρει αλλες.\n",
    "\n",
    "## Τυποι φιλτρων\n",
    "\n",
    "### Low-pass: κραταει χαμηλες, κοβει υψηλες\n",
    "### High-pass: κραταει υψηλες, κοβει χαμηλες\n",
    "### Bandpass: κραταει ενα ευρος (π.χ. 0.5-40 Hz)\n",
    "### Notch: κοβει ακριβως μια συχνοτητα (π.χ. 50 Hz)\n",
    "\n",
    "## Butterworth Filter\n",
    "\n",
    "Ομαλη αποκριση, χωρις κυματισμους. \n",
    "Ταξη 4 ειναι καλη επιλογη για ECG.\n",
    "\n",
    "## Τι ειναι filtfilt;\n",
    "\n",
    "Εφαρμοζει το φιλτρο δυο φορες (μπροστα + πισω) \n",
    "ωστε τα peaks να μην μετατοπιστουν χρονικα."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a626a7f6-053e-4cd7-8dd3-bbcd5ef4eb57",
   "metadata": {},
   "source": [
    "# ΑΣΚΗΣΗ 2 — Φιλτραρισμα\n",
    "\n",
    "---\n",
    "\n",
    "## Στοχος\n",
    "Να κατανοησετε πως η επιλογη συχνοτητων φιλτρου \n",
    "επηρεαζει την ποιοτητα του σηματος.\n",
    "\n",
    "## Οδηγιες\n",
    "\n",
    "### Δοκιμαστε 3 διαφορετικα ευρη:\n",
    "\n",
    "| Δοκιμη | Low | High | Τι περιμενουμε |\n",
    "|--------|-----|------|----------------|\n",
    "| A | 1 Hz | 20 Hz | Χανει μερος QRS |\n",
    "| B | 0.5 Hz | 40 Hz | Βελτιστο |\n",
    "| C | 5 Hz | 50 Hz | Κραταει powerline |\n",
    "\n",
    "## Ερωτησεις\n",
    "1. Ποιο ευρος δινει καλυτερο αποτελεσμα; Γιατι;\n",
    "2. Στη δοκιμη A, τι αλλαζει στο QRS;\n",
    "3. Στη δοκιμη C, γιατι υπαρχει ακομα θορυβος;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3bf1cf0-36af-4fb7-a555-33d0f5d190f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ===============================\n",
    "# ΑΣΚΗΣΗ 2 — ΓΡΑΨΤΕ ΕΔΩ\n",
    "# ===============================\n",
    "\n",
    "# Dokimi A: 1-20 Hz\n",
    "b_a, a_a = signal.butter(4, [______, ______], btype='bandpass', fs=fs)\n",
    "filt_a = signal.filtfilt(b_a, a_a, corrupted)\n",
    "\n",
    "# Dokimi B: 0.5-40 Hz\n",
    "b_b, a_b = signal.butter(4, [______, ______], btype='bandpass', fs=fs)\n",
    "filt_b = signal.filtfilt(b_b, a_b, corrupted)\n",
    "\n",
    "# Dokimi C: 5-50 Hz\n",
    "b_c, a_c = signal.butter(4, [______, ______], btype='bandpass', fs=fs)\n",
    "filt_c = signal.filtfilt(b_c, a_c, corrupted)\n",
    "\n",
    "# SXEDIASTE\n",
    "fig, axes = plt.subplots(5, 1, figsize=(14, 14))\n",
    "\n",
    "axes[0].plot(time, ecg[:samples])\n",
    "axes[0].set_title(\"Original\")\n",
    "\n",
    "axes[1].plot(time, corrupted[:samples])\n",
    "axes[1].set_title(\"Corrupted\")\n",
    "\n",
    "axes[2].plot(time, filt_a[:samples])\n",
    "axes[2].set_title(\"Filter A: 1-20 Hz\")\n",
    "\n",
    "axes[3].plot(time, filt_b[:samples])\n",
    "axes[3].set_title(\"Filter B: 0.5-40 Hz\")\n",
    "\n",
    "axes[4].plot(time, filt_c[:samples])\n",
    "axes[4].set_title(\"Filter C: 5-50 Hz\")\n",
    "\n",
    "for ax in axes:\n",
    "    ax.grid(True, alpha=0.2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "820700db-af0b-411f-9f73-ef3cc819798b",
   "metadata": {},
   "source": [
    "# ΘΕΩΡΙΑ 6 — Ανιχνευση R Peak\n",
    "\n",
    "---\n",
    "\n",
    "## Γιατι τα R peaks;\n",
    "\n",
    "Ειναι τα πιο υψηλα και αιχμηρα σημεια του ECG, \n",
    "αρα τα πιο ευκολα να εντοπιστουν αλγοριθμικα.\n",
    "\n",
    "## Δυο κρισιμες παραμετροι\n",
    "\n",
    "### 1. distance (ελαχιστη αποσταση σε samples)\n",
    "Η καρδια δεν μπορει να χτυπησει πιο γρηγορα \n",
    "απο καποιο οριο. Αρα δυο peaks δεν μπορουν \n",
    "να ειναι πολυ κοντα.\n",
    "\n",
    "- Max HR 200 bpm --> distance = fs * 0.3\n",
    "- Max HR 100 bpm --> distance = fs * 0.6\n",
    "\n",
    "Πολυ μικρο distance: βρισκει ψευτικα peaks\n",
    "Πολυ μεγαλο distance: χανει πραγματικα peaks\n",
    "\n",
