# hemant-kumar-m/ecg-heartbeat-classification-using-machine-learning-and-deep-learning-algorithms. architecture

> Architecture diagram of the GitHub repository hemant-kumar-m/ecg-heartbeat-classification-using-machine-learning-and-deep-learning-algorithms., generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-06-27). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/hemant-kumar-m/ecg-heartbeat-classification-using-machine-learning-and-deep-learning-algorithms.
- Repository: https://github.com/hemant-kumar-m/ecg-heartbeat-classification-using-machine-learning-and-deep-learning-algorithms.

## Overview

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`](https://github.com/hemant-kumar-m/ecg-heartbeat-classification-using-machine-learning-and-deep-learning-algorithms./blob/main/ECG.ipynb) (ECG notebook), [`ptbdb_normal.csv.zip`](https://github.com/hemant-kumar-m/ecg-heartbeat-classification-using-machine-learning-and-deep-learning-algorithms./blob/main/ptbdb_normal.csv.zip) (Normal CSV), [`ptbdb_abnormal.csv.zip`](https://github.com/hemant-kumar-m/ecg-heartbeat-classification-using-machine-learning-and-deep-learning-algorithms./blob/main/ptbdb_abnormal.csv.zip) (Abnormal CSV), [`Output`](https://github.com/hemant-kumar-m/ecg-heartbeat-classification-using-machine-learning-and-deep-learning-algorithms./tree/main/Output) (Plots)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_g1["Data"]
  node_n1[("PhysioNet<br/>external dataset")]
  node_n2["Normal CSV<br/>input data"]
  node_n3["Abnormal CSV<br/>input data"]
end

subgraph group_g2["Notebook workflow"]
  node_n4["ECG notebook<br/>orchestrator<br/>[ECG.ipynb]"]
  node_n5["Load data<br/>ingest step"]
  node_n6["Clean prep<br/>preprocess step"]
  node_n7["Split data<br/>dataset split"]
  node_n8["Feature shape<br/>representation"]
  node_n16["Evaluate<br/>metrics"]
end

subgraph group_g3["Modeling"]
  node_n9["ML branch<br/>classical ML"]
  node_n10["Logistic Reg<br/>classifier"]
  node_n11["KNN<br/>classifier"]
  node_n12["Random Forest<br/>classifier"]
  node_n13["DL branch<br/>deep learning"]
  node_n14["CNN<br/>neural model"]
  node_n15["LSTM<br/>neural model"]
end

subgraph group_g4["Outputs"]
  node_n17["Plots<br/>artifact"]
  node_n18["Report<br/>document<br/>[ECG_Report.docx]"]
  node_n19["README<br/>documentation<br/>[README.md]"]
end

node_n1 -->|"source data"| node_n2
node_n1 -->|"source data"| node_n3
node_n2 -->|"loaded by"| node_n5
node_n3 -->|"loaded by"| node_n5
node_n4 -->|"starts"| node_n5
node_n5 -->|"passes to"| node_n6
node_n6 -->|"prepares"| node_n7
node_n7 -->|"formats"| node_n8
node_n8 -->|"routes to"| node_n9
node_n9 -->|"includes"| node_n10
node_n9 -->|"includes"| node_n11
node_n9 -->|"includes"| node_n12
node_n8 -->|"routes to"| node_n13
node_n13 -->|"includes"| node_n14
node_n13 -->|"includes"| node_n15
node_n10 -->|"measured by"| node_n16
node_n11 -->|"measured by"| node_n16
node_n12 -->|"measured by"| node_n16
node_n14 -->|"measured by"| node_n16
node_n15 -->|"measured by"| node_n16
node_n16 -->|"exports"| node_n17
node_n16 -->|"documents"| node_n18
node_n4 -->|"described by"| node_n19

