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Structural Anomalies Detection from Electrocardiogram (ECG) with Spectrogram and Handcrafted Features
1Department of Computer Science, Faculty of Science, University of Alberta, 116 St and 85 Ave, Edmonton, AB T6G 2R3, Canada.
Insights
This study introduces a novel method using electrocardiogram (ECG) spectrograms and a Convolutional Neural Network to detect complex heart anomalies, improving early cardiovascular disease diagnosis beyond current capabilities.
Area of Science:
- Biomedical Engineering
- Cardiology
- Artificial Intelligence in Medicine
Background:
- Cardiovascular diseases (CVDs) are a leading global cause of mortality, necessitating advanced diagnostic tools.
- Existing commercial devices primarily detect basic rhythm fluctuations, limiting the diagnosis of complex cardiac anomalies.
- Early and accurate detection of diverse heart conditions is crucial for effective patient management and improved health outcomes.
Purpose of the Study:
- To develop an advanced algorithm for detecting a wider range of heart anomalies than currently available commercial products.
- To improve the accuracy and sensitivity of diagnosing both cardiac rhythm and heartbeat irregularities.
- To leverage Short-Time Fourier Transform (STFT) spectrograms and handcrafted features for enhanced ECG analysis.
Main Methods:
- A novel method combining Short-Time Fourier Transform (STFT) spectrograms of ECG signals with handcrafted features was developed.
- A Convolutional Neural Network (CNN) model was proposed and trained to analyze the combined ECG data.
- The algorithm was evaluated on its ability to detect multiple types of cardiac rhythm and heartbeat anomalies.
Main Results:
- The algorithm achieved 99.79% accuracy in detecting 16 different rhythm anomalies, with a low 0.15% false-alarm rate and 99.74% sensitivity.
- It also demonstrated high performance in detecting 13 heartbeat anomalies, achieving 99.18% accuracy, a 0.45% false-alarm rate, and 98.80% sensitivity.
- These results surpass the diagnostic capabilities of many current commercial heart monitoring products.
Conclusions:
- The proposed STFT spectrogram and CNN-based method offers a significant advancement in diagnosing complex cardiovascular conditions.
- This approach enhances the early detection of both rhythm and heartbeat anomalies, potentially improving patient outcomes.
- The high accuracy and sensitivity suggest clinical utility for this advanced ECG analysis technique.
Abstract:
Cardiovascular diseases are the leading cause of death globally, causing nearly 17.9 million deaths per year. Therefore, early detection and treatment are critical to help improve this situation. Many manufacturers have developed products to monitor patients' heart conditions as they perform their daily activities. However, very few can diagnose complex heart anomalies beyond detecting rhythm fluctuation. This paper proposes a new method that combines a Short-Time Fourier Transform (STFT) spectrogram of the ECG signal with handcrafted features to detect heart anomalies beyond commercial product capabilities. Using the proposed Convolutional Neural Network, the algorithm can detect 16 different rhythm anomalies with an accuracy of 99.79% with 0.15% false-alarm rate and 99.74% sensitivity. Additionally, the same algorithm can also detect 13 heartbeat anomalies with 99.18% accuracy with 0.45% false-alarm rate and 98.80% sensitivity.
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