    "### 2. height (ελαχιστο υψος peak)\n",
    "Πολυ χαμηλο: θορυβος = \"peak\" (false positive)\n",
    "Πολυ υψηλο: χανει μικρα peaks (false negative)\n",
    "\n",
    "### Καλη αρχικη τιμη:\n",
    "height = mean(filtered) + 0.5 * std(filtered)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "325377e9-8972-4b4e-8aa2-2b59b63f9779",
   "metadata": {},
   "source": [
    "# ΑΣΚΗΣΗ 3 — Peak Detection Tuning\n",
    "\n",
    "---\n",
    "\n",
    "## Στοχος\n",
    "Να βρειτε τον καλυτερο συνδυασμο distance/height.\n",
    "\n",
    "## Δοκιμαστε 4 συνδυασμους:\n",
    "\n",
    "| Δοκιμη | distance | height |\n",
    "|--------|----------|--------|\n",
    "| 1 | fs * 0.3 | 0.3 |\n",
    "| 2 | fs * 0.6 | 0.5 |\n",
    "| 3 | fs * 0.6 | auto |\n",
    "| 4 | fs * 0.8 | 0.8 |\n",
    "\n",
    "(auto = mean + 0.5*std)\n",
    "\n",
    "## Αναμενομενο σε 10 sec (αν HR περιπου 70 bpm):\n",
    "70 * 10 / 60 = περιπου 12 peaks\n",
    "\n",
    "## Ερωτησεις\n",
    "1. Ποια δοκι��η δινει πιο λογικο αριθμο;\n",
    "2. Γιατι η δοκιμη 1 δινει πολλα peaks;\n",
    "3. Γιατι η δοκιμη 4 μπορει να χασει peaks;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3dd11758-9da0-48f7-a515-e1c83afd4de4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ===============================\n",
    "# ΑΣΚΗΣΗ 3 — ΓΡΑΨΤΕ ΕΔΩ\n",
    "# ===============================\n",
    "\n",
    "# Filtrarismo pragmatikou simatos\n",
    "b, a = signal.butter(4, [0.5, 40], btype='bandpass', fs=fs)\n",
    "filtered = signal.filtfilt(b, a, ecg)\n",
    "\n",
    "# Dokimi 1\n",
    "peaks1, _ = signal.find_peaks(filtered[:samples],\n",
    "                              distance=int(fs * ______),\n",
    "                              height=______)\n",
    "\n",
    "# Dokimi 2\n",
    "peaks2, _ = signal.find_peaks(filtered[:samples],\n",
    "                              distance=int(fs * ______),\n",
    "                              height=______)\n",
    "\n",
    "# Dokimi 3 (auto height)\n",
    "auto_height = np.mean(filtered[:samples]) + 0.5 * np.std(filtered[:samples])\n",
    "peaks3, _ = signal.find_peaks(filtered[:samples],\n",
    "                              distance=int(fs * ______),\n",
    "                              height=auto_height)\n",
    "\n",
    "# Dokimi 4\n",
    "peaks4, _ = signal.find_peaks(filtered[:samples],\n",
    "                              distance=int(fs * ______),\n",
    "                              height=______)\n",
    "\n",
    "print(\"Dokimi 1: \" + str(len(peaks1)) + \" peaks\")\n",
    "print(\"Dokimi 2: \" + str(len(peaks2)) + \" peaks\")\n",
    "print(\"Dokimi 3: \" + str(len(peaks3)) + \" peaks (auto height)\")\n",
    "print(\"Dokimi 4: \" + str(len(peaks4)) + \" peaks\")\n",
    "print(\"\")\n",
    "print(\"Anamenomeno (~70 bpm x 10 sec / 60): ~\" + str(int(70*10/60)) + \" peaks\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "108af8a7-d880-4b32-8168-b7583ca70e9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# SXEDIASTE THN KALYTERH DOKIMH\n",
    "\n",
    "best_peaks = ______  # peaks1, peaks2, peaks3 h peaks4\n",
    "\n",
    "plt.figure(figsize=(14, 4))\n",
    "plt.plot(time, filtered[:samples], label=\"Filtered ECG\")\n",
    "plt.plot(best_peaks / fs,\n",
    "         filtered[best_peaks],\n",
    "         \"rv\", markersize=10, label=\"R Peaks\")\n",
    "plt.title(\"R Peak Detection - Record \" + record_id)\n",
    "plt.xlabel(\"Chronos (sec)\")\n",
    "plt.ylabel(\"Amplitude (mV)\")\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0957101e-8209-4c63-8983-7fb8b2fc51ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# PLHRES PEAK DETECTION SE OLO TO SHMA\n",
    "\n",
    "auto_height_full = np.mean(filtered) + 0.5 * np.std(filtered)\n",
