88 research outputs found
Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation
The electrocardiogram (ECG) is one of the most extensively employed signals
used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG
signals can capture the heart's rhythmic irregularities, commonly known as
arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of
patients' acute and chronic heart conditions. In this study, we propose a
two-dimensional (2-D) convolutional neural network (CNN) model for the
classification of ECG signals into eight classes; namely, normal beat,
premature ventricular contraction beat, paced beat, right bundle branch block
beat, left bundle branch block beat, atrial premature contraction beat,
ventricular flutter wave beat, and ventricular escape beat. The one-dimensional
ECG time series signals are transformed into 2-D spectrograms through
short-time Fourier transform. The 2-D CNN model consisting of four
convolutional layers and four pooling layers is designed for extracting robust
features from the input spectrograms. Our proposed methodology is evaluated on
a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art
average classification accuracy of 99.11\%, which is better than those of
recently reported results in classifying similar types of arrhythmias. The
performance is significant in other indices as well, including sensitivity and
specificity, which indicates the success of the proposed method.Comment: 14 pages, 5 figures, accepted for future publication in Remote
Sensing MDPI Journa
A new method using deep transfer learning on ECG to predict the response to cardiac resynchronization therapy
Background: Cardiac resynchronization therapy (CRT) has emerged as an
effective treatment for heart failure patients with electrical dyssynchrony.
However, accurately predicting which patients will respond to CRT remains a
challenge. This study explores the application of deep transfer learning
techniques to train a predictive model for CRT response. Methods: In this
study, the short-time Fourier transform (STFT) technique was employed to
transform ECG signals into two-dimensional images. A transfer learning approach
was then applied on the MIT-BIT ECG database to pre-train a convolutional
neural network (CNN) model. The model was fine-tuned to extract relevant
features from the ECG images, and then tested on our dataset of CRT patients to
predict their response. Results: Seventy-one CRT patients were enrolled in this
study. The transfer learning model achieved an accuracy of 72% in
distinguishing responders from non-responders in the local dataset.
Furthermore, the model showed good sensitivity (0.78) and specificity (0.79) in
identifying CRT responders. The performance of our model outperformed clinic
guidelines and traditional machine learning approaches. Conclusion: The
utilization of ECG images as input and leveraging the power of transfer
learning allows for improved accuracy in identifying CRT responders. This
approach offers potential for enhancing patient selection and improving
outcomes of CRT
Detection of multi-class arrhythmia using heuristic and deep neural network on edge device
Heart disease is a heart condition that sometimes causes a person to die suddenly. One indication is a rhythm disorder known as arrhythmia. Multi-class Arrhythmia Detection has followed: QRS complex detection procedure and arrhythmia classification based on the QRS complex morphology. We proposed an edge device that detects QRS complexes based on variance analysis (QVAT) and the arrhythmia classification based on the QRS complex spectrogram. The classifier uses two-dimensional convolutional neural network (2D CNN) deep learning. We use a single board computer and neural network compute stick to implement the edge device. The outcomes are a prototype device cardiologists use as a supporting tool for analysing ECG signals, and patients can also use it for self-tests to figure out their heart health. To evaluate the performance of our edge device, we tested using the MIT-BIH database because other methods also use the data. The QVAT sensitivity and predictive positive are 99.81% and 99.90%, respectively. Our classifier's accuracy, sensitivity, predictive positive, specificity, and F1-score are 99.82%, 99.55%, 99.55%, 99.89%, and 99.55%, respectively. The experiment result of arrhythmia classification shows that our method outperforms the others. Still, for r-peak detection, the QVAT implemented in an edge device is comparable to the other methods. In future work, we can improve the performance of r-peak detection using the double-check algorithm in QVAT and cross-check the QRS complex detection by adding 1 class to the classifier, namely the non-QRS class
Short-time fourier transform based ensemble classifiers for detection of atrial fibrillation from ECG datasets
Atrial Fibrillation (Afib) is a common cardiac arrhythmia characterized by irregular and often rapid heart rate, leading to inefficient blood pumping from the atria. It increases the risk of stroke, heart failure, and other heart-related complications. Afib is often associated with symptoms like palpitations, shortness of breath, and fatigue. Diagnosis typically involves electrocardiography (ECG) to detect irregular electrical activity in the heart. Treatment options range from medication to procedures like catheter ablation, aimed at restoring normal heart rhythm and reducing associated risks. Soft computing methods can aid in automating the classification of cardiovascular diseases, assisting clinicians in diagnosing arrhythmias. In this research paper, ensemble classifiers are employed for the classification of Atrial Fibrillation based on ECG datasets. When utilizing the Catboost Classifier in conjunction with the STFT-based GEO implementation, the results indicate an average perfect classification rate of approximately 99%, an error rate of 1%, and a kappa coefficient of 0.9689% for detection of Afib
- …