Electrocardiography (ECG) is a widely used diagnostic tool for monitoring the electrical activity of the heart. The ability to detect and extract key features from an ECG signal, such as the PQRST waveforms, is crucial for analyzing heart conditions like arrhythmias, ischemia, and other cardiovascular diseases. Traditional ECG systems often require high-powered processors and complex algorithms to analyze the signals in real time. However, a project leveraging Atmel AVR microcontrollers, with wavelet transforms for feature extraction, has shown how this can be efficiently achieved with low-power, cost-effective embedded systems.

The Challenge of ECG Signal Processing

The primary challenge in ECG signal processing is accurately detecting and extracting features from the raw signal, such as the P, Q, R, S, and T waves. These features correspond to different phases of the heart’s electrical cycle and are essential for diagnosing various heart conditions.

The complexity of the ECG waveform arises from its low amplitude, noise interference, and variability between individuals. To overcome these challenges, advanced signal processing techniques like wavelet transforms are often used. Wavelet transforms offer a multi-resolution approach that can efficiently handle both high-frequency and low-frequency components, which is crucial for detecting the sharp transitions in ECG signals, particularly the R-wave.

Using Atmel AVR Microcontrollers for ECG Processing

Atmel AVR microcontrollers are known for their simplicity, low power consumption, and ease of integration into embedded systems. These microcontrollers offer an excellent platform for embedded ECG signal processing, as they are capable of handling real-time data collection, basic signal filtering, and even more complex tasks such as feature extraction.

In this project, the Atmel AVR was chosen for its ability to interface with ECG sensors, filter noise, and process the raw ECG signal using a wavelet transform algorithm. The microcontroller-based system can then detect the PQRST complex, extract essential features, and make the results available for further analysis or display.

Project Design: ECG Detection and Feature Extraction

The project follows a structured design that integrates ECG signal acquisition, noise filtering, wavelet transform-based feature extraction, and real-time display.

1. ECG Signal Acquisition

The project begins with the acquisition of raw ECG signals, typically from a commercial ECG sensor or an analog front-end (AFE) circuit. The ECG signal, which is typically a small voltage ranging from -1 to +1 mV, is amplified and digitized by an Analog-to-Digital Converter (ADC) on the AVR microcontroller.

The Atmel AVR microcontroller, such as the ATmega328P, provides sufficient ADC channels and processing power for real-time ECG signal acquisition. The signal is then preprocessed to remove noise and baseline wander.

2. Signal Filtering

Before the signal can be processed for feature extraction, it must undergo preprocessing to remove unwanted noise such as muscle artifacts, power-line interference, and baseline drift. This is typically done using a combination of bandpass filters (to retain the heart rate frequency band) and low-pass filters (to remove high-frequency noise).

In the project, a simple FIR (Finite Impulse Response) filter was implemented in software on the AVR microcontroller to filter out unwanted noise. The filtered signal is then ready for wavelet transform-based feature extraction.

3. Wavelet Transform for Feature Extraction

Wavelet transforms are particularly well-suited for ECG signal analysis because they provide both time and frequency domain information, which is necessary to capture the high-frequency components of the QRS complex (especially the R-wave) while preserving the low-frequency components for the P and T waves.

The project uses the Discrete Wavelet Transform (DWT), specifically with the Haar or Daubechies wavelet, due to their simplicity and efficiency for real-time implementation on embedded systems. The DWT decomposes the ECG signal into several sub-bands, capturing features at multiple resolutions.

The key feature extracted from the wavelet decomposition is the R-wave, the most prominent feature in the ECG signal, which serves as the reference point for detecting the P, Q, S, and T waves. Once the R-wave is detected, the intervals between the waves (such as the RR interval) can be calculated.

4. PQRST Detection

With the wavelet transform providing the necessary signal components, the AVR microcontroller detects the locations of the P, Q, R, S, and T waves. The position of the R-wave is identified first due to its high amplitude and sharp nature. From the R-wave, the system can then approximate the positions of the other waves based on the expected morphology of the ECG signal.

For instance, the P-wave precedes the QRS complex and the T-wave follows the QRS complex. By analyzing the wavelet coefficients, the system identifies the start and end points of each wave and calculates the duration of the intervals, such as the PR interval, QRS duration, and QT interval.

5. Feature Extraction

Once the PQRST complex is detected, various features can be extracted for further analysis. These features include:

  • Heart rate: Calculated from the RR interval (the time between consecutive R-waves).
  • PR interval: The time between the start of the P-wave and the start of the QRS complex.
  • QT interval: The time between the start of the QRS complex and the end of the T-wave.
  • QRS duration: The duration of the QRS complex itself.

These features can then be used for diagnostics or further processing in medical applications, such as heart rate variability analysis or arrhythmia detection.

6. Output and Display

The Atmel AVR microcontroller can interface with a display, such as an LCD or an LED matrix, to show the extracted features and results in real-time. Additionally, the system can store the data for future analysis or transmit it to a remote device via serial communication for further processing.

Conclusion

This project demonstrates the feasibility of using Atmel AVR microcontrollers for ECG PQRST detection and feature extraction using wavelet transforms. By leveraging the microcontroller’s ADC, processing power, and low-power characteristics, it is possible to create a compact, cost-effective, and efficient embedded system for real-time ECG analysis.

The use of wavelet transforms for feature extraction allows the system to effectively handle the complexities of ECG signals, enabling the detection of critical heart-related features. This approach opens the door to low-cost, portable ECG monitoring devices that can be used in clinical settings or by individuals for continuous health monitoring.

The integration of such systems into wearable devices or remote health monitoring tools could significantly enhance the accessibility of healthcare, particularly in underdeveloped areas or for personal health monitoring, paving the way for more proactive healthcare practices.