A Novel SVM Neural Network Based Clinical Diagnosis of Cardiac Rhythm

2017 ◽  
Author(s):  
Arivoli Sundaramurthy
Keyword(s):  
Neural Network ◽  
Clinical Diagnosis ◽  
Cardiac Rhythm

scholarly journals Presymptomatic Prediction of Sepsis in Intensive Care Unit Patients

2008 ◽  
Vol 15 (7) ◽  
pp. 1089-1094 ◽  
Author(s):  
R. A. Lukaszewski ◽  
A. M. Yates ◽  
M. C. Jackson ◽  
K. Swingler ◽  
J. M. Scherer ◽  
...  
Keyword(s):  
Neural Network ◽  
Intensive Care ◽  
Clinical Diagnosis ◽  
Predictive Accuracy ◽  
High Sensitivity ◽  
Icu Patients ◽  
The Neural Network ◽  
Sensitivity And Selectivity ◽  
Ccl2 Mrna ◽  
Factor Α

ABSTRACT Postoperative or posttraumatic sepsis remains one of the leading causes of morbidity and mortality in hospital populations, especially in populations in intensive care units (ICUs). Central to the successful control of sepsis-associated infections is the ability to rapidly diagnose and treat disease. The ability to identify sepsis patients before they show any symptoms would have major benefits for the health care of ICU patients. For this study, 92 ICU patients who had undergone procedures that increased the risk of developing sepsis were recruited upon admission. Blood samples were taken daily until either a clinical diagnosis of sepsis was made or until the patient was discharged from the ICU. In addition to standard clinical and laboratory parameter testing, the levels of expression of interleukin-1β (IL-1β), IL-6, IL-8, and IL-10, tumor necrosis factor-α, FasL, and CCL2 mRNA were also measured by real-time reverse transcriptase PCR. The results of the analysis of the data using a nonlinear technique (neural network analysis) demonstrated discernible differences prior to the onset of overt sepsis. Neural networks using cytokine and chemokine data were able to correctly predict patient outcomes in an average of 83.09% of patient cases between 4 and 1 days before clinical diagnosis with high sensitivity and selectivity (91.43% and 80.20%, respectively). The neural network also had a predictive accuracy of 94.55% when data from 22 healthy volunteers was analyzed in conjunction with the ICU patient data. Our observations from this pilot study indicate that it may be possible to predict the onset of sepsis in a mixed patient population by using a panel of just seven biomarkers.

Neural Network in the Clinical Diagnosis of Acute Pulmonary Embolism

CHEST Journal ◽  
1993 ◽  
Vol 104 (6) ◽  
pp. 1685-1689 ◽  
Author(s):  
Sanjay Patil ◽  
Jerald W. Henry ◽  
Melvyn Rubenfire ◽  
Paul D. Stein
Keyword(s):  
Neural Network ◽  
Pulmonary Embolism ◽  
Clinical Diagnosis ◽  
Acute Pulmonary Embolism

ECG SIGNALS PROCESSING WITH NEURAL NETWORKS

1995 ◽  
Vol 03 (04) ◽  
pp. 419-430 ◽  
Author(s):  
JOSE R. HILERA ◽  
VICTOR J. MARTÍNEZ ◽  
MANUEL MAZO
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Clinical Diagnosis ◽  
Ecg Signals ◽  
Cardiac Disorders ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Nowadays, the digital register process of electrocardiographical signals (ECG) constitutes a common practice for the diagnosis and controlling of patients suffering from cardiac disorders. In this paper we study the usefulness of Artificial Neural Network (ANN) for clinical diagnosis through the detection of arrythmias and the reduction of the large spaces occupied by ECG records.

scholarly journals Towards Clinical Diagnosis: Automated Stroke Lesion Segmentation on Multi-Spectral MR Image Using Convolutional Neural Network

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 57006-57016 ◽  
Author(s):  
Zhiyang Liu ◽  
Chen Cao ◽  
Shuxue Ding ◽  
Zhiang Liu ◽  
Tong Han ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Clinical Diagnosis ◽  
Lesion Segmentation ◽  
Mr Image ◽  
Stroke Lesion
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