    "all_peaks, _ = signal.find_peaks(filtered,\n",
    "                                  distance=int(fs * 0.6),\n",
    "                                  height=auto_height_full)\n",
    "\n",
    "print(\"Synolo R peaks: \" + str(len(all_peaks)))\n",
    "expected = int(len(ecg) / fs * 70 / 60)\n",
    "print(\"Anamenomeno (~70 bpm): ~\" + str(expected))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9936e4b6-4e2e-4c55-8ae0-aeaab9298e5f",
   "metadata": {},
   "source": [
    "# ΑΣΚΗΣΗ 4 — Υπολογισμος Heart Rate\n",
    "\n",
    "---\n",
    "\n",
    "## Οδηγιες\n",
    "\n",
    "### Βημα 1: Υπολογιστε RR intervals\n",
    "Χρησιμοποιηστε np.diff(all_peaks) / fs\n",
    "\n",
    "### Βημα 2: Μεσος HR = 60 / mean(RR)\n",
    "\n",
    "### Βημα 3: Χαρακτηρισμος\n",
    "- Κατω απο 60: Βραδυκαρδια\n",
    "- 60-100: Φυσιολογικο\n",
    "- Πανω απο 100: Ταχυκαρδια\n",
    "\n",
    "### Βημα 4: Σχεδιαστε Instantaneous HR\n",
    "\n",
    "## Ερωτησεις\n",
    "1. Ο ρυθμος ειναι σταθερος ή μεταβαλλεται;\n",
    "2. Υπαρχουν ξαφνικα \"πηδηματα\"; Τι σημαινουν;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7ddd810-45dc-46eb-9112-df0c184a09a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ===============================\n",
    "# ΑΣΚΗΣΗ 4 — ΓΡΑΨΤΕ ΕΔΩ\n",
    "# ===============================\n",
    "\n",
    "rr = np.diff(______) / fs\n",
    "\n",
    "hr_mean = 60 / np.______(rr)\n",
    "\n",
    "print(\"Mesos HR: \" + str(round(hr_mean, 1)) + \" bpm\")\n",
    "print(\"Min HR: \" + str(round(60 / np.max(rr), 1)) + \" bpm\")\n",
    "print(\"Max HR: \" + str(round(60 / np.min(rr), 1)) + \" bpm\")\n",
    "\n",
    "if hr_mean < 60:\n",
    "    print(\"-- Bradycardia\")\n",
    "elif hr_mean > 100:\n",
    "    print(\"-- Tachycardia\")\n",
    "else:\n",
    "    print(\"-- Fysiologikos rythmos\")\n",
    "\n",
    "# Instantaneous HR\n",
    "instant_hr = 60 / rr\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.plot(instant_hr[:200], marker='.', markersize=2, linewidth=0.5)\n",
    "plt.title(\"Instantaneous HR - Record \" + record_id)\n",
    "plt.ylabel(\"BPM\")\n",
    "plt.xlabel(\"Beat Number\")\n",
    "plt.axhline(y=60, color='g', linestyle='--', alpha=0.5, label='60 bpm')\n",
    "plt.axhline(y=100, color='r', linestyle='--', alpha=0.5, label='100 bpm')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5367386-765e-4d77-88e0-0dc450b87eb7",
   "metadata": {},
   "source": [
    "# ΑΣΚΗΣΗ 5 — Tachogram και Histogram\n",
    "\n",
    "---\n",
    "\n",
    "## Τι ειναι Tachogram;\n",
    "Γραφημα RR intervals στη σειρα (beat number vs RR).\n",
    "- Σταθερο: κανονικος ρυθμος\n",
    "- Μεταβλητο: HRV ή αρρυθμια\n",
    "\n",
    "## Τι ειναι RR Histogram;\n",
    "Δειχνει ποσο συχνα εμφανιζεται καθε τιμη RR.\n",
    "- Στενο: σταθερος ρυθμος\n",
    "- Πλατυ: μεγαλη μεταβλητοτητα\n",
    "- 2 κορυφες: πιθανον 2 ειδη beats\n",
    "\n",
    "## Ερωτησεις\n",
    "1. Η κατανομη εχει 1 ή 2 κορυφες;\n",
    "2. Υπαρχουν outliers;\n",
    "3. Τι σημαινουν κλινικα;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "472ccb4f-f4aa-4b0e-b342-695cc7ab3562",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ===============================\n",
    "# ΑΣΚΗΣΗ 5 — ΓΡΑΨΤΕ ΕΔΩ\n",
    "# ===============================\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "axes[0].plot(rr[:200], marker='.', markersize=3, linewidth=0.5)\n",
    "axes[0].set_title(\"Tachogram - Record \" + record_id)\n",
    "axes[0].set_ylabel(\"RR (sec)\")\n",
    "axes[0].set_xlabel(\"Beat Number\")\n",
    "axes[0].axhline(y=np.mean(rr), color='r', linestyle='--', alpha=0.5)\n",
    "axes[0].grid(True, alpha=0.2)\n",
