Abstract 336: A Machine Learning Algorithm for Detecting Pulse During Out-Of-Hospital Cardiac Arrest

Circulation ◽  
2019 ◽  
Vol 140 (Suppl_2) ◽  
Author(s):  
Erik Alonso ◽  
Elisabete Aramendi ◽  
Unai Irusta ◽  
Mohamud R Daya
Keyword(s):  
Machine Learning ◽  
Cardiac Arrest ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Performance Metrics ◽  
Learning Algorithm ◽  
Hidden Markov ◽  
Pulseless Electrical Activity ◽  
Discrete Observation ◽  
Hospital Cardiac Arrest

Introduction: Pulse detection during out-of-hospital cardiac arrest (OHCA) is a challenge still not satisfactorily solved. An automated and accurate method for detecting pulse would reduce hands-off intervals and allow for more prompt post-cardiac arrest care. The aim of this study was to develop a method based on machine learning (ML) to detect pulse during OHCA. Materials and methods: Data were gathered from 187 OHCA patients treated by Tualatin Valley Fire & Rescue (Tigard, OR, USA) using the Philips HeartStart MRx monitor/defibrillator between 2010 and 2014. The dataset used in the study contained 1140 5-s epochs presenting organized rhythms, 792 pulse-generating rhythms (PRs) and 348 pulseless electrical activity (PEA), annotated by consensus between two clinicians and a biomedical engineer using the available clinical information and the capnography signal. The dataset was split patient-wise into training (60%) and test (40%) sets. Each epoch contained the ECG and the thoracic impedance that were first preprocessed and then used to adaptively extract the impedance circulation component (ICC). The ICC shows a small fluctuation with each effective heartbeat. A total of 7 well-known waveform features were computed from the ECG and ICC and fed as observations to a discrete observation density hidden Markov model that classified each observation as PR (pulse) or PEA (no-pulse). The training set was used to develop and optimize the method, while the test set was used to measure the performance in terms of sensitivity (PR detection) and specificity (PEA detection). This procedure was repeated 50 times to estimate the distributions of the performance metrics. Results: The method showed a mean (SD) sensitivity and specificity of 95.4%(2.2) and 91.6% (3.4), respectively. Results were slightly above those previously reported by other authors using different ML techniques. Conclusions: A method based on a discrete observation density hidden Markov model can accurately detect pulse during OHCA. Further studies with larger datasets are needed to confirm these findings.

scholarly journals AA-HMM: An Anti-Adversarial Hidden Markov Model for Network-Based Intrusion Detection

2018 ◽  
Vol 8 (12) ◽  
pp. 2421 ◽  
Author(s):  
Chongya Song ◽  
Alexander Pons ◽  
Kang Yen
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Performance Metrics ◽  
Hidden Markov ◽  
Data Sets ◽  
Learning Abilities ◽  
Benchmark Data ◽  
Malicious Behavior ◽  
Network Intrusion ◽  
The Common

In the field of network intrusion, malware usually evades anomaly detection by disguising malicious behavior as legitimate access. Therefore, detecting these attacks from network traffic has become a challenge in this an adversarial setting. In this paper, an enhanced Hidden Markov Model, called the Anti-Adversarial Hidden Markov Model (AA-HMM), is proposed to effectively detect evasion pattern, using the Dynamic Window and Threshold techniques to achieve adaptive, anti-adversarial, and online-learning abilities. In addition, a concept called Pattern Entropy is defined and acts as the foundation of AA-HMM. We evaluate the effectiveness of our approach employing two well-known benchmark data sets, NSL-KDD and CTU-13, in terms of the common performance metrics and the algorithm’s adaptation and anti-adversary abilities.

scholarly journals Multi-Layer Hidden Markov Model Based Intrusion Detection System

2018 ◽  
Vol 1 (1) ◽  
pp. 265-286 ◽  
Author(s):  
Wondimu Zegeye ◽  
Richard Dean ◽  
Farzad Moazzami
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Detection System ◽  
Traffic Monitoring ◽  
Machine Learning Algorithms ◽  
Full Potential ◽  
Multi Phase

The all IP nature of the next generation (5G) networks is going to open a lot of doors for new vulnerabilities which are going to be challenging in preventing the risk associated with them. Majority of these vulnerabilities might be impossible to detect with simple networking traffic monitoring tools. Intrusion Detection Systems (IDS) which rely on machine learning and artificial intelligence can significantly improve network defense against intruders. This technology can be trained to learn and identify uncommon patterns in massive volume of traffic and notify, using such as alert flags, system administrators for additional investigation. This paper proposes an IDS design which makes use of machine learning algorithms such as Hidden Markov Model (HMM) using a multi-layer approach. This approach has been developed and verified to resolve the common flaws in the application of HMM to IDS commonly referred as the curse of dimensionality. It factors a huge problem of immense dimensionality to a discrete set of manageable and reliable elements. The multi-layer approach can be expanded beyond 2 layers to capture multi-phase attacks over longer spans of time. A pyramid of HMMs can resolve disparate digital events and signatures across protocols and platforms to actionable information where lower layers identify discrete events (such as network scan) and higher layers new states which are the result of multi-phase events of the lower layers. The concepts of this novel approach have been developed but the full potential has not been demonstrated.

