37 research outputs found
Heartbeat Anomaly Detection using Adversarial Oversampling
Cardiovascular diseases are one of the most common causes of death in the
world. Prevention, knowledge of previous cases in the family, and early
detection is the best strategy to reduce this fact. Different machine learning
approaches to automatic diagnostic are being proposed to this task. As in most
health problems, the imbalance between examples and classes is predominant in
this problem and affects the performance of the automated solution. In this
paper, we address the classification of heartbeats images in different
cardiovascular diseases. We propose a two-dimensional Convolutional Neural
Network for classification after using a InfoGAN architecture for generating
synthetic images to unbalanced classes. We call this proposal Adversarial
Oversampling and compare it with the classical oversampling methods as SMOTE,
ADASYN, and RandomOversampling. The results show that the proposed approach
improves the classifier performance for the minority classes without harming
the performance in the balanced classes
Atrial fibrillation classification based on MLP networks by extracting Jitter and Shimmer parameters
Atrial fibrillation (AF) is the most common cardiac anomaly and one that potentially threatens human life. Due to its relation to a
variation in cardiac rhythm during indeterminate periods, long-term observations are necessary for its diagnosis. With the increase
in data volume, fatigue and the complexity of long-term features make analysis an increasingly impractical process. Most medical
diagnostic aid systems based on machine learning, are designed to automatically detect, classify or predict certain behaviors. In
this work, using the PhysioNet MIT-BIH Atrial Fibrillation database, a system based on MLP artificial neural network is proposed
to differentiate, between AF and non-AF, segments and ECG’s features, obtaining average accuracy of 80.67% in test set, for the
10-fold cross-validation method. As a highlight, the extraction of jitter and shimmer parameters from ECG windows is presented
to compose the network input sets, indicating a slight improvement in the model's performance. Added to these, Shannon's and
logarithmic energy entropies are determined, also indicating an improvement in performance related to the use of fewer features.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/05757/2020.info:eu-repo/semantics/publishedVersio
AI Techniques for Efficient Healthcare Systems in ECG Wave Based Cardiac Disease Detection by High Performance Modelling
Heart disease (HD) is extremely lethal by nature and claims a disproportionately large number of lives worldwide. Early and reliable detection techniques are necessary to prevent fatalities from HD. Clinical test results, electrocardiogram (ECG) signal, the heart sound signal, impedance cardiography (ICG), magnetic resonance imaging, and computer tomography (CT) can all be used to determine whether an individual has HD. This research propose novel technique in efficient healthcare system by ECG wave based cardiac disease detection using deep learning architecture with high performance modelling. Here the input is collected as ECG waves which has been processed and obtained as ECG wave fragments. This ECG fragment features has been extracted using deep belief kernel principal neural network. Based on this extracted features the patients 3D heart image has been collected and classified using deep quantum multilayer convolutional neural networks. Here the experimental analysis has been carried out in terms of accuracy, precision, recall, F-score, SNR, RMSE. Proposed technique attained accuracy of 95%, precision of 81%, recall of 69%, F-1score of 73%, SNR of 59% and RMSE of 62%.  
A Compact LSTM-SVM Fusion Model for Long-Duration Cardiovascular Diseases Detection
Globally, cardiovascular diseases (CVDs) are the leading cause of mortality,
accounting for an estimated 17.9 million deaths annually. One critical clinical
objective is the early detection of CVDs using electrocardiogram (ECG) data, an
area that has received significant attention from the research community.
Recent advancements based on machine learning and deep learning have achieved
great progress in this domain. However, existing methodologies exhibit inherent
limitations, including inappropriate model evaluations and instances of data
leakage. In this study, we present a streamlined workflow paradigm for
preprocessing ECG signals into consistent 10-second durations, eliminating the
need for manual feature extraction/beat detection. We also propose a hybrid
model of Long Short-Term Memory (LSTM) with Support Vector Machine (SVM) for
fraud detection. This architecture consists of two LSTM layers and an SVM
classifier, which achieves a SOTA results with an Average precision score of
0.9402 on the MIT-BIH arrhythmia dataset and 0.9563 on the MIT-BIH atrial
fibrillation dataset. Based on the results, we believe our method can
significantly benefit the early detection and management of CVDs