    "\n",
    "axes[1].hist(rr, bins=50, edgecolor='black', alpha=0.7)\n",
    "axes[1].set_title(\"RR Distribution - Record \" + record_id)\n",
    "axes[1].set_xlabel(\"RR (sec)\")\n",
    "axes[1].set_ylabel(\"Count\")\n",
    "axes[1].axvline(x=np.mean(rr), color='r', linestyle='--', label='Mean RR')\n",
    "axes[1].legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"Mean RR: \" + str(round(np.mean(rr), 3)) + \" sec\")\n",
    "print(\"Std RR:  \" + str(round(np.std(rr), 3)) + \" sec\")\n",
    "print(\"Min RR:  \" + str(round(np.min(rr), 3)) + \" sec\")\n",
    "print(\"Max RR:  \" + str(round(np.max(rr), 3)) + \" sec\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7aeff40-360d-4efc-82dc-923568ce4e74",
   "metadata": {},
   "source": [
    "# ΘΕΩΡΙΑ 7 — Heart Rate Variability (HRV)\n",
    "\n",
    "---\n",
    "\n",
    "## Η καρδια ΔΕΝ ειναι μετρονομος\n",
    "\n",
    "Μια υγιης καρδια εχει μεταβλητοτητα στον ρυθμο.\n",
    "\n",
    "## Αυτονομο Νευρικο Συστημα (ANS)\n",
    "\n",
    "- Συμπαθητικο: επιταχυνει (στρες, ασκηση)\n",
    "- Παρασυμπαθητικο: επιβραδυνει (ηρεμια, υπνος)\n",
    "\n",
    "## SDNN = τυπικη αποκλιση RR\n",
    "- Συνολικη μεταβλητοτητα\n",
    "- Πανω απο 100 ms: υγιες\n",
    "- Κατω απο 50 ms: μειωμενο\n",
    "\n",
    "## RMSSD = ριζα μεσου τετραγωνου διαδοχικων διαφορων\n",
    "- Βραχυπροθεσμες αλλαγες\n",
    "- Αντανακλα κυριως παρασυμπαθητικο\n",
    "\n",
    "## ΠΡΟΣΟΧΗ\n",
    "Υψηλο SDNN λογω αρρυθμιων ΔΕΝ ειναι υγιες HRV!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f716f58b-2716-41db-b939-db3d2e0df792",
   "metadata": {},
   "source": [
    "# ΑΣΚΗΣΗ 6 — HRV Time Domain\n",
    "\n",
    "---\n",
    "\n",
    "## Οδηγιες\n",
    "\n",
    "### Βημα 1: SDNN = np.std(rr) (x1000 για ms)\n",
    "### Βημα 2: RMSSD = np.sqrt(np.mean(np.diff(rr)**2)) (x1000 για ms)\n",
    "### Βημα 3: Σημειωστε στον πινακα ολων των ομαδων\n",
    "\n",
    "| Ομαδα | Record | HR | SDNN (ms) | RMSSD (ms) |\n",
    "|-------|--------|----|-----------|------------|\n",
    "| A | 100 | | | |\n",
    "| B | 106 | | | |\n",
    "| C | 119 | | | |\n",
    "\n",
    "## Ερωτησεις\n",
    "1. Ποιο record εχει μεγαλυτερο SDNN;\n",
    "2. Ειναι αυτο \"υγιες\" ή λογω αρρυθμιων;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66d3dcc6-8afc-42f6-9e28-4aea42ce76d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ===============================\n",
    "# ΑΣΚΗΣΗ 6 — ΓΡΑΨΤΕ ΕΔΩ\n",
    "# ===============================\n",
    "\n",
    "sdnn = np.______(rr)\n",
    "rmssd = np.sqrt(np.mean(np.diff(rr) ** ______))\n",
    "\n",
    "print(\"=\" * 40)\n",
    "print(\"HRV - Record \" + record_id)\n",
    "print(\"=\" * 40)\n",
    "print(\"SDNN:  \" + str(round(sdnn * 1000, 1)) + \" ms\")\n",
    "print(\"RMSSD: \" + str(round(rmssd * 1000, 1)) + \" ms\")\n",
    "print(\"HR:    \" + str(round(hr_mean, 1)) + \" bpm\")\n",
    "print(\"=\" * 40)\n",
    "\n",
    "if sdnn * 1000 > 100:\n",
    "    print(\"-- Ypshlh metavlhtothta\")\n",
    "elif sdnn * 1000 > 50:\n",
    "    print(\"-- Metria metavlhtothta\")\n",
    "else:\n",
    "    print(\"-- Xamhlh metavlhtothta\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78c209d6-ea1a-4421-843b-59e6bde31d9f",
   "metadata": {},
   "source": [
    "# ΘΕΩΡΙΑ 8 — Τυποι Καρδιακων Παλμων\n",
    "\n",
    "---\n",
    "\n",
    "## MIT-BIH Annotations\n",
    "\n",
    "| Συμβολο | Ονομασια | Σημασια |\n",
    "|---------|----------|---------|\n",
    "| N | Normal | Φυσιολογικος παλμος |\n",
    "| V | PVC | Πρωιμη Κοιλιακη Συστολη |\n",
    "| A | APC | Πρωιμη Κολπικη Συστολη |\n",
    "| / | Paced | Βηματοδοτουμενος |\n",
    "\n",
    "## Τι ειναι PVC;\n",