Hybrid Physics-Based and Data-Driven Modeling for Improved Standpipe Pressure Prediction

2021 ◽  
Author(s):  
Oney Erge ◽  
Eric van Oort
Keyword(s):  
Machine Learning ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Hybrid Modeling ◽  
Data Driven ◽  
Learning Models ◽  
Drilling Data ◽  
Drilling Operations ◽  
Pump Pressure

Abstract During drilling operations, it is common to see pump pressure spikes when flow is initiated, including after a connection or after a prolonged break in drilling operations. It is important to be able to predict the magnitude of such pressure spikes to avoid compromising wellbore integrity. This study shows how a hybrid approach using data-driven machine learning coupled with physics-based modeling can be used to accurately predict the magnitude of pressure spikes. To model standpipe pressure behavior, machine learning techniques were combined with physics-based models via a rule-based, stochastic decision-making algorithm. To start, neural networks and deep learning models were trained using time-series drilling data. From there, physics-based equations that model the pressure required to break the mud's gel strength as well as the flow of non-Newtonian fluids through the entire circulation system were used to simulate standpipe pressure. Then, these two highly different methods for predicting/modeling standpipe pressure were combined by a hidden Markov model using a set of rules and transition probabilities. By combining machine learning and physics-based approaches, the best features of each model are leveraged by the hidden Markov model, yielding a more accurate and robust prediction of pressure. A similar result is not achievable with a purely data-driven black-box model, because it lacks a connection to the underlying physics. Our study highlights how drilling data analysis can be optimally leveraged. The overarching conclusion: hybrid modeling can more accurately predict pump pressure spikes and capture the transient events at flow initiation when compared to physics-based or machine learning models used in isolation. Moreover, the approach is not limited to pressure behavior but can be applied to a wide range of well construction operations. The proposed approach is easy to implement and the details of implementation are presented in this study. Being able to accurately model and manage the pressure response during drilling operations is essential, especially for wells drilled in narrow-margin environments. Pressure can be more accurately predicted through our proposed hybrid modeling, leading to safer, more optimized operations.

scholarly journals Research on Autoarrangement System of Accompaniment Chords Based on Hidden Markov Model with Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shuo Shi ◽  
Shuting Xi ◽  
Sang-Bing Tsai
Keyword(s):  
Machine Learning ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Template Matching ◽  
Hidden Markov ◽  
Data Set ◽  
Data Format ◽  
Automated Method ◽  
Machine Learning Model ◽  
And Training

Accompaniment production is one of the most important elements in music work, and chord arrangement is the key link of accompaniment production, which usually requires more musical talent and profound music theory knowledge to be competent. In this article, the machine learning model is used to replace manual accompaniment chords’ arrangement, and an automatic computer means is provided to complete and assist accompaniment chords’ arrangement. Also, through music feature extraction, automatic chord label construction, and model construction and training, the whole system finally has the ability of automatic accompaniment chord arrangement for the main melody. Based on the research of automatic chord label construction method and the characteristics of MIDI data format, a chord analysis method based on interval difference is proposed to construct chord labels of the whole track and realize the construction of automatic chord labels. In this study, the hidden Markov model is constructed according to the chord types, in which the input features are the improved theme PCP features proposed in this paper, and the input labels are the label data set constructed by the automated method proposed in this paper. After the training is completed, the PCP features of the theme to be predicted and improved are input to generate the accompaniment chords of the final arrangement. Through PCP features and template-matching model, the system designed in this paper improves the matching accuracy of the generated chords compared with that generated by the traditional method.

scholarly journals Identifying Intrusion Behaviour using Enhanced Hidden Markov Model

2020 ◽  
Vol 9 (4) ◽  
pp. 1618-1623
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Credit Card ◽  
Hidden Markov ◽  
Network Intrusion Detection ◽  
Detection Accuracy ◽  
Constant Attention ◽  
Network Intrusion

Data Mining is a method for detecting network intrusion detection in networks. It brings ideas from variety of areas including statistics, machine learning and database processes. Decreasing price of digital networking is now economically viable for network intrusion detection. This analysis chiefly examines the system intrusion detection with machine learning and DM methods. To improve the accuracy and efficiency of SHMM, we are collecting multiple observation in SHMM that will be called as Multiple Hidden Markov Model (MHMM). It is used to improve better Detection accuracy compare with SHMM. In the standard Hidden Markov Model, we have observed three fundamental problems are Evaluation and decoding another one is learning problem. The Evaluation problem can be used for word recognition. And the Decoding problem is related to constant attention and also the segmentation. In this Proposed Research, the primary purpose is to model the sequence of observation in Network log and credit card log transactions process using Enhanced Hidden Markov Model (EHMM). And show how it can be used for intrusion detection in Network. In this procedure, an EHMM is primarily trained with the conventional manners of a intruders. If the trained EHMM does not recognize an incoming Intruder transaction with adequately high probability, it is thought to be fraudulent.

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