Heart diseases remain one of the leading causes of death and disability worldwide, even withthe immense development of medical diagnosis and treatment. It is very important to diagnose cardiovascular conditions early...
详细信息
ISBN:
(数字)9798331530334
ISBN:
(纸本)9798331530341
Heart diseases remain one of the leading causes of death and disability worldwide, even withthe immense development of medical diagnosis and treatment. It is very important to diagnose cardiovascular conditions early using non-invasive methods, like the analysis of electrocardiogram (ECG) signals, for better prognosis and the reduction of mortality rates. In this regard, an automated system of heart disease detection is presented, based on the utilization of ECG data from the PhysioNet database in collaboration with a newly developed machine learning-based framework. It combines convolutional neural networks (CNNs) for automatic feature extraction and support vector machines (SVMs) for classification to take collective advantage of the strengths of both models. Besides this, the hybrid model utilizes temporal features like QRS-duration and RR intervals, as well as frequency-domain features derived using Fast Fourier Transform (FFT) for its enhanced predictive power. Results yielded a classification accuracy of 100% on boththe training and validation sets, thereby showing the efficacy of the model proposed in this paper. However, the major limitation that was noticed from this model is the clear indication of overfitting-the model does not generalize well on the unseen test data. Future work for us includes working on cross-validation, augmentation techniques, and other regularization methods such as L2 regularization and dropout to generalize better and avoid overfitting. Despite such limitations, our study demonstrates that a hybrid approach using CNN and SVM using both time- and frequency-domain features has great potential in the development of real-time non-invasive diagnostic tools for the detection of heart disease. Once this method is further refined and validated in a larger patient cohort, it may easily be integrated into clinical settings for enabling early interventions and improving patient outcomes.
One of the most critical factors for a successful road trip is a high degree of alertness while driving. Even a split second of inattention or sleepiness in a crucial moment, will make the difference between life and ...
详细信息
ISBN:
(纸本)9781450390699
One of the most critical factors for a successful road trip is a high degree of alertness while driving. Even a split second of inattention or sleepiness in a crucial moment, will make the difference between life and death. Several prestigious car manufacturers are currently pursuing the aim of automated drowsiness identification to resolve this problem. the path between neuro-scientific research in connection with artificial intelligence and the preservation of the dignity of human individual’s and its inviolability, is very narrow. the key contribution of this work is a system of data analysis for EEGs during a driving session, which draws on previous studies analyzing heart rate (ECG), brain waves (EEG), and eye function (EOG). the gathered data is hereby treated as sensitive as possible, taking ethical regulations into consideration. Obtaining evaluable signs of evolving exhaustion includes techniques that obtain sleeping stage frequencies, problematic are hereby the correlated interference’s in the signal. this research focuses on a processing chain for EEG band splitting that involves band-pass filtering, principal component analysis (PCA), independent component analysis (ICA) with automatic artefact severance, and fast fourier transformation (FFT). the classification is based on a step-by-step adaptive deep learning analysis that detects theta rhythms as a drowsiness predictor in the pre-processed data. It was possible to obtain an offline detection rate of 89% and an online detection rate of 73%. the method is linked to the simulated driving scenario for which it was developed. this leaves space for more optimization on laboratory methods and data collection during wakefulness-dependent operations.
暂无评论