    "\n",
    "Παλμος που ξεκιναει μεσα στην κοιλια, χωρις σημα \n",
    "απο τον SA node.\n",
    "\n",
    "Χαρακτηριστικα:\n",
    "1. Πρωιμος (μικρο RR πριν)\n",
    "2. Αντισταθμιστικη παυση (μεγαλο RR μετα)\n",
    "3. Πλατυ QRS (πανω απο 120 ms)\n",
    "4. Διαφορετικη μορφολογια"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "755d1a51-6309-4224-8480-02e62252251a",
   "metadata": {},
   "source": [
    "# ΑΣΚΗΣΗ 7 — Αναλυση Beat Types\n",
    "\n",
    "---\n",
    "\n",
    "## Οδηγιες\n",
    "1. Βρειτε ολους τους τυπους beats (Counter)\n",
    "2. Μετρηστε Normal vs PVC\n",
    "3. Υπολογιστε PVC% = PVC/Total * 100\n",
    "4. Σχεδιαστε bar chart\n",
    "\n",
    "## Ερωτησεις\n",
    "1. Ποσο % ειναι PVC; (κατω απο 1% = φυσιολογικο)\n",
    "2. Ποια ομαδα εχει τα περισσοτερα;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aaf2bbb1-74cf-42f5-9d8d-8d58870e0c03",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ===============================\n",
    "# ΑΣΚΗΣΗ 7 — ΓΡΑΨΤΕ ΕΔΩ\n",
    "# ===============================\n",
    "\n",
    "counts = Counter(annotation.symbol)\n",
    "total = sum(counts.values())\n",
    "normal = counts.get('N', 0)\n",
    "pvc = counts.get('V', 0)\n",
    "pvc_pct = (pvc / total) * 100\n",
    "\n",
    "print(\"Beat Types - Record \" + record_id)\n",
    "print(\"Total: \" + str(total))\n",
    "print(\"Normal (N): \" + str(normal) + \" (\" + str(round(normal / total * 100, 1)) + \"%)\")\n",
    "print(\"PVC (V): \" + str(pvc) + \" (\" + str(round(pvc_pct, 1)) + \"%)\")\n",
    "print(\"Other: \" + str(total - normal - pvc))\n",
    "\n",
    "# Bar chart\n",
    "top_types = dict(counts.most_common(6))\n",
    "colors = []\n",
    "for label in top_types.keys():\n",
    "    if label == 'V':\n",
    "        colors.append('red')\n",
    "    elif label == 'N':\n",
    "        colors.append('green')\n",
    "    else:\n",
    "        colors.append('steelblue')\n",
    "\n",
    "plt.figure(figsize=(8, 4))\n",
    "plt.bar(top_types.keys(), top_types.values(), color=colors, edgecolor='black')\n",
    "plt.title(\"Beat Distribution - Record \" + record_id)\n",
    "plt.ylabel(\"Count\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b765671-ab25-4903-8528-91a5586b24c6",
   "metadata": {},
   "source": [
    "# ΑΣΚΗΣΗ 8 — Μορφολογια Beats\n",
    "\n",
    "---\n",
    "\n",
    "## Στοχος\n",
    "Να δειτε πως μοιαζουν Normal vs PVC beats.\n",
    "\n",
    "## Οδηγιες\n",
    "1. Κοψτε παραθυρα +/- 0.3 sec γυρω απο καθε beat\n",
    "2. Σχεδιαστε 5 Normal beats μαζι (overlay)\n",
    "3. Σχεδιαστε 5 PVC beats μαζι (αν υπαρχουν)\n",
    "4. Σχεδιαστε average Normal vs average PVC\n",
    "\n",
    "## Ερωτησεις\n",
    "1. Γραψτε 3 διαφορες μορφολογιας\n",
    "2. Ποιο beat εχει πλατυτερο QRS;\n",
    "3. Ποιο εχει μεγαλυτερο amplitude;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d98c8828-4432-45e3-9d7e-9e53d0813c4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ===============================\n",
    "# ΑΣΚΗΣΗ 8 — ΓΡΑΨΤΕ ΕΔΩ\n",
    "# ===============================\n",
    "\n",
    "window = int(0.3 * fs)\n",
    "ann_samples = annotation.sample\n",
    "ann_symbols = annotation.symbol\n",
    "\n",
    "normal_beats = []\n",
    "pvc_beats = []\n",
    "\n",
    "for i in range(len(ann_samples)):\n",
    "    peak = ann_samples[i]\n",
    "    if peak - window > 0 and peak + window < len(filtered):\n",
    "        beat = filtered[peak - window : peak + window]\n",
    "        if ann_symbols[i] == 'N' and len(normal_beats) < 20:\n",
    "            normal_beats.append(beat)\n",
    "        elif ann_symbols[i] == 'V' and len(pvc_beats) < 20:\n",
