تکامل عصبی مبتنی بر دستور زبان
Abstract
1. Introduction
2. Related works
3. Deep neural network structure search space
4. Deep neural network topology
5. Adaptive grammar-based deep neuroevolution
6. Data set
7. Evaluation: comparison with other classifiers
8. Evaluation: comparison among different variants
9. Discussion and future work
Conflict of interest
CRediT authorship contribution statement
Acknowledgments
References
Abstract
Ventricular tachycardia is a rapid heart rhythm that begins in the lower chambers of the heart. When it happens continuously, this may result in life-threatening cardiac arrest. In this paper, we apply deep learning techniques to tackle the problem of the physiological signal classification of ventricular tachycardia, since deep learning techniques can attain outstanding performance in many medical applications. Nevertheless, human engineers are required to manually design deep neural networks to handle different tasks. This can be challenging because of many possible deep neural network structures. Therefore, a method, called ADAG-DNE, is presented to automatically design deep neural network structures using deep neuroevolution. Our approach defines a set of structures using probabilistic grammar and searches for best network structures using Probabilistic Model Building Genetic Programming. ADAG-DNE takes advantages of the probabilistic dependencies found among the structures of networks. When applying ADAG-DNE to the classification problem, our discovered model achieves better accuracy than AlexNet, ResNet, and seven non-neural network classifiers. It also uses about 2% of parameters of AlexNet, which means the inference can be made quickly. To summarize, our method evolves a deep neural network, which can be implemented in expert systems. The deep neural network achieves high accuracy. Moreover, it is simpler than existing deep neural networks. Thus, computational efficiency and diagnosis accuracy of the expert system can be improved.
Introduction
Cardiovascular disease is the leading cause of death among people. According to the report released by the American Heart Association (Benjamin et al., 2018), cardiac arrest as an underlying cause of death in 2015 was 17668; any-mention mortality in 2015 was 366807. If patients suffer from endstage renal disease (a type of kidney disease), arrhythmias (i.e. heart rhythm disorder) and sudden cardiac death accounts for nearly 40% of the deaths (Benjamin et al., 2018). In this paper, we are focusing on the diagnosis of ventricular tachycardia in Intensive Care Unit (ICU). Ventricular tachycardia is a heart arrhythmia initiated by abnormal electrical signals in the lower chambers of the heart (Berbari, Scherlag, Hope, & Lazzara, 1978; Bradfield, Boyle, & Shivkumar, 2017; Uther, Dennett, Duffy, Freedman, & Tan, 1979). An ICU in a hospital is a facility dedicated to providing life support and monitoring in patients who are critically ill, for instance, life-threatening illness, injuries, and multiple organ failures. ICU care is important to other medical services, including surgery (World Health Organization, 2003), or care for patients with complications of diseases (Baker, 2009). Higher quality of ICU will also increase citizen confidence in the health care system (Riviello, Letchford, Achieng, & Newton, 2011). In clinical management of ICU, real-time physiological measurement systems help clinicians to continually monitor the physiological status of patients. For example, pulse oximeter provides the oxygen saturation values and shows the plethysmographic waveform of the pulse signal over time (Shamir, Eidelman, Floman, Kaplan, & Pizov, 1999).