All classes EKG Arrhythmia Classification Wavelet Deep Learning Syllabus · Episode 1 of 6

Class EKG Arrhythmia Classification Wavelet Deep Learning #1: EKG & EEG Signals: Characteristics and Public Datasets (MIT-BIH)

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Tutorial on EKG Signals and MIT-BIH Arrhythmia Database with Python: wfdb, AAMI classes, inter-patient splitting. Episode 1 of the Wavelet Deep Learning EKG Classification Series.

Software: Python (wfdb, SciPy, PyWavelets, scikit-learn, PyTorch, Matplotlib)
Level: Intermediate
Duration: ± 10 min
Series total: 56 min · 6 videos
Published: October 25, 2026

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Class EKG Arrhythmia Classification Wavelet Deep Learning #1: EKG & EEG Signals: Characteristics and Public Datasets (MIT-BIH): Cover: Class EKG Arrhythmia Classification Wavelet Deep Learning #1: EKG & EEG Signals: Characteristics and Public Dataset (MIT-BIH)

Class EKG Arrhythmia Classification Wavelet Deep Learning #1: EKG & EEG Signals: Characteristics and Public Datasets (MIT-BIH): Before you start, steps list, and Step 1

Class EKG Arrhythmia Classification Wavelet Deep Learning #1: EKG & EEG Signals: Characteristics and Public Datasets (MIT-BIH): Step 2: AAMI Classes & DS1/DS2

Class EKG Arrhythmia Classification Wavelet Deep Learning #1: EKG & EEG Signals: Characteristics and Public Datasets (MIT-BIH): Step 3–4: Reading records & annotations

Class EKG Arrhythmia Classification Wavelet Deep Learning #1: EKG & EEG Signals: Characteristics and Public Datasets (MIT-BIH): Step 5: Signal & beat morphology

Class EKG Arrhythmia Classification Wavelet Deep Learning #1: EKG & EEG Signals: Characteristics and Public Datasets (MIT-BIH): Done: Final Result and Further Practice

Cover: Class EKG Arrhythmia Classification Wavelet Deep Learning #1: EKG & EEG Signals: Characteristics and Public Dataset (MIT-BIH)

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Transcript

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Introduction

This is the final result, a heart recording from 44 patients of the MITBIH database, more than 100,000 heartbeats labeled as normal and 3 types of arrhythmia, divided so that test patients are never seen during training, like in a real hospital. Let's get started. Hello, welcome to the research tutorial series, EKG signal classification for arrhythmia detection with wavelet and deep learning. Arrhythmia, a heart rhythm disorder, can lead to stroke or cardiac arrest, and reading hours of EKG recordings is very tiring for doctors. In this 6-episode series, we build an automatic classification system from scratch, MITBIH dataset, filtering and segmentation, wavelet transformation, classical machine learning, CNN1 and 2D, up to model evaluation and interpretation. The same concepts also apply to brain signals like EEG.

Step 1: Installing the library

We open the terminal through the terminal menu, new terminal, then install WFDB to read the format visionet, skipy, pi-wave-flats, scikit-learn, torq, and matplotlib. The MITBIH database is already available in the dataset folder, specifically for premium customers.

Step 2: AAMI Class & DS1/D$2

Create a file named data_underscore_ek.py. MITBIH is recorded at 360 Hz. The AAMI standard classifies annotation symbols into 4 classes, N for normal beats and bundle branch block, S for supraventricular ectopic beats from the atrium, V for ventricular ectopic beats, and F for fused ventricular and normal beats. We use inter-patient division decazal, DS1 for training and DS2 for testing containing different patients, and 4 recordings with pacemakers are excluded. Many studies report an accuracy of 99% because the same patient bits are trained and tested together. Inter-patient splitting is much more honest.

Step 3: Reading records & annotations

The load record function reads signals with WFDB, selecting ML2 lit, reading doctor annotations, and converting each symbol into an AAMI class, or minus 1 for non-bit symbols. Create the exploration_underscore ek.py file and run it.

Step 4: Class composition

DS1 contains 22 patients with 51,000 bits, 89.9% normal, 1.9% S, 7.4% V, and 0.8% F. DS2 contains 49,700 bits with S proportion almost double, 3.7%. This imbalance is the main challenge, a model that always guesses normal is already correct 90%. Recording 208 lasts 30 minutes with 3040 annotations.

Step 5: Signal & beat morphology

Let's look at the graph. In an 8-second snippet of recording 208, V bits appear wide and high, different from N bits. A bar chart scale lock shows the dominance of the N class. Average shape per class shows morphological differences. The QRS bit V is much wider and has a different direction, while the F bit is in between them.

Done!

Done. We have now learned about EKG signals, the MITBIH database, AAMI classes, and the inter-patient division of DS1 and DS2. In the next episode, we will filter noise, cut each bit, and calculate RR interval features. Currently working on a thesis, dissertation, or research in the field of biomedical engineering and artificial intelligence? Cerdas Inovasi Digital helps implement biomedical signal processing, from EKG filtering and segmentation, wavelet transformation, classic machine learning, up to CNN1 and 2D models along with their interpretations, as well as opening Python programming courses for electrical and biomedical engineering. Want to master the entire series, from the MITBIH database, to class-wise arrhythmia evaluation? All 6 episodes, complete with step-by-step PDF guides, and datasets and source code, is available for premium tutorial subscribers. Subscribe starting at Rp69.000 per month, save 28% with the 12-month package, or pay once Rp999.000 for lifetime access. Want just one class? Buy single classes with lifetime access. Subscribe now on the premium page cerdasinovasidigital.com. Contact our WhatsApp, or visit cerdasinovasidigital.com. Thank you for watching, see you in the next episode. Thank you for watching,

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Transcript

0:03Introduction

1:00Step 1: Installing the library

1:19Step 2: AAMI Class & DS1/D$2

2:58Step 3: Reading records & annotations

3:51Step 4: Class composition

5:35Step 5: Signal & beat morphology

8:00Done!

Ask the class material

My notes

Questions & answers

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▶ Video Lesson

Recommended classes for you

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Transcript

0:03Introduction

1:00Step 1: Installing the library

1:19Step 2: AAMI Class & DS1/D$2

2:58Step 3: Reading records & annotations

3:51Step 4: Class composition

5:35Step 5: Signal & beat morphology

8:00Done!

Ask the class material

My notes

Questions & answers

Your instructor

Introduction

Step 1: Installing the library

Step 2: AAMI Class & DS1/D$2

Step 3: Reading records & annotations

Step 4: Class composition

Step 5: Signal & beat morphology

Done!

Introduction

Step 1: Installing the library

Step 2: AAMI Class & DS1/D$2

Step 3: Reading records & annotations

Step 4: Class composition

Step 5: Signal & beat morphology

Done!