    "            pvc_beats.append(beat)\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
    "\n",
    "for beat in normal_beats[:5]:\n",
    "    axes[0].plot(beat, alpha=0.5)\n",
    "axes[0].set_title(\"Normal Beats (overlay)\")\n",
    "\n",
    "if len(pvc_beats) > 0:\n",
    "    for beat in pvc_beats[:5]:\n",
    "        axes[1].plot(beat, alpha=0.5, color='red')\n",
    "    axes[1].set_title(\"PVC Beats (overlay)\")\n",
    "else:\n",
    "    axes[1].text(0.5, 0.5, \"No PVC found\",\n",
    "                 ha='center', va='center', transform=axes[1].transAxes)\n",
    "    axes[1].set_title(\"PVC Beats\")\n",
    "\n",
    "avg_normal = np.mean(normal_beats, axis=0)\n",
    "axes[2].plot(avg_normal, label=\"Avg Normal\", color='blue', linewidth=2)\n",
    "if len(pvc_beats) > 0:\n",
    "    avg_pvc = np.mean(pvc_beats, axis=0)\n",
    "    axes[2].plot(avg_pvc, label=\"Avg PVC\", color='red', linewidth=2)\n",
    "axes[2].set_title(\"Average Beat Comparison\")\n",
    "axes[2].legend()\n",
    "\n",
    "plt.suptitle(\"Beat Morphology - Record \" + record_id, fontsize=14)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb232e06-032f-47ef-a4a3-c3ee29acf3b2",
   "metadata": {},
   "source": [
    "# ΑΣΚΗΣΗ 9 — Normal vs PVC RR Analysis\n",
    "\n",
    "---\n",
    "\n",
    "## Υπενθυμιση θεωριας\n",
    "- RR ΠΡΙΝ PVC: μικροτερο (πρωιμος παλμος)\n",
    "- RR ΜΕΤΑ PVC: μεγαλυτερο (αντισταθμιστικη παυση)\n",
    "\n",
    "## Οδηγιες\n",
    "1. Χωριστε RR σε Normal και PVC\n",
    "2. Σχεδιαστε histogram μαζι\n",
    "3. Υπολογιστε μεσο RR καθε κατηγοριας\n",
    "\n",
    "## Ερωτησεις\n",
    "1. Τα PVC RR ειναι μικροτερα ή μεγαλυτερα;\n",
    "2. Ταιριαζει με τη θεωρια; Εξηγηστε."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a9f1bba-98c8-4ebf-8de3-c9576c5cf333",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ===============================\n",
    "# ΑΣΚΗΣΗ 9 — ΓΡΑΨΤΕ ΕΔΩ\n",
    "# ===============================\n",
    "\n",
    "rr_ann = []\n",
    "beat_labels = []\n",
    "\n",
    "for i in range(1, len(ann_samples)):\n",
    "    rr_val = (ann_samples[i] - ann_samples[i - 1]) / fs\n",
    "    rr_ann.append(rr_val)\n",
    "    beat_labels.append(ann_symbols[i])\n",
    "\n",
    "normal_rr = [rr_ann[i] for i in range(len(beat_labels))\n",
    "             if beat_labels[i] == 'N']\n",
    "pvc_rr = [rr_ann[i] for i in range(len(beat_labels))\n",
    "           if beat_labels[i] == 'V']\n",
    "\n",
    "print(\"Normal: n=\" + str(len(normal_rr)) + \", mean=\" + str(round(np.mean(normal_rr), 3)) + \" sec\")\n",
    "if len(pvc_rr) > 0:\n",
    "    print(\"PVC:    n=\" + str(len(pvc_rr)) + \", mean=\" + str(round(np.mean(pvc_rr), 3)) + \" sec\")\n",
    "\n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.hist(normal_rr, bins=50, alpha=0.5,\n",
    "         label=\"Normal (n=\" + str(len(normal_rr)) + \")\", color='blue')\n",
    "if len(pvc_rr) > 0:\n",
    "    plt.hist(pvc_rr, bins=50, alpha=0.5,\n",
    "             label=\"PVC (n=\" + str(len(pvc_rr)) + \")\", color='red')\n",
    "plt.legend()\n",
    "plt.title(\"RR Distribution by Beat Type - Record \" + record_id)\n",
    "plt.xlabel(\"RR (sec)\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9292e58f-4798-4bf7-a96c-3f5817e7ca2d",
   "metadata": {},
   "source": [
    "# ΘΕΩΡΙΑ 9 — Ανιχνευση Αρρυθμιων (Rule-Based)\n",
    "\n",
    "---\n",
    "\n",
    "## Βασικη ιδεα\n",
    "Αν ενα RR interval αποκλινει πολυ απο τον μεσο ορο, \n",
    "τοτε ο αντιστοιχος παλμος ειναι \"υποπτος\".\n",
    "\n",
    "## Κανονας\n",
    "|RR - mean(RR)| > k * std(RR)\n",
    "\n",
    "## Τιμες k\n",
    "\n",
    "| k | Ευαισθησια | False Positives |\n",
    "|---|-----------|-----------------|\n",
