CSE498R Directed Research | Cloud-based Biometric Sensor

Tasks

Task list

To verify that the heart rate monitor is working as expected, open the serial monitor  at 9600 baud

interfacing with arduino and AD8232

/src/sketch.ino

#include <Wire.h>
#include <AD8232.h>

// Define the AD8232 module
AD8232 heartRate;

void setup()
{
  Serial.begin(9600);   // Initialize serial communication
  heartRate.begin();    // Initialize the AD8232 module
}

void loop()
{
  float heart_rate = heartRate.getHeartRate();  // Read the heart rate value
  Serial.println("Heart rate: " + String(heart_rate) + " bpm");  // Print the heart rate value
  delay(1000);  // Wait for 1 second
}

. You should see values printed on the screen. Below is an example output with the sensors connected to the forearms and right leg. Your serial work should spike between +300/-200 around the center value of about ~500.

List of hardware required

• Arduino Rev3 atmega328 + Wi-Fi → as MCU
• AD8232 Heart Sensing Monitor
• Breadboard
• Breadboard connector
• Electrode Sensor Surface pad → Disposable
• Pulse Sensor Module.
• EEG Muscle Sensor.

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Software

• Arduino IDE
• Arduino Cloud - native
• Fritzing
• Excali Draw

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Serial Plotter

Initial Prototype

First Test Case Requires Finetuning

Arduino Rev3 atmega328 + AD8232 Connection.

Wiring

• Cable Color Signal Black RA (Right Arm ) | Blue LA (Left Arm) | Red RL (Right Leg)

Enhancement+

Arduino layout with 7 Segment Led

Research design and problem addressing

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• A research problem is a specific issue, contradiction, or gap in existing knowledge that needs to be addressed.

• Identifying a research problem involves recognizing something problematic that requires attention, explanation, or a solution.

• It is essential to have a well-defined research problem to guide the research process and to contribute to the field of study.

• The process of defining a research problem includes:

  • Identifying a broad topic of interest.

  • Conducting a literature review to understand the current state of knowledge.

  • Narrowing down to a specific issue that is under-explored or controversial.

  • Formulating the problem into a clear, concise statement or question.

• Types of research problems include:

  • Descriptive: Documenting certain phenomena or situations.
  • Exploratory: Investigating a topic to generate new insights or hypotheses.
  • Explanatory: Understanding the causes or effects of certain phenomena.
  • Predictive: Anticipating future occurrences based on current trends or data.
  • Evaluative: Assessing the effectiveness of interventions, programs, or policies.

• Defining a research problem is crucial for:

  • Setting the direction and scope of the study.
  • Ensuring the research is focused and manageable.
  • Avoiding duplication of existing knowledge.
  • Making a meaningful contribution to the academic field or practical application.

• For more detailed guidance on defining a research problem, resources such as Scribbr's articles can provide step-by-step instructions and examples.

Scopes

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BPNN Model Integration

The repository now includes comprehensive mathematical formulations for integrating a Backpropagation Neural Network (BPNN) with the EKG system.

Mathematical Formulations Documentation

See BPNN_MATHEMATICAL_FORMULATIONS.md for complete mathematical documentation including:

Core BPNN Mathematics

• Forward Propagation: $z^{[l]} = W^{[l]} a^{[l-1]} + b^{[l]}$
• Activation Functions: Sigmoid, Tanh, ReLU, Softmax (with derivatives)
• Loss Functions: Binary/Categorical Cross-Entropy, MSE
• Backpropagation: Complete gradient computation formulas
• Parameter Updates: Gradient Descent, Momentum, Adam optimizer

Signal Processing

• Normalization: Min-Max and Z-Score formulas
• Filtering: Moving Average and Butterworth Bandpass filters
• Feature Extraction: HRV metrics (SDNN, RMSSD, pNN50)

EKG Synthesis Model

Mathematical model for generating synthetic EKG signals:

$$\text{ECG}(t) = \sum_{i \in {P, Q, R, S, T}} A_i \exp\left(-\frac{(t - t_i)^2}{2\sigma_i^2}\right)$$

Jupyter Notebooks

1. Data Loading and Preprocessing (dataloading.ipynb)

• Signal normalization implementations
• EKG filtering techniques
• HRV feature extraction
• Window-based segmentation for neural network input
• Complete preprocessing pipeline with visualizations

2. BPNN Implementation (randomdatasetgenerator.ipynb)

• Complete BPNN implementation from scratch
• Synthetic EKG dataset generation (normal and abnormal patterns)
• Training and evaluation pipeline
• Multi-class classification (Normal, Tachycardia, Bradycardia, Arrhythmia)
• Performance visualization and analysis

Quick Start

To use the BPNN model:

# Install required dependencies
pip install numpy pandas matplotlib scipy scikit-learn

# Run the validation script
python3 validate_formulations.py

# Open Jupyter notebooks
jupyter notebook dataloading.ipynb
jupyter notebook randomdatasetgenerator.ipynb

Model Architecture

The implemented BPNN uses the following architecture:

• Input Layer: 250 neurons (EKG window samples)
• Hidden Layer 1: 128 neurons (ReLU activation)
• Hidden Layer 2: 64 neurons (ReLU activation)
• Hidden Layer 3: 32 neurons (ReLU activation)
• Output Layer: 4 neurons (Softmax activation for 4 classes)

Total Parameters: ~54,000 trainable parameters

Classification Classes

The model classifies EKG signals into four categories:

1. Normal: Regular heart rhythm (60-100 bpm)
2. Tachycardia: Fast heart rate (>100 bpm)
3. Bradycardia: Slow heart rate (<60 bpm)
4. Arrhythmia: Irregular heart rhythm

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