GPT-4: Building a Cardiac Anomaly Identifier

Having completed the Quantum Computing detection application for ECG anomalies, Pivotport is now focusing on the identification of anomalies in ECGs. To explore potential approaches, we engaged GPT-4 in this challenge.

GPT-4’s Proposed Solution

In an impressive demonstration of its capabilities, GPT-4 generated a comprehensive solution in under two minutes. The proposed approach includes both traditional machine learning and quantum computing implementations, particularly relevant as Pivotport has been working with the MIT dataset mentioned in the solution.

Traditional Implementation Approach

GPT-4 outlined a systematic process involving:

1. Data Collection: Utilizing the MIT-BIH Arrhythmia Database

import wfdb

# Download the MIT-BIH Arrhythmia Database
wfdb.dl_database('mitdb', os.getcwd())

# Load a recording
record = wfdb.rdrecord('mitdb/100', sampfrom=0, sampto=3000)

2. Data Preprocessing: Implementing noise removal and signal processing

import biosppy.signals.ecg as ecg

# Preprocess the recording
out = ecg.ecg(signal=record.p_signal[:, 0], sampling_rate=record.fs, show=False)
preprocessed_signal = out['filtered']

3. Feature Extraction: Analyzing QRS complex duration, PR interval, and ST segment characteristics

import biosppy.signals.ecg as ecg

# Extract features
out = ecg.ecg(signal=preprocessed_signal, sampling_rate=record.fs, show=False)
qrs_duration = out['rpeaks'][1:] - out['rpeaks'][:-1]
pr_interval = out['rpeaks'] - out['p_peaks']
st_elevation = out['systoles'] - out['isoel']

4. Model Training: Using various machine learning algorithms

from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split

# Load labeled dataset
labels = wfdb.rdann('mitdb/100', 'atr').symbol[1:-1]
labels[labels == 'N'] = 0
labels[labels != 0] = 1

# Combine features into a feature matrix
features = np.column_stack((qrs_duration, pr_interval, st_elevation))

# Split into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2)

# Train a decision tree classifier
clf = DecisionTreeClassifier()
clf.fit(X_train, y_train)

5. Deployment: Creating a web-based application interface

from flask import Flask, request

app = Flask(__name__)

@app.route('/classify', methods=['POST'])
def classify():
    # Load the uploaded recording
    recording = request.files['recording']
    record = wfdb.rdrecord(recording, sampto=3000)
    
    # Preprocess the recording
    out = ecg.ecg(signal=record.p_signal[:, 0], sampling_rate=record.fs, show=False)
    preprocessed_signal = out['filtered']
    
    # Extract features
    out = ecg.ecg(signal=preprocessed_signal

Quantum Computing Approach

The quantum computing implementation suggested by GPT-4 involved using Quantum Fourier Transform (QFT) for signal processing. While the suggestion showed impressive technical understanding (such as noting the importance of do_swaps=False in QFT implementation), it revealed current limitations in practical quantum computing applications for ECG analysis.

Limitations and Reality Check

GPT-4’s response highlighted several important considerations:

1. Current quantum computers lack sufficient qubits for complex signal processing
2. Practical implementation requires extensive expertise across multiple domains
3. Significant resources would be needed for testing and validation

Pivotport’s Path Forward

While GPT-4’s traditional implementation approach aligns with established methodologies, its quantum computing suggestion requires significant adaptation. Pivotport believes that the speed advantage in detection and identification using quantum computing could be a game-changer in Cardiac Anomaly signal processing, but this will require innovative approaches beyond current theoretical frameworks.

We will continue to develop our own quantum computing implementation, leveraging our experience with the MIT dataset and our understanding of both quantum computing and cardiac signal processing.