    "| 1.5 | Υψηλη | Πολλα |\n",
    "| 2.0 | Μετρια | Μετρια |\n",
    "| 2.5 | Χαμηλη | Λιγα |\n",
    "\n",
    "## Περιορισμοι\n",
    "- Δεν εξεταζει μορφολογια QRS\n",
    "- Δεν ξεχωριζει τυπο αρρυθμιας\n",
    "- False positives σε φυσιολογικη μεταβλητοτητα"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06050085-0b53-4a13-aa46-90fe36de1d1f",
   "metadata": {},
   "source": [
    "# ΑΣΚΗΣΗ 10 — Rule-Based Arrhythmia Detector\n",
    "\n",
    "---\n",
    "\n",
    "## Οδηγιες\n",
    "1. Υπολογιστε mean και std των RR\n",
    "2. Εφαρμοστε κανονα για k = 1.5, 2.0, 2.5\n",
    "3. Συγκρινετε με πραγματικο αριθμο PVC\n",
    "\n",
    "## Ερωτησεις\n",
    "1. Ποιο k πλησιαζει τα πραγματικα PVC;\n",
    "2. Γιατι δεν ταιριαζει ακριβως;\n",
    "3. Τι θα βελτιωνατε;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4553eb7-66f2-401d-af47-b658ee7db256",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ===============================\n",
    "# ΑΣΚΗΣΗ 10 — ΓΡΑΨΤΕ ΕΔΩ\n",
    "# ===============================\n",
    "\n",
    "rr_mean_ann = np.mean(rr_ann)\n",
    "rr_std_ann = np.std(rr_ann)\n",
    "\n",
    "print(\"Mean RR: \" + str(round(rr_mean_ann, 3)) + \" sec\")\n",
    "print(\"Std RR:  \" + str(round(rr_std_ann, 3)) + \" sec\")\n",
    "print(\"Real PVC count: \" + str(pvc))\n",
    "print(\"\")\n",
    "\n",
    "for k in [1.5, 2.0, 2.5]:\n",
    "    suspicious = sum(1 for r in rr_ann\n",
    "                     if abs(r - rr_mean_ann) > k * rr_std_ann)\n",
    "    pct = round(suspicious / len(rr_ann) * 100, 1)\n",
    "    print(\"k=\" + str(k) + \": \" + str(suspicious) + \" ypopta (\" + str(pct) + \"%) | Pragmatika PVC: \" + str(pvc))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c30b82e3-5a09-4872-bd41-2e12d3fad946",
   "metadata": {},
   "source": [
    "# ΤΕΛΙΚΗ ΣΥΓΚΡΙΣΗ ΟΜΑΔΩΝ\n",
    "\n",
    "---\n",
    "\n",
    "## Γραψτε τα αποτελεσματα στον πινακα:\n",
    "\n",
    "| Μετρηση | Ομαδα A (100) | Ομαδα B (106) | Ομαδα C (119) |\n",
    "|---------|---------------|---------------|---------------|\n",
    "| HR (bpm) | | | |\n",
    "| SDNN (ms) | | | |\n",
    "| RMSSD (ms) | | | |\n",
    "| Total beats | | | |\n",
    "| PVC count | | | |\n",
    "| PVC % | | | |\n",
    "| Ypopta k=2 | | | |\n",
    "\n",
    "## Ερωτησεις Συζητησης\n",
    "\n",
    "1. Ποιο record ειναι πιο \"υγιες\"; Γιατι;\n",
    "2. Ποιο εχει υψηλοτερο HRV; Ειναι υγιες ή λογω αρρυθμιων;\n",
    "3. Υπαρχει σχεση PVC% και SDNN;\n",
    "4. Ο rule-based detector ειναι αξιοπιστος;\n",
    "5. Τι θα βελτιωνατε;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "feefafe4-0b9f-4fe9-bf5d-a2eeec3aba3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TELIKH ANAFORA OMADAS\n",
    "\n",
    "print(\"=\" * 60)\n",
    "print(\"TELIKH ANAFORA - OMADA: Record \" + record_id)\n",
    "print(\"=\" * 60)\n",
    "print(\"  HR:          \" + str(round(hr_mean, 1)) + \" bpm\")\n",
    "print(\"  SDNN:        \" + str(round(sdnn * 1000, 1)) + \" ms\")\n",
    "print(\"  RMSSD:       \" + str(round(rmssd * 1000, 1)) + \" ms\")\n",
    "print(\"  Total beats: \" + str(total))\n",
    "print(\"  Normal (N):  \" + str(normal) + \" (\" + str(round(normal / total * 100, 1)) + \"%)\")\n",
    "print(\"  PVC (V):     \" + str(pvc) + \" (\" + str(round(pvc_pct, 1)) + \"%)\")\n",
    "print(\"=\" * 60)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1263fcde-5982-46bc-b1d8-717067d0e43a",
   "metadata": {},
   "source": [
    "# BONUS — Frequency Domain HRV\n",
    "\n",
    "---\n",
    "\n",
    "## Ιδεα\n",
    "Εφαρμοζουμε FFT στα RR intervals.\n",
    "\n",
    "## Μπαντες\n",
    "\n",
    "| Μπαντα | Ευρος | Σχετιζεται με |\n",
