Improving the Quality of ECGs Collected using Mobile Phones

With the onset of mobile health technology, there arises the opportunity and need for phone based portable ECG systems to detect and discriminate diagnosable and undiagnosable ECG signals. This project details a real time ECG noise detection algorithm that is able to classify whether an acquired ECG signal is adequate for diagnostic purposes or if another recording is needed. ECG recordings have been collected from MIT_BIH Noise Stress Test Database, in which 12 collections of 6 different signal-to-noise ratios. Recordings begin with five minutes of clean, unaltered ECG data before alternating between noisy and clean ECG data every two minutes. Signal processing is designed to run on the Nucleo F303K8 microcontroller, and output the determined classification to an oscilloscope.

Table of Contents

• Introduction
• How to Use
  • Data Interpretation
  • Installation
• Test Inputs
• Example Test Cases
• Testing Metrics

Introduction

This project operates on two STM32 Nucleo-F303K8 with a sim card for reading ECG data for testing. The two Nucleo-F303K8 boards are based on a sender board and a receiver board, communicating via SPI. The sender board reads a sim card for ECG data and sends the data via SPI to pins D0 and D1 on the receiver board. The receiver board code then converts the serial data into a floating point value that is a single sample of ECG at 360 Hz. This floating point conversion is done in the ISR function "void ISRfxn(){}". At the end of "ISRfxn()", the ISR state flag "FILTERING_STATE" is asserted which starts the main application of thsi project.

After the serial data preprocessing, the floating point data is fed into the Pan-Tompkins QRS detection algorithm broken into two stages:

• float pan_T_Filter(float Ain, float *yOut);
• bool pan_T_Threshold(float *yOut, float *thresholdi1, float *spki, float *npki, float *spki_array, float *npki_array) ;

Details and documentation of these functions can be found in the "BME463_lib.h" file. The function most relevant to the noise detection algorithm is the pan_T_threshold() function and updates the associated values for thresholding and noise detection:

/* Main App, Local Variables */ 
float output[3] = {0.0, 0.0, 0.0};
float NSR = 0.0;
float npki = 0.0;
float spki = 0.0;
float npki_clean = 0.0;
float spki_clean = 0.0;
float npki_array[8] = {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0};
float spki_array[8] = {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0};
float spki_array_clean[8] = {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0};
float npki_array_clean[8] = {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0};
float thresholdi1 = 0.0;
bool QRS_detected = false;
bool cur_noise_state = false;
bool prev_noise_state = false;

The noise detection algorithm is detailed in the following chunk and in main.cpp. When the signal is considered diagnosable, the npki and spki values are saved. When the signal is considered undiagnosable, the npki and spki values update but the saved "clean" values are preserved. The diagnosable/undiagnosable classification exists in the first and second line below and determine the cur_noise_state. When the cur_noise_state changes states, the clean npki and spki values are loaded into the runnign version. Then the prev_noise_state is updated.

NSR = npki/sqrt(npki*npki + spki*spki);
cur_noise_state = NSR > SNR_THRESHOLD;

if(!cur_noise_state){
    // Acceptable amount of noise, save copy to clean array
    save_array(npki_array, npki_array_clean, 8);
    save_array(spki_array, spki_array_clean, 8);
    spki_clean = spki;
    npki_clean = npki;
}          

if(!cur_noise_state && prev_noise_state){    
    // If state transitions from noisy to clean, restore spki and npki arrays to clean state 
    save_array(npki_array_clean, npki_array, 8);
    save_array(spki_array_clean, spki_array, 8);
    spki = array_average(spki_array, 8);
    npki = array_average(npki_array, 8);
}

prev_noise_state = cur_noise_state;

ADC3 = input; 
ADC4 = spki;
ADC5 = TN_rate/3.3;

D12_Out = cur_noise_state;
D11_Out = QRS_detected;

How to Use

Due to implementation constraints, the algorithm input needs to start with a clean signal. To run properly and replicate my results, the following steps must be followed in the exact order:

1. Connect osciliscope probes to pins A3 and D12.
2. Reset Sender Board
3. Reset Receiver Board

• In this current state, the green LED at D13 should be off. If not, restart.

4. Run Sender Board File

• In this current state, the green LED at D13 should be on. If not, restart.

What is happening is that the green LED signals when the receiver board has begun runtime which is at the detection of a signal. Readings and behavior should be present on the osciliscope.f

Data Interpretation

When the input signal is in the expected “clean” state, the duty ratio for when the classification signal is asserted is recorded as false positive rate and the duty ratio for when the classification signal is de-asserted is recorded as a true negative rate. When the input signal is in the expected “noisy” state, the duty ratio for when the classification signal is asserted is recorded as true positive rate and the duty ratio for when the classification signal is de-asserted is recorded as false negative rate.

Installation

Install and run Putty Terminal Emulator with the sender and receiver board detected on their corresponding communciation ports found in your device manager window (windows OS). Then set the buad rate to 115200.

Test Inputs

List the software or hardware inputs required to test your code. This could include specific input files, signal generator settings, or hardware configurations.

Example Test Case

• Test Case:
  • Input: Load the ECG data file "118e_6.txt" onto the sender board using the Putty Terminal Emulator. Follow the instructions in the How to Use section.
  • Expected Output:

Testing Metrics

When the input signal is in the expected “clean” state, the duty ratio for when the classification signal is asserted is recorded as false positive rate and the duty ratio for when the classification signal is de-asserted is recorded as a true negative rate. When the input signal is in the expected “noisy” state, the duty ratio for when the classification signal is asserted is recorded as true positive rate and the duty ratio for when the classification signal is de-asserted is recorded as false negative rate.