How hemant-kumar-m/ecg-heartbeat-classification-using-machine-learning-and-deep-learning-algorithms. works
This repository is a single-project ECG classification pipeline, organized around one Jupyter notebook rather than a multi-service application. The architecture is data-science centric: ingest ECG records, preprocess them, train several classifiers, compare metrics, and export visual/report artifacts.
1. Primary entry point
- ECG.ipynb is the main orchestration layer.
- It likely contains the full workflow: data loading, cleaning, normalization, feature preparation, model training, evaluation, and plotting.
- There is no separate backend, frontend, or API layer in the repo.
2. Data layer
- The raw/processed inputs are stored as compressed CSVs: ptbdb_normal.csv.zip and ptbdb_abnormal.csv.zip.
- These represent the two core classes for binary classification: normal vs abnormal heartbeats.
- The README also references the MIT-BIH Arrhythmia Database as the source dataset, so the repo sits on top of a PhysioNet-derived ECG dataset.
3. Dataset boundary
- The repository’s own boundary is between external ECG source data and the notebook’s model-ready tabular representation.
- The zipped CSVs indicate the signals were already transformed into a form consumable by Pandas/NumPy, reducing the need for low-level signal acquisition logic in the repo itself.
4. Preprocessing subsystem
- The notebook performs data cleaning and normalization before model training.
- For ECG work, this usually means converting raw signal samples into standardized arrays or feature vectors, then splitting into training/test subsets.
- This is the first major transformation boundary: raw ECG samples become machine-learning inputs.
5. Feature representation
- The README mentions both “signal segmentation” and “feature extraction.”
- That suggests the notebook supports two parallel input styles:
- engineered/tabular features for classical ML models
- sequence/tensor representations for deep learning models
- This is an important architectural distinction because the ML and DL paths likely consume different shapes of the same underlying data.
6. Classical machine-learning branch
- The ML branch includes Logistic Regression, K-Nearest Neighbors, and Random Forest.
- These models are likely trained on flattened or engineered feature vectors.
- This branch is useful as a baseline comparison path and probably shares the same preprocessing stage as the DL branch up to the point of tensor shaping.
7. Deep-learning branch
- The DL branch includes CNN and LSTM models built with TensorFlow/Keras.
- CNN implies the ECG input is treated as local temporal patterns; LSTM implies sequence modeling over heartbeat time steps.
- This branch has its own model-shaping boundary, where cleaned ECG data becomes 1D/sequence input suitable for neural networks.
8. Evaluation and comparison layer
- The project evaluates models using Accuracy, F1-score, and ROC-AUC.
- This is a cross-cutting subsystem applied to both ML and DL branches to make results comparable.
- The evaluation layer produces the basis for selecting the best model, which the README identifies as LSTM.
9. Visualization and reporting outputs
- The Output directory likely contains generated plots, charts, or result artifacts from notebook execution.
- ECG_Report.docx is a human-readable report artifact, and the README also mentions a summary PDF, suggesting the notebook feeds into offline documentation rather than an interactive app.
- In architecture terms, outputs are a sink for metrics, charts, and narrative results.
10. Runtime and tooling
- The project is Python-based and notebook-driven, using Pandas, NumPy, Scikit-learn, TensorFlow, Keras, Matplotlib, and Seaborn.
- Jupyter Notebook is the main execution environment, with VS Code mentioned as an editing option.
- This means the repository is designed for exploratory, reproducible experimentation rather than packaged deployment.
11. External dependency
- The only meaningful external system is the dataset source: PhysioNet / MIT-BIH Arrhythmia Database.
- No cloud services, databases, message queues, or web servers appear in the repo.
- The architecture is therefore self-contained once the dataset files are present.
12. Overall flow
- External ECG dataset → packaged CSV inputs → notebook preprocessing → two model branches (ML and DL) → metric comparison → charts/report artifacts.
- The project’s main architectural value is the comparison framework across model families, not a production inference system.
13. Notable repo shape
- The presence of .ipynb, compressed CSVs, a Word report, and an output folder shows this is a research/portfolio-style repository.
- The implementation and the results are bundled together, so the notebook is both code and documentation driver.
14. Important diagram boundary
- If drawing an architecture diagram, the key boundary is between:
- data acquisition/storage,
- preprocessing and feature shaping,
- model training/evaluation,
- generated artifacts/reporting.
- There is no separate serving or deployment tier in the current repository.
Main entry points: ECG.ipynb ECG notebook · ptbdb_normal.csv.zip Normal CSV · ptbdb_abnormal.csv.zip Abnormal CSV · Output Plots
Components 19
Data
Packaged ECG inputs and dataset boundary
- PhysioNet · Original ECG source referenced by the project
- Normal CSV · Packaged normal-class ECG records
ptbdb_normal.csv.zip - Abnormal CSV · Packaged abnormal-class ECG records
ptbdb_abnormal.csv.zip
Notebook workflow
Primary orchestration and preprocessing inside the notebook
- ECG notebook · Main end-to-end workflow entry point
ECG.ipynb - Load data · Reads the packaged ECG tables into memory
- Clean prep · Cleans and normalizes ECG inputs
- Split data · Creates training and evaluation subsets
- Feature shape · Converts cleaned ECG into model-ready vectors or sequences
- Evaluate · Computes accuracy, F1, and ROC-AUC across models
Modeling
Classical and deep-learning branches
- ML branch · Baseline tabular model path
- Logistic Reg · Linear baseline classifier
- KNN · Neighborhood-based classifier
- Random Forest · Tree ensemble classifier
- DL branch · Sequence and tensor model path
- CNN · Convolutional ECG model
- LSTM · Sequence model selected as best performer
Connections 23
- PhysioNet → Normal CSV: source data · External ECG data is packaged into the normal class file
- PhysioNet → Abnormal CSV: source data · External ECG data is packaged into the abnormal class file
- Normal CSV → Load data: loaded by · Normal records are ingested into the notebook workflow
- Abnormal CSV → Load data: loaded by · Abnormal records are ingested into the notebook workflow
- ECG notebook → Load data: starts · Notebook drives data loading
- Load data → Clean prep: passes to · Loaded tables are cleaned and normalized
- Clean prep → Split data: prepares · Clean data is split for training and evaluation
- Split data → Feature shape: formats · Splits are shaped into tabular or sequential inputs
- Feature shape → ML branch: routes to · Engineered/tabular representations feed classical ML
- ML branch → Logistic Reg: includes · One baseline classifier in the ML branch
- ML branch → KNN: includes · One baseline classifier in the ML branch
- ML branch → Random Forest: includes · One ensemble classifier in the ML branch
- Feature shape → DL branch: routes to · Sequence/tensor representations feed deep learning
- DL branch → CNN: includes · CNN model within the DL branch
- DL branch → LSTM: includes · LSTM model within the DL branch
- Logistic Reg → Evaluate: measured by · Models are compared using shared metrics
- KNN → Evaluate: measured by · Models are compared using shared metrics
- Random Forest → Evaluate: measured by · Models are compared using shared metrics
- CNN → Evaluate: measured by · Models are compared using shared metrics
- LSTM → Evaluate: measured by · Models are compared using shared metrics
- Evaluate → Plots: exports · Metrics and comparisons become plots and charts
- Evaluate → Report: documents · Evaluation results are written into a report
- ECG notebook → README: described by · README summarizes the notebook-driven workflow