    "|--------|-------|---------------|\n",
    "| LF | 0.04-0.15 Hz | Μικτο (συμπαθητικο + παρασυμπαθητικο) |\n",
    "| HF | 0.15-0.40 Hz | Παρασυμπαθητικο (αναπνοη) |\n",
    "\n",
    "## LF/HF Ratio\n",
    "- Υψηλο: συμπαθητικη κυριαρχια (στρες)\n",
    "- Χαμηλο: παρασυμπαθητικη κυριαρχια (χαλαρωση)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "904ca175-c0d5-479a-810d-e234892fdad6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# BONUS - Frequency HRV\n",
    "\n",
    "from scipy.interpolate import interp1d\n",
    "\n",
    "rr_times = np.cumsum(rr_ann)\n",
    "rr_values = np.array(rr_ann)\n",
    "\n",
    "fs_hrv = 4\n",
    "t_interp = np.arange(rr_times[0], rr_times[-1], 1.0 / fs_hrv)\n",
    "interp_func = interp1d(rr_times, rr_values, kind='cubic')\n",
    "rr_interp = interp_func(t_interp)\n",
    "\n",
    "rr_centered = rr_interp - np.mean(rr_interp)\n",
    "\n",
    "N_fft = len(rr_centered)\n",
    "yf = np.abs(fft(rr_centered)) ** 2\n",
    "xf = fftfreq(N_fft, 1.0 / fs_hrv)\n",
    "\n",
    "mask = (xf > 0) & (xf < 0.5)\n",
    "\n",
    "plt.figure(figsize=(12, 5))\n",
    "plt.plot(xf[mask], yf[mask])\n",
    "plt.axvspan(0.04, 0.15, alpha=0.2, color='blue', label='LF (0.04-0.15)')\n",
    "plt.axvspan(0.15, 0.40, alpha=0.2, color='green', label='HF (0.15-0.40)')\n",
    "plt.title(\"HRV Power Spectrum - Record \" + record_id)\n",
    "plt.xlabel(\"Frequency (Hz)\")\n",
    "plt.ylabel(\"Power\")\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.show()\n",
    "\n",
    "lf_mask = (xf > 0.04) & (xf < 0.15)\n",
    "hf_mask = (xf > 0.15) & (xf < 0.40)\n",
    "\n",
    "lf_power = np.sum(yf[lf_mask])\n",
    "hf_power = np.sum(yf[hf_mask])\n",
    "\n",
    "print(\"LF power:  \" + str(round(lf_power, 2)))\n",
    "print(\"HF power:  \" + str(round(hf_power, 2)))\n",
    "if hf_power > 0:\n",
    "    print(\"LF/HF:    \" + str(round(lf_power / hf_power, 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "358dcad5-0ce0-417a-a78d-f4897773bf91",
   "metadata": {},
   "source": [
    "# BONUS — Mikro ML Classification\n",
    "\n",
    "---\n",
    "\n",
    "## Στοχος\n",
    "Logistic Regression: Normal vs PVC βαση RR intervals.\n",
    "\n",
    "## Features: RR_before, RR_current, RR_after\n",
    "## Label: 0=Normal, 1=PVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c1034cd-49c1-434b-b792-d253b94cfb02",
   "metadata": {},
   "outputs": [],
   "source": [
    "# BONUS - Simple ML Classification\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "X = []\n",
    "y = []\n",
    "\n",
    "for i in range(1, len(rr_ann) - 1):\n",
    "    if beat_labels[i] in ['N', 'V']:\n",
    "        rr_before = rr_ann[i - 1]\n",
    "        rr_current = rr_ann[i]\n",
    "        rr_after = rr_ann[i + 1] if i + 1 < len(rr_ann) else rr_ann[i]\n",
    "        X.append([rr_before, rr_current, rr_after])\n",
    "        y.append(1 if beat_labels[i] == 'V' else 0)\n",
    "\n",
    "X = np.array(X)\n",
    "y = np.array(y)\n",
    "\n",
    "print(\"Total samples: \" + str(len(y)))\n",
    "print(\"Normal: \" + str(sum(y == 0)))\n",
    "print(\"PVC:    \" + str(sum(y == 1)))\n",
    "\n",
    "model = LogisticRegression(max_iter=1000)\n",
    "scores = cross_val_score(model, X, y, cv=5, scoring='accuracy')\n",
    "\n",
    "print(\"\")\n",
    "print(\"5-fold CV Accuracy: \" + str(round(scores.mean() * 100, 1)) + \"% +/- \" + str(round(scores.std() * 100, 1)) + \"%\")\n",
    "\n",
    "model.fit(X, y)\n",
    "y_pred = model.predict(X)\n",
    "\n",
    "print(\"\")\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(y, y_pred, target_names=['Normal', 'PVC']))"
   ]
  }
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