click node_n2 "https://github.com/hemant-kumar-m/ecg-heartbeat-classification-using-machine-learning-and-deep-learning-algorithms./blob/main/ptbdb_normal.csv.zip"
click node_n3 "https://github.com/hemant-kumar-m/ecg-heartbeat-classification-using-machine-learning-and-deep-learning-algorithms./blob/main/ptbdb_abnormal.csv.zip"
click node_n4 "https://github.com/hemant-kumar-m/ecg-heartbeat-classification-using-machine-learning-and-deep-learning-algorithms./blob/main/ECG.ipynb"
click node_n17 "https://github.com/hemant-kumar-m/ecg-heartbeat-classification-using-machine-learning-and-deep-learning-algorithms./tree/main/Output"
click node_n18 "https://github.com/hemant-kumar-m/ecg-heartbeat-classification-using-machine-learning-and-deep-learning-algorithms./blob/main/ECG_Report.docx"
click node_n19 "https://github.com/hemant-kumar-m/ecg-heartbeat-classification-using-machine-learning-and-deep-learning-algorithms./blob/main/README.md"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_n1,node_n2,node_n3 toneBlue
class node_n4,node_n5,node_n6,node_n7,node_n8,node_n16 toneAmber
class node_n9,node_n10,node_n11,node_n12,node_n13,node_n14,node_n15 toneMint
class node_n17,node_n18,node_n19 toneRose
```

## Components

### Data

Packaged ECG inputs and dataset boundary

- **PhysioNet**: external dataset · Original ECG source referenced by the project
- [Normal CSV](https://github.com/hemant-kumar-m/ecg-heartbeat-classification-using-machine-learning-and-deep-learning-algorithms./blob/main/ptbdb_normal.csv.zip): input data · Packaged normal-class ECG records · `ptbdb_normal.csv.zip`
- [Abnormal CSV](https://github.com/hemant-kumar-m/ecg-heartbeat-classification-using-machine-learning-and-deep-learning-algorithms./blob/main/ptbdb_abnormal.csv.zip): input data · Packaged abnormal-class ECG records · `ptbdb_abnormal.csv.zip`

### Notebook workflow

Primary orchestration and preprocessing inside the notebook

- [ECG notebook](https://github.com/hemant-kumar-m/ecg-heartbeat-classification-using-machine-learning-and-deep-learning-algorithms./blob/main/ECG.ipynb): orchestrator · Main end-to-end workflow entry point · `ECG.ipynb`
- **Load data**: ingest step · Reads the packaged ECG tables into memory
- **Clean prep**: preprocess step · Cleans and normalizes ECG inputs
- **Split data**: dataset split · Creates training and evaluation subsets
- **Feature shape**: representation · Converts cleaned ECG into model-ready vectors or sequences
- **Evaluate**: metrics · Computes accuracy, F1, and ROC-AUC across models

### Modeling

Classical and deep-learning branches

- **ML branch**: classical ML · Baseline tabular model path
- **Logistic Reg**: classifier · Linear baseline classifier
- **KNN**: classifier · Neighborhood-based classifier
- **Random Forest**: classifier · Tree ensemble classifier
- **DL branch**: deep learning · Sequence and tensor model path
- **CNN**: neural model · Convolutional ECG model
- **LSTM**: neural model · Sequence model selected as best performer

### Outputs

Generated reports and visual artifacts

- [Plots](https://github.com/hemant-kumar-m/ecg-heartbeat-classification-using-machine-learning-and-deep-learning-algorithms./tree/main/Output): artifact · Charts and comparison visuals · `Output`
- [Report](https://github.com/hemant-kumar-m/ecg-heartbeat-classification-using-machine-learning-and-deep-learning-algorithms./blob/main/ECG_Report.docx): document · Narrative project report · `ECG_Report.docx`
- [README](https://github.com/hemant-kumar-m/ecg-heartbeat-classification-using-machine-learning-and-deep-learning-algorithms./blob/main/README.md): documentation · Project summary and usage context · `README.md`

## Connections

- 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